Using Simulation Technique to overcome the multi-collinearity problem for estimating fuzzy linear regression parameters.

NASA Astrophysics Data System (ADS)

Mansoor Gorgees, Hazim; Hilal, Mariam Mohammed

2018-05-01

Fatigue cracking is one of the common types of pavement distresses and is an indicator of structural failure; cracks allow moisture infiltration, roughness, may further deteriorate to a pothole. Some causes of pavement deterioration are: traffic loading; environment influences; drainage deficiencies; materials quality problems; construction deficiencies and external contributors. Many researchers have made models that contain many variables like asphalt content, asphalt viscosity, fatigue life, stiffness of asphalt mixture, temperature and other parameters that affect the fatigue life. For this situation, a fuzzy linear regression model was employed and analyzed by using the traditional methods and our proposed method in order to overcome the multi-collinearity problem. The total spread error was used as a criterion to compare the performance of the studied methods. Simulation program was used to obtain the required results.

Comparison of multi-subject ICA methods for analysis of fMRI data

PubMed Central

Erhardt, Erik Barry; Rachakonda, Srinivas; Bedrick, Edward; Allen, Elena; Adali, Tülay; Calhoun, Vince D.

2010-01-01

Spatial independent component analysis (ICA) applied to functional magnetic resonance imaging (fMRI) data identifies functionally connected networks by estimating spatially independent patterns from their linearly mixed fMRI signals. Several multi-subject ICA approaches estimating subject-specific time courses (TCs) and spatial maps (SMs) have been developed, however there has not yet been a full comparison of the implications of their use. Here, we provide extensive comparisons of four multi-subject ICA approaches in combination with data reduction methods for simulated and fMRI task data. For multi-subject ICA, the data first undergo reduction at the subject and group levels using principal component analysis (PCA). Comparisons of subject-specific, spatial concatenation, and group data mean subject-level reduction strategies using PCA and probabilistic PCA (PPCA) show that computationally intensive PPCA is equivalent to PCA, and that subject-specific and group data mean subject-level PCA are preferred because of well-estimated TCs and SMs. Second, aggregate independent components are estimated using either noise free ICA or probabilistic ICA (PICA). Third, subject-specific SMs and TCs are estimated using back-reconstruction. We compare several direct group ICA (GICA) back-reconstruction approaches (GICA1-GICA3) and an indirect back-reconstruction approach, spatio-temporal regression (STR, or dual regression). Results show the earlier group ICA (GICA1) approximates STR, however STR has contradictory assumptions and may show mixed-component artifacts in estimated SMs. Our evidence-based recommendation is to use GICA3, introduced here, with subject-specific PCA and noise-free ICA, providing the most robust and accurate estimated SMs and TCs in addition to offering an intuitive interpretation. PMID:21162045

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

PubMed

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

2012-06-01

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

Modelling the Relationship Between Land Surface Temperature and Landscape Patterns of Land Use Land Cover Classification Using Multi Linear Regression Models

NASA Astrophysics Data System (ADS)

Bernales, A. M.; Antolihao, J. A.; Samonte, C.; Campomanes, F.; Rojas, R. J.; dela Serna, A. M.; Silapan, J.

2016-06-01

The threat of the ailments related to urbanization like heat stress is very prevalent. There are a lot of things that can be done to lessen the effect of urbanization to the surface temperature of the area like using green roofs or planting trees in the area. So land use really matters in both increasing and decreasing surface temperature. It is known that there is a relationship between land use land cover (LULC) and land surface temperature (LST). Quantifying this relationship in terms of a mathematical model is very important so as to provide a way to predict LST based on the LULC alone. This study aims to examine the relationship between LST and LULC as well as to create a model that can predict LST using class-level spatial metrics from LULC. LST was derived from a Landsat 8 image and LULC classification was derived from LiDAR and Orthophoto datasets. Class-level spatial metrics were created in FRAGSTATS with the LULC and LST as inputs and these metrics were analysed using a statistical framework. Multi linear regression was done to create models that would predict LST for each class and it was found that the spatial metric "Effective mesh size" was a top predictor for LST in 6 out of 7 classes. The model created can still be refined by adding a temporal aspect by analysing the LST of another farming period (for rural areas) and looking for common predictors between LSTs of these two different farming periods.

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

PubMed

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

2011-10-01

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

Determination of pKa values of alendronate sodium in aqueous solution by piecewise linear regression based on acid-base potentiometric titration.

PubMed

Ke, Jing; Dou, Hanfei; Zhang, Ximin; Uhagaze, Dushimabararezi Serge; Ding, Xiali; Dong, Yuming

2016-12-01

As a mono-sodium salt form of alendronic acid, alendronate sodium presents multi-level ionization for the dissociation of its four hydroxyl groups. The dissociation constants of alendronate sodium were determined in this work by studying the piecewise linear relationship between volume of titrant and pH value based on acid-base potentiometric titration reaction. The distribution curves of alendronate sodium were drawn according to the determined pKa values. There were 4 dissociation constants (pKa 1 =2.43, pKa 2 =7.55, pKa 3 =10.80, pKa 4 =11.99, respectively) of alendronate sodium, and 12 existing forms, of which 4 could be ignored, existing in different pH environments.

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

PubMed

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

2014-12-01

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

Investigating the detection of multi-homed devices independent of operating systems

DTIC Science & Technology

2017-09-01

timestamp data was used to estimate clock skews using linear regression and linear optimization methods. Analysis revealed that detection depends on...the consistency of the estimated clock skew. Through vertical testing, it was also shown that clock skew consistency depends on the installed...optimization methods. Analysis revealed that detection depends on the consistency of the estimated clock skew. Through vertical testing, it was also

Evaluation of a Microbiological Multi-Residue System on the detection of antibacterial substances in ewe milk.

PubMed

Althaus, Rafael; Berruga, Maria Isabel; Montero, Ana; Roca, Marta; Molina, Maria Pilar

2009-01-19

To protect both, public health and the dairy industry, from the presence of antibiotic residues in milk, control programmes have been established, which include the needed screening tests. This work focuses on the application of a Microbiological Multi-Residue System in ewe milk, a method based on the use of six different plates, each seeded with one of the following bacteria: Geobacillus stearothermophilus var. calidolactis (beta-lactams), Bacillus subtilis at pH 8.0 (aminoglycosides), Kocuria rhizophila (macrolides), Escherichia coli (quinolones), B. cereus (tetracyclines) and B. subtilis at pH 7.0 (sulphonamides), respectively. Twenty-three antimicrobial substances were analysed and a logistic regression was established for each substance assayed to relate the antibiotic concentration and the zone of microbial growth inhibition. Great linearity in the response was observed (regression coefficients of over 0.97). This fact suggests the possibility of establishing a decision level of antibiotic concentrations near to the Maximum Residue Limits (MRL). Zones of inhibition were suggested as proposed action levels for the different antimicrobial groups (diameters of inhibition of 18 mm for the aminoglycoside, beta-lactam and sulphonamide plates; 19 mm for the tetracycline plate, 21 mm for the macrolide plate, and 24 mm for the quinolone plate). Specificity and cross-reactivity were also assayed.

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.

High Maternal Blood Mercury Level Is Associated with Low Verbal IQ in Children.

PubMed

Jeong, Kyoung Sook; Park, Hyewon; Ha, Eunhee; Shin, Jiyoung; Hong, Yun Chul; Ha, Mina; Park, Hyesook; Kim, Bung Nyun; Lee, Boeun; Lee, Soo Jeong; Lee, Kyung Yeon; Kim, Ja Hyeong; Kim, Yangho

2017-07-01

The objective of the present study was to investigate the relationship of IQ in children with maternal blood mercury concentration during late pregnancy. The present study is a component of the Mothers and Children's Environmental Health (MOCEH) study, a multi-center birth cohort project in Korea that began in 2006. The study cohort consisted of 553 children whose mothers underwent testing for blood mercury during late pregnancy. The children were given the Korean language version of the Wechsler Preschool and Primary Scale of Intelligence, revised edition (WPPSI-R) at 60 months of age. Multivariate linear regression analysis, with adjustment for covariates, was used to assess the relationship between verbal, performance, and total IQ in children and blood mercury concentration of mothers during late pregnancy. The results of multivariate linear regression analysis indicated that a doubling of blood mercury was associated with the decrease in verbal and total IQ by 2.482 (95% confidence interval [CI], 0.749-4.214) and 2.402 (95% CI, 0.526-4.279), respectively, after adjustment. This inverse association remained after further adjustment for blood lead concentration. Fish intake is an effect modifier of child IQ. In conclusion, high maternal blood mercury level is associated with low verbal IQ in children. © 2017 The Korean Academy of Medical Sciences.

Multi-Axis Identifiability Using Single-Surface Parameter Estimation Maneuvers on the X-48B Blended Wing Body

NASA Technical Reports Server (NTRS)

Ratnayake, Nalin A.; Koshimoto, Ed T.; Taylor, Brian R.

2011-01-01

The problem of parameter estimation on hybrid-wing-body type aircraft is complicated by the fact that many design candidates for such aircraft involve a large number of aero- dynamic control effectors that act in coplanar motion. This fact adds to the complexity already present in the parameter estimation problem for any aircraft with a closed-loop control system. Decorrelation of system inputs must be performed in order to ascertain individual surface derivatives with any sort of mathematical confidence. Non-standard control surface configurations, such as clamshell surfaces and drag-rudder modes, further complicate the modeling task. In this paper, asymmetric, single-surface maneuvers are used to excite multiple axes of aircraft motion simultaneously. Time history reconstructions of the moment coefficients computed by the solved regression models are then compared to each other in order to assess relative model accuracy. The reduced flight-test time required for inner surface parameter estimation using multi-axis methods was found to come at the cost of slightly reduced accuracy and statistical confidence for linear regression methods. Since the multi-axis maneuvers captured parameter estimates similar to both longitudinal and lateral-directional maneuvers combined, the number of test points required for the inner, aileron-like surfaces could in theory have been reduced by 50%. While trends were similar, however, individual parameters as estimated by a multi-axis model were typically different by an average absolute difference of roughly 15-20%, with decreased statistical significance, than those estimated by a single-axis model. The multi-axis model exhibited an increase in overall fit error of roughly 1-5% for the linear regression estimates with respect to the single-axis model, when applied to flight data designed for each, respectively.

Multi-disease analysis of maternal antibody decay using non-linear mixed models accounting for censoring.

PubMed

Goeyvaerts, Nele; Leuridan, Elke; Faes, Christel; Van Damme, Pierre; Hens, Niel

2015-09-10

Biomedical studies often generate repeated measures of multiple outcomes on a set of subjects. It may be of interest to develop a biologically intuitive model for the joint evolution of these outcomes while assessing inter-subject heterogeneity. Even though it is common for biological processes to entail non-linear relationships, examples of multivariate non-linear mixed models (MNMMs) are still fairly rare. We contribute to this area by jointly analyzing the maternal antibody decay for measles, mumps, rubella, and varicella, allowing for a different non-linear decay model for each infectious disease. We present a general modeling framework to analyze multivariate non-linear longitudinal profiles subject to censoring, by combining multivariate random effects, non-linear growth and Tobit regression. We explore the hypothesis of a common infant-specific mechanism underlying maternal immunity using a pairwise correlated random-effects approach and evaluating different correlation matrix structures. The implied marginal correlation between maternal antibody levels is estimated using simulations. The mean duration of passive immunity was less than 4 months for all diseases with substantial heterogeneity between infants. The maternal antibody levels against rubella and varicella were found to be positively correlated, while little to no correlation could be inferred for the other disease pairs. For some pairs, computational issues occurred with increasing correlation matrix complexity, which underlines the importance of further developing estimation methods for MNMMs. Copyright © 2015 John Wiley & Sons, Ltd.

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.

Simulation of multi-stage nonlinear bone remodeling induced by fixed partial dentures of different configurations: a comparative clinical and numerical study.

PubMed

Liao, Zhipeng; Yoda, Nobuhiro; Chen, Junning; Zheng, Keke; Sasaki, Keiichi; Swain, Michael V; Li, Qing

2017-04-01

This paper aimed to develop a clinically validated bone remodeling algorithm by integrating bone's dynamic properties in a multi-stage fashion based on a four-year clinical follow-up of implant treatment. The configurational effects of fixed partial dentures (FPDs) were explored using a multi-stage remodeling rule. Three-dimensional real-time occlusal loads during maximum voluntary clenching were measured with a piezoelectric force transducer and were incorporated into a computerized tomography-based finite element mandibular model. Virtual X-ray images were generated based on simulation and statistically correlated with clinical data using linear regressions. The strain energy density-driven remodeling parameters were regulated over the time frame considered. A linear single-stage bone remodeling algorithm, with a single set of constant remodeling parameters, was found to poorly fit with clinical data through linear regression (low [Formula: see text] and R), whereas a time-dependent multi-stage algorithm better simulated the remodeling process (high [Formula: see text] and R) against the clinical results. The three-implant-supported and distally cantilevered FPDs presented noticeable and continuous bone apposition, mainly adjacent to the cervical and apical regions. The bridged and mesially cantilevered FPDs showed bone resorption or no visible bone formation in some areas. Time-dependent variation of bone remodeling parameters is recommended to better correlate remodeling simulation with clinical follow-up. The position of FPD pontics plays a critical role in mechanobiological functionality and bone remodeling. Caution should be exercised when selecting the cantilever FPD due to the risk of overloading bone resorption.

Estimation and Selection via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications

PubMed Central

Huang, Jian; Zhang, Cun-Hui

2013-01-01

The ℓ1-penalized method, or the Lasso, has emerged as an important tool for the analysis of large data sets. Many important results have been obtained for the Lasso in linear regression which have led to a deeper understanding of high-dimensional statistical problems. In this article, we consider a class of weighted ℓ1-penalized estimators for convex loss functions of a general form, including the generalized linear models. We study the estimation, prediction, selection and sparsity properties of the weighted ℓ1-penalized estimator in sparse, high-dimensional settings where the number of predictors p can be much larger than the sample size n. Adaptive Lasso is considered as a special case. A multistage method is developed to approximate concave regularized estimation by applying an adaptive Lasso recursively. We provide prediction and estimation oracle inequalities for single- and multi-stage estimators, a general selection consistency theorem, and an upper bound for the dimension of the Lasso estimator. Important models including the linear regression, logistic regression and log-linear models are used throughout to illustrate the applications of the general results. PMID:24348100

Horses Auto-Recruit Their Lungs by Inspiratory Breath Holding Following Recovery from General Anaesthesia

PubMed Central

Mosing, Martina; Waldmann, Andreas D.; MacFarlane, Paul; Iff, Samuel; Auer, Ulrike; Bohm, Stephan H.; Bettschart-Wolfensberger, Regula; Bardell, David

2016-01-01

This study evaluated the breathing pattern and distribution of ventilation in horses prior to and following recovery from general anaesthesia using electrical impedance tomography (EIT). Six horses were anaesthetised for 6 hours in dorsal recumbency. Arterial blood gas and EIT measurements were performed 24 hours before (baseline) and 1, 2, 3, 4, 5 and 6 hours after horses stood following anaesthesia. At each time point 4 representative spontaneous breaths were analysed. The percentage of the total breath length during which impedance remained greater than 50% of the maximum inspiratory impedance change (breath holding), the fraction of total tidal ventilation within each of four stacked regions of interest (ROI) (distribution of ventilation) and the filling time and inflation period of seven ROI evenly distributed over the dorso-ventral height of the lungs were calculated. Mixed effects multi-linear regression and linear regression were used and significance was set at p<0.05. All horses demonstrated inspiratory breath holding until 5 hours after standing. No change from baseline was seen for the distribution of ventilation during inspiration. Filling time and inflation period were more rapid and shorter in ventral and slower and longer in most dorsal ROI compared to baseline, respectively. In a mixed effects multi-linear regression, breath holding was significantly correlated with PaCO2 in both the univariate and multivariate regression. Following recovery from anaesthesia, horses showed inspiratory breath holding during which gas redistributed from ventral into dorsal regions of the lungs. This suggests auto-recruitment of lung tissue which would have been dependent and likely atelectic during anaesthesia. PMID:27331910

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

USGS Publications Warehouse

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

2008-01-01

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

Multi-model ensemble combinations of the water budget in the East/Japan Sea

NASA Astrophysics Data System (ADS)

HAN, S.; Hirose, N.; Usui, N.; Miyazawa, Y.

2016-02-01

The water balance of East/Japan Sea is determined mainly by inflow and outflow through the Korea/Tsushima, Tsugaru and Soya/La Perouse Straits. However, the volume transports measured at three straits remain quantitatively unbalanced. This study examined the seasonal variation of the volume transport using the multiple linear regression and ridge regression of multi-model ensemble (MME) methods to estimate physically consistent circulation in East/Japan Sea by using four different data assimilation models. The MME outperformed all of the single models by reducing uncertainties, especially the multicollinearity problem with the ridge regression. However, the regression constants turned out to be inconsistent with each other if the MME was applied separately for each strait. The MME for a connected system was thus performed to find common constants for these straits. The estimation of this MME was found to be similar to the MME result of sea level difference (SLD). The estimated mean transport (2.42 Sv) was smaller than the measurement data at the Korea/Tsushima Strait, but the calibrated transport of the Tsugaru Strait (1.63 Sv) was larger than the observed data. The MME results of transport and SLD also suggested that the standard deviation (STD) of the Korea/Tsushima Strait is larger than the STD of the observation, whereas the estimated results were almost identical to that observed for the Tsugaru and Soya/La Perouse Straits. The similarity between MME results enhances the reliability of the present MME estimation.

Multi-model ensemble estimation of volume transport through the straits of the East/Japan Sea

NASA Astrophysics Data System (ADS)

Han, Sooyeon; Hirose, Naoki; Usui, Norihisa; Miyazawa, Yasumasa

2016-01-01

The volume transports measured at the Korea/Tsushima, Tsugaru, and Soya/La Perouse Straits remain quantitatively inconsistent. However, data assimilation models at least provide a self-consistent budget despite subtle differences among the models. This study examined the seasonal variation of the volume transport using the multiple linear regression and ridge regression of multi-model ensemble (MME) methods to estimate more accurately transport at these straits by using four different data assimilation models. The MME outperformed all of the single models by reducing uncertainties, especially the multicollinearity problem with the ridge regression. However, the regression constants turned out to be inconsistent with each other if the MME was applied separately for each strait. The MME for a connected system was thus performed to find common constants for these straits. The estimation of this MME was found to be similar to the MME result of sea level difference (SLD). The estimated mean transport (2.43 Sv) was smaller than the measurement data at the Korea/Tsushima Strait, but the calibrated transport of the Tsugaru Strait (1.63 Sv) was larger than the observed data. The MME results of transport and SLD also suggested that the standard deviation (STD) of the Korea/Tsushima Strait is larger than the STD of the observation, whereas the estimated results were almost identical to that observed for the Tsugaru and Soya/La Perouse Straits. The similarity between MME results enhances the reliability of the present MME estimation.

What Is Wrong with ANOVA and Multiple Regression? Analyzing Sentence Reading Times with Hierarchical Linear Models

ERIC Educational Resources Information Center

Richter, Tobias

2006-01-01

Most reading time studies using naturalistic texts yield data sets characterized by a multilevel structure: Sentences (sentence level) are nested within persons (person level). In contrast to analysis of variance and multiple regression techniques, hierarchical linear models take the multilevel structure of reading time data into account. They…

Mixed effect Poisson log-linear models for clinical and epidemiological sleep hypnogram data

PubMed Central

Swihart, Bruce J.; Caffo, Brian S.; Crainiceanu, Ciprian; Punjabi, Naresh M.

2013-01-01

Bayesian Poisson log-linear multilevel models scalable to epidemiological studies are proposed to investigate population variability in sleep state transition rates. Hierarchical random effects are used to account for pairings of subjects and repeated measures within those subjects, as comparing diseased to non-diseased subjects while minimizing bias is of importance. Essentially, non-parametric piecewise constant hazards are estimated and smoothed, allowing for time-varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming exponentially distributed survival times. Such re-derivation allows synthesis of two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear models with GEE for transition counts. An example data set from the Sleep Heart Health Study is analyzed. Supplementary material includes the analyzed data set as well as the code for a reproducible analysis. PMID:22241689

Dynamic whole body PET parametric imaging: II. Task-oriented statistical estimation

PubMed Central

Karakatsanis, Nicolas A.; Lodge, Martin A.; Zhou, Y.; Wahl, Richard L.; Rahmim, Arman

2013-01-01

In the context of oncology, dynamic PET imaging coupled with standard graphical linear analysis has been previously employed to enable quantitative estimation of tracer kinetic parameters of physiological interest at the voxel level, thus, enabling quantitative PET parametric imaging. However, dynamic PET acquisition protocols have been confined to the limited axial field-of-view (~15–20cm) of a single bed position and have not been translated to the whole-body clinical imaging domain. On the contrary, standardized uptake value (SUV) PET imaging, considered as the routine approach in clinical oncology, commonly involves multi-bed acquisitions, but is performed statically, thus not allowing for dynamic tracking of the tracer distribution. Here, we pursue a transition to dynamic whole body PET parametric imaging, by presenting, within a unified framework, clinically feasible multi-bed dynamic PET acquisition protocols and parametric imaging methods. In a companion study, we presented a novel clinically feasible dynamic (4D) multi-bed PET acquisition protocol as well as the concept of whole body PET parametric imaging employing Patlak ordinary least squares (OLS) regression to estimate the quantitative parameters of tracer uptake rate Ki and total blood distribution volume V. In the present study, we propose an advanced hybrid linear regression framework, driven by Patlak kinetic voxel correlations, to achieve superior trade-off between contrast-to-noise ratio (CNR) and mean squared error (MSE) than provided by OLS for the final Ki parametric images, enabling task-based performance optimization. Overall, whether the observer's task is to detect a tumor or quantitatively assess treatment response, the proposed statistical estimation framework can be adapted to satisfy the specific task performance criteria, by adjusting the Patlak correlation-coefficient (WR) reference value. The multi-bed dynamic acquisition protocol, as optimized in the preceding companion study, was employed along with extensive Monte Carlo simulations and an initial clinical FDG patient dataset to validate and demonstrate the potential of the proposed statistical estimation methods. Both simulated and clinical results suggest that hybrid regression in the context of whole-body Patlak Ki imaging considerably reduces MSE without compromising high CNR. Alternatively, for a given CNR, hybrid regression enables larger reductions than OLS in the number of dynamic frames per bed, allowing for even shorter acquisitions of ~30min, thus further contributing to the clinical adoption of the proposed framework. Compared to the SUV approach, whole body parametric imaging can provide better tumor quantification, and can act as a complement to SUV, for the task of tumor detection. PMID:24080994

Dynamic whole-body PET parametric imaging: II. Task-oriented statistical estimation.

PubMed

Karakatsanis, Nicolas A; Lodge, Martin A; Zhou, Y; Wahl, Richard L; Rahmim, Arman

2013-10-21

In the context of oncology, dynamic PET imaging coupled with standard graphical linear analysis has been previously employed to enable quantitative estimation of tracer kinetic parameters of physiological interest at the voxel level, thus, enabling quantitative PET parametric imaging. However, dynamic PET acquisition protocols have been confined to the limited axial field-of-view (~15-20 cm) of a single-bed position and have not been translated to the whole-body clinical imaging domain. On the contrary, standardized uptake value (SUV) PET imaging, considered as the routine approach in clinical oncology, commonly involves multi-bed acquisitions, but is performed statically, thus not allowing for dynamic tracking of the tracer distribution. Here, we pursue a transition to dynamic whole-body PET parametric imaging, by presenting, within a unified framework, clinically feasible multi-bed dynamic PET acquisition protocols and parametric imaging methods. In a companion study, we presented a novel clinically feasible dynamic (4D) multi-bed PET acquisition protocol as well as the concept of whole-body PET parametric imaging employing Patlak ordinary least squares (OLS) regression to estimate the quantitative parameters of tracer uptake rate Ki and total blood distribution volume V. In the present study, we propose an advanced hybrid linear regression framework, driven by Patlak kinetic voxel correlations, to achieve superior trade-off between contrast-to-noise ratio (CNR) and mean squared error (MSE) than provided by OLS for the final Ki parametric images, enabling task-based performance optimization. Overall, whether the observer's task is to detect a tumor or quantitatively assess treatment response, the proposed statistical estimation framework can be adapted to satisfy the specific task performance criteria, by adjusting the Patlak correlation-coefficient (WR) reference value. The multi-bed dynamic acquisition protocol, as optimized in the preceding companion study, was employed along with extensive Monte Carlo simulations and an initial clinical (18)F-deoxyglucose patient dataset to validate and demonstrate the potential of the proposed statistical estimation methods. Both simulated and clinical results suggest that hybrid regression in the context of whole-body Patlak Ki imaging considerably reduces MSE without compromising high CNR. Alternatively, for a given CNR, hybrid regression enables larger reductions than OLS in the number of dynamic frames per bed, allowing for even shorter acquisitions of ~30 min, thus further contributing to the clinical adoption of the proposed framework. Compared to the SUV approach, whole-body parametric imaging can provide better tumor quantification, and can act as a complement to SUV, for the task of tumor detection.

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

PubMed

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

2011-06-28

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

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.

Combined analysis of magnetic and gravity anomalies using normalized source strength (NSS)

NASA Astrophysics Data System (ADS)

Li, L.; Wu, Y.

2017-12-01

Gravity field and magnetic field belong to potential fields which lead inherent multi-solution. Combined analysis of magnetic and gravity anomalies based on Poisson's relation is used to determinate homology gravity and magnetic anomalies and decrease the ambiguity. The traditional combined analysis uses the linear regression of the reduction to pole (RTP) magnetic anomaly to the first order vertical derivative of the gravity anomaly, and provides the quantitative or semi-quantitative interpretation by calculating the correlation coefficient, slope and intercept. In the calculation process, due to the effect of remanent magnetization, the RTP anomaly still contains the effect of oblique magnetization. In this case the homology gravity and magnetic anomalies display irrelevant results in the linear regression calculation. The normalized source strength (NSS) can be transformed from the magnetic tensor matrix, which is insensitive to the remanence. Here we present a new combined analysis using NSS. Based on the Poisson's relation, the gravity tensor matrix can be transformed into the pseudomagnetic tensor matrix of the direction of geomagnetic field magnetization under the homologous condition. The NSS of pseudomagnetic tensor matrix and original magnetic tensor matrix are calculated and linear regression analysis is carried out. The calculated correlation coefficient, slope and intercept indicate the homology level, Poisson's ratio and the distribution of remanent respectively. We test the approach using synthetic model under complex magnetization, the results show that it can still distinguish the same source under the condition of strong remanence, and establish the Poisson's ratio. Finally, this approach is applied in China. The results demonstrated that our approach is feasible.

Analyzing Multilevel Data: An Empirical Comparison of Parameter Estimates of Hierarchical Linear Modeling and Ordinary Least Squares Regression

ERIC Educational Resources Information Center

Rocconi, Louis M.

2011-01-01

Hierarchical linear models (HLM) solve the problems associated with the unit of analysis problem such as misestimated standard errors, heterogeneity of regression and aggregation bias by modeling all levels of interest simultaneously. Hierarchical linear modeling resolves the problem of misestimated standard errors by incorporating a unique random…

USING HYDROGRAPHIC DATA AND THE EPA VIRTUAL BEACH MODEL TO TEST PREDICTIONS OF BEACH BACTERIA CONCENTRATIONS

EPA Science Inventory

A modeling study of 2006 Huntington Beach (Lake Erie) beach bacteria concentrations indicates multi-variable linear regression (MLR) can effectively estimate bacteria concentrations compared to the persistence model. Our use of the Virtual Beach (VB) model affirms that fact. VB i...

A novel multi-target regression framework for time-series prediction of drug efficacy.

PubMed

Li, Haiqing; Zhang, Wei; Chen, Ying; Guo, Yumeng; Li, Guo-Zheng; Zhu, Xiaoxin

2017-01-18

Excavating from small samples is a challenging pharmacokinetic problem, where statistical methods can be applied. Pharmacokinetic data is special due to the small samples of high dimensionality, which makes it difficult to adopt conventional methods to predict the efficacy of traditional Chinese medicine (TCM) prescription. The main purpose of our study is to obtain some knowledge of the correlation in TCM prescription. Here, a novel method named Multi-target Regression Framework to deal with the problem of efficacy prediction is proposed. We employ the correlation between the values of different time sequences and add predictive targets of previous time as features to predict the value of current time. Several experiments are conducted to test the validity of our method and the results of leave-one-out cross-validation clearly manifest the competitiveness of our framework. Compared with linear regression, artificial neural networks, and partial least squares, support vector regression combined with our framework demonstrates the best performance, and appears to be more suitable for this task.

A novel multi-target regression framework for time-series prediction of drug efficacy

PubMed Central

Li, Haiqing; Zhang, Wei; Chen, Ying; Guo, Yumeng; Li, Guo-Zheng; Zhu, Xiaoxin

2017-01-01

Excavating from small samples is a challenging pharmacokinetic problem, where statistical methods can be applied. Pharmacokinetic data is special due to the small samples of high dimensionality, which makes it difficult to adopt conventional methods to predict the efficacy of traditional Chinese medicine (TCM) prescription. The main purpose of our study is to obtain some knowledge of the correlation in TCM prescription. Here, a novel method named Multi-target Regression Framework to deal with the problem of efficacy prediction is proposed. We employ the correlation between the values of different time sequences and add predictive targets of previous time as features to predict the value of current time. Several experiments are conducted to test the validity of our method and the results of leave-one-out cross-validation clearly manifest the competitiveness of our framework. Compared with linear regression, artificial neural networks, and partial least squares, support vector regression combined with our framework demonstrates the best performance, and appears to be more suitable for this task. PMID:28098186

Comparative data mining analysis for information retrieval of MODIS images: monitoring lake turbidity changes at Lake Okeechobee, Florida

NASA Astrophysics Data System (ADS)

Chang, Ni-Bin; Daranpob, Ammarin; Yang, Y. Jeffrey; Jin, Kang-Ren

2009-09-01

In the remote sensing field, a frequently recurring question is: Which computational intelligence or data mining algorithms are most suitable for the retrieval of essential information given that most natural systems exhibit very high non-linearity. Among potential candidates might be empirical regression, neural network model, support vector machine, genetic algorithm/genetic programming, analytical equation, etc. This paper compares three types of data mining techniques, including multiple non-linear regression, artificial neural networks, and genetic programming, for estimating multi-temporal turbidity changes following hurricane events at Lake Okeechobee, Florida. This retrospective analysis aims to identify how the major hurricanes impacted the water quality management in 2003-2004. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra 8-day composite imageries were used to retrieve the spatial patterns of turbidity distributions for comparison against the visual patterns discernible in the in-situ observations. By evaluating four statistical parameters, the genetic programming model was finally selected as the most suitable data mining tool for classification in which the MODIS band 1 image and wind speed were recognized as the major determinants by the model. The multi-temporal turbidity maps generated before and after the major hurricane events in 2003-2004 showed that turbidity levels were substantially higher after hurricane episodes. The spatial patterns of turbidity confirm that sediment-laden water travels to the shore where it reduces the intensity of the light necessary to submerged plants for photosynthesis. This reduction results in substantial loss of biomass during the post-hurricane period.

A Multilevel Study of Students in Vietnam: Drinking Motives and Drinking Context as Predictors of Alcohol Consumption

PubMed Central

Diep, Pham Bich; Tan, Frans E. S.; Knibbe, Ronald A.; De Vries, Nanne

2016-01-01

Background: This study used multi-level analysis to estimate which type of factor explains most of the variance in alcohol consumption of Vietnamese students. Methods: Data were collected among 6011 students attending 12 universities/faculties in four provinces in Vietnam. The three most recent drinking occasions were investigated per student, resulting in 12,795 drinking occasions among 4265 drinkers. Students reported on 10 aspects of the drinking context per drinking occasion. A multi-level mixed-effects linear regression model was constructed in which aspects of drinking context composed the first level; the age of students and four drinking motives comprised the second level. The dependent variable was the number of drinks. Results: Of the aspects of context, drinking duration had the strongest association with alcohol consumption while, at the individual level, coping motive had the strongest association. The drinking context characteristics explained more variance than the individual characteristics in alcohol intake per occasion. Conclusions: These findings suggest that, among students in Vietnam, the drinking context explains a larger proportion of the variance in alcohol consumption than the drinking motives. Therefore, measures that reduce the availability of alcohol in specific drinking situations are an essential part of an effective prevention policy. PMID:27420089

Applying the methodology of Design of Experiments to stability studies: a Partial Least Squares approach for evaluation of drug stability.

PubMed

Jordan, Nika; Zakrajšek, Jure; Bohanec, Simona; Roškar, Robert; Grabnar, Iztok

2018-05-01

The aim of the present research is to show that the methodology of Design of Experiments can be applied to stability data evaluation, as they can be seen as multi-factor and multi-level experimental designs. Linear regression analysis is usually an approach for analyzing stability data, but multivariate statistical methods could also be used to assess drug stability during the development phase. Data from a stability study for a pharmaceutical product with hydrochlorothiazide (HCTZ) as an unstable drug substance was used as a case example in this paper. The design space of the stability study was modeled using Umetrics MODDE 10.1 software. We showed that a Partial Least Squares model could be used for a multi-dimensional presentation of all data generated in a stability study and for determination of the relationship among factors that influence drug stability. It might also be used for stability predictions and potentially for the optimization of the extent of stability testing needed to determine shelf life and storage conditions, which would be time and cost-effective for the pharmaceutical industry.

Crush Analyses of Multi-Level Equipment

DOT National Transportation Integrated Search

2006-11-06

Non-linear large deformation crush analyses were conducted on a multi-level cab car typical of those in operation by the Southern California Regional Rail Authority (SCRRA) in California. The motivation for these analyses was a collision, which occur...

Predicting multi-level drug response with gene expression profile in multiple myeloma using hierarchical ordinal regression.

PubMed

Zhang, Xinyan; Li, Bingzong; Han, Huiying; Song, Sha; Xu, Hongxia; Hong, Yating; Yi, Nengjun; Zhuang, Wenzhuo

2018-05-10

Multiple myeloma (MM), like other cancers, is caused by the accumulation of genetic abnormalities. Heterogeneity exists in the patients' response to treatments, for example, bortezomib. This urges efforts to identify biomarkers from numerous molecular features and build predictive models for identifying patients that can benefit from a certain treatment scheme. However, previous studies treated the multi-level ordinal drug response as a binary response where only responsive and non-responsive groups are considered. It is desirable to directly analyze the multi-level drug response, rather than combining the response to two groups. In this study, we present a novel method to identify significantly associated biomarkers and then develop ordinal genomic classifier using the hierarchical ordinal logistic model. The proposed hierarchical ordinal logistic model employs the heavy-tailed Cauchy prior on the coefficients and is fitted by an efficient quasi-Newton algorithm. We apply our hierarchical ordinal regression approach to analyze two publicly available datasets for MM with five-level drug response and numerous gene expression measures. Our results show that our method is able to identify genes associated with the multi-level drug response and to generate powerful predictive models for predicting the multi-level response. The proposed method allows us to jointly fit numerous correlated predictors and thus build efficient models for predicting the multi-level drug response. The predictive model for the multi-level drug response can be more informative than the previous approaches. Thus, the proposed approach provides a powerful tool for predicting multi-level drug response and has important impact on cancer studies.

An improved multiple linear regression and data analysis computer program package

NASA Technical Reports Server (NTRS)

Sidik, S. M.

1972-01-01

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

Modeling of thermal degradation kinetics of the C-glucosyl xanthone mangiferin in an aqueous model solution as a function of pH and temperature and protective effect of honeybush extract matrix.

PubMed

Beelders, Theresa; de Beer, Dalene; Kidd, Martin; Joubert, Elizabeth

2018-01-01

Mangiferin, a C-glucosyl xanthone, abundant in mango and honeybush, is increasingly targeted for its bioactive properties and thus to enhance functional properties of food. The thermal degradation kinetics of mangiferin at pH3, 4, 5, 6 and 7 were each modeled at five temperatures ranging between 60 and 140°C. First-order reaction models were fitted to the data using non-linear regression to determine the reaction rate constant at each pH-temperature combination. The reaction rate constant increased with increasing temperature and pH. Comparison of the reaction rate constants at 100°C revealed an exponential relationship between the reaction rate constant and pH. The data for each pH were also modeled with the Arrhenius equation using non-linear and linear regression to determine the activation energy and pre-exponential factor. Activation energies decreased slightly with increasing pH. Finally, a multi-linear model taking into account both temperature and pH was developed for mangiferin degradation. Sterilization (121°C for 4min) of honeybush extracts dissolved at pH4, 5 and 7 did not cause noticeable degradation of mangiferin, although the multi-linear model predicted 34% degradation at pH7. The extract matrix is postulated to exert a protective effect as changes in potential precursor content could not fully explain the stability of mangiferin. Copyright © 2017 Elsevier Ltd. All rights reserved.

Development of efficiency indicators of operating room management for multi-institutional comparisons.

PubMed

Tanaka, Masayuki; Lee, Jason; Ikai, Hiroshi; Imanaka, Yuichi

2013-04-01

The efficiency of a hospital's operating room (OR) management can affect its overall profitability. However, existing indicators that assess OR management efficiency do not take into account differences in hospital size, manpower and functional characteristics, thereby rendering them unsuitable for multi-institutional comparisons. The aim of this study was to develop indicators of OR management efficiency that would take into account differences in hospital size and manpower, which may then be applied to multi-institutional comparisons. Using administrative data from 224 hospitals in Japan from 2008 to 2010, we performed four multiple linear regression analyses at the hospital level, in which the dependent variables were the number of operations per OR per month, procedural fees per OR per month, total utilization times per OR per month and total fees per OR per month for each of the models. The expected values of these four indicators were produced using multiple regression analysis results, adjusting for differences in hospital size and manpower, which are beyond the control of process owners' management. However, more than half of the variations in three of these four indicators were shown to be explained by differences in hospital size and manpower. Using the ratio of observed to expected values (OE ratio), as well as the difference between the two values (OE difference) allows hospitals to identify weaknesses in efficiency with more validity when compared to unadjusted indicators. The new indicators may support the improvement and sustainment of a high-quality health care system. © 2012 Blackwell Publishing Ltd.

Multi-variant study of obesity risk genes in African Americans: The Jackson Heart Study.

PubMed

Liu, Shijian; Wilson, James G; Jiang, Fan; Griswold, Michael; Correa, Adolfo; Mei, Hao

2016-11-30

Genome-wide association study (GWAS) has been successful in identifying obesity risk genes by single-variant association analysis. For this study, we designed steps of analysis strategy and aimed to identify multi-variant effects on obesity risk among candidate genes. Our analyses were focused on 2137 African American participants with body mass index measured in the Jackson Heart Study and 657 common single nucleotide polymorphisms (SNPs) genotyped at 8 GWAS-identified obesity risk genes. Single-variant association test showed that no SNPs reached significance after multiple testing adjustment. The following gene-gene interaction analysis, which was focused on SNPs with unadjusted p-value<0.10, identified 6 significant multi-variant associations. Logistic regression showed that SNPs in these associations did not have significant linear interactions; examination of genetic risk score evidenced that 4 multi-variant associations had significant additive effects of risk SNPs; and haplotype association test presented that all multi-variant associations contained one or several combinations of particular alleles or haplotypes, associated with increased obesity risk. Our study evidenced that obesity risk genes generated multi-variant effects, which can be additive or non-linear interactions, and multi-variant study is an important supplement to existing GWAS for understanding genetic effects of obesity risk genes. Copyright © 2016 Elsevier B.V. All rights reserved.

An hourly PM10 diagnosis model for the Bilbao metropolitan area using a linear regression methodology.

PubMed

González-Aparicio, I; Hidalgo, J; Baklanov, A; Padró, A; Santa-Coloma, O

2013-07-01

There is extensive evidence of the negative impacts on health linked to the rise of the regional background of particulate matter (PM) 10 levels. These levels are often increased over urban areas becoming one of the main air pollution concerns. This is the case on the Bilbao metropolitan area, Spain. This study describes a data-driven model to diagnose PM10 levels in Bilbao at hourly intervals. The model is built with a training period of 7-year historical data covering different urban environments (inland, city centre and coastal sites). The explanatory variables are quantitative-log [NO2], temperature, short-wave incoming radiation, wind speed and direction, specific humidity, hour and vehicle intensity-and qualitative-working days/weekends, season (winter/summer), the hour (from 00 to 23 UTC) and precipitation/no precipitation. Three different linear regression models are compared: simple linear regression; linear regression with interaction terms (INT); and linear regression with interaction terms following the Sawa's Bayesian Information Criteria (INT-BIC). Each type of model is calculated selecting two different periods: the training (it consists of 6 years) and the testing dataset (it consists of 1 year). The results of each type of model show that the INT-BIC-based model (R(2) = 0.42) is the best. Results were R of 0.65, 0.63 and 0.60 for the city centre, inland and coastal sites, respectively, a level of confidence similar to the state-of-the art methodology. The related error calculated for longer time intervals (monthly or seasonal means) diminished significantly (R of 0.75-0.80 for monthly means and R of 0.80 to 0.98 at seasonally means) with respect to shorter periods.

Differences in Student Evaluations of Limited-Term Lecturers and Full-Time Faculty

ERIC Educational Resources Information Center

Cho, Jeong-Il; Otani, Koichiro; Kim, B. Joon

2014-01-01

This study compared student evaluations of teaching (SET) for limited-term lecturers (LTLs) and full-time faculty (FTF) using a Likert-scaled survey administered to students (N = 1,410) at the end of university courses. Data were analyzed using a general linear regression model to investigate the influence of multi-dimensional evaluation items on…

Time-resolved flow reconstruction with indirect measurements using regression models and Kalman-filtered POD ROM

NASA Astrophysics Data System (ADS)

Leroux, Romain; Chatellier, Ludovic; David, Laurent

2018-01-01

This article is devoted to the estimation of time-resolved particle image velocimetry (TR-PIV) flow fields using a time-resolved point measurements of a voltage signal obtained by hot-film anemometry. A multiple linear regression model is first defined to map the TR-PIV flow fields onto the voltage signal. Due to the high temporal resolution of the signal acquired by the hot-film sensor, the estimates of the TR-PIV flow fields are obtained with a multiple linear regression method called orthonormalized partial least squares regression (OPLSR). Subsequently, this model is incorporated as the observation equation in an ensemble Kalman filter (EnKF) applied on a proper orthogonal decomposition reduced-order model to stabilize it while reducing the effects of the hot-film sensor noise. This method is assessed for the reconstruction of the flow around a NACA0012 airfoil at a Reynolds number of 1000 and an angle of attack of {20}°. Comparisons with multi-time delay-modified linear stochastic estimation show that both the OPLSR and EnKF combined with OPLSR are more accurate as they produce a much lower relative estimation error, and provide a faithful reconstruction of the time evolution of the velocity flow fields.

Computerized tomography measured liver fat is associated with low levels of N-terminal pro-brain natriuretic protein (NT-proBNP). Multi-Ethnic Study of Atherosclerosis

PubMed Central

Sanchez, Otto A.; Mariana, Lazo-Elizondo; Irfan, Zeb; Tracy, Russell P; Bradley, Ryan; Duprez, Daniel A.; Bahrami, Hossein; Peralta, Carmen A.; Daniels, Lori B.; Lima, João A.; Maisel, Alan; Jacobs, David R.; MJ, Budoff

2016-01-01

Background and aims N-terminal pro B-type natriuretic peptide (NT-proBNP) is inversely associated with diabetes mellitus, obesity and metabolic syndrome. We aim to characterize the association between NT-proBNP and nonalcoholic fatty liver disease (NAFLD), a condition strongly associated with metabolic syndrome. Methods 4529 participants from the Multi-Ethnic Study of Atherosclerosis (MESA) free of cardiovascular disease, without self-reported liver disease and not diabetic at their baseline visit in 2000- 2002 were included in this analysis. NAFLD was defined by a liver attenuation <40 HU. Relative prevalence (RP) for NAFLD was assessed adjusted for age, race, and sex, percent of dietary calories derived from fat, total intentional exercise, alcoholic drinks per week, and interleukin-6 by quintiles of NT-proBNP. Adjusted linear spline model was used to characterize a non-linear association between NT-proBNP and liver fat. The inflection point (IP) was the NT-proBNP concentration where there was a change in slope in the association between liver attenuation and NT-proBNP. Results RP for NAFLD decreased by 30% from the lowest to the highest quintile of NT-proBNP, p = 0.01. We observed an inverse linear association between NT-proBNP and liver fat, which plateaued (IP) at an NT-proBNP concentration of 45 pg/mL. Linear regression coefficient (SE) per unit of NT-proBNP < and ≥ IP was of 0.05 (0.02), p = 0.001 and 0.0006 (0.0008), p = 0.5, respectively, differences between slopes p < 0.0001. Conclusions In this cross-sectional study of a community based multiethnic sample of non-diabetic adults, low levels of NT-proBNP are associated with greater prevalence of NAFLD. PMID:27085779

Exploring the impact of different multi-level measures of physician communities in patient-centric care networks on healthcare outcomes: A multi-level regression approach.

PubMed

Uddin, Shahadat

2016-02-04

A patient-centric care network can be defined as a network among a group of healthcare professionals who provide treatments to common patients. Various multi-level attributes of the members of this network have substantial influence to its perceived level of performance. In order to assess the impact different multi-level attributes of patient-centric care networks on healthcare outcomes, this study first captured patient-centric care networks for 85 hospitals using health insurance claim dataset. From these networks, this study then constructed physician collaboration networks based on the concept of patient-sharing network among physicians. A multi-level regression model was then developed to explore the impact of different attributes that are organised at two levels on hospitalisation cost and hospital length of stay. For Level-1 model, the average visit per physician significantly predicted both hospitalisation cost and hospital length of stay. The number of different physicians significantly predicted only the hospitalisation cost, which has significantly been moderated by age, gender and Comorbidity score of patients. All Level-1 findings showed significance variance across physician collaboration networks having different community structure and density. These findings could be utilised as a reflective measure by healthcare decision makers. Moreover, healthcare managers could consider them in developing effective healthcare environments.

Familial and Religious Influences on Adolescent Alcohol Use: A Multi-Level Study of Students and School Communities

ERIC Educational Resources Information Center

Bjarnason, Thoroddur; Thorlindsson, Thorolfur; Sigfusdottir, Inga D.; Welch, Michael R.

2005-01-01

A multi-level Durkheimian theory of familial and religious influences on adolescent alcohol use is developed and tested with hierarchical linear modeling of data from Icelandic schools and students. On the individual level, traditional family structure, parental monitoring, parental support, religious participation, and perceptions of divine…

A Multi-Level Examination of College and Its Influence on Ecumenical Worldview Development

ERIC Educational Resources Information Center

Mayhew, Matthew J.

2012-01-01

This multi-level, longitudinal study investigated the ecumenical worldview development of 13,932 students enrolled in one of 126 institutions. Results indicated that the final hierarchical linear model, consisting of institution-and-student-level predictors as well as slopes explaining the relationships among some of these predictors, explained…

Vitamin D status of human immunodeficiency virus-positive patients with advanced liver disease enrolled in the solid organ transplantation in HIV: multi-site study.

PubMed

Branch, Andrea D; Barin, Burc; Rahman, Adeeb; Stock, Peter; Schiano, Thomas D

2014-02-01

An optimal vitamin D status may benefit liver transplantation (LT) patients. Higher levels of 25-hydroxyvitamin D [25(OH)D] mitigate steroid-induced bone loss after LT, correlate with better hepatitis C virus treatment responses, and increase graft survival. This study investigated 25(OH)D levels and assessed strategies for vitamin D deficiency prevention in human immunodeficiency virus (HIV)-positive patients with advanced liver disease who were enrolled in the Solid Organ Transplantation in HIV: Multi-Site Study. 25(OH)D was measured in banked specimens from 154 LT candidates/recipients with the DiaSorin assay; deficiency was defined as a 25(OH)D level < 20 ng/mL. Information about vitamin D supplement use after LT was obtained from medication logs and via surveys. Logistic regression, Cox regression, and linear repeated measures analyses were performed with SAS software. We found that none of the 17 academic medical centers in the United States routinely recommended vitamin D supplements before LT, and only a minority (4/17) recommended vitamin D supplements to all patients after LT. Seventy-one percent of the 139 patients with pre-LT values had vitamin D deficiency, which was significantly associated with cirrhosis (P = 0.01) but no other variable. The vitamin D status improved modestly after LT; however, the status was deficient for 40% of the patients 1 year after LT. In a multivariate linear repeated measures model, a higher pre-LT 25(OH)D level (P < 0.001), specimen collection in the summer (P < 0.001), a routine vitamin D supplementation strategy after LT (P < 0.001), and the time elapsing since LT (P = 0.01) were significantly associated with increases in the post-LT 25(OH)D level; black race was associated with a decreased level (P = 0.02). In conclusion, the majority of patients awaiting LT were vitamin D deficient, and approximately half were vitamin D deficient after LT. More extensive use of vitamin D supplements, more sun exposure, or both are needed to prevent this deficiency in HIV-positive LT candidates and recipients. © 2013 American Association for the Study of Liver Diseases.

MultiDK: A Multiple Descriptor Multiple Kernel Approach for Molecular Discovery and Its Application to Organic Flow Battery Electrolytes.

PubMed

Kim, Sungjin; Jinich, Adrián; Aspuru-Guzik, Alán

2017-04-24

We propose a multiple descriptor multiple kernel (MultiDK) method for efficient molecular discovery using machine learning. We show that the MultiDK method improves both the speed and accuracy of molecular property prediction. We apply the method to the discovery of electrolyte molecules for aqueous redox flow batteries. Using multiple-type-as opposed to single-type-descriptors, we obtain more relevant features for machine learning. Following the principle of "wisdom of the crowds", the combination of multiple-type descriptors significantly boosts prediction performance. Moreover, by employing multiple kernels-more than one kernel function for a set of the input descriptors-MultiDK exploits nonlinear relations between molecular structure and properties better than a linear regression approach. The multiple kernels consist of a Tanimoto similarity kernel and a linear kernel for a set of binary descriptors and a set of nonbinary descriptors, respectively. Using MultiDK, we achieve an average performance of r 2 = 0.92 with a test set of molecules for solubility prediction. We also extend MultiDK to predict pH-dependent solubility and apply it to a set of quinone molecules with different ionizable functional groups to assess their performance as flow battery electrolytes.

A comparison of methods for the analysis of binomial clustered outcomes in behavioral research.

PubMed

Ferrari, Alberto; Comelli, Mario

2016-12-01

In behavioral research, data consisting of a per-subject proportion of "successes" and "failures" over a finite number of trials often arise. This clustered binary data are usually non-normally distributed, which can distort inference if the usual general linear model is applied and sample size is small. A number of more advanced methods is available, but they are often technically challenging and a comparative assessment of their performances in behavioral setups has not been performed. We studied the performances of some methods applicable to the analysis of proportions; namely linear regression, Poisson regression, beta-binomial regression and Generalized Linear Mixed Models (GLMMs). We report on a simulation study evaluating power and Type I error rate of these models in hypothetical scenarios met by behavioral researchers; plus, we describe results from the application of these methods on data from real experiments. Our results show that, while GLMMs are powerful instruments for the analysis of clustered binary outcomes, beta-binomial regression can outperform them in a range of scenarios. Linear regression gave results consistent with the nominal level of significance, but was overall less powerful. Poisson regression, instead, mostly led to anticonservative inference. GLMMs and beta-binomial regression are generally more powerful than linear regression; yet linear regression is robust to model misspecification in some conditions, whereas Poisson regression suffers heavily from violations of the assumptions when used to model proportion data. We conclude providing directions to behavioral scientists dealing with clustered binary data and small sample sizes. Copyright © 2016 Elsevier B.V. All rights reserved.

Research on critical groundwater level under the threshold value of land subsidence in the typical region of Beijing

NASA Astrophysics Data System (ADS)

Jiang, Y.; Liu, J.-R.; Luo, Y.; Yang, Y.; Tian, F.; Lei, K.-C.

2015-11-01

Groundwater in Beijing has been excessively exploited in a long time, causing the groundwater level continued to declining and land subsidence areas expanding, which restrained the economic and social sustainable development. Long years of study show good time-space corresponding relationship between groundwater level and land subsidence. To providing scientific basis for the following land subsidence prevention and treatment, quantitative research between groundwater level and settlement is necessary. Multi-linear regression models are set up by long series factual monitoring data about layered water table and settlement in the Tianzhu monitoring station. The results show that: layered settlement is closely related to water table, water level variation and amplitude, especially the water table. Finally, according to the threshold value in the land subsidence prevention and control plan of China (45, 30, 25 mm), the minimum allowable layered water level in this region while settlement achieving the threshold value is calculated between -18.448 and -10.082 m. The results provide a reasonable and operable control target of groundwater level for rational adjustment of groundwater exploited horizon in the future.

Gender and racial differences in risk factors for sexually transmitted diseases among justice-involved youth

PubMed Central

Dembo, Richard; Childs, Kristina; Belenko, Steven; Schmeidler, James; Wareham, Jennifer

2010-01-01

Gender and racial differences in infection rates for chlamydia and gonorrhea have been reported within community-based populations, but little is known of such differences within juvenile offending populations. Moreover, while research has demonstrated that certain individual-level and community-level factors affect risky behaviors associated with sexually transmitted disease (STD), less is known about how multi-level factors affect STD infection, particularly among delinquent populations. The present study investigated gender and racial differences in STD infection among a sample of 924 juvenile offenders. Generalized linear model regression analyses were conducted to examine the influence of individual-level factors such as age, offense history, and substance use and community-level factors such as concentrated disadvantage, ethnic heterogeneity, and family disruption on STD status. Results revealed significant racial and STD status differences across gender, as well as interaction effects for race and STD status for males only. Gender differences in individual-level and community-level predictors were also found. Implications of these findings for future research and public health policy are discussed. PMID:20700475

Quantification of endocrine disruptors and pesticides in water by gas chromatography-tandem mass spectrometry. Method validation using weighted linear regression schemes.

PubMed

Mansilha, C; Melo, A; Rebelo, H; Ferreira, I M P L V O; Pinho, O; Domingues, V; Pinho, C; Gameiro, P

2010-10-22

A multi-residue methodology based on a solid phase extraction followed by gas chromatography-tandem mass spectrometry was developed for trace analysis of 32 compounds in water matrices, including estrogens and several pesticides from different chemical families, some of them with endocrine disrupting properties. Matrix standard calibration solutions were prepared by adding known amounts of the analytes to a residue-free sample to compensate matrix-induced chromatographic response enhancement observed for certain pesticides. Validation was done mainly according to the International Conference on Harmonisation recommendations, as well as some European and American validation guidelines with specifications for pesticides analysis and/or GC-MS methodology. As the assumption of homoscedasticity was not met for analytical data, weighted least squares linear regression procedure was applied as a simple and effective way to counteract the greater influence of the greater concentrations on the fitted regression line, improving accuracy at the lower end of the calibration curve. The method was considered validated for 31 compounds after consistent evaluation of the key analytical parameters: specificity, linearity, limit of detection and quantification, range, precision, accuracy, extraction efficiency, stability and robustness. Copyright © 2010 Elsevier B.V. All rights reserved.

Can multi-slice or navigator-gated R2* MRI replace single-slice breath-hold acquisition for hepatic iron quantification?

PubMed

Loeffler, Ralf B; McCarville, M Beth; Wagstaff, Anne W; Smeltzer, Matthew P; Krafft, Axel J; Song, Ruitian; Hankins, Jane S; Hillenbrand, Claudia M

2017-01-01

Liver R2* values calculated from multi-gradient echo (mGRE) magnetic resonance images (MRI) are strongly correlated with hepatic iron concentration (HIC) as shown in several independently derived biopsy calibration studies. These calibrations were established for axial single-slice breath-hold imaging at the location of the portal vein. Scanning in multi-slice mode makes the exam more efficient, since whole-liver coverage can be achieved with two breath-holds and the optimal slice can be selected afterward. Navigator echoes remove the need for breath-holds and allow use in sedated patients. To evaluate if the existing biopsy calibrations can be applied to multi-slice and navigator-controlled mGRE imaging in children with hepatic iron overload, by testing if there is a bias-free correlation between single-slice R2* and multi-slice or multi-slice navigator controlled R2*. This study included MRI data from 71 patients with transfusional iron overload, who received an MRI exam to estimate HIC using gradient echo sequences. Patient scans contained 2 or 3 of the following imaging methods used for analysis: single-slice images (n = 71), multi-slice images (n = 69) and navigator-controlled images (n = 17). Small and large blood corrected region of interests were selected on axial images of the liver to obtain R2* values for all data sets. Bland-Altman and linear regression analysis were used to compare R2* values from single-slice images to those of multi-slice images and navigator-controlled images. Bland-Altman analysis showed that all imaging method comparisons were strongly associated with each other and had high correlation coefficients (0.98 ≤ r ≤ 1.00) with P-values ≤0.0001. Linear regression yielded slopes that were close to 1. We found that navigator-gated or breath-held multi-slice R2* MRI for HIC determination measures R2* values comparable to the biopsy-validated single-slice, single breath-hold scan. We conclude that these three R2* methods can be interchangeably used in existing R2*-HIC calibrations.

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.

Factors influencing time-location patterns and their impact on estimates of exposure: the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air).

PubMed

Spalt, Elizabeth W; Curl, Cynthia L; Allen, Ryan W; Cohen, Martin; Williams, Kayleen; Hirsch, Jana A; Adar, Sara D; Kaufman, Joel D

2016-06-01

We assessed time-location patterns and the role of individual- and residential-level characteristics on these patterns within the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) cohort and also investigated the impact of individual-level time-location patterns on individual-level estimates of exposure to outdoor air pollution. Reported time-location patterns varied significantly by demographic factors such as age, gender, race/ethnicity, income, education, and employment status. On average, Chinese participants reported spending significantly more time indoors and less time outdoors and in transit than White, Black, or Hispanic participants. Using a tiered linear regression approach, we predicted time indoors at home and total time indoors. Our model, developed using forward-selection procedures, explained 43% of the variability in time spent indoors at home, and incorporated demographic, health, lifestyle, and built environment factors. Time-weighted air pollution predictions calculated using recommended time indoors from USEPA overestimated exposures as compared with predictions made with MESA Air participant-specific information. These data fill an important gap in the literature by describing the impact of individual and residential characteristics on time-location patterns and by demonstrating the impact of population-specific data on exposure estimates.

Macro-level gender equality and alcohol consumption: a multi-level analysis across U.S. States.

PubMed

Roberts, Sarah C M

2012-07-01

Higher levels of women's alcohol consumption have long been attributed to increases in gender equality. However, only limited research examines the relationship between gender equality and alcohol consumption. This study examined associations between five measures of state-level gender equality and five alcohol consumption measures in the United States. Survey data regarding men's and women's alcohol consumption from the 2005 Behavioral Risk Factor Surveillance System were linked to state-level indicators of gender equality. Gender equality indicators included state-level women's socioeconomic status, gender equality in socioeconomic status, reproductive rights, policies relating to violence against women, and women's political participation. Alcohol consumption measures included past 30-day drinker status, drinking frequency, binge drinking, volume, and risky drinking. Other than drinker status, consumption is measured for drinkers only. Multi-level linear and logistic regression models adjusted for individual demographics as well as state-level income inequality, median income, and % Evangelical Protestant/Mormon. All gender equality indicators were positively associated with both women's and men's drinker status in models adjusting only for individual-level covariates; associations were not significant in models adjusting for other state-level characteristics. All other associations between gender equality and alcohol consumption were either negative or non-significant for both women and men in models adjusting for other state-level factors. Findings do not support the hypothesis that higher levels of gender equality are associated with higher levels of alcohol consumption by women or by men. In fact, most significant findings suggest that higher levels of equality are associated with less alcohol consumption overall. Copyright © 2012 Elsevier Ltd. All rights reserved.

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

PubMed

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

2015-10-08

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

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

PubMed Central

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

2015-01-01

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

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...

Differential gene expression detection and sample classification using penalized linear regression models.

PubMed

Wu, Baolin

2006-02-15

Differential gene expression detection and sample classification using microarray data have received much research interest recently. Owing to the large number of genes p and small number of samples n (p > n), microarray data analysis poses big challenges for statistical analysis. An obvious problem owing to the 'large p small n' is over-fitting. Just by chance, we are likely to find some non-differentially expressed genes that can classify the samples very well. The idea of shrinkage is to regularize the model parameters to reduce the effects of noise and produce reliable inferences. Shrinkage has been successfully applied in the microarray data analysis. The SAM statistics proposed by Tusher et al. and the 'nearest shrunken centroid' proposed by Tibshirani et al. are ad hoc shrinkage methods. Both methods are simple, intuitive and prove to be useful in empirical studies. Recently Wu proposed the penalized t/F-statistics with shrinkage by formally using the (1) penalized linear regression models for two-class microarray data, showing good performance. In this paper we systematically discussed the use of penalized regression models for analyzing microarray data. We generalize the two-class penalized t/F-statistics proposed by Wu to multi-class microarray data. We formally derive the ad hoc shrunken centroid used by Tibshirani et al. using the (1) penalized regression models. And we show that the penalized linear regression models provide a rigorous and unified statistical framework for sample classification and differential gene expression detection.

Electronic and spectroscopic characterizations of SNP isomers

NASA Astrophysics Data System (ADS)

Trabelsi, Tarek; Al Mogren, Muneerah Mogren; Hochlaf, Majdi; Francisco, Joseph S.

2018-02-01

High-level ab initio electronic structure calculations were performed to characterize SNP isomers. In addition to the known linear SNP, cyc-PSN, and linear SPN isomers, we identified a fourth isomer, linear PSN, which is located ˜2.4 eV above the linear SNP isomer. The low-lying singlet and triplet electronic states of the linear SNP and SPN isomers were investigated using a multi-reference configuration interaction method and large basis set. Several bound electronic states were identified. However, their upper rovibrational levels were predicted to pre-dissociate, leading to S + PN, P + NS products, and multi-step pathways were discovered. For the ground states, a set of spectroscopic parameters were derived using standard and explicitly correlated coupled-cluster methods in conjunction with augmented correlation-consistent basis sets extrapolated to the complete basis set limit. We also considered scalar and core-valence effects. For linear isomers, the rovibrational spectra were deduced after generation of their 3D-potential energy surfaces along the stretching and bending coordinates and variational treatments of the nuclear motions.

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.

Multivariate Regression Analysis of Winter Ozone Events in the Uinta Basin of Eastern Utah, USA

NASA Astrophysics Data System (ADS)

Mansfield, M. L.

2012-12-01

I report on a regression analysis of a number of variables that are involved in the formation of winter ozone in the Uinta Basin of Eastern Utah. One goal of the analysis is to develop a mathematical model capable of predicting the daily maximum ozone concentration from values of a number of independent variables. The dependent variable is the daily maximum ozone concentration at a particular site in the basin. Independent variables are (1) daily lapse rate, (2) daily "basin temperature" (defined below), (3) snow cover, (4) midday solar zenith angle, (5) monthly oil production, (6) monthly gas production, and (7) the number of days since the beginning of a multi-day inversion event. Daily maximum temperature and daily snow cover data are available at ten or fifteen different sites throughout the basin. The daily lapse rate is defined operationally as the slope of the linear least-squares fit to the temperature-altitude plot, and the "basin temperature" is defined as the value assumed by the same least-squares line at an altitude of 1400 m. A multi-day inversion event is defined as a set of consecutive days for which the lapse rate remains positive. The standard deviation in the accuracy of the model is about 10 ppb. The model has been combined with historical climate and oil & gas production data to estimate historical ozone levels.

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

NASA Astrophysics Data System (ADS)

Indarsih, Indrati, Ch. Rini

2016-02-01

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

Non-linear education gradient across the nutrition transition: mothers’ overweight and the population education transition

PubMed Central

Salinas, Daniel; Baker, David P

2015-01-01

Objective Previous studies found that developed and developing countries present opposite education-overweight gradients but have not considered the dynamics at different levels of national development. A U-inverted curve is hypothesized to best describe the education-overweight association. It is also hypothesized that as the nutrition transition unfolds within nations the shape of education-overweight curve change. Design Multi-level logistic regression estimates the moderating effect of the nutrition transition at the population level on education-overweight gradient. At the individual level, a non-linear estimate of the education association assesses the optimal functional form of the association across the nutrition transition. Setting Twenty-two administrations of the Demographic and Health Survey, collected at different time points across the nutrition transition in nine Latin American/Caribbean countries. Subjects Mothers of reproductive age (15–49) in each administration (n 143,258). Results In the pooled sample, a non-linear education gradient on mothers‘ overweight is found; each additional year of schooling increases the probability of being overweight up to the end of primary schooling, after which each additional year of schooling decreases the probability of overweight. Also, as access to diets of high animal fats and sweeteners increases over time, the curve‘s critical point moves to lower education levels, the detrimental positive effect of education diminishes, and both occur as the overall risk of overweight increases with greater access to harmful diets. Conclusions Both hypotheses are supported. As the nutrition transition progresses, the education-overweight curve steadily shifts to a negative linear association with higher average risk of overweight; and education, at increasingly lower levels, acts as a “social vaccine” against increasing risk of overweight. These empirical patterns fit the general “population education transition” (PET) curve hypothesis about how education influences on health risks are contextualized across population transitions. PMID:26054756

A systematic study on the influencing parameters and improvement of quantitative analysis of multi-component with single marker method using notoginseng as research subject.

PubMed

Wang, Chao-Qun; Jia, Xiu-Hong; Zhu, Shu; Komatsu, Katsuko; Wang, Xuan; Cai, Shao-Qing

2015-03-01

A new quantitative analysis of multi-component with single marker (QAMS) method for 11 saponins (ginsenosides Rg1, Rb1, Rg2, Rh1, Rf, Re and Rd; notoginsenosides R1, R4, Fa and K) in notoginseng was established, when 6 of these saponins were individually used as internal referring substances to investigate the influences of chemical structure, concentrations of quantitative components, and purities of the standard substances on the accuracy of the QAMS method. The results showed that the concentration of the analyte in sample solution was the major influencing parameter, whereas the other parameters had minimal influence on the accuracy of the QAMS method. A new method for calculating the relative correction factors by linear regression was established (linear regression method), which demonstrated to decrease standard method differences of the QAMS method from 1.20%±0.02% - 23.29%±3.23% to 0.10%±0.09% - 8.84%±2.85% in comparison with the previous method. And the differences between external standard method and the QAMS method using relative correction factors calculated by linear regression method were below 5% in the quantitative determination of Rg1, Re, R1, Rd and Fa in 24 notoginseng samples and Rb1 in 21 notoginseng samples. And the differences were mostly below 10% in the quantitative determination of Rf, Rg2, R4 and N-K (the differences of these 4 constituents bigger because their contents lower) in all the 24 notoginseng samples. The results indicated that the contents assayed by the new QAMS method could be considered as accurate as those assayed by external standard method. In addition, a method for determining applicable concentration ranges of the quantitative components assayed by QAMS method was established for the first time, which could ensure its high accuracy and could be applied to QAMS methods of other TCMs. The present study demonstrated the practicability of the application of the QAMS method for the quantitative analysis of multi-component and the quality control of TCMs and TCM prescriptions. Copyright © 2014 Elsevier B.V. All rights reserved.

Gender difference in cycling speed and age of winning performers in ultra-cycling - the 508-mile "Furnace Creek" from 1983 to 2012.

PubMed

Rüst, Christoph Alexander; Rosemann, Thomas; Lepers, Romuald; Knechtle, Beat

2015-01-01

We analysed (i) the gender difference in cycling speed and (ii) the age of winning performers in the 508-mile "Furnace Creek 508". Changes in cycling speeds and gender differences from 1983 to 2012 were analysed using linear, non-linear and hierarchical multi-level regression analyses for the annual three fastest women and men. Cycling speed increased non-linearly in men from 14.6 (s = 0.3) km · h(-1) (1983) to 27.1 (s = 0.7) km · h(-1) (2012) and non-linearly in women from 11.0 (s = 0.3) km · h(-1) (1984) to 24.2 (s = 0.2) km · h(-1) (2012). The gender difference in cycling speed decreased linearly from 26.2 (s = 0.5)% (1984) to 10.7 (s = 1.9)% (2012). The age of winning performers increased from 26 (s = 2) years (1984) to 43 (s = 11) years (2012) in women and from 33 (s = 6) years (1983) to 50 (s = 5) years (2012) in men. To summarise, these results suggest that (i) women will be able to narrow the gender gap in cycling speed in the near future in an ultra-endurance cycling race such as the "Furnace Creek 508" due to the linear decrease in gender difference and (ii) the maturity of these athletes has changed during the last three decades where winning performers become older and faster across years.

Computerized dynamic posturography: the influence of platform stability on postural control.

PubMed

Palm, Hans-Georg; Lang, Patricia; Strobel, Johannes; Riesner, Hans-Joachim; Friemert, Benedikt

2014-01-01

Postural stability can be quantified using posturography systems, which allow different foot platform stability settings to be selected. It is unclear, however, how platform stability and postural control are mathematically correlated. Twenty subjects performed tests on the Biodex Stability System at all 13 stability levels. Overall stability index, medial-lateral stability index, and anterior-posterior stability index scores were calculated, and data were analyzed using analysis of variance and linear regression analysis. A decrease in platform stability from the static level to the second least stable level was associated with a linear decrease in postural control. The overall stability index scores were 1.5 ± 0.8 degrees (static), 2.2 ± 0.9 degrees (level 8), and 3.6 ± 1.7 degrees (level 2). The slope of the regression lines was 0.17 for the men and 0.10 for the women. A linear correlation was demonstrated between platform stability and postural control. The influence of stability levels seems to be almost twice as high in men as in women.

A clinical study of electrophysiological correlates of behavioural comfort levels in cochlear implantees.

PubMed

Raghunandhan, S; Ravikumar, A; Kameswaran, Mohan; Mandke, Kalyani; Ranjith, R

2014-05-01

Indications for cochlear implantation have expanded today to include very young children and those with syndromes/multiple handicaps. Programming the implant based on behavioural responses may be tedious for audiologists in such cases, wherein matching an effective Measurable Auditory Percept (MAP) and appropriate MAP becomes the key issue in the habilitation program. In 'Difficult to MAP' scenarios, objective measures become paramount to predict optimal current levels to be set in the MAP. We aimed to (a) study the trends in multi-modal electrophysiological tests and behavioural responses sequentially over the first year of implant use; (b) generate normative data from the above; (c) correlate the multi-modal electrophysiological thresholds levels with behavioural comfort levels; and (d) create predictive formulae for deriving optimal comfort levels (if unknown), using linear and multiple regression analysis. This prospective study included 10 profoundly hearing impaired children aged between 2 and 7 years with normal inner ear anatomy and no additional handicaps. They received the Advanced Bionics HiRes 90 K Implant with Harmony Speech processor and used HiRes-P with Fidelity 120 strategy. They underwent, impedance telemetry, neural response imaging, electrically evoked stapedial response telemetry (ESRT), and electrically evoked auditory brainstem response (EABR) tests at 1, 4, 8, and 12 months of implant use, in conjunction with behavioural mapping. Trends in electrophysiological and behavioural responses were analyzed using paired t-test. By Karl Pearson's correlation method, electrode-wise correlations were derived for neural response imaging (NRI) thresholds versus most comfortable level (M-levels) and offset based (apical, mid-array, and basal array) correlations for EABR and ESRT thresholds versus M-levels were calculated over time. These were used to derive predictive formulae by linear and multiple regression analysis. Such statistically predicted M-levels were compared with the behaviourally recorded M-levels among the cohort, using Cronbach's alpha reliability test method for confirming the efficacy of this method. NRI, ESRT, and EABR thresholds showed statistically significant positive correlations with behavioural M-levels, which improved with implant use over time. These correlations were used to derive predicted M-levels using regression analysis. On an average, predicted M-levels were found to be statistically reliable and they were a fair match to the actual behavioural M-levels. When applied in clinical practice, the predicted values were found to be useful for programming members of the study group. However, individuals showed considerable deviations in behavioural M-levels, above and below the electrophysiologically predicted values, due to various factors. While the current method appears helpful as a reference to predict initial maps in 'difficult to Map' subjects, it is recommended that behavioural measures are mandatory to further optimize the maps for these individuals. The study explores the trends, correlations and individual variabilities that occur between electrophysiological tests and behavioural responses, recorded over time among a cohort of cochlear implantees. The statistical method shown may be used as a guideline to predict optimal behavioural levels in difficult situations among future implantees, bearing in mind that optimal M-levels for individuals can vary from predicted values. In 'Difficult to MAP' scenarios, following a protocol of sequential behavioural programming, in conjunction with electrophysiological correlates will provide the best outcomes.

Macro-level gender equality and alcohol consumption: A multi-level analysis across U.S. States

PubMed Central

Roberts, Sarah C.M.

2014-01-01

Higher levels of women’s alcohol consumption have long been attributed to increases in gender equality. However, only limited research examines the relationship between gender equality and alcohol consumption. This study examined associations between five measures of state-level gender equality and five alcohol consumption measures in the United States. Survey data regarding men’s and women’s alcohol consumption from the 2005 Behavioral Risk Factor Surveillance System were linked to state-level indicators of gender equality. Gender equality indicators included state-level women’s socioeconomic status, gender equality in socioeconomic status, reproductive rights, policies relating to violence against women, and women’s political participation. Alcohol consumption measures included past 30-day drinker status, drinking frequency, binge drinking, volume, and risky drinking. Other than drinker status, consumption is measured for drinkers only. Multi-level linear and logistic regression models adjusted for individual demographics as well as state-level income inequality, median income, and % Evangelical Protestant/Mormon. All gender equality indicators were positively associated with both women’s and men’s drinker status in models adjusting only for individual-level covariates; associations were not significant in models adjusting for other state-level characteristics. All other associations between gender equality and alcohol consumption were either negative or non-significant for both women and men in models adjusting for other state-level factors. Findings do not support the hypothesis that higher levels of gender equality are associated with higher levels of alcohol consumption by women or by men. In fact, most significant findings suggest that higher levels of equality are associated with less alcohol consumption overall. PMID:22521679

Data Assimilation and Propagation of Uncertainty in Multiscale Cardiovascular Simulation

NASA Astrophysics Data System (ADS)

Schiavazzi, Daniele; Marsden, Alison

2015-11-01

Cardiovascular modeling is the application of computational tools to predict hemodynamics. State-of-the-art techniques couple a 3D incompressible Navier-Stokes solver with a boundary circulation model and can predict local and peripheral hemodynamics, analyze the post-operative performance of surgical designs and complement clinical data collection minimizing invasive and risky measurement practices. The ability of these tools to make useful predictions is directly related to their accuracy in representing measured physiologies. Tuning of model parameters is therefore a topic of paramount importance and should include clinical data uncertainty, revealing how this uncertainty will affect the predictions. We propose a fully Bayesian, multi-level approach to data assimilation of uncertain clinical data in multiscale circulation models. To reduce the computational cost, we use a stable, condensed approximation of the 3D model build by linear sparse regression of the pressure/flow rate relationship at the outlets. Finally, we consider the problem of non-invasively propagating the uncertainty in model parameters to the resulting hemodynamics and compare Monte Carlo simulation with Stochastic Collocation approaches based on Polynomial or Multi-resolution Chaos expansions.

Reasons for Hierarchical Linear Modeling: A Reminder.

ERIC Educational Resources Information Center

Wang, Jianjun

1999-01-01

Uses examples of hierarchical linear modeling (HLM) at local and national levels to illustrate proper applications of HLM and dummy variable regression. Raises cautions about the circumstances under which hierarchical data do not need HLM. (SLD)

Boosting multi-state models.

PubMed

Reulen, Holger; Kneib, Thomas

2016-04-01

One important goal in multi-state modelling is to explore information about conditional transition-type-specific hazard rate functions by estimating influencing effects of explanatory variables. This may be performed using single transition-type-specific models if these covariate effects are assumed to be different across transition-types. To investigate whether this assumption holds or whether one of the effects is equal across several transition-types (cross-transition-type effect), a combined model has to be applied, for instance with the use of a stratified partial likelihood formulation. Here, prior knowledge about the underlying covariate effect mechanisms is often sparse, especially about ineffectivenesses of transition-type-specific or cross-transition-type effects. As a consequence, data-driven variable selection is an important task: a large number of estimable effects has to be taken into account if joint modelling of all transition-types is performed. A related but subsequent task is model choice: is an effect satisfactory estimated assuming linearity, or is the true underlying nature strongly deviating from linearity? This article introduces component-wise Functional Gradient Descent Boosting (short boosting) for multi-state models, an approach performing unsupervised variable selection and model choice simultaneously within a single estimation run. We demonstrate that features and advantages in the application of boosting introduced and illustrated in classical regression scenarios remain present in the transfer to multi-state models. As a consequence, boosting provides an effective means to answer questions about ineffectiveness and non-linearity of single transition-type-specific or cross-transition-type effects.

Monthly monsoon rainfall forecasting using artificial neural networks

NASA Astrophysics Data System (ADS)

Ganti, Ravikumar

2014-10-01

Indian agriculture sector heavily depends on monsoon rainfall for successful harvesting. In the past, prediction of rainfall was mainly performed using regression models, which provide reasonable accuracy in the modelling and forecasting of complex physical systems. Recently, Artificial Neural Networks (ANNs) have been proposed as efficient tools for modelling and forecasting. A feed-forward multi-layer perceptron type of ANN architecture trained using the popular back-propagation algorithm was employed in this study. Other techniques investigated for modeling monthly monsoon rainfall include linear and non-linear regression models for comparison purposes. The data employed in this study include monthly rainfall and monthly average of the daily maximum temperature in the North Central region in India. Specifically, four regression models and two ANN model's were developed. The performance of various models was evaluated using a wide variety of standard statistical parameters and scatter plots. The results obtained in this study for forecasting monsoon rainfalls using ANNs have been encouraging. India's economy and agricultural activities can be effectively managed with the help of the availability of the accurate monsoon rainfall forecasts.

Hourly predictive Levenberg-Marquardt ANN and multi linear regression models for predicting of dew point temperature

NASA Astrophysics Data System (ADS)

Zounemat-Kermani, Mohammad

2012-08-01

In this study, the ability of two models of multi linear regression (MLR) and Levenberg-Marquardt (LM) feed-forward neural network was examined to estimate the hourly dew point temperature. Dew point temperature is the temperature at which water vapor in the air condenses into liquid. This temperature can be useful in estimating meteorological variables such as fog, rain, snow, dew, and evapotranspiration and in investigating agronomical issues as stomatal closure in plants. The availability of hourly records of climatic data (air temperature, relative humidity and pressure) which could be used to predict dew point temperature initiated the practice of modeling. Additionally, the wind vector (wind speed magnitude and direction) and conceptual input of weather condition were employed as other input variables. The three quantitative standard statistical performance evaluation measures, i.e. the root mean squared error, mean absolute error, and absolute logarithmic Nash-Sutcliffe efficiency coefficient ( {| {{{Log}}({{NS}})} |} ) were employed to evaluate the performances of the developed models. The results showed that applying wind vector and weather condition as input vectors along with meteorological variables could slightly increase the ANN and MLR predictive accuracy. The results also revealed that LM-NN was superior to MLR model and the best performance was obtained by considering all potential input variables in terms of different evaluation criteria.

New Approach To Hour-By-Hour Weather Forecast

NASA Astrophysics Data System (ADS)

Liao, Q. Q.; Wang, B.

2017-12-01

Fine hourly forecast in single station weather forecast is required in many human production and life application situations. Most previous MOS (Model Output Statistics) which used a linear regression model are hard to solve nonlinear natures of the weather prediction and forecast accuracy has not been sufficient at high temporal resolution. This study is to predict the future meteorological elements including temperature, precipitation, relative humidity and wind speed in a local region over a relatively short period of time at hourly level. By means of hour-to-hour NWP (Numeral Weather Prediction)meteorological field from Forcastio (https://darksky.net/dev/docs/forecast) and real-time instrumental observation including 29 stations in Yunnan and 3 stations in Tianjin of China from June to October 2016, predictions are made of the 24-hour hour-by-hour ahead. This study presents an ensemble approach to combine the information of instrumental observation itself and NWP. Use autoregressive-moving-average (ARMA) model to predict future values of the observation time series. Put newest NWP products into the equations derived from the multiple linear regression MOS technique. Handle residual series of MOS outputs with autoregressive (AR) model for the linear property presented in time series. Due to the complexity of non-linear property of atmospheric flow, support vector machine (SVM) is also introduced . Therefore basic data quality control and cross validation makes it able to optimize the model function parameters , and do 24 hours ahead residual reduction with AR/SVM model. Results show that AR model technique is better than corresponding multi-variant MOS regression method especially at the early 4 hours when the predictor is temperature. MOS-AR combined model which is comparable to MOS-SVM model outperform than MOS. Both of their root mean square error and correlation coefficients for 2 m temperature are reduced to 1.6 degree Celsius and 0.91 respectively. The forecast accuracy of 24- hour forecast deviation no more than 2 degree Celsius is 78.75 % for MOS-AR model and 81.23 % for AR model.

Photometric redshift estimation based on data mining with PhotoRApToR

NASA Astrophysics Data System (ADS)

Cavuoti, S.; Brescia, M.; De Stefano, V.; Longo, G.

2015-03-01

Photometric redshifts (photo-z) are crucial to the scientific exploitation of modern panchromatic digital surveys. In this paper we present PhotoRApToR (Photometric Research Application To Redshift): a Java/C ++ based desktop application capable to solve non-linear regression and multi-variate classification problems, in particular specialized for photo-z estimation. It embeds a machine learning algorithm, namely a multi-layer neural network trained by the Quasi Newton learning rule, and special tools dedicated to pre- and post-processing data. PhotoRApToR has been successfully tested on several scientific cases. The application is available for free download from the DAME Program web site.

Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter

PubMed Central

Gao, Bingbing; Hu, Gaoge; Gao, Shesheng; Gu, Chengfan

2018-01-01

This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation. PMID:29415509

Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter.

PubMed

Gao, Bingbing; Hu, Gaoge; Gao, Shesheng; Zhong, Yongmin; Gu, Chengfan

2018-02-06

This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation.

Multi-segmental movements as a function of experience in karate.

PubMed

Zago, Matteo; Codari, Marina; Iaia, F Marcello; Sforza, Chiarella

2017-08-01

Karate is a martial art that partly depends on subjective scoring of complex movements. Principal component analysis (PCA)-based methods can identify the fundamental synergies (principal movements) of motor system, providing a quantitative global analysis of technique. In this study, we aimed at describing the fundamental multi-joint synergies of a karate performance, under the hypothesis that the latter are skilldependent; estimate karateka's experience level, expressed as years of practice. A motion capture system recorded traditional karate techniques of 10 professional and amateur karateka. At any time point, the 3D-coordinates of body markers produced posture vectors that were normalised, concatenated from all karateka and submitted to a first PCA. Five principal movements described both gross movement synergies and individual differences. A second PCA followed by linear regression estimated the years of practice using principal movements (eigenpostures and weighting curves) and centre of mass kinematics (error: 3.71 years; R2 = 0.91, P ≪ 0.001). Principal movements and eigenpostures varied among different karateka and as functions of experience. This approach provides a framework to develop visual tools for the analysis of motor synergies in karate, allowing to detect the multi-joint motor patterns that should be restored after an injury, or to be specifically trained to increase performance.

Support vector regression-guided unravelling: antioxidant capacity and quantitative structure-activity relationship predict reduction and promotion effects of flavonoids on acrylamide formation

PubMed Central

Huang, Mengmeng; Wei, Yan; Wang, Jun; Zhang, Yu

2016-01-01

We used the support vector regression (SVR) approach to predict and unravel reduction/promotion effect of characteristic flavonoids on the acrylamide formation under a low-moisture Maillard reaction system. Results demonstrated the reduction/promotion effects by flavonoids at addition levels of 1–10000 μmol/L. The maximal inhibition rates (51.7%, 68.8% and 26.1%) and promote rates (57.7%, 178.8% and 27.5%) caused by flavones, flavonols and isoflavones were observed at addition levels of 100 μmol/L and 10000 μmol/L, respectively. The reduction/promotion effects were closely related to the change of trolox equivalent antioxidant capacity (ΔTEAC) and well predicted by triple ΔTEAC measurements via SVR models (R: 0.633–0.900). Flavonols exhibit stronger effects on the acrylamide formation than flavones and isoflavones as well as their O-glycosides derivatives, which may be attributed to the number and position of phenolic and 3-enolic hydroxyls. The reduction/promotion effects were well predicted by using optimized quantitative structure-activity relationship (QSAR) descriptors and SVR models (R: 0.926–0.994). Compared to artificial neural network and multi-linear regression models, SVR models exhibited better fitting performance for both TEAC-dependent and QSAR descriptor-dependent predicting work. These observations demonstrated that the SVR models are competent for predicting our understanding on the future use of natural antioxidants for decreasing the acrylamide formation. PMID:27586851

Support vector regression-guided unravelling: antioxidant capacity and quantitative structure-activity relationship predict reduction and promotion effects of flavonoids on acrylamide formation

NASA Astrophysics Data System (ADS)

Huang, Mengmeng; Wei, Yan; Wang, Jun; Zhang, Yu

2016-09-01

We used the support vector regression (SVR) approach to predict and unravel reduction/promotion effect of characteristic flavonoids on the acrylamide formation under a low-moisture Maillard reaction system. Results demonstrated the reduction/promotion effects by flavonoids at addition levels of 1-10000 μmol/L. The maximal inhibition rates (51.7%, 68.8% and 26.1%) and promote rates (57.7%, 178.8% and 27.5%) caused by flavones, flavonols and isoflavones were observed at addition levels of 100 μmol/L and 10000 μmol/L, respectively. The reduction/promotion effects were closely related to the change of trolox equivalent antioxidant capacity (ΔTEAC) and well predicted by triple ΔTEAC measurements via SVR models (R: 0.633-0.900). Flavonols exhibit stronger effects on the acrylamide formation than flavones and isoflavones as well as their O-glycosides derivatives, which may be attributed to the number and position of phenolic and 3-enolic hydroxyls. The reduction/promotion effects were well predicted by using optimized quantitative structure-activity relationship (QSAR) descriptors and SVR models (R: 0.926-0.994). Compared to artificial neural network and multi-linear regression models, SVR models exhibited better fitting performance for both TEAC-dependent and QSAR descriptor-dependent predicting work. These observations demonstrated that the SVR models are competent for predicting our understanding on the future use of natural antioxidants for decreasing the acrylamide formation.

Maintenance Operations in Mission Oriented Protective Posture Level IV (MOPPIV)

DTIC Science & Technology

1987-10-01

Repair FADAC Printed Circuit Board ............. 6 3. Data Analysis Techniques ............................. 6 a. Multiple Linear Regression... ANALYSIS /DISCUSSION ............................... 12 1. Exa-ple of Regression Analysis ..................... 12 S2. Regression results for all tasks...6 * TABLE 9. Task Grouping for Analysis ........................ 7 "TABXLE 10. Remove/Replace H60A3 Power Pack................. 8 TABLE

Regression analysis using dependent Polya trees.

PubMed

Schörgendorfer, Angela; Branscum, Adam J

2013-11-30

Many commonly used models for linear regression analysis force overly simplistic shape and scale constraints on the residual structure of data. We propose a semiparametric Bayesian model for regression analysis that produces data-driven inference by using a new type of dependent Polya tree prior to model arbitrary residual distributions that are allowed to evolve across increasing levels of an ordinal covariate (e.g., time, in repeated measurement studies). By modeling residual distributions at consecutive covariate levels or time points using separate, but dependent Polya tree priors, distributional information is pooled while allowing for broad pliability to accommodate many types of changing residual distributions. We can use the proposed dependent residual structure in a wide range of regression settings, including fixed-effects and mixed-effects linear and nonlinear models for cross-sectional, prospective, and repeated measurement data. A simulation study illustrates the flexibility of our novel semiparametric regression model to accurately capture evolving residual distributions. In an application to immune development data on immunoglobulin G antibodies in children, our new model outperforms several contemporary semiparametric regression models based on a predictive model selection criterion. Copyright © 2013 John Wiley & Sons, Ltd.

Mid-Level Healthcare Personnel Training: An Evaluation of the Revised, Nationally-Standardized, Pre-Service Curriculum for Clinical Officers in Mozambique

PubMed Central

Feldacker, Caryl; Chicumbe, Sergio; Dgedge, Martinho; Augusto, Gerito; Cesar, Freide; Robertson, Molly; Mbofana, Francisco; O'Malley, Gabrielle

2014-01-01

Introduction Mozambique suffers from a critical shortage of healthcare workers. Mid-level healthcare workers, (Tecnicos de Medicina Geral (TMG)), in Mozambique require less money and time to train than physicians. From 2009–2010, the Mozambique Ministry of Health (MoH) and the International Training and Education Center for Health (I-TECH), University of Washington, Seattle, revised the TMG curriculum. To evaluate the effect of the curriculum revision, we used mixed methods to determine: 1) if TMGs meet the MoH's basic standards of clinical competency; and 2) do scores on measurements of clinical knowledge, physical exam, and clinical case scenarios differ by curriculum? Methods T-tests of differences in means examined differences in continuous score variables between curriculum groups. Univariate and multivariate linear regression models assess curriculum-related and demographic factors associated with assessment scores on each of the three evaluation methods at the p<0.05 level. Qualitative interviews and focus groups inform interpretation. Results We found no significant differences in sex, marital status and age between the 112 and 189 TMGs in initial and revised curriculum, respectively. Mean scores at graduation of initial curriculum TMGs were 56.7%, 63.5%, and 49.1% on the clinical cases, knowledge test, and physical exam, respectively. Scores did not differ significantly from TMGs in the revised curriculum. Results from linear regression models find that training institute was the most significant predictor of TMG scores on both the clinical cases and physical exam. Conclusion TMGs trained in either curriculum may be inadequately prepared to provide quality care. Curriculum changes are a necessary, but insufficient, part of improving TMG knowledge and skills overall. A more comprehensive, multi-level approach to improving TMG training that includes post-graduation mentoring, strengthening the pre-service internship training, and greater resources for training institute faculty may result in improvements in TMG capacity and patient care over time. PMID:25068590

Mid-level healthcare personnel training: an evaluation of the revised, nationally-standardized, pre-service curriculum for clinical officers in Mozambique.

PubMed

Feldacker, Caryl; Chicumbe, Sergio; Dgedge, Martinho; Augusto, Gerito; Cesar, Freide; Robertson, Molly; Mbofana, Francisco; O'Malley, Gabrielle

2014-01-01

Mozambique suffers from a critical shortage of healthcare workers. Mid-level healthcare workers, (Tecnicos de Medicina Geral (TMG)), in Mozambique require less money and time to train than physicians. From 2009-2010, the Mozambique Ministry of Health (MoH) and the International Training and Education Center for Health (I-TECH), University of Washington, Seattle, revised the TMG curriculum. To evaluate the effect of the curriculum revision, we used mixed methods to determine: 1) if TMGs meet the MoH's basic standards of clinical competency; and 2) do scores on measurements of clinical knowledge, physical exam, and clinical case scenarios differ by curriculum? T-tests of differences in means examined differences in continuous score variables between curriculum groups. Univariate and multivariate linear regression models assess curriculum-related and demographic factors associated with assessment scores on each of the three evaluation methods at the p<0.05 level. Qualitative interviews and focus groups inform interpretation. We found no significant differences in sex, marital status and age between the 112 and 189 TMGs in initial and revised curriculum, respectively. Mean scores at graduation of initial curriculum TMGs were 56.7%, 63.5%, and 49.1% on the clinical cases, knowledge test, and physical exam, respectively. Scores did not differ significantly from TMGs in the revised curriculum. Results from linear regression models find that training institute was the most significant predictor of TMG scores on both the clinical cases and physical exam. TMGs trained in either curriculum may be inadequately prepared to provide quality care. Curriculum changes are a necessary, but insufficient, part of improving TMG knowledge and skills overall. A more comprehensive, multi-level approach to improving TMG training that includes post-graduation mentoring, strengthening the pre-service internship training, and greater resources for training institute faculty may result in improvements in TMG capacity and patient care over time.

Uncertainty Analysis in Large Area Aboveground Biomass Mapping

NASA Astrophysics Data System (ADS)

Baccini, A.; Carvalho, L.; Dubayah, R.; Goetz, S. J.; Friedl, M. A.

2011-12-01

Satellite and aircraft-based remote sensing observations are being more frequently used to generate spatially explicit estimates of aboveground carbon stock of forest ecosystems. Because deforestation and forest degradation account for circa 10% of anthropogenic carbon emissions to the atmosphere, policy mechanisms are increasingly recognized as a low-cost mitigation option to reduce carbon emission. They are, however, contingent upon the capacity to accurately measures carbon stored in the forests. Here we examine the sources of uncertainty and error propagation in generating maps of aboveground biomass. We focus on characterizing uncertainties associated with maps at the pixel and spatially aggregated national scales. We pursue three strategies to describe the error and uncertainty properties of aboveground biomass maps, including: (1) model-based assessment using confidence intervals derived from linear regression methods; (2) data-mining algorithms such as regression trees and ensembles of these; (3) empirical assessments using independently collected data sets.. The latter effort explores error propagation using field data acquired within satellite-based lidar (GLAS) acquisitions versus alternative in situ methods that rely upon field measurements that have not been systematically collected for this purpose (e.g. from forest inventory data sets). A key goal of our effort is to provide multi-level characterizations that provide both pixel and biome-level estimates of uncertainties at different scales.

Spacebased Estimation of Moisture Transport in Marine Atmosphere Using Support Vector Regression

NASA Technical Reports Server (NTRS)

Xie, Xiaosu; Liu, W. Timothy; Tang, Benyang

2007-01-01

An improved algorithm is developed based on support vector regression (SVR) to estimate horizonal water vapor transport integrated through the depth of the atmosphere ((Theta)) over the global ocean from observations of surface wind-stress vector by QuikSCAT, cloud drift wind vector derived from the Multi-angle Imaging SpectroRadiometer (MISR) and geostationary satellites, and precipitable water from the Special Sensor Microwave/Imager (SSM/I). The statistical relation is established between the input parameters (the surface wind stress, the 850 mb wind, the precipitable water, time and location) and the target data ((Theta) calculated from rawinsondes and reanalysis of numerical weather prediction model). The results are validated with independent daily rawinsonde observations, monthly mean reanalysis data, and through regional water balance. This study clearly demonstrates the improvement of (Theta) derived from satellite data using SVR over previous data sets based on linear regression and neural network. The SVR methodology reduces both mean bias and standard deviation comparedwith rawinsonde observations. It agrees better with observations from synoptic to seasonal time scales, and compare more favorably with the reanalysis data on seasonal variations. Only the SVR result can achieve the water balance over South America. The rationale of the advantage by SVR method and the impact of adding the upper level wind will also be discussed.

An Analysis of Turkey's PISA 2015 Results Using Two-Level Hierarchical Linear Modelling

ERIC Educational Resources Information Center

Atas, Dogu; Karadag, Özge

2017-01-01

In the field of education, most of the data collected are multi-level structured. Cities, city based schools, school based classes and finally students in the classrooms constitute a hierarchical structure. Hierarchical linear models give more accurate results compared to standard models when the data set has a structure going far as individuals,…

Plasma and serum L-selectin and clinical and subclinical the Multi-Ethnic Study of Atherosclerosis (MESA)cardiovascular disease

PubMed Central

BERARDI, CECILIA; DECKER, PAUL A.; KIRSCH, PHILLIP S.; DE ANDRADE, MARIZA; TSAI, MICHAEL Y.; PANKOW, JAMES S.; SALE, MICHELE M.; SICOTTE, HUGUES; TANG, WEIHONG; HANSON, NAOMI; POLAK, JOSEPH F.; BIELINSKI, SUZETTE J.

2014-01-01

L-selectin has been suggested to play a role in atherosclerosis. Previous studies on cardiovascular disease (CVD) and serum or plasma L-selectin are inconsistent. The association of serum L-selectin (sL-selectin) with carotid intima-media thickness, coronary artery calcium, ankle-brachial index (subclinical CVD) and incident CVD was assessed within 2403 participants in the Multi-Ethnic Study of Atherosclerosis (MESA). Regression analysis and the Tobit model were used to study subclinical disease; Cox Proportional Hazards regression for incident CVD. Mean age was 63 ± 10, 47% were males; mean sL-selectin was significantly different across ethnicities. Within each race/ethnicity, sL-selectin was associated with age and sex; among Caucasians and African Americans, it was associated with smoking status and current alcohol use. sL-selectin levels did not predict subclinical or clinical CVD after correction for multiple comparisons. Conditional logistic regression models were used to study plasma L-selectin and CVD within 154 incident CVD cases, occurred in a median follow up of 8.5 years, and 306 age-, sex-, and ethnicity-matched controls. L-selectin levels in plasma were significantly lower than in serum and the overall concordance was low. Plasma levels were not associated with CVD. In conclusion, this large multi-ethnic population, soluble L-selectin levels did not predict clinical or subclinical CVD. PMID:24631064

[Study of Cervical Exfoliated Cell's DNA Quantitative Analysis Based on Multi-Spectral Imaging Technology].

PubMed

Wu, Zheng; Zeng, Li-bo; Wu, Qiong-shui

2016-02-01

The conventional cervical cancer screening methods mainly include TBS (the bethesda system) classification method and cellular DNA quantitative analysis, however, by using multiple staining method in one cell slide, which is staining the cytoplasm with Papanicolaou reagent and the nucleus with Feulgen reagent, the study of achieving both two methods in the cervical cancer screening at the same time is still blank. Because the difficulty of this multiple staining method is that the absorbance of the non-DNA material may interfere with the absorbance of DNA, so that this paper has set up a multi-spectral imaging system, and established an absorbance unmixing model by using multiple linear regression method based on absorbance's linear superposition character, and successfully stripped out the absorbance of DNA to run the DNA quantitative analysis, and achieved the perfect combination of those two kinds of conventional screening method. Through a series of experiment we have proved that between the absorbance of DNA which is calculated by the absorbance unmixxing model and the absorbance of DNA which is measured there is no significant difference in statistics when the test level is 1%, also the result of actual application has shown that there is no intersection between the confidence interval of the DNA index of the tetraploid cells which are screened by using this paper's analysis method when the confidence level is 99% and the DNA index's judging interval of cancer cells, so that the accuracy and feasibility of the quantitative DNA analysis with multiple staining method expounded by this paper have been verified, therefore this analytical method has a broad application prospect and considerable market potential in early diagnosis of cervical cancer and other cancers.

Quantification of trace metals in infant formula premixes using laser-induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Cama-Moncunill, Raquel; Casado-Gavalda, Maria P.; Cama-Moncunill, Xavier; Markiewicz-Keszycka, Maria; Dixit, Yash; Cullen, Patrick J.; Sullivan, Carl

2017-09-01

Infant formula is a human milk substitute generally based upon fortified cow milk components. In order to mimic the composition of breast milk, trace elements such as copper, iron and zinc are usually added in a single operation using a premix. The correct addition of premixes must be verified to ensure that the target levels in infant formulae are achieved. In this study, a laser-induced breakdown spectroscopy (LIBS) system was assessed as a fast validation tool for trace element premixes. LIBS is a promising emission spectroscopic technique for elemental analysis, which offers real-time analyses, little to no sample preparation and ease of use. LIBS was employed for copper and iron determinations of premix samples ranging approximately from 0 to 120 mg/kg Cu/1640 mg/kg Fe. LIBS spectra are affected by several parameters, hindering subsequent quantitative analyses. This work aimed at testing three matrix-matched calibration approaches (simple-linear regression, multi-linear regression and partial least squares regression (PLS)) as means for precision and accuracy enhancement of LIBS quantitative analysis. All calibration models were first developed using a training set and then validated with an independent test set. PLS yielded the best results. For instance, the PLS model for copper provided a coefficient of determination (R2) of 0.995 and a root mean square error of prediction (RMSEP) of 14 mg/kg. Furthermore, LIBS was employed to penetrate through the samples by repetitively measuring the same spot. Consequently, LIBS spectra can be obtained as a function of sample layers. This information was used to explore whether measuring deeper into the sample could reduce possible surface-contaminant effects and provide better quantifications.

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

DOE PAGES

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

2015-12-04

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

Monopole and dipole estimation for multi-frequency sky maps by linear regression

NASA Astrophysics Data System (ADS)

Wehus, I. K.; Fuskeland, U.; Eriksen, H. K.; Banday, A. J.; Dickinson, C.; Ghosh, T.; Górski, K. M.; Lawrence, C. R.; Leahy, J. P.; Maino, D.; Reich, P.; Reich, W.

2017-01-01

We describe a simple but efficient method for deriving a consistent set of monopole and dipole corrections for multi-frequency sky map data sets, allowing robust parametric component separation with the same data set. The computational core of this method is linear regression between pairs of frequency maps, often called T-T plots. Individual contributions from monopole and dipole terms are determined by performing the regression locally in patches on the sky, while the degeneracy between different frequencies is lifted whenever the dominant foreground component exhibits a significant spatial spectral index variation. Based on this method, we present two different, but each internally consistent, sets of monopole and dipole coefficients for the nine-year WMAP, Planck 2013, SFD 100 μm, Haslam 408 MHz and Reich & Reich 1420 MHz maps. The two sets have been derived with different analysis assumptions and data selection, and provide an estimate of residual systematic uncertainties. In general, our values are in good agreement with previously published results. Among the most notable results are a relative dipole between the WMAP and Planck experiments of 10-15μK (depending on frequency), an estimate of the 408 MHz map monopole of 8.9 ± 1.3 K, and a non-zero dipole in the 1420 MHz map of 0.15 ± 0.03 K pointing towards Galactic coordinates (l,b) = (308°,-36°) ± 14°. These values represent the sum of any instrumental and data processing offsets, as well as any Galactic or extra-Galactic component that is spectrally uniform over the full sky.

Association of Brain-Derived Neurotrophic Factor and Vitamin D with Depression and Obesity: A Population-Based Study.

PubMed

Goltz, Annemarie; Janowitz, Deborah; Hannemann, Anke; Nauck, Matthias; Hoffmann, Johanna; Seyfart, Tom; Völzke, Henry; Terock, Jan; Grabe, Hans Jörgen

2018-06-19

Depression and obesity are widespread and closely linked. Brain-derived neurotrophic factor (BDNF) and vitamin D are both assumed to be associated with depression and obesity. Little is known about the interplay between vitamin D and BDNF. We explored the putative associations and interactions between serum BDNF and vitamin D levels with depressive symptoms and abdominal obesity in a large population-based cohort. Data were obtained from the population-based Study of Health in Pomerania (SHIP)-Trend (n = 3,926). The associations of serum BDNF and vitamin D levels with depressive symptoms (measured using the Patient Health Questionnaire) were assessed with binary and multinomial logistic regression models. The associations of serum BDNF and vitamin D levels with obesity (measured by the waist-to-hip ratio [WHR]) were assessed with binary logistic and linear regression models with restricted cubic splines. Logistic regression models revealed inverse associations of vitamin D with depression (OR = 0.966; 95% CI 0.951-0.981) and obesity (OR = 0.976; 95% CI 0.967-0.985). No linear association of serum BDNF with depression or obesity was found. However, linear regression models revealed a U-shaped association of BDNF with WHR (p < 0.001). Vitamin D was inversely associated with depression and obesity. BDNF was associated with abdominal obesity, but not with depression. At the population level, our results support the relevant roles of vitamin D and BDNF in mental and physical health-related outcomes. © 2018 S. Karger AG, Basel.

Factors associated with single-vehicle and multi-vehicle road traffic collision injuries in Ireland.

PubMed

Donnelly-Swift, Erica; Kelly, Alan

2016-12-01

Generalised linear regression models were used to identify factors associated with fatal/serious road traffic collision injuries for single- and multi-vehicle collisions. Single-vehicle collisions and multi-vehicle collisions occurring during the hours of darkness or on a wet road surface had reduced likelihood of a fatal/serious injury. Single-vehicle 'driver with passengers' collisions occurring at junctions or on a hill/gradient were less likely to result in a fatal/serious injury. Multi-vehicle rear-end/angle collisions had reduced likelihood of a fatal/serious injury. Single-vehicle 'driver only' collisions and multi-vehicle collisions occurring on a public/bank holiday or on a hill/gradient were more likely to result in a fatal/serious injury. Single-vehicle collisions involving male drivers had increased likelihood of a fatal/serious injury and single-vehicle 'driver with passengers' collisions involving drivers under the age of 25 years also had increased likelihood of a fatal/serious injury. Findings can enlighten decision-makers to circumstances leading to fatal/serious injuries.

Physical Interpretation of the Correlation Between Multi-Angle Spectral Data and Canopy Height

NASA Technical Reports Server (NTRS)

Schull, M. A.; Ganguly, S.; Samanta, A.; Huang, D.; Shabanov, N. V.; Jenkins, J. P.; Chiu, J. C.; Marshak, A.; Blair, J. B.; Myneni, R. B.;

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

USGS Publications Warehouse

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

1996-01-01

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

The importance of proximal fusion level selection for outcomes of multi-level lumbar posterolateral fusion.

PubMed

Nam, Woo Dong; Cho, Jae Hwan

2015-03-01

There are few studies about risk factors for poor outcomes from multi-level lumbar posterolateral fusion limited to three or four level lumbar posterolateral fusions. The purpose of this study was to analyze the outcomes of multi-level lumbar posterolateral fusion and to search for possible risk factors for poor surgical outcomes. We retrospectively analyzed 37 consecutive patients who underwent multi-level lumbar or lumbosacral posterolateral fusion with posterior instrumentation. The outcomes were deemed either 'good' or 'bad' based on clinical and radiological results. Many demographic and radiological factors were analyzed to examine potential risk factors for poor outcomes. Student t-test, Fisher exact test, and the chi-square test were used based on the nature of the variables. Multiple logistic regression analysis was used to exclude confounding factors. Twenty cases showed a good outcome (group A, 54.1%) and 17 cases showed a bad outcome (group B, 45.9%). The overall fusion rate was 70.3%. The revision procedures (group A: 1/20, 5.0%; group B: 4/17, 23.5%), proximal fusion to L2 (group A: 5/20, 25.0%; group B: 10/17, 58.8%), and severity of stenosis (group A: 12/19, 63.3%; group B: 3/11, 27.3%) were adopted as possible related factors to the outcome in univariate analysis. Multiple logistic regression analysis revealed that only the proximal fusion level (superior instrumented vertebra, SIV) was a significant risk factor. The cases in which SIV was L2 showed inferior outcomes than those in which SIV was L3. The odds ratio was 6.562 (95% confidence interval, 1.259 to 34.203). The overall outcome of multi-level lumbar or lumbosacral posterolateral fusion was not as high as we had hoped it would be. Whether the SIV was L2 or L3 was the only significant risk factor identified for poor outcomes in multi-level lumbar or lumbosacral posterolateral fusion in the current study. Thus, the authors recommend that proximal fusion levels be carefully determined when multi-level lumbar fusions are considered.

The Importance of Proximal Fusion Level Selection for Outcomes of Multi-Level Lumbar Posterolateral Fusion

PubMed Central

Nam, Woo Dong

2015-01-01

Background There are few studies about risk factors for poor outcomes from multi-level lumbar posterolateral fusion limited to three or four level lumbar posterolateral fusions. The purpose of this study was to analyze the outcomes of multi-level lumbar posterolateral fusion and to search for possible risk factors for poor surgical outcomes. Methods We retrospectively analyzed 37 consecutive patients who underwent multi-level lumbar or lumbosacral posterolateral fusion with posterior instrumentation. The outcomes were deemed either 'good' or 'bad' based on clinical and radiological results. Many demographic and radiological factors were analyzed to examine potential risk factors for poor outcomes. Student t-test, Fisher exact test, and the chi-square test were used based on the nature of the variables. Multiple logistic regression analysis was used to exclude confounding factors. Results Twenty cases showed a good outcome (group A, 54.1%) and 17 cases showed a bad outcome (group B, 45.9%). The overall fusion rate was 70.3%. The revision procedures (group A: 1/20, 5.0%; group B: 4/17, 23.5%), proximal fusion to L2 (group A: 5/20, 25.0%; group B: 10/17, 58.8%), and severity of stenosis (group A: 12/19, 63.3%; group B: 3/11, 27.3%) were adopted as possible related factors to the outcome in univariate analysis. Multiple logistic regression analysis revealed that only the proximal fusion level (superior instrumented vertebra, SIV) was a significant risk factor. The cases in which SIV was L2 showed inferior outcomes than those in which SIV was L3. The odds ratio was 6.562 (95% confidence interval, 1.259 to 34.203). Conclusions The overall outcome of multi-level lumbar or lumbosacral posterolateral fusion was not as high as we had hoped it would be. Whether the SIV was L2 or L3 was the only significant risk factor identified for poor outcomes in multi-level lumbar or lumbosacral posterolateral fusion in the current study. Thus, the authors recommend that proximal fusion levels be carefully determined when multi-level lumbar fusions are considered. PMID:25729522

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

ERIC Educational Resources Information Center

Cai, Li; Hayes, Andrew F.

2008-01-01

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

The Age in Swimming of Champions in World Championships (1994–2013) and Olympic Games (1992–2012): A Cross-Sectional Data Analysis

PubMed Central

Knechtle, Beat; Bragazzi, Nicola Luigi; König, Stefan; Nikolaidis, Pantelis Theodoros; Wild, Stefanie; Rosemann, Thomas; Rüst, Christoph Alexander

2016-01-01

(1) Background: We investigated the age of swimming champions in all strokes and race distances in World Championships (1994–2013) and Olympic Games (1992–2012); (2) Methods: Changes in age and swimming performance across calendar years for 412 Olympic and world champions were analysed using linear, non-linear, multi-level regression analyses and MultiLayer Perceptron (MLP); (3) Results: The age of peak swimming performance remained stable in most of all race distances for world champions and in all race distances for Olympic champions. Longer (i.e., 200 m and more) race distances were completed by younger (~20 years old for women and ~22 years old for men) champions than shorter (i.e., 50 m and 100 m) race distances (~22 years old for women and ~24 years old for men). There was a sex difference in the age of champions of ~2 years with a mean age of ~21 and ~23 years for women and men, respectively. Swimming performance improved in most race distances for world and Olympic champions with a larger trend of increase in Olympic champions; (4) Conclusion: Swimmers at younger ages (<20 years) may benefit from training and competing in longer race distances (i.e., 200 m and longer) before they change to shorter distances (i.e., 50 m and 100 m) when they become older (>22 years). PMID:29910265

Cardiovascular diseases and air pollution in Novi Sad, Serbia.

PubMed

Jevtić, Marija; Dragić, Nataša; Bijelović, Sanja; Popović, Milka

2014-04-01

A large body of evidence has documented that air pollutants have adverse effect on human health as well as on the environment. The aim of this study was to determine whether there was an association between outdoor concentrations of sulfur dioxide (SO2) and nitrogen dioxide (NO2) and a daily number of hospital admissions due to cardiovascular diseases (CVD) in Novi Sad, Serbia among patients aged above 18. The investigation was carried out during over a 3-year period (from January 1, 2007 to December 31, 2009) in the area of Novi Sad. The number (N = 10 469) of daily CVD (ICD-10: I00-I99) hospital admissions was collected according to patients' addresses. Daily mean levels of NO2 and SO2, measured in the ambient air of Novi Sad via a network of fixed samplers, have been used to put forward outdoor air pollution. Associations between air pollutants and hospital admissions were firstly analyzed by the use of the linear regression in a single polluted model, and then trough a single and multi-polluted adjusted generalized linear Poisson model. The single polluted model (without confounding factors) indicated that there was a linear increase in the number of hospital admissions due to CVD in relation to the linear increase in concentrations of SO2 (p = 0.015; 95% confidence interval (95% CI): 0.144-1.329, R(2) = 0.005) and NO2 (p = 0.007; 95% CI: 0.214-1.361, R(2) = 0.007). However, the single and multi-polluted adjusted models revealed that only NO2 was associated with the CVD (p = 0.016, relative risk (RR) = 1.049, 95% CI: 1.009-1.091 and p = 0.022, RR = 1.047, 95% CI: 1.007-1.089, respectively). This study shows a significant positive association between hospital admissions due to CVD and outdoor NO2 concentrations in the area of Novi Sad, Serbia.

Prenatal Lead Exposure and Fetal Growth: Smaller Infants Have Heightened Susceptibility

PubMed Central

Rodosthenous, Rodosthenis S.; Burris, Heather H.; Svensson, Katherine; Amarasiriwardena, Chitra J.; Cantoral, Alejandra; Schnaas, Lourdes; Mercado-García, Adriana; Coull, Brent A.; Wright, Robert O.; Téllez-Rojo, Martha M.; Baccarelli, Andrea A.

2016-01-01

Background As population lead levels decrease, the toxic effects of lead may be distributed to more sensitive populations, such as infants with poor fetal growth. Objectives To determine the association of prenatal lead exposure and fetal growth; and to evaluate whether infants with poor fetal growth are more susceptible to lead toxicity than those with normal fetal growth. Methods We examined the association of second trimester maternal blood lead levels (BLL) with birthweight-for-gestational age (BWGA) z-score in 944 mother-infant participants of the PROGRESS cohort. We determined the association between maternal BLL and BWGA z-score by using both linear and quantile regression. We estimated odds ratios for small-for-gestational age (SGA) infants between maternal BLL quartiles using logistic regression. Maternal age, body mass index, socioeconomic status, parity, household smoking exposure, hemoglobin levels, and infant sex were included as confounders. Results While linear regression showed a negative association between maternal BLL and BWGA z-score (β=−0.06 z-score units per log2 BLL increase; 95% CI: −0.13, 0.003; P=0.06), quantile regression revealed larger magnitudes of this association in the <30th percentiles of BWGA z-score (β range [−0.08, −0.13] z-score units per log2 BLL increase; all P values <0.05). Mothers in the highest BLL quartile had an odds ratio of 1.62 (95% CI: 0.99–2.65) for having a SGA infant compared to the lowest BLL quartile. Conclusions While both linear and quantile regression showed a negative association between prenatal lead exposure and birthweight, quantile regression revealed that smaller infants may represent a more susceptible subpopulation. PMID:27923585

Prenatal lead exposure and fetal growth: Smaller infants have heightened susceptibility.

PubMed

Rodosthenous, Rodosthenis S; Burris, Heather H; Svensson, Katherine; Amarasiriwardena, Chitra J; Cantoral, Alejandra; Schnaas, Lourdes; Mercado-García, Adriana; Coull, Brent A; Wright, Robert O; Téllez-Rojo, Martha M; Baccarelli, Andrea A

2017-02-01

As population lead levels decrease, the toxic effects of lead may be distributed to more sensitive populations, such as infants with poor fetal growth. To determine the association of prenatal lead exposure and fetal growth; and to evaluate whether infants with poor fetal growth are more susceptible to lead toxicity than those with normal fetal growth. We examined the association of second trimester maternal blood lead levels (BLL) with birthweight-for-gestational age (BWGA) z-score in 944 mother-infant participants of the PROGRESS cohort. We determined the association between maternal BLL and BWGA z-score by using both linear and quantile regression. We estimated odds ratios for small-for-gestational age (SGA) infants between maternal BLL quartiles using logistic regression. Maternal age, body mass index, socioeconomic status, parity, household smoking exposure, hemoglobin levels, and infant sex were included as confounders. While linear regression showed a negative association between maternal BLL and BWGA z-score (β=-0.06 z-score units per log 2 BLL increase; 95% CI: -0.13, 0.003; P=0.06), quantile regression revealed larger magnitudes of this association in the <30th percentiles of BWGA z-score (β range [-0.08, -0.13] z-score units per log 2 BLL increase; all P values<0.05). Mothers in the highest BLL quartile had an odds ratio of 1.62 (95% CI: 0.99-2.65) for having a SGA infant compared to the lowest BLL quartile. While both linear and quantile regression showed a negative association between prenatal lead exposure and birthweight, quantile regression revealed that smaller infants may represent a more susceptible subpopulation. Copyright © 2016 Elsevier Ltd. All rights reserved.

More green space is related to less antidepressant prescription rates in the Netherlands: A Bayesian geoadditive quantile regression approach.

PubMed

Helbich, Marco; Klein, Nadja; Roberts, Hannah; Hagedoorn, Paulien; Groenewegen, Peter P

2018-06-20

Exposure to green space seems to be beneficial for self-reported mental health. In this study we used an objective health indicator, namely antidepressant prescription rates. Current studies rely exclusively upon mean regression models assuming linear associations. It is, however, plausible that the presence of green space is non-linearly related with different quantiles of the outcome antidepressant prescription rates. These restrictions may contribute to inconsistent findings. Our aim was: a) to assess antidepressant prescription rates in relation to green space, and b) to analyze how the relationship varies non-linearly across different quantiles of antidepressant prescription rates. We used cross-sectional data for the year 2014 at a municipality level in the Netherlands. Ecological Bayesian geoadditive quantile regressions were fitted for the 15%, 50%, and 85% quantiles to estimate green space-prescription rate correlations, controlling for physical activity levels, socio-demographics, urbanicity, etc. RESULTS: The results suggested that green space was overall inversely and non-linearly associated with antidepressant prescription rates. More important, the associations differed across the quantiles, although the variation was modest. Significant non-linearities were apparent: The associations were slightly positive in the lower quantile and strongly negative in the upper one. Our findings imply that an increased availability of green space within a municipality may contribute to a reduction in the number of antidepressant prescriptions dispensed. Green space is thus a central health and community asset, whilst a minimum level of 28% needs to be established for health gains. The highest effectiveness occurred at a municipality surface percentage higher than 79%. This inverse dose-dependent relation has important implications for setting future community-level health and planning policies. Copyright © 2018 Elsevier Inc. All rights reserved.

Performance characteristics of LOX-H2, tangential-entry, swirl-coaxial, rocket injectors

NASA Technical Reports Server (NTRS)

Howell, Doug; Petersen, Eric; Clark, Jim

1993-01-01

Development of a high performing swirl-coaxial injector requires an understanding of fundamental performance characteristics. This paper addresses the findings of studies on cold flow atomic characterizations which provided information on the influence of fluid properties and element operating conditions on the produced droplet sprays. These findings are applied to actual rocket conditions. The performance characteristics of swirl-coaxial injection elements under multi-element hot-fire conditions were obtained by analysis of combustion performance data from three separate test series. The injection elements are described and test results are analyzed using multi-variable linear regression. A direct comparison of test results indicated that reduced fuel injection velocity improved injection element performance through improved propellant mixing.

The effect of coffee, tea, and caffeine consumption on serum uric acid and the risk of hyperuricemia in Korean Multi-Rural Communities Cohort.

PubMed

Bae, Jisuk; Park, Pil Sook; Chun, Byung-Yeol; Choi, Bo Youl; Kim, Mi Kyung; Shin, Min-Ho; Lee, Young-Hoon; Shin, Dong Hoon; Kim, Seong-Kyu

2015-02-01

Caffeine, a commonly consumed food constituent, is known to exert beneficial physiological effects in humans. There is a lack of comprehensive population data for the effects of caffeine intake on urate metabolism. Therefore, the aim of this study was to determine whether coffee, tea, and caffeine intake influences serum uric acid and the risk of hyperuricemia in the Korean Multi-Rural Communities Cohort. We enrolled 9,400 participants in this study. An assessment of various dietary intake amounts of substances such as coffee and tea was performed using a food frequency questionnaire. The content of caffeine was calculated from coffee (74 mg/cup) and tea (15 mg/cup) intake information from the past year. Multivariate logistic regression models, multiple linear regression models, and analysis of covariance were applied to identify any association of dietary intake with serum uric acid levels or the risk of hyperuricemia. No trends for coffee, tea, or caffeine intake were found according to each quintile with serum uric acid in males, although there were weak, marginally significant trends between the content of coffee and caffeine intake and serum uric acid level in females (p = 0.07 for both). Tea intake in males and caffeine intake in females were significantly different between non-hyperuricemia and hyperuricemia (p = 0.04 and p = 0.04, respectively). In addition, a significant association of serum uric acid level with tea intake in males (β = 0.0006, p = 0.02) and with tea intake and caffeine intake in females (β = 0.0003, p = 0.04 and β = 0.0006, p = 0.02, respectively) was observed. There was no effect of coffee, tea, or caffeine intake on the risk of hyperuricemia in either males or females. This study suggests that caffeine consumption might have an effect on serum uric acid in females. However, coffee, tea, and caffeine intake amounts were not associated with the risk of hyperuricemia.

An ensemble Kalman filter for statistical estimation of physics constrained nonlinear regression models

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

Harlim, John, E-mail: jharlim@psu.edu; Mahdi, Adam, E-mail: amahdi@ncsu.edu; Majda, Andrew J., E-mail: jonjon@cims.nyu.edu

2014-01-15

A central issue in contemporary science is the development of nonlinear data driven statistical–dynamical models for time series of noisy partial observations from nature or a complex model. It has been established recently that ad-hoc quadratic multi-level regression models can have finite-time blow-up of statistical solutions and/or pathological behavior of their invariant measure. Recently, a new class of physics constrained nonlinear regression models were developed to ameliorate this pathological behavior. Here a new finite ensemble Kalman filtering algorithm is developed for estimating the state, the linear and nonlinear model coefficients, the model and the observation noise covariances from available partialmore » noisy observations of the state. Several stringent tests and applications of the method are developed here. In the most complex application, the perfect model has 57 degrees of freedom involving a zonal (east–west) jet, two topographic Rossby waves, and 54 nonlinearly interacting Rossby waves; the perfect model has significant non-Gaussian statistics in the zonal jet with blocked and unblocked regimes and a non-Gaussian skewed distribution due to interaction with the other 56 modes. We only observe the zonal jet contaminated by noise and apply the ensemble filter algorithm for estimation. Numerically, we find that a three dimensional nonlinear stochastic model with one level of memory mimics the statistical effect of the other 56 modes on the zonal jet in an accurate fashion, including the skew non-Gaussian distribution and autocorrelation decay. On the other hand, a similar stochastic model with zero memory levels fails to capture the crucial non-Gaussian behavior of the zonal jet from the perfect 57-mode model.« less

Multi-objective Optimization of Solar Irradiance and Variance at Pertinent Inclination Angles

NASA Astrophysics Data System (ADS)

Jain, Dhanesh; Lalwani, Mahendra

2018-05-01

The performance of photovoltaic panel gets highly affected bychange in atmospheric conditions and angle of inclination. This article evaluates the optimum tilt angle and orientation angle (surface azimuth angle) for solar photovoltaic array in order to get maximum solar irradiance and to reduce variance of radiation at different sets or subsets of time periods. Non-linear regression and adaptive neural fuzzy interference system (ANFIS) methods are used for predicting the solar radiation. The results of ANFIS are more accurate in comparison to non-linear regression. These results are further used for evaluating the correlation and applied for estimating the optimum combination of tilt angle and orientation angle with the help of general algebraic modelling system and multi-objective genetic algorithm. The hourly average solar irradiation is calculated at different combinations of tilt angle and orientation angle with the help of horizontal surface radiation data of Jodhpur (Rajasthan, India). The hourly average solar irradiance is calculated for three cases: zero variance, with actual variance and with double variance at different time scenarios. It is concluded that monthly collected solar radiation produces better result as compared to bimonthly, seasonally, half-yearly and yearly collected solar radiation. The profit obtained for monthly varying angle has 4.6% more with zero variance and 3.8% more with actual variance, than the annually fixed angle.

Searching for the main anti-bacterial components in artificial Calculus bovis using UPLC and microcalorimetry coupled with multi-linear regression analysis.

PubMed

Zang, Qing-Ce; Wang, Jia-Bo; Kong, Wei-Jun; Jin, Cheng; Ma, Zhi-Jie; Chen, Jing; Gong, Qian-Feng; Xiao, Xiao-He

2011-12-01

The fingerprints of artificial Calculus bovis extracts from different solvents were established by ultra-performance liquid chromatography (UPLC) and the anti-bacterial activities of artificial C. bovis extracts on Staphylococcus aureus (S. aureus) growth were studied by microcalorimetry. The UPLC fingerprints were evaluated using hierarchical clustering analysis. Some quantitative parameters obtained from the thermogenic curves of S. aureus growth affected by artificial C. bovis extracts were analyzed using principal component analysis. The spectrum-effect relationships between UPLC fingerprints and anti-bacterial activities were investigated using multi-linear regression analysis. The results showed that peak 1 (taurocholate sodium), peak 3 (unknown compound), peak 4 (cholic acid), and peak 6 (chenodeoxycholic acid) are more significant than the other peaks with the standard parameter estimate 0.453, -0.166, 0.749, 0.025, respectively. So, compounds cholic acid, taurocholate sodium, and chenodeoxycholic acid might be the major anti-bacterial components in artificial C. bovis. Altogether, this work provides a general model of the combination of UPLC chromatography and anti-bacterial effect to study the spectrum-effect relationships of artificial C. bovis extracts, which can be used to discover the main anti-bacterial components in artificial C. bovis or other Chinese herbal medicines with anti-bacterial effects. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Addressing data privacy in matched studies via virtual pooling.

PubMed

Saha-Chaudhuri, P; Weinberg, C R

2017-09-07

Data confidentiality and shared use of research data are two desirable but sometimes conflicting goals in research with multi-center studies and distributed data. While ideal for straightforward analysis, confidentiality restrictions forbid creation of a single dataset that includes covariate information of all participants. Current approaches such as aggregate data sharing, distributed regression, meta-analysis and score-based methods can have important limitations. We propose a novel application of an existing epidemiologic tool, specimen pooling, to enable confidentiality-preserving analysis of data arising from a matched case-control, multi-center design. Instead of pooling specimens prior to assay, we apply the methodology to virtually pool (aggregate) covariates within nodes. Such virtual pooling retains most of the information used in an analysis with individual data and since individual participant data is not shared externally, within-node virtual pooling preserves data confidentiality. We show that aggregated covariate levels can be used in a conditional logistic regression model to estimate individual-level odds ratios of interest. The parameter estimates from the standard conditional logistic regression are compared to the estimates based on a conditional logistic regression model with aggregated data. The parameter estimates are shown to be similar to those without pooling and to have comparable standard errors and confidence interval coverage. Virtual data pooling can be used to maintain confidentiality of data from multi-center study and can be particularly useful in research with large-scale distributed data.

The associations between metabolic variables and NT-proBNP are blunted at pathological ranges: the Multi-Ethnic Study of Atherosclerosis

PubMed Central

Sanchez, Otto A.; Duprez, Daniel A.; Bahrami, Hossein; Daniels, Lori B.; Folsom, Aaron R.; Lima, Joao A.; Maisel, Alan; Peralta, Carmen A.; Jacobs, David R.

2014-01-01

Objective Under physiological conditions brain natriuretic peptide (BNP) is inversely associated with metabolic risk factors, but under pathological conditions these associations may tend to plateau. Material end methods 5597 individuals in the Multi-Ethnic Study of Atherosclerosis (MESA), 45–84 years of age, free of overt cardiovascular disease in 2000–02 and then again in 2003–05 participated in this study. Associations between NT-proBNP and BMI, blood lipids, homeostasis model of insulin resistance (HOMA-IR) using linear regression models were adjusted for age, race, sex, BMI, % of energy from saturated fats, intentional exercise, statin use, antihypertensive medication use, diabetes and glomerular filtration rate. The inflection points (IP) at which these associations became nonlinear were determined using linear splines with knots at different levels of NT-proBNP. Results Participants with NT-proBNP ≥100 pg/mL (29%) tended to be older, on statins and anti-hypertensive medications vs. those with NT-proBNP <100 pg/mL. The IP point varies among variables and ranged from 50–120 pg/mL. NT-proBNP

Using Linear Equating to Map PROMIS(®) Global Health Items and the PROMIS-29 V2.0 Profile Measure to the Health Utilities Index Mark 3.

PubMed

Hays, Ron D; Revicki, Dennis A; Feeny, David; Fayers, Peter; Spritzer, Karen L; Cella, David

2016-10-01

Preference-based health-related quality of life (HR-QOL) scores are useful as outcome measures in clinical studies, for monitoring the health of populations, and for estimating quality-adjusted life-years. This was a secondary analysis of data collected in an internet survey as part of the Patient-Reported Outcomes Measurement Information System (PROMIS(®)) project. To estimate Health Utilities Index Mark 3 (HUI-3) preference scores, we used the ten PROMIS(®) global health items, the PROMIS-29 V2.0 single pain intensity item and seven multi-item scales (physical functioning, fatigue, pain interference, depressive symptoms, anxiety, ability to participate in social roles and activities, sleep disturbance), and the PROMIS-29 V2.0 items. Linear regression analyses were used to identify significant predictors, followed by simple linear equating to avoid regression to the mean. The regression models explained 48 % (global health items), 61 % (PROMIS-29 V2.0 scales), and 64 % (PROMIS-29 V2.0 items) of the variance in the HUI-3 preference score. Linear equated scores were similar to observed scores, although differences tended to be larger for older study participants. HUI-3 preference scores can be estimated from the PROMIS(®) global health items or PROMIS-29 V2.0. The estimated HUI-3 scores from the PROMIS(®) health measures can be used for economic applications and as a measure of overall HR-QOL in research.

“I don’t know enough to feel comfortable using them:” Women’s knowledge of and perceived barriers to long acting reversible contraceptives on a college campus

PubMed Central

Ela, Elizabeth; Zochowski, Melissa K.; Caldwell, Amy; Moniz, Michelle; McAndrew, Laura; Steel, Monique; Challa, Sneha; Dalton, Vanessa K.; Ernst, Susan

2016-01-01

Objective To assess multiple dimensions of long acting reversible contraception (LARC) knowledge and perceived multi-level barriers to LARC use among a sample of college women. Study Design We conducted an internet-based study of 1,982 female undergraduates at a large mid-western university. Our 55-item survey used a multi-level framework to measure young women’s understanding of, experiences with intrauterine devices (IUD) and implants and their perceived barriers to LARC at individual, health systems, and community levels. The survey included a 20-item knowledge scale. We estimated and compared LARC knowledge scores and barriers using descriptive, bivariate, and linear regression statistics. Results Few college women had used (5%) or heard of (22%) LARC, and most self-reported “little” or “no” knowledge of IUDs (79%) and implants (88%). Women answered 50% of LARC knowledge items correctly (mean 10.4, range 0–20), and scores differed across sociodemographic groups (p-values<0.04). Factors associated with scores in multivariable models included race/ethnicity, program year, sorority participation, religious affiliation and service attendance, employment status, sexual orientation, and contraceptive history. Perceived barriers to IUDs included: not wanting a foreign object in body (44%); not knowing enough about the method (42%); preferring a “controllable” method (42%); cost (27%); and not being in a long-term relationship (23%). Implant results were similar. “Not knowing enough” was women’s primary reason for IUD (18%) and implant (22%) nonuse. Conclusion Lack of knowledge (both perceived and actual) was the most common barrier among many perceived individual, systems, and community-level factors precluding these college women’s LARC use. Findings can inform innovative, multi-level interventions to improve understanding, acceptability, and uptake of LARC on campuses. PMID:26879627

Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia

NASA Astrophysics Data System (ADS)

Karimi, Sepideh; Kisi, Ozgur; Shiri, Jalal; Makarynskyy, Oleg

2013-03-01

Accurate predictions of sea level with different forecast horizons are important for coastal and ocean engineering applications, as well as in land drainage and reclamation studies. The methodology of tidal harmonic analysis, which is generally used for obtaining a mathematical description of the tides, is data demanding requiring processing of tidal observation collected over several years. In the present study, hourly sea levels for Darwin Harbor, Australia were predicted using two different, data driven techniques, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Multi linear regression (MLR) technique was used for selecting the optimal input combinations (lag times) of hourly sea level. The input combination comprises current sea level as well as five previous level values found to be optimal. For the ANFIS models, five different membership functions namely triangular, trapezoidal, generalized bell, Gaussian and two Gaussian membership function were tested and employed for predicting sea level for the next 1 h, 24 h, 48 h and 72 h. The used ANN models were trained using three different algorithms, namely, Levenberg-Marquardt, conjugate gradient and gradient descent. Predictions of optimal ANFIS and ANN models were compared with those of the optimal auto-regressive moving average (ARMA) models. The coefficient of determination, root mean square error and variance account statistics were used as comparison criteria. The obtained results indicated that triangular membership function was optimal for predictions with the ANFIS models while adaptive learning rate and Levenberg-Marquardt were most suitable for training the ANN models. Consequently, ANFIS and ANN models gave similar forecasts and performed better than the developed for the same purpose ARMA models for all the prediction intervals.

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).

Social networks and health-related quality of life: a population based study among older adults.

PubMed

Gallegos-Carrillo, Katia; Mudgal, Jyoti; Sánchez-García, Sergio; Wagner, Fernando A; Gallo, Joseph J; Salmerón, Jorge; García-Peña, Carmen

2009-01-01

To examine the relationship between components of social networks and health-related quality of life (HRQL) in older adults with and without depressive symptoms. Comparative cross-sectional study with data from the cohort study 'Integral Study of Depression', carried out in Mexico City during 2004. The sample was selected through a multi-stage probability design. HRQL was measured with the SF-36. Geriatric Depression Scale (GDS) and the Short Anxiety Screening Test (SAST) determined depressive symptoms and anxiety. T-test and multiple linear regressions were conducted. Older adults with depressive symptoms had the lowest scores in all HRQL scales. A larger network of close relatives and friends was associated with better HRQL on several scales. Living alone did not significantly affect HRQL level, in either the study or comparison group. A positive association between some components of social networks and good HRQL exists even in older adults with depressive symptoms.

Exposure to tobacco and nicotine product advertising: Associations with perceived prevalence of use among college students.

PubMed

Kreitzberg, Daniel S; Herrera, Ana Laura; Loukas, Alexandra; Pasch, Keryn E

2018-03-22

The purpose of this study was to examine the relationship between exposure to tobacco marketing and perceptions of peer tobacco use among college students. Participants were 5,767 undergraduate students from 19 colleges/universities in the State of Texas. Students completed an online survey, in the spring of 2016, that assessed past 30 day exposure to e-cigarette, cigar, smokeless tobacco, and traditional cigarette advertising across multiple marketing channels, past 30 day use of each product, and perceived prevalence of peer use. Multi-level linear regression models were run to examine the associations between exposure to tobacco advertising and perceptions of peer tobacco use controlling for age, gender, race/ethnicity, use and school. Greater exposure to advertising was associated with greater perceived prevalence of peer use. Given the normative effects of advertising on perceived peer tobacco use, college tobacco initiatives should include descriptive norms education to counteract inaccurate perceptions.

Lateral-Directional Parameter Estimation on the X-48B Aircraft Using an Abstracted, Multi-Objective Effector Model

NASA Technical Reports Server (NTRS)

Ratnayake, Nalin A.; Waggoner, Erin R.; Taylor, Brian R.

2011-01-01

The problem of parameter estimation on hybrid-wing-body aircraft is complicated by the fact that many design candidates for such aircraft involve a large number of aerodynamic control effectors that act in coplanar motion. This adds to the complexity already present in the parameter estimation problem for any aircraft with a closed-loop control system. Decorrelation of flight and simulation data must be performed in order to ascertain individual surface derivatives with any sort of mathematical confidence. Non-standard control surface configurations, such as clamshell surfaces and drag-rudder modes, further complicate the modeling task. In this paper, time-decorrelation techniques are applied to a model structure selected through stepwise regression for simulated and flight-generated lateral-directional parameter estimation data. A virtual effector model that uses mathematical abstractions to describe the multi-axis effects of clamshell surfaces is developed and applied. Comparisons are made between time history reconstructions and observed data in order to assess the accuracy of the regression model. The Cram r-Rao lower bounds of the estimated parameters are used to assess the uncertainty of the regression model relative to alternative models. Stepwise regression was found to be a useful technique for lateral-directional model design for hybrid-wing-body aircraft, as suggested by available flight data. Based on the results of this study, linear regression parameter estimation methods using abstracted effectors are expected to perform well for hybrid-wing-body aircraft properly equipped for the task.

Can single empirical algorithms accurately predict inland shallow water quality status from high resolution, multi-sensor, multi-temporal satellite data?

NASA Astrophysics Data System (ADS)

Theologou, I.; Patelaki, M.; Karantzalos, K.

2015-04-01

Assessing and monitoring water quality status through timely, cost effective and accurate manner is of fundamental importance for numerous environmental management and policy making purposes. Therefore, there is a current need for validated methodologies which can effectively exploit, in an unsupervised way, the enormous amount of earth observation imaging datasets from various high-resolution satellite multispectral sensors. To this end, many research efforts are based on building concrete relationships and empirical algorithms from concurrent satellite and in-situ data collection campaigns. We have experimented with Landsat 7 and Landsat 8 multi-temporal satellite data, coupled with hyperspectral data from a field spectroradiometer and in-situ ground truth data with several physico-chemical and other key monitoring indicators. All available datasets, covering a 4 years period, in our case study Lake Karla in Greece, were processed and fused under a quantitative evaluation framework. The performed comprehensive analysis posed certain questions regarding the applicability of single empirical models across multi-temporal, multi-sensor datasets towards the accurate prediction of key water quality indicators for shallow inland systems. Single linear regression models didn't establish concrete relations across multi-temporal, multi-sensor observations. Moreover, the shallower parts of the inland system followed, in accordance with the literature, different regression patterns. Landsat 7 and 8 resulted in quite promising results indicating that from the recreation of the lake and onward consistent per-sensor, per-depth prediction models can be successfully established. The highest rates were for chl-a (r2=89.80%), dissolved oxygen (r2=88.53%), conductivity (r2=88.18%), ammonium (r2=87.2%) and pH (r2=86.35%), while the total phosphorus (r2=70.55%) and nitrates (r2=55.50%) resulted in lower correlation rates.

Multi-frame linear regressive filter for the measurement of infrared pixel spatial response and MTF from sparse data.

PubMed

Huard, Edouard; Derelle, Sophie; Jaeck, Julien; Nghiem, Jean; Haïdar, Riad; Primot, Jérôme

2018-03-05

A challenging point in the prediction of the image quality of infrared imaging systems is the evaluation of the detector modulation transfer function (MTF). In this paper, we present a linear method to get a 2D continuous MTF from sparse spectral data. Within the method, an object with a predictable sparse spatial spectrum is imaged by the focal plane array. The sparse data is then treated to return the 2D continuous MTF with the hypothesis that all the pixels have an identical spatial response. The linearity of the treatment is a key point to estimate directly the error bars of the resulting detector MTF. The test bench will be presented along with measurement tests on a 25 μm pitch InGaAs detector.

A practical data processing workflow for multi-OMICS projects.

PubMed

Kohl, Michael; Megger, Dominik A; Trippler, Martin; Meckel, Hagen; Ahrens, Maike; Bracht, Thilo; Weber, Frank; Hoffmann, Andreas-Claudius; Baba, Hideo A; Sitek, Barbara; Schlaak, Jörg F; Meyer, Helmut E; Stephan, Christian; Eisenacher, Martin

2014-01-01

Multi-OMICS approaches aim on the integration of quantitative data obtained for different biological molecules in order to understand their interrelation and the functioning of larger systems. This paper deals with several data integration and data processing issues that frequently occur within this context. To this end, the data processing workflow within the PROFILE project is presented, a multi-OMICS project that aims on identification of novel biomarkers and the development of new therapeutic targets for seven important liver diseases. Furthermore, a software called CrossPlatformCommander is sketched, which facilitates several steps of the proposed workflow in a semi-automatic manner. Application of the software is presented for the detection of novel biomarkers, their ranking and annotation with existing knowledge using the example of corresponding Transcriptomics and Proteomics data sets obtained from patients suffering from hepatocellular carcinoma. Additionally, a linear regression analysis of Transcriptomics vs. Proteomics data is presented and its performance assessed. It was shown, that for capturing profound relations between Transcriptomics and Proteomics data, a simple linear regression analysis is not sufficient and implementation and evaluation of alternative statistical approaches are needed. Additionally, the integration of multivariate variable selection and classification approaches is intended for further development of the software. Although this paper focuses only on the combination of data obtained from quantitative Proteomics and Transcriptomics experiments, several approaches and data integration steps are also applicable for other OMICS technologies. Keeping specific restrictions in mind the suggested workflow (or at least parts of it) may be used as a template for similar projects that make use of different high throughput techniques. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan. Copyright © 2013 Elsevier B.V. All rights reserved.

Effect of conductance linearity and multi-level cell characteristics of TaOx-based synapse device on pattern recognition accuracy of neuromorphic system

NASA Astrophysics Data System (ADS)

Sung, Changhyuck; Lim, Seokjae; Kim, Hyungjun; Kim, Taesu; Moon, Kibong; Song, Jeonghwan; Kim, Jae-Joon; Hwang, Hyunsang

2018-03-01

To improve the classification accuracy of an image data set (CIFAR-10) by using analog input voltage, synapse devices with excellent conductance linearity (CL) and multi-level cell (MLC) characteristics are required. We analyze the CL and MLC characteristics of TaOx-based filamentary resistive random access memory (RRAM) to implement the synapse device in neural network hardware. Our findings show that the number of oxygen vacancies in the filament constriction region of the RRAM directly controls the CL and MLC characteristics. By adopting a Ta electrode (instead of Ti) and the hot-forming step, we could form a dense conductive filament. As a result, a wide range of conductance levels with CL is achieved and significantly improved image classification accuracy is confirmed.

Commentary on the statistical properties of noise and its implication on general linear models in functional near-infrared spectroscopy.

PubMed

Huppert, Theodore J

2016-01-01

Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique that uses low levels of light to measure changes in cerebral blood oxygenation levels. In the majority of NIRS functional brain studies, analysis of this data is based on a statistical comparison of hemodynamic levels between a baseline and task or between multiple task conditions by means of a linear regression model: the so-called general linear model. Although these methods are similar to their implementation in other fields, particularly for functional magnetic resonance imaging, the specific application of these methods in fNIRS research differs in several key ways related to the sources of noise and artifacts unique to fNIRS. In this brief communication, we discuss the application of linear regression models in fNIRS and the modifications needed to generalize these models in order to deal with structured (colored) noise due to systemic physiology and noise heteroscedasticity due to motion artifacts. The objective of this work is to present an overview of these noise properties in the context of the linear model as it applies to fNIRS data. This work is aimed at explaining these mathematical issues to the general fNIRS experimental researcher but is not intended to be a complete mathematical treatment of these concepts.

Elevated ambulatory blood pressure in a multi-ethnic population of obese children and adolescents.

PubMed

Aguilar, Alexandra; Ostrow, Vlady; De Luca, Francesco; Suarez, Elizabeth

2010-06-01

To evaluate the relationship among ambulatory blood pressure (ABP), body mass index (BMI), and homeostasis model assessment (HOMA) in a multi-ethnic population of obese children with clinic blood pressure in the reference range. A total of 43 obese normotensive children (7-17 years old) were recruited. ABP monitoring, oral glucose tolerance test, lipid levels, and urine microalbumin levels were obtained. Fourteen percent of the subjects had elevated 24-hour systolic blood pressure (SBP), 9.3% had elevated daytime SBP, and 32.6 % elevated nighttime SBP. For diastolic blood pressure, 4.7% of the sample had an elevated mean nighttime value. Children with more severe obesity (BMI SD score >2.5) had higher 24-hour and nighttime SBP than children with less severe obesity (BMI SD score < or =2.5). Children with HOMA values in the highest quartile had larger waist circumference and higher clinic blood pressure than children with HOMA values in the lowest quartile, and no difference in the mean ABP values was found in the 2 groups . Multiple linear regression analysis showed that 24-hour and nighttime SBP were significantly correlated with BMI SD score. Obese children with normal clinic blood pressure often exhibit elevated ABP. The risk for ambulatory hypertension appears to be correlated with the degree of obesity. Copyright 2010 Mosby, Inc. All rights reserved.

Neighborhood built environment and cognition in non-demented older adults: The Multi-Ethnic Study of Atherosclerosis.

PubMed

Besser, Lilah M; Rodriguez, Daniel A; McDonald, Noreen; Kukull, Walter A; Fitzpatrick, Annette L; Rapp, Stephen R; Seeman, Teresa

2018-03-01

Preliminary studies suggest that neighborhood social and built environment (BE) characteristics may affect cognition in older adults. Older adults are particularly vulnerable to the neighborhood environment due to a decreasing range of routine travel with increasing age. We examined if multiple neighborhood BE characteristics are cross-sectionally associated with cognition in a diverse sample of older adults, and if the BE-cognition associations vary by individual-level demographics. The sample included 4539 participants from the Multi-Ethnic Study of Atherosclerosis. Multivariable linear regression was used to examine the associations between five BE measures and four cognitive measures, and effect modification by individual-level education and race/ethnicity. In the overall sample, increasing social destination density, walking destination density, and intersection density were associated with worse overall cognition, whereas increasing proportion of land dedicated to retail was associated with better processing speed. Effect modification results suggest that the association between urban density and worse cognition may be limited to or strongest in those of non-white race/ethnicity. Although an increase in neighborhood retail destinations was associated with better cognition in the overall sample, these results suggest that certain BE characteristics in dense urban environments may have a disproportionately negative association with cognition in vulnerable populations. However, our findings must be replicated in longitudinal studies and other regional samples. Copyright © 2018 Elsevier Ltd. All rights reserved.

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

Using the PDD Behavior Inventory as a Level 2 Screener: A Classification and Regression Trees Analysis

ERIC Educational Resources Information Center

Cohen, Ira L.; Liu, Xudong; Hudson, Melissa; Gillis, Jennifer; Cavalari, Rachel N. S.; Romanczyk, Raymond G.; Karmel, Bernard Z.; Gardner, Judith M.

2016-01-01

In order to improve discrimination accuracy between Autism Spectrum Disorder (ASD) and similar neurodevelopmental disorders, a data mining procedure, Classification and Regression Trees (CART), was used on a large multi-site sample of PDD Behavior Inventory (PDDBI) forms on children with and without ASD. Discrimination accuracy exceeded 80%,…

Electromyographic analyses of muscle pre-activation induced by single joint exercise.

PubMed

Júnior, Valdinar A R; Bottaro, Martim; Pereira, Maria C C; Andrade, Marcelino M; P Júnior, Paulo R W; Carmo, Jake C

2010-01-01

To investigate whether performing a low-intensity, single-joint exercises for knee extensors was an efficient strategy for increasing the number of motor units recruited in the vastus lateralis muscle during a subsequent multi-joint exercises. Nine healthy male participants (23.33+/-3.46 yrs) underwent bouts of exercise in which knee extension and 45 degrees , and leg press exercises were performed in sequence. In the low-intensity bout (R30), 15 unilateral knee extensions were performed, followed by 15 repetitions of the leg presses at 30% and 60% of one maximum repetition load (1-MR), respectively. In the high-intensity bout (R60), the same sequence was performed, but the applied load was 60% of 1-MR for both exercises. A single set of 15 repetitions of the leg press at 60% of 1-MR was performed as a control exercise (CR). The surface electromyographic signals of the vastus lateralis muscle were recorded by means of a linear electrode array. The root mean square (RMS) values were determined for each repetition of the leg press, and linear regressions were calculated from these results. The slopes of the straight lines obtained were then normalized using the linear coefficients of the regression equations and compared using one-way ANOVAs for repeated measures. The slopes observed in the CR were significantly lower than those in the R30 and R60 (p<0.05). The results indicated that the recruitment of motor units was more effective when a single-joint exercise preceded the multi-joint exercise. Article registered in the Australian New Zealand Clinical Trials Registry (ANZCTR) under the number ACTRN12609000413224.

An automated ranking platform for machine learning regression models for meat spoilage prediction using multi-spectral imaging and metabolic profiling.

PubMed

Estelles-Lopez, Lucia; Ropodi, Athina; Pavlidis, Dimitris; Fotopoulou, Jenny; Gkousari, Christina; Peyrodie, Audrey; Panagou, Efstathios; Nychas, George-John; Mohareb, Fady

2017-09-01

Over the past decade, analytical approaches based on vibrational spectroscopy, hyperspectral/multispectral imagining and biomimetic sensors started gaining popularity as rapid and efficient methods for assessing food quality, safety and authentication; as a sensible alternative to the expensive and time-consuming conventional microbiological techniques. Due to the multi-dimensional nature of the data generated from such analyses, the output needs to be coupled with a suitable statistical approach or machine-learning algorithms before the results can be interpreted. Choosing the optimum pattern recognition or machine learning approach for a given analytical platform is often challenging and involves a comparative analysis between various algorithms in order to achieve the best possible prediction accuracy. In this work, "MeatReg", a web-based application is presented, able to automate the procedure of identifying the best machine learning method for comparing data from several analytical techniques, to predict the counts of microorganisms responsible of meat spoilage regardless of the packaging system applied. In particularly up to 7 regression methods were applied and these are ordinary least squares regression, stepwise linear regression, partial least square regression, principal component regression, support vector regression, random forest and k-nearest neighbours. MeatReg" was tested with minced beef samples stored under aerobic and modified atmosphere packaging and analysed with electronic nose, HPLC, FT-IR, GC-MS and Multispectral imaging instrument. Population of total viable count, lactic acid bacteria, pseudomonads, Enterobacteriaceae and B. thermosphacta, were predicted. As a result, recommendations of which analytical platforms are suitable to predict each type of bacteria and which machine learning methods to use in each case were obtained. The developed system is accessible via the link: www.sorfml.com. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

PubMed

Marill, Keith A

2004-01-01

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

Association of menopause age and N-terminal pro brain natriuretic peptide: the Multi-Ethnic Study of Atherosclerosis.

PubMed

Ebong, Imo A; Watson, Karol E; Goff, David C; Bluemke, David A; Srikanthan, Preethi; Horwich, Tamara; Bertoni, Alain G

2015-05-01

Menopause age can affect the risk of developing cardiovascular disease (CVD). The purpose of this study was to investigate the associations of early menopause (menopause occurring before age 45 y) and menopause age with N-terminal pro brain natriuretic peptide (NT-proBNP), a potential risk marker of CVD and heart failure. Our cross-sectional study included 2,275 postmenopausal women, aged 45 to 85 years and without clinical CVD (2000-2002), from the Multi-Ethnic Study of Atherosclerosis. Participants were classified as having or not having early menopause. NT-proBNP was log-transformed. Multivariable linear regression was used for analysis. Five hundred sixty-one women had early menopause. The median (25th-75th percentiles) NT-proBNP value was 79.0 (41.1-151.6) pg/mL for all participants, 83.4 (41.4-164.9) pg/mL for women with early menopause, and 78.0 (40.8-148.3) pg/mL for women without early menopause. The mean (SD) age was 65 (10.1) and 65 (8.9) years for women with and without early menopause, respectively. No significant interactions between menopause age and ethnicity were observed. In multivariable analysis, early menopause was associated with a 10.7% increase in NT-proBNP levels, whereas each 1-year increase in menopause age was associated with a 0.7% decrease in NT-proBNP levels. Early menopause is associated with greater NT-proBNP levels, whereas each 1-year increase in menopause age is associated with lower NT-proBNP levels, in postmenopausal women.

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

PubMed

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

2017-01-01

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

Gas demand forecasting by a new artificial intelligent algorithm

NASA Astrophysics Data System (ADS)

Khatibi. B, Vahid; Khatibi, Elham

2012-01-01

Energy demand forecasting is a key issue for consumers and generators in all energy markets in the world. This paper presents a new forecasting algorithm for daily gas demand prediction. This algorithm combines a wavelet transform and forecasting models such as multi-layer perceptron (MLP), linear regression or GARCH. The proposed method is applied to real data from the UK gas markets to evaluate their performance. The results show that the forecasting accuracy is improved significantly by using the proposed method.

Impact of temperature on mortality in Hubei, China: a multi-county time series analysis

NASA Astrophysics Data System (ADS)

Zhang, Yunquan; Yu, Chuanhua; Bao, Junzhe; Li, Xudong

2017-03-01

We examined the impact of extreme temperatures on mortality in 12 counties across Hubei Province, central China, during 2009-2012. Quasi-Poisson generalized linear regression combined with distributed lag non-linear model was first applied to estimate county-specific relationship between temperature and mortality. A multivariable meta-analysis was then used to pool the estimates of county-specific mortality effects of extreme cold temperature (1st percentile) and hot temperature (99th percentile). An inverse J-shaped relationship was observed between temperature and mortality at the provincial level. Heat effect occurred immediately and persisted for 2-3 days, whereas cold effect was 1-2 days delayed and much longer lasting. Higher mortality risks were observed among females, the elderly aged over 75 years, persons dying outside the hospital and those with high education attainment, especially for cold effects. Our data revealed some slight differences in heat- and cold- related mortality effects on urban and rural residents. These findings may have important implications for developing locally-based preventive and intervention strategies to reduce temperature-related mortality, especially for those susceptible subpopulations. Also, urbanization should be considered as a potential influence factor when evaluating temperature-mortality association in future researches.

Hepatic fat quantification: a prospective comparison of magnetic resonance spectroscopy and analysis methods for chemical-shift gradient echo magnetic resonance imaging with histologic assessment as the reference standard.

PubMed

Kang, Bo-Kyeong; Yu, Eun Sil; Lee, Seung Soo; Lee, Youngjoo; Kim, Namkug; Sirlin, Claude B; Cho, Eun Yoon; Yeom, Suk Keu; Byun, Jae Ho; Park, Seong Ho; Lee, Moon-Gyu

2012-06-01

The aims of this study were to assess the confounding effects of hepatic iron deposition, inflammation, and fibrosis on hepatic steatosis (HS) evaluation by magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) and to assess the accuracies of MRI and MRS for HS evaluation, using histology as the reference standard. In this institutional review board-approved prospective study, 56 patients gave informed consents and underwent chemical-shift MRI and MRS of the liver on a 1.5-T magnetic resonance scanner. To estimate MRI fat fraction (FF), 4 analysis methods were used (dual-echo, triple-echo, multiecho, and multi-interference), and MRS FF was calculated with T2 correction. Degrees of HS, iron deposition, inflammation, and fibrosis were analyzed in liver resection (n = 37) and biopsy (n = 19) specimens. The confounding effects of histology on fat quantification were assessed by multiple linear regression analysis. Using the histologic degree of HS as the reference standard, the accuracies of each method in estimating HS and diagnosing an HS of 5% or greater were determined by linear regression and receiver operating characteristic analyses. Iron deposition significantly confounded estimations of FF by the dual-echo (P < 0.001) and triple-echo (P = 0.033) methods, whereas no histologic feature confounded the multiecho and multi-interference methods or MRS. The MRS (r = 0.95) showed the strongest correlation with histologic degree of HS, followed by the multiecho (r = 0.92), multi-interference (r = 0.91), triple-echo (r = 0.90), and dual-echo (r = 0.85) methods. For diagnosing HS, the areas under the curve tended to be higher for MRS (0.96) and the multiecho (0.95), multi-interference (0.95), and triple-echo (0.95) methods than for the dual-echo method (0.88) (P ≥ 0.13). The multiecho and multi-interference MRI methods and MRS can accurately quantify hepatic fat, with coexisting histologic abnormalities having no confounding effects.

Stochastic search, optimization and regression with energy applications

NASA Astrophysics Data System (ADS)

Hannah, Lauren A.

Designing clean energy systems will be an important task over the next few decades. One of the major roadblocks is a lack of mathematical tools to economically evaluate those energy systems. However, solutions to these mathematical problems are also of interest to the operations research and statistical communities in general. This thesis studies three problems that are of interest to the energy community itself or provide support for solution methods: R&D portfolio optimization, nonparametric regression and stochastic search with an observable state variable. First, we consider the one stage R&D portfolio optimization problem to avoid the sequential decision process associated with the multi-stage. The one stage problem is still difficult because of a non-convex, combinatorial decision space and a non-convex objective function. We propose a heuristic solution method that uses marginal project values---which depend on the selected portfolio---to create a linear objective function. In conjunction with the 0-1 decision space, this new problem can be solved as a knapsack linear program. This method scales well to large decision spaces. We also propose an alternate, provably convergent algorithm that does not exploit problem structure. These methods are compared on a solid oxide fuel cell R&D portfolio problem. Next, we propose Dirichlet Process mixtures of Generalized Linear Models (DPGLM), a new method of nonparametric regression that accommodates continuous and categorical inputs, and responses that can be modeled by a generalized linear model. We prove conditions for the asymptotic unbiasedness of the DP-GLM regression mean function estimate. We also give examples for when those conditions hold, including models for compactly supported continuous distributions and a model with continuous covariates and categorical response. We empirically analyze the properties of the DP-GLM and why it provides better results than existing Dirichlet process mixture regression models. We evaluate DP-GLM on several data sets, comparing it to modern methods of nonparametric regression like CART, Bayesian trees and Gaussian processes. Compared to existing techniques, the DP-GLM provides a single model (and corresponding inference algorithms) that performs well in many regression settings. Finally, we study convex stochastic search problems where a noisy objective function value is observed after a decision is made. There are many stochastic search problems whose behavior depends on an exogenous state variable which affects the shape of the objective function. Currently, there is no general purpose algorithm to solve this class of problems. We use nonparametric density estimation to take observations from the joint state-outcome distribution and use them to infer the optimal decision for a given query state. We propose two solution methods that depend on the problem characteristics: function-based and gradient-based optimization. We examine two weighting schemes, kernel-based weights and Dirichlet process-based weights, for use with the solution methods. The weights and solution methods are tested on a synthetic multi-product newsvendor problem and the hour-ahead wind commitment problem. Our results show that in some cases Dirichlet process weights offer substantial benefits over kernel based weights and more generally that nonparametric estimation methods provide good solutions to otherwise intractable problems.

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

PubMed

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

2015-12-07

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

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

PubMed Central

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

2015-01-01

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

Higher direct bilirubin levels during mid-pregnancy are associated with lower risk of gestational diabetes mellitus.

PubMed

Liu, Chaoqun; Zhong, Chunrong; Zhou, Xuezhen; Chen, Renjuan; Wu, Jiangyue; Wang, Weiye; Li, Xiating; Ding, Huisi; Guo, Yanfang; Gao, Qin; Hu, Xingwen; Xiong, Guoping; Yang, Xuefeng; Hao, Liping; Xiao, Mei; Yang, Nianhong

2017-01-01

Bilirubin concentrations have been recently reported to be negatively associated with type 2 diabetes mellitus. We examined the association between bilirubin concentrations and gestational diabetes mellitus. In a prospective cohort study, 2969 pregnant women were recruited prior to 16 weeks of gestation and were followed up until delivery. The value of bilirubin was tested and oral glucose tolerance test was conducted to screen gestational diabetes mellitus. The relationship between serum bilirubin concentration and gestational weeks was studied by two-piecewise linear regression. A subsample of 1135 participants with serum bilirubin test during 16-18 weeks gestation was conducted to research the association between serum bilirubin levels and risk of gestational diabetes mellitus by logistic regression. Gestational diabetes mellitus developed in 8.5 % of the participants (223 of 2969). Two-piecewise linear regression analyses demonstrated that the levels of bilirubin decreased with gestational week up to the turning point 23 and after that point, levels of bilirubin were increased slightly. In multiple logistic regression analysis, the relative risk of developing gestational diabetes mellitus was lower in the highest tertile of direct bilirubin than that in the lowest tertile (RR 0.60; 95 % CI, 0.35-0.89). The results suggested that women with higher serum direct bilirubin levels during the second trimester of pregnancy have lower risk for development of gestational diabetes mellitus.

Multi-functional surface acoustic wave sensor for monitoring enviromental and structural condition

NASA Astrophysics Data System (ADS)

Furuya, Y.; Kon, T.; Okazaki, T.; Saigusa, Y.; Nomura, T.

2006-03-01

As a first step to develop a health monitoring system with active and embedded nondestructive evaluation devices for the machineries and structures, multi-functional SAW (surface acoustic wave) device was developed. A piezoelectric LiNbO3(x-y cut) materials were used as a SAW substrate on which IDT(20μm pitch) was produced by lithography. On the surface of a path of SAW between IDTs, environmentally active material films of shape memory Ti50Ni41Cu(at%) with non-linear hysteresis and superelastic Ti48Ni43Cu(at%) with linear deformation behavior were formed by magnetron-sputtering technique. In this study, these two kinds of shape memory alloys SMA) system were used to measure 1) loading level, 2) phase transformation and 3)stress-strain hysteresis under cyclic loading by utilizing their linearity and non-linearity deformation behaviors. Temperature and stress dependencies of SAW signal were also investigated in the non-sputtered film state. Signal amplitude and phase change of SAW were chosen to measure as the sensing parameters. As a result, temperature, stress level, phase transformation in SMA depending on temperature and mechanical damage accumulation could be measured by the proposed multi-functional SAW sensor. Moreover, the wireless SAW sensing system which has a unique feature of no supplying electric battery was constructed, and the same characteristic evaluation is confirmed in comparison with wired case.

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.

Use of experimental design for optimisation of the cold plasma ICP-MS determination of lithium, aluminum and iron in soft drinks and alcoholic beverages.

PubMed

Bianchi, F; Careri, M; Maffini, M; Mangia, A; Mucchino, C

2003-01-01

A sensitive method for the simultaneous determination of (7)Li, (27)Al and (56)Fe by cold plasma ICP-MS was developed and validated. Experimental design was used to investigate the effects of torch position, torch power, lens 2 voltage, and coolant flow. Regression models and desirability functions were applied to find the experimental conditions providing the highest global sensitivity in a multi-elemental analysis. Validation was performed in terms of limits of detection (LOD), limits of quantitation (LOQ), linearity and precision. LODs were 1.4 and 159 ng L(-1) for (7)Li and (56)Fe, respectively; the highest LOD found being that for (27)Al (425 ng L(-1)). Linear ranges of 5 orders of magnitude for Li and 3 orders for Fe were statistically verified for each compound. Precision was evaluated by testing two concentration levels, and good results in terms of both intra-day repeatability and intermediate precision were obtained. RSD values lower than 4.8% at the lowest concentration level were calculated for intra-day repeatability. Commercially available soft drinks and alcoholic beverages contained in different packaging materials (TetraPack, polyethylene terephthalate (PET), commercial cans and glass) were analysed, and all the analytes were detected and quantitated. Copyright 2002 John Wiley & Sons, Ltd.

Individual relocation decisions after tornadoes: a multi-level analysis.

PubMed

Cong, Zhen; Nejat, Ali; Liang, Daan; Pei, Yaolin; Javid, Roxana J

2018-04-01

This study examines how multi-level factors affected individuals' relocation decisions after EF4 and EF5 (Enhanced Fujita Tornado Intensity Scale) tornadoes struck the United States in 2013. A telephone survey was conducted with 536 respondents, including oversampled older adults, one year after these two disaster events. Respondents' addresses were used to associate individual information with block group-level variables recorded by the American Community Survey. Logistic regression revealed that residential damage and homeownership are important predictors of relocation. There was also significant interaction between these two variables, indicating less difference between homeowners and renters at higher damage levels. Homeownership diminished the likelihood of relocation among younger respondents. Random effects logistic regression found that the percentage of homeownership and of higher income households in the community buffered the effect of damage on relocation; the percentage of older adults reduced the likelihood of this group relocating. The findings are assessed from the standpoint of age difference, policy implications, and social capital and vulnerability. © 2018 The Author(s). Disasters © Overseas Development Institute, 2018.

Evaluation of force-velocity and power-velocity relationship of arm muscles.

PubMed

Sreckovic, Sreten; Cuk, Ivan; Djuric, Sasa; Nedeljkovic, Aleksandar; Mirkov, Dragan; Jaric, Slobodan

2015-08-01

A number of recent studies have revealed an approximately linear force-velocity (F-V) and, consequently, a parabolic power-velocity (P-V) relationship of multi-joint tasks. However, the measurement characteristics of their parameters have been neglected, particularly those regarding arm muscles, which could be a problem for using the linear F-V model in both research and routine testing. Therefore, the aims of the present study were to evaluate the strength, shape, reliability, and concurrent validity of the F-V relationship of arm muscles. Twelve healthy participants performed maximum bench press throws against loads ranging from 20 to 70 % of their maximum strength, and linear regression model was applied on the obtained range of F and V data. One-repetition maximum bench press and medicine ball throw tests were also conducted. The observed individual F-V relationships were exceptionally strong (r = 0.96-0.99; all P < 0.05) and fairly linear, although it remains unresolved whether a polynomial fit could provide even stronger relationships. The reliability of parameters obtained from the linear F-V regressions proved to be mainly high (ICC > 0.80), while their concurrent validity regarding directly measured F, P, and V ranged from high (for maximum F) to medium-to-low (for maximum P and V). The findings add to the evidence that the linear F-V and, consequently, parabolic P-V models could be used to study the mechanical properties of muscular systems, as well as to design a relatively simple, reliable, and ecologically valid routine test of the muscle ability of force, power, and velocity production.

A dynamic multi-level optimal design method with embedded finite-element modeling for power transformers

NASA Astrophysics Data System (ADS)

Zhang, Yunpeng; Ho, Siu-lau; Fu, Weinong

2018-05-01

This paper proposes a dynamic multi-level optimal design method for power transformer design optimization (TDO) problems. A response surface generated by second-order polynomial regression analysis is updated dynamically by adding more design points, which are selected by Shifted Hammersley Method (SHM) and calculated by finite-element method (FEM). The updating stops when the accuracy requirement is satisfied, and optimized solutions of the preliminary design are derived simultaneously. The optimal design level is modulated through changing the level of error tolerance. Based on the response surface of the preliminary design, a refined optimal design is added using multi-objective genetic algorithm (MOGA). The effectiveness of the proposed optimal design method is validated through a classic three-phase power TDO problem.

Changes in US extreme sea levels and the role of large scale climate variations

NASA Astrophysics Data System (ADS)

Wahl, T.; Chambers, D. P.

2015-12-01

We analyze a set of 20 tide gauge records covering the contiguous United States (US) coastline and the period from 1929 to 2013 to identify long-term trends and multi-decadal variations in extreme sea levels (ESLs) relative to changes in mean sea level (MSL). Significant but small long-term trends in ESLs above/below MSL are found at individual sites along most coastline stretches, but are mostly confined to the southeast coast and the winter season when storm surges are primarily driven by extra-tropical cyclones. We identify six regions with broadly coherent and considerable multi-decadal ESL variations unrelated to MSL changes. Using a quasi-non-stationary extreme value analysis approach we show that the latter would have caused variations in design relevant return water levels (RWLs; 50 to 200 year return periods) ranging from ~10 cm to as much as 110 cm across the six regions. To explore the origin of these temporal changes and the role of large-scale climate variability we develop different sets of simple and multiple linear regression models with RWLs as dependent variables and climate indices, or tailored (toward the goal of predicting multi-decadal RWL changes) versions of them, and wind stress curl as independent predictors. The models, after being tested for spatial and temporal stability, explain up to 97% of the observed variability at individual sites and almost 80% on average. Using the model predictions as covariates for the quasi-non-stationary extreme value analysis also significantly reduces the range of change in the 100-year RWLs over time, turning a non-stationary process into a stationary one. This highlights that the models - when used with regional and global climate model output of the predictors - should also be capable of projecting future RWL changes to be used by decision makers for improved flood preparedness and long-term resiliency.

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

PubMed

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

2010-05-01

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

Reconstructed and projected U.S. residential natural gas consumption during 1896-2043

USDA-ARS?s Scientific Manuscript database

Using state-level monthly heating degree day (HDD) data, per-capita natural gas (NG) consumption records for each state of the continental U.S. were calculated during 1895-2014 using linear regressions. The regressed monthly NG values estimate the effects of 20th and early 21st century climate varia...

Physical activity in European adolescents and associations with anxiety, depression and well-being.

PubMed

McMahon, Elaine M; Corcoran, Paul; O'Regan, Grace; Keeley, Helen; Cannon, Mary; Carli, Vladimir; Wasserman, Camilla; Hadlaczky, Gergö; Sarchiapone, Marco; Apter, Alan; Balazs, Judit; Balint, Maria; Bobes, Julio; Brunner, Romuald; Cozman, Doina; Haring, Christian; Iosue, Miriam; Kaess, Michael; Kahn, Jean-Pierre; Nemes, Bogdan; Podlogar, Tina; Poštuvan, Vita; Sáiz, Pilar; Sisask, Merike; Tubiana, Alexandra; Värnik, Peeter; Hoven, Christina W; Wasserman, Danuta

2017-01-01

In this cross-sectional study, physical activity, sport participation and associations with well-being, anxiety and depressive symptoms were examined in a large representative sample of European adolescents. A school-based survey was completed by 11,110 adolescents from ten European countries who took part in the SEYLE (Saving and Empowering Young Lives in Europe) study. The questionnaire included items assessing physical activity, sport participation and validated instruments assessing well-being (WHO-5), depressive symptoms (BDI-II) and anxiety (SAS). Multi-level mixed effects linear regression was used to examine associations between physical activity/sport participation and mental health measures. A minority of the sample (17.9 % of boys and 10.7 % of girls; p < 0.0005) reported sufficient activity based on WHO guidelines (60 min + daily). The mean number of days of at least 60 min of moderate-to-vigorous activity in the past 2 weeks was 7.5 ± 4.4 among boys and 5.9 days ± 4.3 among girls. Frequency of activity was positively correlated with well-being and negatively correlated with both anxiety and depressive symptoms, up to a threshold of moderate frequency of activity. In a multi-level mixed effects model more frequent physical activity and participation in sport were both found to independently contribute to greater well-being and lower levels of anxiety and depressive symptoms in both sexes. Increasing activity levels and sports participation among the least active young people should be a target of community and school-based interventions to promote well-being. There does not appear to be an additional benefit to mental health associated with meeting the WHO-recommended levels of activity.

Non-Linear Approach in Kinesiology Should Be Preferred to the Linear--A Case of Basketball.

PubMed

Trninić, Marko; Jeličić, Mario; Papić, Vladan

2015-07-01

In kinesiology, medicine, biology and psychology, in which research focus is on dynamical self-organized systems, complex connections exist between variables. Non-linear nature of complex systems has been discussed and explained by the example of non-linear anthropometric predictors of performance in basketball. Previous studies interpreted relations between anthropometric features and measures of effectiveness in basketball by (a) using linear correlation models, and by (b) including all basketball athletes in the same sample of participants regardless of their playing position. In this paper the significance and character of linear and non-linear relations between simple anthropometric predictors (AP) and performance criteria consisting of situation-related measures of effectiveness (SE) in basketball were determined and evaluated. The sample of participants consisted of top-level junior basketball players divided in three groups according to their playing time (8 minutes and more per game) and playing position: guards (N = 42), forwards (N = 26) and centers (N = 40). Linear (general model) and non-linear (general model) regression models were calculated simultaneously and separately for each group. The conclusion is viable: non-linear regressions are frequently superior to linear correlations when interpreting actual association logic among research variables.

Verification of spectrophotometric method for nitrate analysis in water samples

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Elevated Levels of Adhesion Proteins Are Associated With Low Ankle-Brachial Index.

PubMed

Berardi, Cecilia; Wassel, Christine L; Decker, Paul A; Larson, Nicholas B; Kirsch, Phillip S; Andrade, Mariza de; Tsai, Michael Y; Pankow, James S; Sale, Michele M; Sicotte, Hugues; Tang, Weihong; Hanson, Naomi Q; McDermott, Mary M; Criqui, Michael H; Allison, Michael A; Bielinski, Suzette J

2017-04-01

Inflammation plays a pivotal role in peripheral artery disease (PAD). Cellular adhesion proteins mediate the interaction of leukocytes with endothelial cells during inflammation. To determine the association of cellular adhesion molecules with ankle-brachial index (ABI) and ABI category (≤1.0 vs >1.0) in a diverse population, 15 adhesion proteins were measured in the Multi-Ethnic Study of Atherosclerosis (MESA). To assess multivariable associations of each protein with ABI and ABI category, linear and logistic regression was used, respectively. Among 2364 participants, 23 presented with poorly compressible arteries (ABI > 1.4) and were excluded and 261 had ABI ≤ 1.0. Adjusting for traditional risk factors, elevated levels of soluble P-selectin, hepatocyte growth factor, and secretory leukocyte protease inhibitor were associated with lower ABI ( P = .0004, .001, and .002, respectively). Per each standard deviation of protein, we found 26%, 20%, and 19% greater odds of lower ABI category ( P = .001, .01, and .02, respectively). Further investigation into the adhesion pathway may shed new light on biological mechanisms implicated in PAD.

Walkability and cardiometabolic risk factors: Cross-sectional and longitudinal associations from the Multi-Ethnic Study of Atherosclerosis.

PubMed

Braun, Lindsay M; Rodríguez, Daniel A; Evenson, Kelly R; Hirsch, Jana A; Moore, Kari A; Diez Roux, Ana V

2016-05-01

We used data from 3227 older adults in the Multi-Ethnic Study of Atherosclerosis (2004-2012) to explore cross-sectional and longitudinal associations between walkability and cardiometabolic risk factors. In cross-sectional analyses, linear regression was used to estimate associations of Street Smart Walk Score® with glucose, triglycerides, HDL and LDL cholesterol, systolic and diastolic blood pressure, and waist circumference, while logistic regression was used to estimate associations with odds of metabolic syndrome. Econometric fixed effects models were used to estimate longitudinal associations of changes in walkability with changes in each risk factor among participants who moved residential locations between 2004 and 2012 (n=583). Most cross-sectional and longitudinal associations were small and statistically non-significant. We found limited evidence that higher walkability was cross-sectionally associated with lower blood pressure but that increases in walkability were associated with increases in triglycerides and blood pressure over time. Further research over longer time periods is needed to understand the potential for built environment interventions to improve cardiometabolic health. Copyright © 2016 Elsevier Ltd. All rights reserved.

Influence of school-level and family-level variables on Chinese college students' aggression.

PubMed

Zhou, Jiawei; Yang, Jiarun; Yu, Yunmiao; Wang, Lin; Han, Dong; Zhu, Xiongzhao; He, Jincai; Qiu, Xiaohui; Yang, Xiuxian; Qiao, Zhengxue; Sui, Hong; Yang, Yanjie

2017-08-01

With the frequent occurrence of campus violence, scholars have devoted increasing attention to college students' aggression. This study aims to estimate the prevalence of aggression in Chinese university students and identify factors that could influence their aggression. We can thus find methods to reduce the incidence of college students' aggression in the future. A multi-stage stratified sampling procedure was used to select university students (N = 4565) aged 16-25 years in Harbin. The Aggression Questionnaire, the Adolescent Self-Rating Life Events Checklist and the Social Support Revalued Scale were used to collect data. Females reported lower levels of aggression than males (p < .001). A multiple linear regression analysis was conducted to determine the influence of factors of aggression, and the model was highly significant (R 2  = .233, Ad R 2  = .230, p < .01). The results show that the aggression is affected by gender, family-level and school-level variables. Aggression scores are significantly correlated with not only family-level or school-level variables independently, but their combination as well. We find that the risk factors for aggression include a dissatisfying profession, higher levels of study pressure, poor parental relationships, poor interpersonal relationships, the presence of siblings, punishment, health maladjustment, less subjective support, and lower levels of utilization of social support.

Street-service-level approach towards the calculation of CO emission in Malang City, Indonesia

NASA Astrophysics Data System (ADS)

Utomo, D. M.; Bakkara, A.; Sari, K. E.

2017-06-01

Malang has shown an annual vehicle growth of 15%. However, it is an umfortunate fact that 32% of 44 main streets are identified as having low service level, according to a local transportation ranking report. Such condition results in the decline of average vehicle velocity, approaching to the level of velocity (v) = 0; or in other words, street saturation degree could reach >1. The condition is not proportional to the increase of CO concentration in Malang in 2013-2014 as shown in the result of Evaluation of City Air Quality in 2014 which jumped from 3000 µm/m3 in 2013 to almost 5000 µm/m3 in 2014. This study was aimed at evaluating the extend to which street-service-level variables influence the production of CO emission from motorized transportation activity in an urban street in Malang. Gatot Subroto Street is chosen as a case study according to Multi Criteria Analysis. Furthermore, the street-service-level variables being evaluated include vehicle volume, velocity, side friction, effective roadside width and effective street width. Through a qualitative statistical analysis approach using a multiple linear regression analysis, the result suggests that vehicle volume and side friction are the most dominant factors (Xi) that significantly influence CO emission loads (Y).

Patterns of medicinal plant use: an examination of the Ecuadorian Shuar medicinal flora using contingency table and binomial analyses.

PubMed

Bennett, Bradley C; Husby, Chad E

2008-03-28

Botanical pharmacopoeias are non-random subsets of floras, with some taxonomic groups over- or under-represented. Moerman [Moerman, D.E., 1979. Symbols and selectivity: a statistical analysis of Native American medical ethnobotany, Journal of Ethnopharmacology 1, 111-119] introduced linear regression/residual analysis to examine these patterns. However, regression, the commonly-employed analysis, suffers from several statistical flaws. We use contingency table and binomial analyses to examine patterns of Shuar medicinal plant use (from Amazonian Ecuador). We first analyzed the Shuar data using Moerman's approach, modified to better meet requirements of linear regression analysis. Second, we assessed the exact randomization contingency table test for goodness of fit. Third, we developed a binomial model to test for non-random selection of plants in individual families. Modified regression models (which accommodated assumptions of linear regression) reduced R(2) to from 0.59 to 0.38, but did not eliminate all problems associated with regression analyses. Contingency table analyses revealed that the entire flora departs from the null model of equal proportions of medicinal plants in all families. In the binomial analysis, only 10 angiosperm families (of 115) differed significantly from the null model. These 10 families are largely responsible for patterns seen at higher taxonomic levels. Contingency table and binomial analyses offer an easy and statistically valid alternative to the regression approach.

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.

[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.

Extraction of object skeletons in multispectral imagery by the orthogonal regression fitting

NASA Astrophysics Data System (ADS)

Palenichka, Roman M.; Zaremba, Marek B.

2003-03-01

Accurate and automatic extraction of skeletal shape of objects of interest from satellite images provides an efficient solution to such image analysis tasks as object detection, object identification, and shape description. The problem of skeletal shape extraction can be effectively solved in three basic steps: intensity clustering (i.e. segmentation) of objects, extraction of a structural graph of the object shape, and refinement of structural graph by the orthogonal regression fitting. The objects of interest are segmented from the background by a clustering transformation of primary features (spectral components) with respect to each pixel. The structural graph is composed of connected skeleton vertices and represents the topology of the skeleton. In the general case, it is a quite rough piecewise-linear representation of object skeletons. The positions of skeleton vertices on the image plane are adjusted by means of the orthogonal regression fitting. It consists of changing positions of existing vertices according to the minimum of the mean orthogonal distances and, eventually, adding new vertices in-between if a given accuracy if not yet satisfied. Vertices of initial piecewise-linear skeletons are extracted by using a multi-scale image relevance function. The relevance function is an image local operator that has local maximums at the centers of the objects of interest.

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.

Electrochemical Behavior and Determination of Chlorogenic Acid Based on Multi-Walled Carbon Nanotubes Modified Screen-Printed Electrode

PubMed Central

Ma, Xiaoyan; Yang, Hongqiao; Xiong, Huabin; Li, Xiaofen; Gao, Jinting; Gao, Yuntao

2016-01-01

In this paper, the multi-walled carbon nanotubes modified screen-printed electrode (MWCNTs/SPE) was prepared and the MWCNTs/SPE was employed for the electrochemical determination of the antioxidant substance chlorogenic acids (CGAs). A pair of well-defined redox peaks of CGA was observed at the MWCNTs/SPE in 0.10 mol/L acetic acid-sodium acetate buffer (pH 6.2) and the electrode process was adsorption-controlled. Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) methods for the determination of CGA were proposed based on the MWCNTs/SPE. Under the optimal conditions, the proposed method exhibited linear ranges from 0.17 to 15.8 µg/mL, and the linear regression equation was Ipa (µA) = 4.1993 C (×10−5 mol/L) + 1.1039 (r = 0.9976) and the detection limit for CGA could reach 0.12 µg/mL. The recovery of matrine was 94.74%–106.65% (RSD = 2.92%) in coffee beans. The proposed method is quick, sensitive, reliable, and can be used for the determination of CGA. PMID:27801797

Riemannian multi-manifold modeling and clustering in brain networks

NASA Astrophysics Data System (ADS)

Slavakis, Konstantinos; Salsabilian, Shiva; Wack, David S.; Muldoon, Sarah F.; Baidoo-Williams, Henry E.; Vettel, Jean M.; Cieslak, Matthew; Grafton, Scott T.

2017-08-01

This paper introduces Riemannian multi-manifold modeling in the context of brain-network analytics: Brainnetwork time-series yield features which are modeled as points lying in or close to a union of a finite number of submanifolds within a known Riemannian manifold. Distinguishing disparate time series amounts thus to clustering multiple Riemannian submanifolds. To this end, two feature-generation schemes for brain-network time series are put forth. The first one is motivated by Granger-causality arguments and uses an auto-regressive moving average model to map low-rank linear vector subspaces, spanned by column vectors of appropriately defined observability matrices, to points into the Grassmann manifold. The second one utilizes (non-linear) dependencies among network nodes by introducing kernel-based partial correlations to generate points in the manifold of positivedefinite matrices. Based on recently developed research on clustering Riemannian submanifolds, an algorithm is provided for distinguishing time series based on their Riemannian-geometry properties. Numerical tests on time series, synthetically generated from real brain-network structural connectivity matrices, reveal that the proposed scheme outperforms classical and state-of-the-art techniques in clustering brain-network states/structures.

Prediction of siRNA potency using sparse logistic regression.

PubMed

Hu, Wei; Hu, John

2014-06-01

RNA interference (RNAi) can modulate gene expression at post-transcriptional as well as transcriptional levels. Short interfering RNA (siRNA) serves as a trigger for the RNAi gene inhibition mechanism, and therefore is a crucial intermediate step in RNAi. There have been extensive studies to identify the sequence characteristics of potent siRNAs. One such study built a linear model using LASSO (Least Absolute Shrinkage and Selection Operator) to measure the contribution of each siRNA sequence feature. This model is simple and interpretable, but it requires a large number of nonzero weights. We have introduced a novel technique, sparse logistic regression, to build a linear model using single-position specific nucleotide compositions which has the same prediction accuracy of the linear model based on LASSO. The weights in our new model share the same general trend as those in the previous model, but have only 25 nonzero weights out of a total 84 weights, a 54% reduction compared to the previous model. Contrary to the linear model based on LASSO, our model suggests that only a few positions are influential on the efficacy of the siRNA, which are the 5' and 3' ends and the seed region of siRNA sequences. We also employed sparse logistic regression to build a linear model using dual-position specific nucleotide compositions, a task LASSO is not able to accomplish well due to its high dimensional nature. Our results demonstrate the superiority of sparse logistic regression as a technique for both feature selection and regression over LASSO in the context of siRNA design.

Centering Effects in HLM Level-1 Predictor Variables.

ERIC Educational Resources Information Center

Schumacker, Randall E.; Bembry, Karen

Research has suggested that important research questions can be addressed with meaningful interpretations using hierarchical linear modeling (HLM). The proper interpretation of results, however, is invariably linked to the choice of centering for the Level-1 predictor variables that produce the outcome measure for the Level-2 regression analysis.…

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.

Lung nodule malignancy prediction using multi-task convolutional neural network

NASA Astrophysics Data System (ADS)

Li, Xiuli; Kao, Yueying; Shen, Wei; Li, Xiang; Xie, Guotong

2017-03-01

In this paper, we investigated the problem of diagnostic lung nodule malignancy prediction using thoracic Computed Tomography (CT) screening. Unlike most existing studies classify the nodules into two types benign and malignancy, we interpreted the nodule malignancy prediction as a regression problem to predict continuous malignancy level. We proposed a joint multi-task learning algorithm using Convolutional Neural Network (CNN) to capture nodule heterogeneity by extracting discriminative features from alternatingly stacked layers. We trained a CNN regression model to predict the nodule malignancy, and designed a multi-task learning mechanism to simultaneously share knowledge among 9 different nodule characteristics (Subtlety, Calcification, Sphericity, Margin, Lobulation, Spiculation, Texture, Diameter and Malignancy), and improved the final prediction result. Each CNN would generate characteristic-specific feature representations, and then we applied multi-task learning on the features to predict the corresponding likelihood for that characteristic. We evaluated the proposed method on 2620 nodules CT scans from LIDC-IDRI dataset with the 5-fold cross validation strategy. The multitask CNN regression result for regression RMSE and mapped classification ACC were 0.830 and 83.03%, while the results for single task regression RMSE 0.894 and mapped classification ACC 74.9%. Experiments show that the proposed method could predict the lung nodule malignancy likelihood effectively and outperforms the state-of-the-art methods. The learning framework could easily be applied in other anomaly likelihood prediction problem, such as skin cancer and breast cancer. It demonstrated the possibility of our method facilitating the radiologists for nodule staging assessment and individual therapeutic planning.

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

PubMed

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

2016-01-01

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

A menu-driven software package of Bayesian nonparametric (and parametric) mixed models for regression analysis and density estimation.

PubMed

Karabatsos, George

2017-02-01

Most of applied statistics involves regression analysis of data. In practice, it is important to specify a regression model that has minimal assumptions which are not violated by data, to ensure that statistical inferences from the model are informative and not misleading. This paper presents a stand-alone and menu-driven software package, Bayesian Regression: Nonparametric and Parametric Models, constructed from MATLAB Compiler. Currently, this package gives the user a choice from 83 Bayesian models for data analysis. They include 47 Bayesian nonparametric (BNP) infinite-mixture regression models; 5 BNP infinite-mixture models for density estimation; and 31 normal random effects models (HLMs), including normal linear models. Each of the 78 regression models handles either a continuous, binary, or ordinal dependent variable, and can handle multi-level (grouped) data. All 83 Bayesian models can handle the analysis of weighted observations (e.g., for meta-analysis), and the analysis of left-censored, right-censored, and/or interval-censored data. Each BNP infinite-mixture model has a mixture distribution assigned one of various BNP prior distributions, including priors defined by either the Dirichlet process, Pitman-Yor process (including the normalized stable process), beta (two-parameter) process, normalized inverse-Gaussian process, geometric weights prior, dependent Dirichlet process, or the dependent infinite-probits prior. The software user can mouse-click to select a Bayesian model and perform data analysis via Markov chain Monte Carlo (MCMC) sampling. After the sampling completes, the software automatically opens text output that reports MCMC-based estimates of the model's posterior distribution and model predictive fit to the data. Additional text and/or graphical output can be generated by mouse-clicking other menu options. This includes output of MCMC convergence analyses, and estimates of the model's posterior predictive distribution, for selected functionals and values of covariates. The software is illustrated through the BNP regression analysis of real data.

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.

A structured sparse regression method for estimating isoform expression level from multi-sample RNA-seq data.

PubMed

Zhang, L; Liu, X J

2016-06-03

With the rapid development of next-generation high-throughput sequencing technology, RNA-seq has become a standard and important technique for transcriptome analysis. For multi-sample RNA-seq data, the existing expression estimation methods usually deal with each single-RNA-seq sample, and ignore that the read distributions are consistent across multiple samples. In the current study, we propose a structured sparse regression method, SSRSeq, to estimate isoform expression using multi-sample RNA-seq data. SSRSeq uses a non-parameter model to capture the general tendency of non-uniformity read distribution for all genes across multiple samples. Additionally, our method adds a structured sparse regularization, which not only incorporates the sparse specificity between a gene and its corresponding isoform expression levels, but also reduces the effects of noisy reads, especially for lowly expressed genes and isoforms. Four real datasets were used to evaluate our method on isoform expression estimation. Compared with other popular methods, SSRSeq reduced the variance between multiple samples, and produced more accurate isoform expression estimations, and thus more meaningful biological interpretations.

Multi-level significance of vulnerability indicators. Case study: Eastern Romania

NASA Astrophysics Data System (ADS)

Stanga, I. C.; Grozavu, A.

2012-04-01

Vulnerability assessment aims, most frequently, to emphasize internal fragility of a system comparing to a reference standard, to similar systems or in relation to a given hazard. Internal fragility, either biophysical or structural, may affect the capacity to predict, to prepare for, to cope with or to recover from a disaster. Thus, vulnerability is linked to resilience and adaptive capacity. From local level to global one, vulnerability factors and corresponding indicators are different and their significance must be tested and validated in a well-structured conceptual and methodological framework. In this paper, the authors aim to show the real vulnerability of rural settlements in Eastern Romania in a multi-level approach. The research area, Tutova Hills, counts about 3421 sq.km and more than 200.000 inhabitants in 421 villages characterized by deficient accessibility, lack of endowments, subsistential agriculture, high pressure on natural environment (especially on forest and soil resources), poverty and aging process of population. Factors that could influence the vulnerability of these rural settlements have been inventoried and assigned into groups through a cluster analysis: habitat and technical urban facilities, infrastructure, economical, social and demographical indicators, environment quality, management of emergency situations etc. Firstly, the main difficulty was to convert qualitative variable in quantitative indicators and to standardize all values to make possible mathematical and statistical processing of data. Secondly, the great variability of vulnerability factors, their different measuring units and their high amplitude of variation require different method of standardization in order to obtain values between zero (minimum vulnerability) and one (maximum vulnerability). Final vulnerability indicators were selected and integrated in a general scheme, according to their significance resulted from an appropriate factor analysis: linear and logistic regression, varimax rotation, multiple-criteria decision analysis, weight of evidence, multi-criteria evaluation method etc. The approach started from the local level which allows a functional and structural analysis and was progressively translated to an upper level and to a spatial analysis. The model shows that changing the level of analysis diminishes the functional significance of some indicators and increases the capacity of discretization in the case of others, highlighting the spatial and functional complexity of vulnerability.

Decreased levels of sRAGE in follicular fluid from patients with PCOS.

PubMed

Wang, BiJun; Li, Jing; Yang, QingLing; Zhang, FuLi; Hao, MengMeng; Guo, YiHong

2017-03-01

This study aimed to explore the association between soluble receptor for advanced glycation end products (sRAGE) levels in follicular fluid and the number of oocytes retrieved and to evaluate the effect of sRAGE on vascular endothelial growth factor (VEGF) in granulosa cells in patients with polycystic ovarian syndrome (PCOS). Two sets of experiments were performed in this study. In part one, sRAGE and VEGF protein levels in follicular fluid samples from 39 patients with PCOS and 35 non-PCOS patients were measured by ELISA. In part two, ovarian granulosa cells were isolated from an additional 10 patients with PCOS and cultured. VEGF and SP1 mRNA and protein levels, as well as pAKT levels, were detected by real-time PCR and Western blotting after cultured cells were treated with different concentrations of sRAGE. Compared with the non-PCOS patients, patients with PCOS had lower sRAGE levels in follicular fluid. Multi-adjusted regression analysis showed that high sRAGE levels in follicular fluid predicted a lower Gn dose, more oocytes retrieved, and a better IVF outcome in the non-PCOS group. Logistic regression analysis showed that higher sRAGE levels predicted favorably IVF outcomes in the non-PCOS group. Multi-adjusted regression analysis also showed that high sRAGE levels in follicular fluid predicted a lower Gn dose in the PCOS group. Treating granulosa cells isolated from patients with PCOS with recombinant sRAGE decreased VEGF and SP1 mRNA and protein expression and pAKT levels in a dose-dependent manner. © 2017 Society for Reproduction and Fertility.

High performance liquid level monitoring system based on polymer fiber Bragg gratings embedded in silicone rubber diaphragms

NASA Astrophysics Data System (ADS)

Marques, Carlos A. F.; Peng, Gang-Ding; Webb, David J.

2015-05-01

Liquid-level sensing technologies have attracted great prominence, because such measurements are essential to industrial applications, such as fuel storage, flood warning and in the biochemical industry. Traditional liquid level sensors are based on electromechanical techniques; however they suffer from intrinsic safety concerns in explosive environments. In recent years, given that optical fiber sensors have lots of well-established advantages such as high accuracy, costeffectiveness, compact size, and ease of multiplexing, several optical fiber liquid level sensors have been investigated which are based on different operating principles such as side-polishing the cladding and a portion of core, using a spiral side-emitting optical fiber or using silica fiber gratings. The present work proposes a novel and highly sensitive liquid level sensor making use of polymer optical fiber Bragg gratings (POFBGs). The key elements of the system are a set of POFBGs embedded in silicone rubber diaphragms. This is a new development building on the idea of determining liquid level by measuring the pressure at the bottom of a liquid container, however it has a number of critical advantages. The system features several FBG-based pressure sensors as described above placed at different depths. Any sensor above the surface of the liquid will read the same ambient pressure. Sensors below the surface of the liquid will read pressures that increase linearly with depth. The position of the liquid surface can therefore be approximately identified as lying between the first sensor to read an above-ambient pressure and the next higher sensor. This level of precision would not in general be sufficient for most liquid level monitoring applications; however a much more precise determination of liquid level can be made by linear regression to the pressure readings from the sub-surface sensors. There are numerous advantages to this multi-sensor approach. First, the use of linear regression using multiple sensors is inherently more accurate than using a single pressure reading to estimate depth. Second, common mode temperature induced wavelength shifts in the individual sensors are automatically compensated. Thirdly, temperature induced changes in the sensor pressure sensitivity are also compensated. Fourthly, the approach provides the possibility to detect and compensate for malfunctioning sensors. Finally, the system is immune to changes in the density of the monitored fluid and even to changes in the effective force of gravity, as might be obtained in an aerospace application. The performance of an individual sensor was characterized and displays a sensitivity (54 pm/cm), enhanced by more than a factor of 2 when compared to a sensor head configuration based on a silica FBG published in the literature, resulting from the much lower elastic modulus of POF. Furthermore, the temperature/humidity behavior and measurement resolution were also studied in detail. The proposed configuration also displays a highly linear response, high resolution and good repeatability. The results suggest the new configuration can be a useful tool in many different applications, such as aircraft fuel monitoring, and biochemical and environmental sensing, where accuracy and stability are fundamental.

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

PubMed Central

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

2012-01-01

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

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

PubMed

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

2012-12-01

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

Relation of Sex Hormone Levels With Prevalent and 10-Year Change in Aortic Distensibility Assessed by MRI: The Multi-Ethnic Study of Atherosclerosis.

PubMed

Subramanya, Vinita; Ambale-Venkatesh, Bharath; Ohyama, Yoshiaki; Zhao, Di; Nwabuo, Chike C; Post, Wendy S; Guallar, Eliseo; Ouyang, Pamela; Shah, Sanjiv J; Allison, Matthew A; Ndumele, Chiadi E; Vaidya, Dhananjay; Bluemke, David A; Lima, Joao A; Michos, Erin D

2018-06-11

Women experience a steeper decline in aortic elasticity related to aging compared to men. We examined whether sex hormone levels were associated with ascending aortic distensibility (AAD) in the Multi-Ethnic Study of Atherosclerosis. We studied 1,345 postmenopausal women and 1,532 men aged 45-84 years, who had serum sex hormone levels, AAD measured by phase-contrast cardiac magnetic resonance imaging, and ejection fraction>50% at baseline. Among these participants, 457 women and 548 men returned for follow-up magnetic resonance imaging 10-years later. Stratified by sex, and using mixed effects linear regression methods, we examined associations of sex hormones (as tertiles) with baseline and annual change in log-transformed AAD (mm Hg-110-3), adjusting for demographics, body size, lifestyle factors, mean arterial pressure, heart rate, hypertensive medication use (and in women, for hormone therapy use and years since menopause). The mean (SD) age was 65 (9) for women and 62 (10) years for men. AAD was lower in women than men (P < 0.001). In adjusted cross-sectional analysis, the highest tertile of free testosterone (compared to lowest) in women was significantly associated with lower AAD [-0.10 (-0.19, -0.01)] and the highest tertile of estradiol in men was associated with greater AAD [0.12 (0.04, 0.20)]. There were no associations of sex hormones with change in AAD over 10 years, albeit in a smaller sample size. Lower free testosterone in women and higher estradiol in men were associated with greater aortic distensibility at baseline, but not longitudinally. Sex hormone levels may account for differences in AAD between women and men.

Investigation of Alien Wavelength Quality in Live Multi-Domain, Multi-Vendor Link Using Advanced Simulation Tool

NASA Astrophysics Data System (ADS)

Nordal Petersen, Martin; Nuijts, Roeland; Lange Bjørn, Lars

2014-05-01

This article presents an advanced optical model for simulation of alien wavelengths in multi-domain and multi-vendor dense wavelength-division multiplexing networks. The model aids optical network planners with a better understanding of the non-linear effects present in dense wavelength-division multiplexing systems and better utilization of alien wavelengths in future applications. The limiting physical effects for alien wavelengths are investigated in relation to power levels, channel spacing, and other factors. The simulation results are verified through experimental setup in live multi-domain dense wavelength-division multiplexing systems between two national research networks: SURFnet in Holland and NORDUnet in Denmark.

Development of non-linear models predicting daily fine particle concentrations using aerosol optical depth retrievals and ground-based measurements at a municipality in the Brazilian Amazon region

NASA Astrophysics Data System (ADS)

Gonçalves, Karen dos Santos; Winkler, Mirko S.; Benchimol-Barbosa, Paulo Roberto; de Hoogh, Kees; Artaxo, Paulo Eduardo; de Souza Hacon, Sandra; Schindler, Christian; Künzli, Nino

2018-07-01

Epidemiological studies generally use particulate matter measurements with diameter less 2.5 μm (PM2.5) from monitoring networks. Satellite aerosol optical depth (AOD) data has considerable potential in predicting PM2.5 concentrations, and thus provides an alternative method for producing knowledge regarding the level of pollution and its health impact in areas where no ground PM2.5 measurements are available. This is the case in the Brazilian Amazon rainforest region where forest fires are frequent sources of high pollution. In this study, we applied a non-linear model for predicting PM2.5 concentration from AOD retrievals using interaction terms between average temperature, relative humidity, sine, cosine of date in a period of 365,25 days and the square of the lagged relative residual. Regression performance statistics were tested comparing the goodness of fit and R2 based on results from linear regression and non-linear regression for six different models. The regression results for non-linear prediction showed the best performance, explaining on average 82% of the daily PM2.5 concentrations when considering the whole period studied. In the context of Amazonia, it was the first study predicting PM2.5 concentrations using the latest high-resolution AOD products also in combination with the testing of a non-linear model performance. Our results permitted a reliable prediction considering the AOD-PM2.5 relationship and set the basis for further investigations on air pollution impacts in the complex context of Brazilian Amazon Region.

Personalized metabolomics for predicting glucose tolerance changes in sedentary women after high-intensity interval training.

PubMed

Kuehnbaum, Naomi L; Gillen, Jenna B; Gibala, Martin J; Britz-McKibbin, Philip

2014-08-28

High-intensity interval training (HIIT) offers a practical approach for enhancing cardiorespiratory fitness, however its role in improving glucose regulation among sedentary yet normoglycemic women remains unclear. Herein, multi-segment injection capillary electrophoresis-mass spectrometry is used as a high-throughput platform in metabolomics to assess dynamic responses of overweight/obese women (BMI > 25, n = 11) to standardized oral glucose tolerance tests (OGTTs) performed before and after a 6-week HIIT intervention. Various statistical methods were used to classify plasma metabolic signatures associated with post-prandial glucose and/or training status when using a repeated measures/cross-over study design. Branched-chain/aromatic amino acids and other intermediates of urea cycle and carnitine metabolism decreased over time in plasma after oral glucose loading. Adaptive exercise-induced changes to plasma thiol redox and orthinine status were measured for trained subjects while at rest in a fasting state. A multi-linear regression model was developed to predict changes in glucose tolerance based on a panel of plasma metabolites measured for naïve subjects in their untrained state. Since treatment outcomes to physical activity are variable between-subjects, prognostic markers offer a novel approach to screen for potential negative responders while designing lifestyle modifications that maximize the salutary benefits of exercise for diabetes prevention on an individual level.

[Transcultural self-efficacy and educational needs for cultural competence in nursing of Korean nurses].

PubMed

Kim, Sun-Hee

2013-02-01

This study was done to investigate the level of transcultural self-efficacy (TSE) and related factors and educational needs for cultural competence in nursing (CCN) of Korean hospital nurses. A self-assessment instrument was used to measure TSE and educational needs for CCN. Questionnaires were completed by 285 nurses working in four Korean hospitals. Descriptive statistics, t-test, ANOVA, Pearson correlation coefficients, and multiple regression were used to analyze the data. Mean TSE score for all items was 4.54 and score for mean CCN educational needs, 5.77. Nurses with master's degrees or higher had significantly higher levels of TSE than nurses with bachelor's degrees. TSE positively correlated with English language proficiency, degrees of interest in multi-culture, degree of experience in caring for multi-cultural clients, and educational needs for CCN. The regression model explained 28% of TSE. Factors affecting TSE were degree of interest in multi-culture, degree of experience in caring for multi-cultural clients, and educational needs for CCN. The results of the study indicate a need for nurse educators to support nurses to strengthen TSE and provide educational program for TSE to provide nurses with strategies for raising interests in cultural diversity and successful experiences of cultural congruent care.

The Correlation Between Metacognition Level with Self-Efficacy of Biology Education College Students

NASA Astrophysics Data System (ADS)

Ridlo, S.; Lutfiya, F.

2017-04-01

Self-efficacy is a strong predictor of academic achievement. Self-efficacy refers to the ability of college students to achieve the desired results. The metacognition level can influence college student’s self-efficacy. This study aims to identify college student’s metacognition level and self-efficacy, as well as determine the relationship between self-efficacy and metacognition level for college students of Biology Education 2013, Semarang State University. The ex-post facto quantitative research was conducted on 99 students Academic Year 2015/2016. Saturation sampling technique determined samples. E-D scale collected data for self-efficacy identification. Data for assess the metacognition level collected by Metacognitive Awareness Inventory. Data were analysed quantitatively by Pearson correlation and linear regression. Most college students have the high level of metacognition and average self-efficacy. Pearson correlation coefficient result was 0.367. This result showed that metacognition level and self-efficacy has a weak relationship. Based on linear regression test, self-efficacy influenced by metacognition level up to 13.5%. The results of the study showed that positive and significant relationships exist between metacognition level and self-efficacy. Therefore, if the metacognition level is high, then self-efficacy will also be high (appropriate).

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

NASA Technical Reports Server (NTRS)

Fu, Lee-Lueng; Pihos, Greg

1994-01-01

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

Measuring multi-joint stiffness during single movements: numerical validation of a novel time-frequency approach.

PubMed

Piovesan, Davide; Pierobon, Alberto; DiZio, Paul; Lackner, James R

2012-01-01

This study presents and validates a Time-Frequency technique for measuring 2-dimensional multijoint arm stiffness throughout a single planar movement as well as during static posture. It is proposed as an alternative to current regressive methods which require numerous repetitions to obtain average stiffness on a small segment of the hand trajectory. The method is based on the analysis of the reassigned spectrogram of the arm's response to impulsive perturbations and can estimate arm stiffness on a trial-by-trial basis. Analytic and empirical methods are first derived and tested through modal analysis on synthetic data. The technique's accuracy and robustness are assessed by modeling the estimation of stiffness time profiles changing at different rates and affected by different noise levels. Our method obtains results comparable with two well-known regressive techniques. We also test how the technique can identify the viscoelastic component of non-linear and higher than second order systems with a non-parametrical approach. The technique proposed here is very impervious to noise and can be used easily for both postural and movement tasks. Estimations of stiffness profiles are possible with only one perturbation, making our method a useful tool for estimating limb stiffness during motor learning and adaptation tasks, and for understanding the modulation of stiffness in individuals with neurodegenerative diseases.

A non-linear optimization programming model for air quality planning including co-benefits for GHG emissions.

PubMed

Turrini, Enrico; Carnevale, Claudio; Finzi, Giovanna; Volta, Marialuisa

2018-04-15

This paper introduces the MAQ (Multi-dimensional Air Quality) model aimed at defining cost-effective air quality plans at different scales (urban to national) and assessing the co-benefits for GHG emissions. The model implements and solves a non-linear multi-objective, multi-pollutant decision problem where the decision variables are the application levels of emission abatement measures allowing the reduction of energy consumption, end-of pipe technologies and fuel switch options. The objectives of the decision problem are the minimization of tropospheric secondary pollution exposure and of internal costs. The model assesses CO 2 equivalent emissions in order to support decision makers in the selection of win-win policies. The methodology is tested on Lombardy region, a heavily polluted area in northern Italy. Copyright © 2017 Elsevier B.V. All rights reserved.

Quantitative detection of glucose level based on radiofrequency patch biosensor combined with volume-fixed structures.

PubMed

Qiang, Tian; Wang, Cong; Kim, Nam-Young

2017-12-15

A concept for characterizing a radiofrequency (RF) patch biosensor combined with volume-fixed structures is presented for timely monitoring of an individual's glucose levels based on frequency variation. Two types of patch biosensors-separately integrated with a backside slot (0.53μL) and a front-side tank (0.70μL) structure-were developed to achieve precise and efficient detection while excluding the effects of interference due to the liquidity, shape, and thickness of the tested glucose sample. A glucose test analyte at different concentrations (50-600mg/dL) was dropped into the volume-fixed structures. It fully interacted with the RF patch electromagnetic field, effectively and sensitively changing the resonance frequency and magnitude of the reflection coefficient. Measurement results based on the resonance frequency showed high sensitivity up to 1.13MHz and 1.97MHz per mg/dL, and low detection limits of 26.54mg/dL and 15.22mg/dL, for the two types of patch biosensors, respectively, as well as a short response time of less than 1s. Excellent reusability of the proposed biosensors was verified through three sets of measurements for each individual glucose sample. Regression analysis revealed a good linear correlation between glucose concentrations and the resonance frequency shift. Moreover, to facilitate a multi-parameter-sensitive detection of glucose, the magnitude of the reflection coefficient was also tested, and it showed a good linear correlation with the glucose concentration. Thus, the proposed approach can be adopted for distinguishing glucose solution levels, and it is a potential candidate for early-stage detection of glucose levels in diabetes patients. Copyright © 2017 Elsevier B.V. All rights reserved.

Differences in musculoskeletal health due to gender in a rural multiethnic cohort: a Project FRONTIER study.

PubMed

Brismée, J M; Yang, S; Lambert, M E; Chyu, M C; Tsai, P; Zhang, Y; Han, J; Hudson, C; Chung, Eunhee; Shen, C L

2016-04-26

Very few studies have investigated differences in musculoskeletal health due to gender in a large rural population. The aim of this study is to investigate factors affecting musculoskeletal health in terms of hand grip strength, musculoskeletal discomfort, and gait disturbance in a rural-dwelling, multi-ethnic cohort. Data for 1117 participants (40 years and older, 70% female) of an ongoing rural healthcare study, Project FRONTIER, were analyzed. Subjects with a history of neurological disease, stroke and movement disorder were excluded. Dominant hand grip strength was assessed by dynamometry. Gait disturbance including stiff, spastic, narrow-based, wide-based, unstable or shuffling gait was rated. Musculoskeletal discomfort was assessed by self-reported survey. Data were analyzed by linear, logistic regression and negative binomial regressions as appropriate. Demographic and socioeconomic factors were adjusted in the multiple variable analyses. In both genders, advanced age was a risk factor for weaker hand grip strength; arthritis was positively associated with musculoskeletal discomfort, and fair or poor health was significantly associated with increased risk of gait disturbance. Greater waist circumference was associated with greater musculoskeletal discomfort in males only. In females, advanced age is the risk factor for musculoskeletal discomfort as well as gait disturbance. Females with fair or poor health had weaker hand grip strength. Higher C-reactive protein and HbA1c levels were also positively associated with gait disturbance in females, but not in males. This cross-sectional study demonstrates how gender affects hand grip strength, musculoskeletal discomfort, and gait in a rural-dwelling multi-ethnic cohort. Our results suggest that musculoskeletal health may need to be assessed differently between males and females.

Labor market integration, immigration experience, and psychological distress in a multi-ethnic sample of immigrants residing in Portugal.

PubMed

Teixeira, Ana F; Dias, Sónia F

2018-01-01

This study aims at examining how factors relating to immigrants' experience in the host country affect psychological distress (PD). Specifically, we analyzed the association among socio-economic status (SES), integration in the labor market, specific immigration experience characteristics, and PD in a multi-ethnic sample of immigrant individuals residing in Lisbon, Portugal. Using a sample (n = 1375) consisting of all main immigrant groups residing in Portugal's metropolitan area of Lisbon, we estimated multivariable linear regression models of PD regressed on selected sets of socio-economic independent variables. A psychological distress scale was constructed based on five items (feeling physically tired, feeling psychologically tired, feeling happy, feeling full of energy, and feeling lonely). Variables associated with a decrease in PD are being a male (demographic), being satisfied with their income level (SES), living with the core family and having higher number of children (social isolation), planning to remain for longer periods of time in Portugal (migration project), and whether respondents considered themselves to be in good health condition (subjective health status). Study variables negatively associated with immigrants' PD were job insecurity (labor market), and the perception that health professionals were not willing to understand immigrants during a clinical interaction. The study findings emphasized the importance of labor market integration and access to good quality jobs for immigrants' psychological well-being, as well as the existence of family ties in the host country, intention to reside long term in the host country, and high subjective (physical) health. Our research suggests the need to foster cross-national studies of immigrant populations in order to understand the social mechanisms that transverse all migrant groups and contribute to lower psychological well-being.

Visual field progression in glaucoma: estimating the overall significance of deterioration with permutation analyses of pointwise linear regression (PoPLR).

PubMed

O'Leary, Neil; Chauhan, Balwantray C; Artes, Paul H

2012-10-01

To establish a method for estimating the overall statistical significance of visual field deterioration from an individual patient's data, and to compare its performance to pointwise linear regression. The Truncated Product Method was used to calculate a statistic S that combines evidence of deterioration from individual test locations in the visual field. The overall statistical significance (P value) of visual field deterioration was inferred by comparing S with its permutation distribution, derived from repeated reordering of the visual field series. Permutation of pointwise linear regression (PoPLR) and pointwise linear regression were evaluated in data from patients with glaucoma (944 eyes, median mean deviation -2.9 dB, interquartile range: -6.3, -1.2 dB) followed for more than 4 years (median 10 examinations over 8 years). False-positive rates were estimated from randomly reordered series of this dataset, and hit rates (proportion of eyes with significant deterioration) were estimated from the original series. The false-positive rates of PoPLR were indistinguishable from the corresponding nominal significance levels and were independent of baseline visual field damage and length of follow-up. At P < 0.05, the hit rates of PoPLR were 12, 29, and 42%, at the fifth, eighth, and final examinations, respectively, and at matching specificities they were consistently higher than those of pointwise linear regression. In contrast to population-based progression analyses, PoPLR provides a continuous estimate of statistical significance for visual field deterioration individualized to a particular patient's data. This allows close control over specificity, essential for monitoring patients in clinical practice and in clinical trials.

Statistical post-processing of seasonal multi-model forecasts: Why is it so hard to beat the multi-model mean?

NASA Astrophysics Data System (ADS)

Siegert, Stefan

2017-04-01

Initialised climate forecasts on seasonal time scales, run several months or even years ahead, are now an integral part of the battery of products offered by climate services world-wide. The availability of seasonal climate forecasts from various modeling centres gives rise to multi-model ensemble forecasts. Post-processing such seasonal-to-decadal multi-model forecasts is challenging 1) because the cross-correlation structure between multiple models and observations can be complicated, 2) because the amount of training data to fit the post-processing parameters is very limited, and 3) because the forecast skill of numerical models tends to be low on seasonal time scales. In this talk I will review new statistical post-processing frameworks for multi-model ensembles. I will focus particularly on Bayesian hierarchical modelling approaches, which are flexible enough to capture commonly made assumptions about collective and model-specific biases of multi-model ensembles. Despite the advances in statistical methodology, it turns out to be very difficult to out-perform the simplest post-processing method, which just recalibrates the multi-model ensemble mean by linear regression. I will discuss reasons for this, which are closely linked to the specific characteristics of seasonal multi-model forecasts. I explore possible directions for improvements, for example using informative priors on the post-processing parameters, and jointly modelling forecasts and observations.

Investigation of Cumulative Fatigue Damage Through Sequential Low Cycle Fatigue and High Cycle Fatigue Cycling at High Temperature for a Type 316LN Stainless Steel: Life-Prediction Techniques and Associated Mechanisms

NASA Astrophysics Data System (ADS)

Sarkar, Aritra; Nagesha, A.; Parameswaran, P.; Sandhya, R.; Laha, K.; Okazaki, M.

2017-03-01

Cumulative fatigue damage under sequential low cycle fatigue (LCF) and high cycle fatigue (HCF) cycling was investigated at 923 K (650 °C) by conducting HCF tests on specimens subjected to prior LCF cycling at various strain amplitudes. Remnant HCF lives were found to decrease drastically with increase in prior fatigue exposure as a result of strong LCF-HCF interactions. The rate of decrease in remnant lives varied as a function of the applied strain amplitude. A threshold damage in terms of prior LCF life-fraction was found, below which no significant LCF-HCF interaction takes place. Similarly, a critical damage during the LCF pre-cycling marking the highest degree of LCF-HCF interaction was identified which was found to depend on the applied strain amplitude. In view of the non-linear damage accumulation behavior, Miner's linear damage rule proved to be highly non-conservative. Manson's damage curve approach, suitably modified, was found to be a better alternative for predicting the remnant HCF life. The single constant ( β) employed in the model, which reflects the damage accumulation of the material under two/multi-level loading conditions is derived from the regression analysis of the experimental results and validated further.

Characterization, photophysical and DFT calculation study on 2-(2,4-difluorophenyl)-1-(4-methoxyphenyl)-1H-imidazo[4,5-f][1,10]phenanthroline ligand.

PubMed

Jayabharathi, Jayaraman; Thanikachalam, Venugopal; Venkatesh Perumal, Marimuthu

2012-09-01

The synthesized imidazole derivative 2-(2,4-difluorophenyl)-1-(4-methoxyphenyl)-1H-imidazo[4,5-f][1,10] phenanthroline (dfpmpip) has been characterized using IR, mass, (1)H, (13)C NMR and elemental analysis. The photophysical properties of dfpmpip have been studied using UV-visible and fluorescence spectroscopy in different solvents. The solvent effect on the absorption and fluorescence bands has been analyzed by a multi-component linear regression. Theoretically calculated bond lengths, bond angles and dihedral angles are found to be slightly higher than that of X-ray Diffraction (XRD) values of its parent compound. The charge distribution has been calculated from the atomic charges by non-linear optical (NLO) and natural bond orbital (NBO) analysis. Since the synthesized imidazole derivative has the largest μ(g)β(0) value, the reported imidazole can be used as potential NLO material. The energies of the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) levels and the molecular electrostatic potential (MEP) energy surface studies evidenced the existence of intramolecular charge transfer (ICT) within the molecule. Theoretical calculations regarding the chemical potential (μ), hardness (η) and electrophilicity index (ω) have also been calculated. Copyright © 2012 Elsevier B.V. All rights reserved.

Medical Expenditures among Immigrant and Non-Immigrant Groups in the U.S.: Findings from the Medical Expenditures Panel Survey (2000–2008)

PubMed Central

Tarraf, Wassim; Miranda, Patricia Y.; González, Hector M.

2011-01-01

Objective To examine time trends and differences in medical expenditures between non-citizens, foreign-born, and U.S.-born citizens. Methods We used multi-year Medical Expenditures Panel Survey (2000–2008) data on non-institutionalized adults in the U.S. (N=190,965). Source specific and total medical expenditures were analyzed using regression models, bootstrap prediction techniques, and linear and non-linear decomposition methods to evaluate the relationship between immigration status and expenditures, controlling for confounding effects. Results We found that the average health expenditures between 2000 and 2008 for non-citizens immigrants ($1,836) were substantially lower compared to both foreign-born ($3,737) and U.S.-born citizens ($4,478). Differences were maintained after controlling for confounding effects. Decomposition techniques showed that the main determinants of these differences were the availability of a usual source of healthcare, insurance, and ethnicity/race. Conclusion Lower healthcare expenditures among immigrants result from disparate access to healthcare. The dissipation of demographic advantages among immigrants could prospectively produce higher pressures on the U.S. healthcare system as immigrants age and levels of chronic conditions rise. Barring a shift in policy, the brunt of the effects could be borne by an already overextended public healthcare system. PMID:22222383

Design of convolutional tornado code

NASA Astrophysics Data System (ADS)

Zhou, Hui; Yang, Yao; Gao, Hongmin; Tan, Lu

2017-09-01

As a linear block code, the traditional tornado (tTN) code is inefficient in burst-erasure environment and its multi-level structure may lead to high encoding/decoding complexity. This paper presents a convolutional tornado (cTN) code which is able to improve the burst-erasure protection capability by applying the convolution property to the tTN code, and reduce computational complexity by abrogating the multi-level structure. The simulation results show that cTN code can provide a better packet loss protection performance with lower computation complexity than tTN code.

SPReM: Sparse Projection Regression Model For High-dimensional Linear Regression *

PubMed Central

Sun, Qiang; Zhu, Hongtu; Liu, Yufeng; Ibrahim, Joseph G.

2014-01-01

The aim of this paper is to develop a sparse projection regression modeling (SPReM) framework to perform multivariate regression modeling with a large number of responses and a multivariate covariate of interest. We propose two novel heritability ratios to simultaneously perform dimension reduction, response selection, estimation, and testing, while explicitly accounting for correlations among multivariate responses. Our SPReM is devised to specifically address the low statistical power issue of many standard statistical approaches, such as the Hotelling’s T2 test statistic or a mass univariate analysis, for high-dimensional data. We formulate the estimation problem of SPREM as a novel sparse unit rank projection (SURP) problem and propose a fast optimization algorithm for SURP. Furthermore, we extend SURP to the sparse multi-rank projection (SMURP) by adopting a sequential SURP approximation. Theoretically, we have systematically investigated the convergence properties of SURP and the convergence rate of SURP estimates. Our simulation results and real data analysis have shown that SPReM out-performs other state-of-the-art methods. PMID:26527844

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

PubMed

Kumar, K Vasanth

2007-04-02

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

Multilevel Hierarchical Modeling of Benthic Macroinvertebrate Responses to Urbanization in Nine Metropolitan Regions across the Conterminous United States

USGS Publications Warehouse

Kashuba, Roxolana; Cha, YoonKyung; Alameddine, Ibrahim; Lee, Boknam; Cuffney, Thomas F.

2010-01-01

Multilevel hierarchical modeling methodology has been developed for use in ecological data analysis. The effect of urbanization on stream macroinvertebrate communities was measured across a gradient of basins in each of nine metropolitan regions across the conterminous United States. The hierarchical nature of this dataset was harnessed in a multi-tiered model structure, predicting both invertebrate response at the basin scale and differences in invertebrate response at the region scale. Ordination site scores, total taxa richness, Ephemeroptera, Plecoptera, Trichoptera (EPT) taxa richness, and richness-weighted mean tolerance of organisms at a site were used to describe invertebrate responses. Percentage of urban land cover was used as a basin-level predictor variable. Regional mean precipitation, air temperature, and antecedent agriculture were used as region-level predictor variables. Multilevel hierarchical models were fit to both levels of data simultaneously, borrowing statistical strength from the complete dataset to reduce uncertainty in regional coefficient estimates. Additionally, whereas non-hierarchical regressions were only able to show differing relations between invertebrate responses and urban intensity separately for each region, the multilevel hierarchical regressions were able to explain and quantify those differences within a single model. In this way, this modeling approach directly establishes the importance of antecedent agricultural conditions in masking the response of invertebrates to urbanization in metropolitan regions such as Milwaukee-Green Bay, Wisconsin; Denver, Colorado; and Dallas-Fort Worth, Texas. Also, these models show that regions with high precipitation, such as Atlanta, Georgia; Birmingham, Alabama; and Portland, Oregon, start out with better regional background conditions of invertebrates prior to urbanization but experience faster negative rates of change with urbanization. Ultimately, this urbanization-invertebrate response example is used to detail the multilevel hierarchical construction methodology, showing how the result is a set of models that are both statistically more rigorous and ecologically more interpretable than simple linear regression models.

Modeling Joint Exposures and Health Outcomes for Cumulative Risk Assessment: The Case of Radon and Smoking

PubMed Central

Chahine, Teresa; Schultz, Bradley D.; Zartarian, Valerie G.; Xue, Jianping; Subramanian, SV; Levy, Jonathan I.

2011-01-01

Community-based cumulative risk assessment requires characterization of exposures to multiple chemical and non-chemical stressors, with consideration of how the non-chemical stressors may influence risks from chemical stressors. Residential radon provides an interesting case example, given its large attributable risk, effect modification due to smoking, and significant variability in radon concentrations and smoking patterns. In spite of this fact, no study to date has estimated geographic and sociodemographic patterns of both radon and smoking in a manner that would allow for inclusion of radon in community-based cumulative risk assessment. In this study, we apply multi-level regression models to explain variability in radon based on housing characteristics and geological variables, and construct a regression model predicting housing characteristics using U.S. Census data. Multi-level regression models of smoking based on predictors common to the housing model allow us to link the exposures. We estimate county-average lifetime lung cancer risks from radon ranging from 0.15 to 1.8 in 100, with high-risk clusters in areas and for subpopulations with high predicted radon and smoking rates. Our findings demonstrate the viability of screening-level assessment to characterize patterns of lung cancer risk from radon, with an approach that can be generalized to multiple chemical and non-chemical stressors. PMID:22016710

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.

Optimization of autoregressive, exogenous inputs-based typhoon inundation forecasting models using a multi-objective genetic algorithm

NASA Astrophysics Data System (ADS)

Ouyang, Huei-Tau

2017-07-01

Three types of model for forecasting inundation levels during typhoons were optimized: the linear autoregressive model with exogenous inputs (LARX), the nonlinear autoregressive model with exogenous inputs with wavelet function (NLARX-W) and the nonlinear autoregressive model with exogenous inputs with sigmoid function (NLARX-S). The forecast performance was evaluated by three indices: coefficient of efficiency, error in peak water level and relative time shift. Historical typhoon data were used to establish water-level forecasting models that satisfy all three objectives. A multi-objective genetic algorithm was employed to search for the Pareto-optimal model set that satisfies all three objectives and select the ideal models for the three indices. Findings showed that the optimized nonlinear models (NLARX-W and NLARX-S) outperformed the linear model (LARX). Among the nonlinear models, the optimized NLARX-W model achieved a more balanced performance on the three indices than the NLARX-S models and is recommended for inundation forecasting during typhoons.

Multi-linear regression models predict the effects of water chemistry on acute lead toxicity to Ceriodaphnia dubia and Pimephales promelas.

PubMed

Esbaugh, A J; Brix, K V; Mager, E M; Grosell, M

2011-09-01

The current study examined the acute toxicity of lead (Pb) to Ceriodaphnia dubia and Pimephales promelas in a variety of natural waters. The natural waters were selected to range in pertinent water chemistry parameters such as calcium, pH, total CO(2) and dissolved organic carbon (DOC). Acute toxicity was determined for C. dubia and P. promelas using standard 48h and 96h protocols, respectively. For both organisms acute toxicity varied markedly according to water chemistry, with C. dubia LC50s ranging from 29 to 180μg/L and P. promelas LC50s ranging from 41 to 3598μg/L. Additionally, no Pb toxicity was observed for P. promelas in three alkaline natural waters. With respect to water chemistry parameters, DOC had the strongest protective impact for both organisms. A multi-linear regression (MLR) approach combining previous lab data and the current data was used to identify the relative importance of individual water chemistry components in predicting acute Pb toxicity for both species. As anticipated, the P. promelas best-fit MLR model combined DOC, calcium and pH. Unexpectedly, in the C. dubiaMLR model the importance of pH, TCO(2) and calcium was minimal while DOC and ionic strength were the controlling water quality variables. Adjusted R(2) values of 0.82 and 0.64 for the P. promelas and C. dubia models, respectively, are comparable to previously developed biotic ligand models for other metals. Copyright © 2011 Elsevier Inc. All rights reserved.

Does Group-Level Commitment Predict Employee Well-Being?: A Prospective Analysis.

PubMed

Clausen, Thomas; Christensen, Karl Bang; Nielsen, Karina

2015-11-01

To investigate the links between group-level affective organizational commitment (AOC) and individual-level psychological well-being, self-reported sickness absence, and sleep disturbances. A total of 5085 care workers from 301 workgroups in the Danish eldercare services participated in both waves of the study (T1 [2005] and T2 [2006]). The three outcomes were analyzed using linear multilevel regression analysis, multilevel Poisson regression analysis, and multilevel logistic regression analysis, respectively. Group-level AOC (T1) significantly predicted individual-level psychological well-being, self-reported sickness absence, and sleep disturbances (T2). The association between group-level AOC (T1) and psychological well-being (T2) was fully mediated by individual-level AOC (T1), and the associations between group-level AOC (T1) and self-reported sickness absence and sleep disturbances (T2) were partially mediated by individual-level AOC (T1). Group-level AOC is an important predictor of employee well-being in contemporary health care organizations.

Geographical variation of cerebrovascular disease in New York State: the correlation with income

PubMed Central

Han, Daikwon; Carrow, Shannon S; Rogerson, Peter A; Munschauer, Frederick E

2005-01-01

Background Income is known to be associated with cerebrovascular disease; however, little is known about the more detailed relationship between cerebrovascular disease and income. We examined the hypothesis that the geographical distribution of cerebrovascular disease in New York State may be predicted by a nonlinear model using income as a surrogate socioeconomic risk factor. Results We used spatial clustering methods to identify areas with high and low prevalence of cerebrovascular disease at the ZIP code level after smoothing rates and correcting for edge effects; geographic locations of high and low clusters of cerebrovascular disease in New York State were identified with and without income adjustment. To examine effects of income, we calculated the excess number of cases using a non-linear regression with cerebrovascular disease rates taken as the dependent variable and income and income squared taken as independent variables. The resulting regression equation was: excess rate = 32.075 - 1.22*10-4(income) + 8.068*10-10(income2), and both income and income squared variables were significant at the 0.01 level. When income was included as a covariate in the non-linear regression, the number and size of clusters of high cerebrovascular disease prevalence decreased. Some 87 ZIP codes exceeded the critical value of the local statistic yielding a relative risk of 1.2. The majority of low cerebrovascular disease prevalence geographic clusters disappeared when the non-linear income effect was included. For linear regression, the excess rate of cerebrovascular disease falls with income; each $10,000 increase in median income of each ZIP code resulted in an average reduction of 3.83 observed cases. The significant nonlinear effect indicates a lessening of this income effect with increasing income. Conclusion Income is a non-linear predictor of excess cerebrovascular disease rates, with both low and high observed cerebrovascular disease rate areas associated with higher income. Income alone explains a significant amount of the geographical variance in cerebrovascular disease across New York State since both high and low clusters of cerebrovascular disease dissipate or disappear with income adjustment. Geographical modeling, including non-linear effects of income, may allow for better identification of other non-traditional risk factors. PMID:16242043

Physical activity in Iranian older adults who experienced fall during the past 12 months.

PubMed

Salehi, Leili; Shokrvash, Behjat; Jamshidi, Ensiyeh; Montazeri, Ali

2014-10-31

Physical activity may have several benefits for elderly people. However, the risk of falling might prevent this population from showing interest in physical activity. This research was aimed to explore facilitators and barriers to physical activity in older persons who have experienced at least one fall in the past 12 months. This cross sectional study was conducted in 2010-2011, in Tehran, Iran. Using a multistage sampling method a group of elderly people entered into the study. A multi-section questionnaire was used to collect data on demographic information, physical activity level, and different determinants that might influence physical activity. Several statistical tests including linear regression were used to analyze the data. In all, 180 old people from 40 elderly centers (49 men and 131 women) took part in the study. The mean age of participants was 65.9 ± 6.1 years. The result indicated that most participants experienced two or more falls during the last year (54.5%). Those who had more falls significantly scored lower on the Physical Activity Scale for Elderly (p < 0.0001). 'Keeping in touch with friends' was the most important advantage cited by participants for performing physical activity. The results obtained from linear regression analysis showed that 'perceived benefits' was the only significant factor that associated with physical activity (β = 1.03, p < 0.001). The findings suggest that perceived benefits could facilitate physical activity among elderly regardless of number of falls, self-reported health and daily living activities. However, we observed inverse association between number of falls and physical activity. Indeed the findings suggest that we should reinforce benefits exist when designing programs to increase physical activity among elderly population.

Impact of preoperative calculation of nephron volume loss on future of partial nephrectomy techniques; planning a strategic roadmap for improving functional preservation and securing oncological safety.

PubMed

Rha, Koon H; Abdel Raheem, Ali; Park, Sung Y; Kim, Kwang H; Kim, Hyung J; Koo, Kyo C; Choi, Young D; Jung, Byung H; Lee, Sang K; Lee, Won K; Krishnan, Jayram; Shin, Tae Y; Cho, Jin-Seon

2017-11-01

To assess the correlation of the resected and ischaemic volume (RAIV), which is a preoperatively calculated volume of nephron loss, with the amount of postoperative renal function (PRF) decline after minimally invasive partial nephrectomy (PN) in a multi-institutional dataset. We identified 348 patients from March 2005 to December 2013 at six institutions. Data on all cases of laparoscopic (n = 85) and robot-assisted PN (n = 263) performed were retrospectively gathered. Univariable and multivariable linear regression analyses were used to identify the associations between various time points of PRF and the RAIV, as a continuous variable. The mean (sd) RAIV was 24.2 (29.2) cm 3 . The mean preoperative estimated glomerular filtration rate (eGFR) and the eGFRs at postoperative day 1, 6 and 36 months after PN were 91.0 and 76.8, 80.2 and 87.7 mL/min/1.73 m 2 , respectively. In multivariable linear regression analysis, the amount of decline in PRF at follow-up was significantly correlated with the RAIV (β 0.261, 0.165, 0.260 at postoperative day 1, 6 and 36 months after PN, respectively). This study has the limitation of its retrospective nature. Preoperatively calculated RAIV significantly correlates with the amount of decline in PRF during long-term follow-up. The RAIV could lead our research to the level of prediction of the amount of PRF decline after PN and thus would be appropriate for assessing the technical advantages of emerging techniques. © 2017 The Authors BJU International © 2017 BJU International Published by John Wiley & Sons Ltd.

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

PubMed

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

2006-07-01

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

Fuzzy regression modeling for tool performance prediction and degradation detection.

PubMed

Li, X; Er, M J; Lim, B S; Zhou, J H; Gan, O P; Rutkowski, L

2010-10-01

In this paper, the viability of using Fuzzy-Rule-Based Regression Modeling (FRM) algorithm for tool performance and degradation detection is investigated. The FRM is developed based on a multi-layered fuzzy-rule-based hybrid system with Multiple Regression Models (MRM) embedded into a fuzzy logic inference engine that employs Self Organizing Maps (SOM) for clustering. The FRM converts a complex nonlinear problem to a simplified linear format in order to further increase the accuracy in prediction and rate of convergence. The efficacy of the proposed FRM is tested through a case study - namely to predict the remaining useful life of a ball nose milling cutter during a dry machining process of hardened tool steel with a hardness of 52-54 HRc. A comparative study is further made between four predictive models using the same set of experimental data. It is shown that the FRM is superior as compared with conventional MRM, Back Propagation Neural Networks (BPNN) and Radial Basis Function Networks (RBFN) in terms of prediction accuracy and learning speed.

A novel method for determination of aragonite saturation state on the continental shelf of central Oregon using multi-parameter relationships with hydrographic data

NASA Astrophysics Data System (ADS)

Juranek, L. W.; Feely, R. A.; Peterson, W. T.; Alin, S. R.; Hales, B.; Lee, K.; Sabine, C. L.; Peterson, J.

2009-12-01

We developed a multiple linear regression model to robustly determine aragonite saturation state (Ωarag) from observations of temperature and oxygen (R2 = 0.987, RMS error 0.053), using data collected in the Pacific Northwest region in late May 2007. The seasonal evolution of Ωarag near central Oregon was evaluated by applying the regression model to a monthly (winter)/bi-weekly (summer) water-column hydrographic time-series collected over the shelf and slope in 2007. The Ωarag predicted by the regression model was less than 1, the thermodynamic calcification/dissolution threshold, over shelf/slope bottom waters throughout the entire 2007 upwelling season (May-November), with the Ωarag = 1 horizon shoaling to 30 m by late summer. The persistence of water with Ωarag < 1 on the continental shelf has not been previously noted and could have notable ecological consequences for benthic and pelagic calcifying organisms such as mussels, oysters, abalone, echinoderms, and pteropods.

Redefining ADHD Using an Adult Population: Should Inattention be Viewed as a Separate Dimension From Cognitive and Physiological Activity Level?

PubMed

Miller, Nathan; Prevatt, Frances

2017-10-01

The purpose of this study was to reexamine the latent structure of ADHD and sluggish cognitive tempo (SCT) due to issues with construct validity. Two proposed changes to the construct include viewing hyperactivity and sluggishness (hypoactivity) as a single continuum of activity level, and viewing inattention as a separate dimension from activity level. Data were collected from 1,398 adults using Amazon's MTurk. A new scale measuring activity level was developed, and scores of Inattention were regressed onto scores of Activity Level using curvilinear regression. The Activity Level scale showed acceptable levels of internal consistency, normality, and unimodality. Curvilinear regression indicates that a quadratic (curvilinear) model accurately explains a small but significant portion of the variance in levels of inattention. Hyperactivity and hypoactivity may be viewed as a continuum, rather than separate disorders. Inattention may have a U-shaped relationship with activity level. Linear analyses may be insufficient and inaccurate for studying ADHD.

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

Yang, Qichun; Zhang, Xuesong; Xu, Xingya

Riverine carbon cycling is an important, but insufficiently investigated component of the global carbon cycle. Analyses of environmental controls on riverine carbon cycling are critical for improved understanding of mechanisms regulating carbon processing and storage along the terrestrial-aquatic continuum. Here, we compile and analyze riverine dissolved organic carbon (DOC) concentration data from 1402 United States Geological Survey (USGS) gauge stations to examine the spatial variability and environmental controls of DOC concentrations in the United States (U.S.) surface waters. DOC concentrations exhibit high spatial variability, with an average of 6.42 ± 6.47 mg C/ L (Mean ± Standard Deviation). In general,more » high DOC concentrations occur in the Upper Mississippi River basin and the Southeastern U.S., while low concentrations are mainly distributed in the Western U.S. Single-factor analysis indicates that slope of drainage areas, wetlands, forests, percentage of first-order streams, and instream nutrients (such as nitrogen and phosphorus) pronouncedly influence DOC concentrations, but the explanatory power of each bivariate model is lower than 35%. Analyses based on the general multi-linear regression models suggest DOC concentrations are jointly impacted by multiple factors. Soil properties mainly show positive correlations with DOC concentrations; forest and shrub lands have positive correlations with DOC concentrations, but urban area and croplands demonstrate negative impacts; total instream phosphorus and dam density correlate positively with DOC concentrations. Notably, the relative importance of these environmental controls varies substantially across major U.S. water resource regions. In addition, DOC concentrations and environmental controls also show significant variability from small streams to large rivers, which may be caused by changing carbon sources and removal rates by river orders. In sum, our results reveal that general multi-linear regression analysis of twenty one terrestrial and aquatic environmental factors can partially explain (56%) the DOC concentration variation. In conclusion, this study highlights the complexity of the interactions among these environmental factors in determining DOC concentrations, thus calls for processes-based, non-linear methodologies to constrain uncertainties in riverine DOC cycling.« less

Linear regression crash prediction models : issues and proposed solutions.

DOT National Transportation Integrated Search

2010-05-01

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

Temporal Drivers of Liking Based on Functional Data Analysis and Non-Additive Models for Multi-Attribute Time-Intensity Data of Fruit Chews.

PubMed

Kuesten, Carla; Bi, Jian

2018-06-03

Conventional drivers of liking analysis was extended with a time dimension into temporal drivers of liking (TDOL) based on functional data analysis methodology and non-additive models for multiple-attribute time-intensity (MATI) data. The non-additive models, which consider both direct effects and interaction effects of attributes to consumer overall liking, include Choquet integral and fuzzy measure in the multi-criteria decision-making, and linear regression based on variance decomposition. Dynamics of TDOL, i.e., the derivatives of the relative importance functional curves were also explored. Well-established R packages 'fda', 'kappalab' and 'relaimpo' were used in the paper for developing TDOL. Applied use of these methods shows that the relative importance of MATI curves offers insights for understanding the temporal aspects of consumer liking for fruit chews.

Advances in simultaneous atmospheric profile and cloud parameter regression based retrieval from high-spectral resolution radiance measurements

NASA Astrophysics Data System (ADS)

Weisz, Elisabeth; Smith, William L.; Smith, Nadia

2013-06-01

The dual-regression (DR) method retrieves information about the Earth surface and vertical atmospheric conditions from measurements made by any high-spectral resolution infrared sounder in space. The retrieved information includes temperature and atmospheric gases (such as water vapor, ozone, and carbon species) as well as surface and cloud top parameters. The algorithm was designed to produce a high-quality product with low latency and has been demonstrated to yield accurate results in real-time environments. The speed of the retrieval is achieved through linear regression, while accuracy is achieved through a series of classification schemes and decision-making steps. These steps are necessary to account for the nonlinearity of hyperspectral retrievals. In this work, we detail the key steps that have been developed in the DR method to advance accuracy in the retrieval of nonlinear parameters, specifically cloud top pressure. The steps and their impact on retrieval results are discussed in-depth and illustrated through relevant case studies. In addition to discussing and demonstrating advances made in addressing nonlinearity in a linear geophysical retrieval method, advances toward multi-instrument geophysical analysis by applying the DR to three different operational sounders in polar orbit are also noted. For any area on the globe, the DR method achieves consistent accuracy and precision, making it potentially very valuable to both the meteorological and environmental user communities.

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

ERIC Educational Resources Information Center

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

2013-01-01

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

Periodontitis in coronary heart disease patients: strong association between bleeding on probing and systemic biomarkers.

PubMed

Bokhari, Syed Akhtar H; Khan, Ayyaz A; Butt, Arshad K; Hanif, Mohammad; Izhar, Mateen; Tatakis, Dimitris N; Ashfaq, Mohammad

2014-11-01

Few studies have examined the relationship of individual periodontal parameters with individual systemic biomarkers. This study assessed the possible association between specific clinical parameters of periodontitis and systemic biomarkers of coronary heart disease risk in coronary heart disease patients with periodontitis. Angiographically proven coronary heart disease patients with periodontitis (n = 317), aged >30 years and without other systemic illness were examined. Periodontal clinical parameters of bleeding on probing (BOP), probing depth (PD), and clinical attachment level (CAL) and systemic levels of high-sensitivity C-reactive protein (CRP), fibrinogen (FIB) and white blood cells (WBC) were noted and analyzed to identify associations through linear and stepwise multiple regression analyses. Unadjusted linear regression showed significant associations between periodontal and systemic parameters; the strongest association (r = 0.629; p < 0.001) was found between BOP and CRP levels, the periodontal and systemic inflammation marker, respectively. Stepwise regression analysis models revealed that BOP was a predictor of systemic CRP levels (p < 0.0001). BOP was the only periodontal parameter significantly associated with each systemic parameter (CRP, FIB, and WBC). In coronary heart disease patients with periodontitis, BOP is strongly associated with systemic CRP levels; this association possibly reflects the potential significance of the local periodontal inflammatory burden for systemic inflammation. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Advanced microwave soil moisture studies. [Big Sioux River Basin, Iowa

NASA Technical Reports Server (NTRS)

Dalsted, K. J.; Harlan, J. C.

1983-01-01

Comparisons of low level L-band brightness temperature (TB) and thermal infrared (TIR) data as well as the following data sets: soil map and land cover data; direct soil moisture measurement; and a computer generated contour map were statistically evaluated using regression analysis and linear discriminant analysis. Regression analysis of footprint data shows that statistical groupings of ground variables (soil features and land cover) hold promise for qualitative assessment of soil moisture and for reducing variance within the sampling space. Dry conditions appear to be more conductive to producing meaningful statistics than wet conditions. Regression analysis using field averaged TB and TIR data did not approach the higher sq R values obtained using within-field variations. The linear discriminant analysis indicates some capacity to distinguish categories with the results being somewhat better on a field basis than a footprint basis.

Covariance functions for body weight from birth to maturity in Nellore cows.

PubMed

Boligon, A A; Mercadante, M E Z; Forni, S; Lôbo, R B; Albuquerque, L G

2010-03-01

The objective of this study was to estimate (co)variance functions using random regression models on Legendre polynomials for the analysis of repeated measures of BW from birth to adult age. A total of 82,064 records from 8,145 females were analyzed. Different models were compared. The models included additive direct and maternal effects, and animal and maternal permanent environmental effects as random terms. Contemporary group and dam age at calving (linear and quadratic effect) were included as fixed effects, and orthogonal Legendre polynomials of animal age (cubic regression) were considered as random covariables. Eight models with polynomials of third to sixth order were used to describe additive direct and maternal effects, and animal and maternal permanent environmental effects. Residual effects were modeled using 1 (i.e., assuming homogeneity of variances across all ages) or 5 age classes. The model with 5 classes was the best to describe the trajectory of residuals along the growth curve. The model including fourth- and sixth-order polynomials for additive direct and animal permanent environmental effects, respectively, and third-order polynomials for maternal genetic and maternal permanent environmental effects were the best. Estimates of (co)variance obtained with the multi-trait and random regression models were similar. Direct heritability estimates obtained with the random regression models followed a trend similar to that obtained with the multi-trait model. The largest estimates of maternal heritability were those of BW taken close to 240 d of age. In general, estimates of correlation between BW from birth to 8 yr of age decreased with increasing distance between ages.

Effectiveness of a worksite mindfulness-based multi-component intervention on lifestyle behaviors

PubMed Central

2014-01-01

Introduction Overweight and obesity are associated with an increased risk of morbidity. Mindfulness training could be an effective strategy to optimize lifestyle behaviors related to body weight gain. The aim of this study was to evaluate the effectiveness of a worksite mindfulness-based multi-component intervention on vigorous physical activity in leisure time, sedentary behavior at work, fruit intake and determinants of these behaviors. The control group received information on existing lifestyle behavior- related facilities that were already available at the worksite. Methods In a randomized controlled trial design (n = 257), 129 workers received a mindfulness training, followed by e-coaching, lunch walking routes and fruit. Outcome measures were assessed at baseline and after 6 and 12 months using questionnaires. Physical activity was also measured using accelerometers. Effects were analyzed using linear mixed effect models according to the intention-to-treat principle. Linear regression models (complete case analyses) were used as sensitivity analyses. Results There were no significant differences in lifestyle behaviors and determinants of these behaviors between the intervention and control group after 6 or 12 months. The sensitivity analyses showed effect modification for gender in sedentary behavior at work at 6-month follow-up, although the main analyses did not. Conclusions This study did not show an effect of a worksite mindfulness-based multi-component intervention on lifestyle behaviors and behavioral determinants after 6 and 12 months. The effectiveness of a worksite mindfulness-based multi-component intervention as a health promotion intervention for all workers could not be established. PMID:24467802

Synthesis, spectral studies and antimicrobial activities of some 2-naphthyl pyrazoline derivatives

NASA Astrophysics Data System (ADS)

Sakthinathan, S. P.; Vanangamudi, G.; Thirunarayanan, G.

A series of 2-naphthyl pyrazolines were synthesized by the cyclization of 2-naphthyl chalcones and phenylhydrazine hydrochloride in the presence of sodium acetate. The yields of pyrazoline derivatives are more than 80%. The synthesized pyrazolines were characterized by their physical constants, IR, 1H, 13C and MS spectra. From the IR and NMR spectra the Cdbnd N (cm-1) stretches, the pyrazoline ring proton chemical shifts (ppm) of δ, Hb and Hc and also the carbon chemical shifts (ppm) of δCdbnd N are correlated with Hammett substituent constants, F and R, and Swain-Lupton's parameters using single and multi-regression analyses. From the results of linear regression analysis, the effect of substituents on the group frequencies has been predicted. The antimicrobial activities of all synthesized pyrazolines have been studied.

[Use of multiple regression models in observational studies (1970-2013) and requirements of the STROBE guidelines in Spanish scientific journals].

PubMed

Real, J; Cleries, R; Forné, C; Roso-Llorach, A; Martínez-Sánchez, J M

In medicine and biomedical research, statistical techniques like logistic, linear, Cox and Poisson regression are widely known. The main objective is to describe the evolution of multivariate techniques used in observational studies indexed in PubMed (1970-2013), and to check the requirements of the STROBE guidelines in the author guidelines in Spanish journals indexed in PubMed. A targeted PubMed search was performed to identify papers that used logistic linear Cox and Poisson models. Furthermore, a review was also made of the author guidelines of journals published in Spain and indexed in PubMed and Web of Science. Only 6.1% of the indexed manuscripts included a term related to multivariate analysis, increasing from 0.14% in 1980 to 12.3% in 2013. In 2013, 6.7, 2.5, 3.5, and 0.31% of the manuscripts contained terms related to logistic, linear, Cox and Poisson regression, respectively. On the other hand, 12.8% of journals author guidelines explicitly recommend to follow the STROBE guidelines, and 35.9% recommend the CONSORT guideline. A low percentage of Spanish scientific journals indexed in PubMed include the STROBE statement requirement in the author guidelines. Multivariate regression models in published observational studies such as logistic regression, linear, Cox and Poisson are increasingly used both at international level, as well as in journals published in Spanish. Copyright © 2015 Sociedad Española de Médicos de Atención Primaria (SEMERGEN). Publicado por Elsevier España, S.L.U. All rights reserved.

The Application of the Cumulative Logistic Regression Model to Automated Essay Scoring

ERIC Educational Resources Information Center

Haberman, Shelby J.; Sinharay, Sandip

2010-01-01

Most automated essay scoring programs use a linear regression model to predict an essay score from several essay features. This article applied a cumulative logit model instead of the linear regression model to automated essay scoring. Comparison of the performances of the linear regression model and the cumulative logit model was performed on a…

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.

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

Estimating PM2.5 Concentrations in Xi'an City Using a Generalized Additive Model with Multi-Source Monitoring Data

PubMed Central

Song, Yong-Ze; Yang, Hong-Lei; Peng, Jun-Huan; Song, Yi-Rong; Sun, Qian; Li, Yuan

2015-01-01

Particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) represents a severe environmental problem and is of negative impact on human health. Xi'an City, with a population of 6.5 million, is among the highest concentrations of PM2.5 in China. In 2013, in total, there were 191 days in Xi’an City on which PM2.5 concentrations were greater than 100 μg/m3. Recently, a few studies have explored the potential causes of high PM2.5 concentration using remote sensing data such as the MODIS aerosol optical thickness (AOT) product. Linear regression is a commonly used method to find statistical relationships among PM2.5 concentrations and other pollutants, including CO, NO2, SO2, and O3, which can be indicative of emission sources. The relationships of these variables, however, are usually complicated and non-linear. Therefore, a generalized additive model (GAM) is used to estimate the statistical relationships between potential variables and PM2.5 concentrations. This model contains linear functions of SO2 and CO, univariate smoothing non-linear functions of NO2, O3, AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM2.5 concentrations, with R2 = 0.691, which improves the result of a stepwise linear regression (R2 = 0.582) by 18.73%. The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO2, NO2, and O3 account for 10.88% of the total deviance. These results show that in Xi'an City, the traffic and other industrial emissions are the primary source of PM2.5. Temperature, location, and wind variables also non-linearly related with PM2.5. PMID:26540446

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

Luszczek, Piotr R; Tomov, Stanimire Z; Dongarra, Jack J

We present an efficient and scalable programming model for the development of linear algebra in heterogeneous multi-coprocessor environments. The model incorporates some of the current best design and implementation practices for the heterogeneous acceleration of dense linear algebra (DLA). Examples are given as the basis for solving linear systems' algorithms - the LU, QR, and Cholesky factorizations. To generate the extreme level of parallelism needed for the efficient use of coprocessors, algorithms of interest are redesigned and then split into well-chosen computational tasks. The tasks execution is scheduled over the computational components of a hybrid system of multi-core CPUs andmore » coprocessors using a light-weight runtime system. The use of lightweight runtime systems keeps scheduling overhead low, while enabling the expression of parallelism through otherwise sequential code. This simplifies the development efforts and allows the exploration of the unique strengths of the various hardware components.« less

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.

Percent Emphysema and Daily Motor Activity Levels in the General Population: Multi-Ethnic Study of Atherosclerosis.

PubMed

Lo Cascio, Christian M; Quante, Mirja; Hoffman, Eric A; Bertoni, Alain G; Aaron, Carrie P; Schwartz, Joseph E; Avdalovic, Mark V; Fan, Vincent S; Lovasi, Gina S; Kawut, Steven M; Austin, John H M; Redline, Susan; Barr, R Graham

2017-05-01

COPD is associated with reduced physical capacity. However, it is unclear whether pulmonary emphysema, which can occur without COPD, is associated with reduced physical activity in daily life, particularly among people without COPD and never smokers. We hypothesized that greater percentage of emphysema-like lung on CT scan is associated with reduced physical activity assessed by actigraphy and self-report. The Multi-Ethnic Study of Atherosclerosis (MESA) enrolled participants free of clinical cardiovascular disease from the general population. Percent emphysema was defined as percentage of voxels < -950 Hounsfield units on full-lung CT scans. Physical activity was measured by wrist actigraphy over 7 days and a questionnaire. Multivariable linear regression was used to adjust for age, sex, race/ethnicity, height, weight, education, smoking, pack-years, and lung function. Among 1,435 participants with actigraphy and lung measures, 47% had never smoked, and 8% had COPD. Percent emphysema was associated with lower activity levels on actigraphy (P = .001), corresponding to 1.5 hour less per week of moderately paced walking for the average participant in quintile 2 vs 4 of percent emphysema. This association was significant among participants without COPD (P = .004) and among ever (P = .01) and never smokers (P = .03). It was also independent of coronary artery calcium and left ventricular ejection fraction. There was no evidence that percent emphysema was associated with self-reported activity levels. Percent emphysema was associated with decreased physical activity in daily life objectively assessed by actigraphy in the general population, among participants without COPD, and nonsmokers. Copyright © 2016 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

Association between sex hormones and adiposity: qualitative differences in women and men in the multi-ethnic study of atherosclerosis.

PubMed

Mongraw-Chaffin, Morgana L; Anderson, Cheryl A M; Allison, Matthew A; Ouyang, Pamela; Szklo, Moyses; Vaidya, Dhananjay; Woodward, Mark; Golden, Sherita Hill

2015-04-01

Sex hormones may influence adipose tissue deposition, possibly contributing to sex disparities in cardiovascular disease risk. We hypothesized that associations of sex hormone levels with visceral and subcutaneous fat differ by sex. Participants were from the Multi-Ethnic Study of Atherosclerosis with sex hormone levels at baseline and visceral and subcutaneous fat measurements from computed tomography at visit 2 or 3 (n = 1835). Multivariable linear regression was used to investigate the relationships between sex hormones and adiposity. Testing for interaction by sex, race/ethnicity, and age was conducted. In adjusted models, there was a modest significant positive association between estradiol and visceral fat in both sexes (percent difference in visceral fat for 1% difference in hormone [95% confidence interval] in women, 5.44 [1.82, 9.09]; and in men, 8.22 [0.61, 16.18]). Higher bioavailable T was significantly associated with higher visceral and subcutaneous fat in women and with the reverse in men (women, 14.38 [10.23, 18.69]; men, -7.69 [-13.06, -1.00]). Higher dehydroepiandrosterone was associated with higher visceral fat in women (7.57 [1.71, 13.88]), but not in men (-2.47 [-8.88, 4.29]). Higher SHBG was associated with significantly lower levels of adiposity in both sexes (women, -24.42 [-28.11, -20.55]; men, -27.39 [-32.97, -21.34]). There was no significant interaction by race/ethnicity or age. Sex hormones are significantly associated with adiposity, and the associations of androgens differ qualitatively by sex. This heterogeneity may help explain the complexity of the contribution of sex hormones to sex differences in cardiovascular disease.

Tobacco exposure-related alterations in DNA methylation and gene expression in human monocytes: the Multi-Ethnic Study of Atherosclerosis (MESA)

PubMed Central

Reynolds, Lindsay M.; Lohman, Kurt; Pittman, Gary S.; Barr, R. Graham; Chi, Gloria C.; Kaufman, Joel; Wan, Ma; Bell, Douglas A.; Blaha, Michael J.; Rodriguez, Carlos J.; Liu, Yongmei

2017-01-01

ABSTRACT Alterations in DNA methylation and gene expression in blood leukocytes are potential biomarkers of harm and mediators of the deleterious effects of tobacco exposure. However, methodological issues, including the use of self-reported smoking status and mixed cell types have made previously identified alterations in DNA methylation and gene expression difficult to interpret. In this study, we examined associations of tobacco exposure with DNA methylation and gene expression, utilizing a biomarker of tobacco exposure (urine cotinine) and CD14+ purified monocyte samples from 934 participants of the community-based Multi-Ethnic Study of Atherosclerosis (MESA). Urine cotinine levels were measured using an immunoassay. DNA methylation and gene expression were measured with microarrays. Multivariate linear regression was used to test for associations adjusting for age, sex, race/ethnicity, education, and study site. Urine cotinine levels were associated with methylation of 176 CpGs [false discovery rate (FDR)<0.01]. Four CpGs not previously identified by studies of non-purified blood samples nominally replicated (P value<0.05) with plasma cotinine-associated methylation in 128 independent monocyte samples. Urine cotinine levels associated with expression of 12 genes (FDR<0.01), including increased expression of P2RY6 (Beta ± standard error = 0.078 ± 0.008, P = 1.99 × 10−22), a gene previously identified to be involved in the release of pro-inflammatory cytokines. No cotinine-associated (FDR<0.01) methylation profiles significantly (FDR<0.01) correlated with cotinine-associated (FDR<0.01) gene expression profiles. In conclusion, our findings i) identify potential monocyte-specific smoking-associated methylation patterns and ii) suggest that alterations in methylation may not be a main mechanism regulating gene expression in monocytes in response to cigarette smoking. PMID:29166816

Does visual impairment lead to additional disability in adults with intellectual disabilities?

PubMed

Evenhuis, H M; Sjoukes, L; Koot, H M; Kooijman, A C

2009-01-01

This study addresses the question to what extent visual impairment leads to additional disability in adults with intellectual disabilities (ID). In a multi-centre cross-sectional study of 269 adults with mild to profound ID, social and behavioural functioning was assessed with observant-based questionnaires, prior to expert assessment of visual function. With linear regression analysis the percentage of variance, explained by levels of visual function, was calculated for the total population and per ID level. A total of 107/269 participants were visually impaired or blind (WHO criteria). On top of the decrease by ID visual impairment significantly decreased daily living skills, communication & language, recognition/communication. Visual impairment did not cause more self-absorbed and withdrawn behaviour or anxiety. Peculiar looking habits correlated with visual impairment and not with ID. In the groups with moderate and severe ID this effect seems stronger than in the group with profound ID. Although ID alone impairs daily functioning, visual impairment diminishes the daily functioning even more. Timely detection and treatment or rehabilitation of visual impairment may positively influence daily functioning, language development, initiative and persistence, social skills, communication skills and insecure movement.

A parameter estimation subroutine package

NASA Technical Reports Server (NTRS)

Bierman, G. J.; Nead, M. W.

1978-01-01

Linear least squares estimation and regression analyses continue to play a major role in orbit determination and related areas. A library of FORTRAN subroutines were developed to facilitate analyses of a variety of estimation problems. An easy to use, multi-purpose set of algorithms that are reasonably efficient and which use a minimal amount of computer storage are presented. Subroutine inputs, outputs, usage and listings are given, along with examples of how these routines can be used. The routines are compact and efficient and are far superior to the normal equation and Kalman filter data processing algorithms that are often used for least squares analyses.

Quantifying Main Trends in Lysozyme Nucleation: The Effects of Precipitant Concentration, Supersaturation and Impurities

NASA Technical Reports Server (NTRS)

Burke, Michael W.; Leardi, Riccardo; Judge, Russell A.; Pusey, Marc L.; Whitaker, Ann F. (Technical Monitor)

2001-01-01

Full factorial experimental design incorporating multi-linear regression analysis of the experimental data allows quick identification of main trends and effects using a limited number of experiments. In this study these techniques were employed to identify the effect of precipitant concentration, supersaturation, and the presence of an impurity, the physiological lysozyme dimer, on the nucleation rate and crystal dimensions of the tetragonal forin of chicken egg white lysozyme. Decreasing precipitant concentration, increasing supers aturation, and increasing impurity, were found to increase crystal numbers. The crystal axial ratio decreased with increasing precipitant concentration, independent of impurity.

Sex, race, and age distributions of mean aortic wall thickness in a multiethnic population-based sample.

PubMed

Rosero, Eric B; Peshock, Ronald M; Khera, Amit; Clagett, Patrick; Lo, Hao; Timaran, Carlos H

2011-04-01

Reference values and age-related changes of the wall thickness of the abdominal aorta have not been described in the general population. We characterized age-, race-, and gender-specific distributions, and yearly rates of change of mean aortic wall thickness (MAWT), and associations between MAWT and cardiovascular risk factors in a multi-ethnic population-based probability sample. Magnetic resonance imaging measurements of MAWT were performed on 2466 free-living white, black, and Hispanic adult subjects. MAWT race/ethnicity- and gender-specific percentile values across age were estimated using regression analyses. MAWT was greater in men than in women and increased linearly with age in all the groups and across all the percentiles. Hispanic women had the thinnest and black men the thickest aortas. Black men had the highest and white women the lowest age-related MAWT increase. Age, gender, ethnicity, smoking status, systolic blood pressure, low-density lipoprotein-cholesterol levels, high-density lipoprotein-cholesterol levels, and fasting glucose levels were independent predictors of MAWT. Age, gender, and racial/ethnic differences in MAWT distributions exist in the general population. Such differences should be considered in future investigations assessing aortic atherosclerosis and the effects of anti-atherosclerotic therapies. Published by Mosby, Inc.

Head circumference, atrophy, and cognition: implications for brain reserve in Alzheimer disease.

PubMed

Perneczky, R; Wagenpfeil, S; Lunetta, K L; Cupples, L A; Green, R C; Decarli, C; Farrer, L A; Kurz, A

2010-07-13

Clinical and epidemiologic studies suggest that patients with Alzheimer disease (AD) with larger head circumference have better cognitive performance at the same level of brain pathology than subjects with smaller head circumference. A total of 270 patients with AD participating in the Multi-Institutional Research in Alzheimer's Genetic Epidemiology (MIRAGE) study underwent cognitive testing, APOE genotyping, and MRI of the brain in a cross-sectional study. Linear regression analysis was used to examine the association between cerebral atrophy, as a proxy for AD pathology, and level of cognitive function, adjusting for age, duration of AD symptoms, gender, head circumference, APOE genotype, diabetes mellitus, hypertension, major depression, and ethnicity. An interaction term between atrophy and head circumference was introduced to explore if head circumference modified the association between cerebral atrophy and cognition. There was a significant inverse association between atrophy and cognitive function, and a significant interaction between atrophy and head circumference. With greater levels of atrophy, cognition was higher for individuals with greater head circumference. This study suggests that larger head circumference is associated with less cognitive impairment in the face of cerebral atrophy. This finding supports the notion that head circumference (and presumably brain size) offers protection against AD symptoms through enhanced brain reserve.

Transmission of linear regression patterns between time series: From relationship in time series to complex networks

NASA Astrophysics Data System (ADS)

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

2014-07-01

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

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

PubMed

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

2014-07-01

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

Dynamic characteristics of a multi-wavelength Brillouin-Raman fiber laser assisted by multiple four-wave mixing processes in a ring cavity

NASA Astrophysics Data System (ADS)

Shirazi, M. R.; Mohamed Taib, J.; De La Rue, R. M.; Harun, S. W.; Ahmad, H.

2015-03-01

Dynamic characteristics of a multi-wavelength Brillouin-Raman fiber laser (MBRFL) assisted by four-wave mixing have been investigated through the development of Stokes and anti-Stokes lines under different combinations of Brillouin and Raman pump power levels and different Raman pumping schemes in a ring cavity. For a Stokes line of order higher than three, the threshold power was less than the saturation power of its last-order Stokes line. By increasing the Brillouin pump power, the nth order anti-Stokes and the (n+4)th order Stokes power levels were unexpectedly increased almost the same before the Stokes line threshold power. It was also found out that the SBS threshold reduction (SBSTR) depended linearly on the gain factor for the 1st and 2nd Stokes lines, as the first set. This relation for the 3rd and 4th Stokes lines as the second set, however, was almost linear with the same slope before SBSTR -6 dB, then, it approached to the linear relation in the first set when the gain factor was increased to 50 dB. Therefore, the threshold power levels of Stokes lines for a given Raman gain can be readily estimated only by knowing the threshold power levels in which there is no Raman amplification.

Linking brain-wide multivoxel activation patterns to behaviour: Examples from language and math.

PubMed

Raizada, Rajeev D S; Tsao, Feng-Ming; Liu, Huei-Mei; Holloway, Ian D; Ansari, Daniel; Kuhl, Patricia K

2010-05-15

A key goal of cognitive neuroscience is to find simple and direct connections between brain and behaviour. However, fMRI analysis typically involves choices between many possible options, with each choice potentially biasing any brain-behaviour correlations that emerge. Standard methods of fMRI analysis assess each voxel individually, but then face the problem of selection bias when combining those voxels into a region-of-interest, or ROI. Multivariate pattern-based fMRI analysis methods use classifiers to analyse multiple voxels together, but can also introduce selection bias via data-reduction steps as feature selection of voxels, pre-selecting activated regions, or principal components analysis. We show here that strong brain-behaviour links can be revealed without any voxel selection or data reduction, using just plain linear regression as a classifier applied to the whole brain at once, i.e. treating each entire brain volume as a single multi-voxel pattern. The brain-behaviour correlations emerged despite the fact that the classifier was not provided with any information at all about subjects' behaviour, but instead was given only the neural data and its condition-labels. Surprisingly, more powerful classifiers such as a linear SVM and regularised logistic regression produce very similar results. We discuss some possible reasons why the very simple brain-wide linear regression model is able to find correlations with behaviour that are as strong as those obtained on the one hand from a specific ROI and on the other hand from more complex classifiers. In a manner which is unencumbered by arbitrary choices, our approach offers a method for investigating connections between brain and behaviour which is simple, rigorous and direct. Copyright (c) 2010 Elsevier Inc. All rights reserved.

Validating Variational Bayes Linear Regression Method With Multi-Central Datasets.

PubMed

Murata, Hiroshi; Zangwill, Linda M; Fujino, Yuri; Matsuura, Masato; Miki, Atsuya; Hirasawa, Kazunori; Tanito, Masaki; Mizoue, Shiro; Mori, Kazuhiko; Suzuki, Katsuyoshi; Yamashita, Takehiro; Kashiwagi, Kenji; Shoji, Nobuyuki; Asaoka, Ryo

2018-04-01

To validate the prediction accuracy of variational Bayes linear regression (VBLR) with two datasets external to the training dataset. The training dataset consisted of 7268 eyes of 4278 subjects from the University of Tokyo Hospital. The Japanese Archive of Multicentral Databases in Glaucoma (JAMDIG) dataset consisted of 271 eyes of 177 patients, and the Diagnostic Innovations in Glaucoma Study (DIGS) dataset includes 248 eyes of 173 patients, which were used for validation. Prediction accuracy was compared between the VBLR and ordinary least squared linear regression (OLSLR). First, OLSLR and VBLR were carried out using total deviation (TD) values at each of the 52 test points from the second to fourth visual fields (VFs) (VF2-4) to 2nd to 10th VF (VF2-10) of each patient in JAMDIG and DIGS datasets, and the TD values of the 11th VF test were predicted every time. The predictive accuracy of each method was compared through the root mean squared error (RMSE) statistic. OLSLR RMSEs with the JAMDIG and DIGS datasets were between 31 and 4.3 dB, and between 19.5 and 3.9 dB. On the other hand, VBLR RMSEs with JAMDIG and DIGS datasets were between 5.0 and 3.7, and between 4.6 and 3.6 dB. There was statistically significant difference between VBLR and OLSLR for both datasets at every series (VF2-4 to VF2-10) (P < 0.01 for all tests). However, there was no statistically significant difference in VBLR RMSEs between JAMDIG and DIGS datasets at any series of VFs (VF2-2 to VF2-10) (P > 0.05). VBLR outperformed OLSLR to predict future VF progression, and the VBLR has a potential to be a helpful tool at clinical settings.

Linking brain-wide multivoxel activation patterns to behaviour: Examples from language and math

PubMed Central

Raizada, Rajeev D.S.; Tsao, Feng-Ming; Liu, Huei-Mei; Holloway, Ian D.; Ansari, Daniel; Kuhl, Patricia K.

2010-01-01

A key goal of cognitive neuroscience is to find simple and direct connections between brain and behaviour. However, fMRI analysis typically involves choices between many possible options, with each choice potentially biasing any brain–behaviour correlations that emerge. Standard methods of fMRI analysis assess each voxel individually, but then face the problem of selection bias when combining those voxels into a region-of-interest, or ROI. Multivariate pattern-based fMRI analysis methods use classifiers to analyse multiple voxels together, but can also introduce selection bias via data-reduction steps as feature selection of voxels, pre-selecting activated regions, or principal components analysis. We show here that strong brain–behaviour links can be revealed without any voxel selection or data reduction, using just plain linear regression as a classifier applied to the whole brain at once, i.e. treating each entire brain volume as a single multi-voxel pattern. The brain–behaviour correlations emerged despite the fact that the classifier was not provided with any information at all about subjects' behaviour, but instead was given only the neural data and its condition-labels. Surprisingly, more powerful classifiers such as a linear SVM and regularised logistic regression produce very similar results. We discuss some possible reasons why the very simple brain-wide linear regression model is able to find correlations with behaviour that are as strong as those obtained on the one hand from a specific ROI and on the other hand from more complex classifiers. In a manner which is unencumbered by arbitrary choices, our approach offers a method for investigating connections between brain and behaviour which is simple, rigorous and direct. PMID:20132896

Estimating severity of sideways fall using a generic multi linear regression model based on kinematic input variables.

PubMed

van der Zijden, A M; Groen, B E; Tanck, E; Nienhuis, B; Verdonschot, N; Weerdesteyn, V

2017-03-21

Many research groups have studied fall impact mechanics to understand how fall severity can be reduced to prevent hip fractures. Yet, direct impact force measurements with force plates are restricted to a very limited repertoire of experimental falls. The purpose of this study was to develop a generic model for estimating hip impact forces (i.e. fall severity) in in vivo sideways falls without the use of force plates. Twelve experienced judokas performed sideways Martial Arts (MA) and Block ('natural') falls on a force plate, both with and without a mat on top. Data were analyzed to determine the hip impact force and to derive 11 selected (subject-specific and kinematic) variables. Falls from kneeling height were used to perform a stepwise regression procedure to assess the effects of these input variables and build the model. The final model includes four input variables, involving one subject-specific measure and three kinematic variables: maximum upper body deceleration, body mass, shoulder angle at the instant of 'maximum impact' and maximum hip deceleration. The results showed that estimated and measured hip impact forces were linearly related (explained variances ranging from 46 to 63%). Hip impact forces of MA falls onto the mat from a standing position (3650±916N) estimated by the final model were comparable with measured values (3698±689N), even though these data were not used for training the model. In conclusion, a generic linear regression model was developed that enables the assessment of fall severity through kinematic measures of sideways falls, without using force plates. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

PubMed

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

2015-01-01

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

Non-Linear Relationship between Economic Growth and CO2 Emissions in China: An Empirical Study Based on Panel Smooth Transition Regression Models

PubMed Central

Wang, Zheng-Xin; Hao, Peng; Yao, Pei-Yi

2017-01-01

The non-linear relationship between provincial economic growth and carbon emissions is investigated by using panel smooth transition regression (PSTR) models. The research indicates that, on the condition of separately taking Gross Domestic Product per capita (GDPpc), energy structure (Es), and urbanisation level (Ul) as transition variables, three models all reject the null hypothesis of a linear relationship, i.e., a non-linear relationship exists. The results show that the three models all contain only one transition function but different numbers of location parameters. The model taking GDPpc as the transition variable has two location parameters, while the other two models separately considering Es and Ul as the transition variables both contain one location parameter. The three models applied in the study all favourably describe the non-linear relationship between economic growth and CO2 emissions in China. It also can be seen that the conversion rate of the influence of Ul on per capita CO2 emissions is significantly higher than those of GDPpc and Es on per capita CO2 emissions. PMID:29236083

Non-Linear Relationship between Economic Growth and CO₂ Emissions in China: An Empirical Study Based on Panel Smooth Transition Regression Models.

PubMed

Wang, Zheng-Xin; Hao, Peng; Yao, Pei-Yi

2017-12-13

The non-linear relationship between provincial economic growth and carbon emissions is investigated by using panel smooth transition regression (PSTR) models. The research indicates that, on the condition of separately taking Gross Domestic Product per capita (GDPpc), energy structure (Es), and urbanisation level (Ul) as transition variables, three models all reject the null hypothesis of a linear relationship, i.e., a non-linear relationship exists. The results show that the three models all contain only one transition function but different numbers of location parameters. The model taking GDPpc as the transition variable has two location parameters, while the other two models separately considering Es and Ul as the transition variables both contain one location parameter. The three models applied in the study all favourably describe the non-linear relationship between economic growth and CO₂ emissions in China. It also can be seen that the conversion rate of the influence of Ul on per capita CO₂ emissions is significantly higher than those of GDPpc and Es on per capita CO₂ emissions.

PsiQuaSP-A library for efficient computation of symmetric open quantum systems.

PubMed

Gegg, Michael; Richter, Marten

2017-11-24

In a recent publication we showed that permutation symmetry reduces the numerical complexity of Lindblad quantum master equations for identical multi-level systems from exponential to polynomial scaling. This is important for open system dynamics including realistic system bath interactions and dephasing in, for instance, the Dicke model, multi-Λ system setups etc. Here we present an object-oriented C++ library that allows to setup and solve arbitrary quantum optical Lindblad master equations, especially those that are permutationally symmetric in the multi-level systems. PsiQuaSP (Permutation symmetry for identical Quantum Systems Package) uses the PETSc package for sparse linear algebra methods and differential equations as basis. The aim of PsiQuaSP is to provide flexible, storage efficient and scalable code while being as user friendly as possible. It is easily applied to many quantum optical or quantum information systems with more than one multi-level system. We first review the basics of the permutation symmetry for multi-level systems in quantum master equations. The application of PsiQuaSP to quantum dynamical problems is illustrated with several typical, simple examples of open quantum optical systems.

Discovering variable fractional orders of advection-dispersion equations from field data using multi-fidelity Bayesian optimization

NASA Astrophysics Data System (ADS)

Pang, Guofei; Perdikaris, Paris; Cai, Wei; Karniadakis, George Em

2017-11-01

The fractional advection-dispersion equation (FADE) can describe accurately the solute transport in groundwater but its fractional order has to be determined a priori. Here, we employ multi-fidelity Bayesian optimization to obtain the fractional order under various conditions, and we obtain more accurate results compared to previously published data. Moreover, the present method is very efficient as we use different levels of resolution to construct a stochastic surrogate model and quantify its uncertainty. We consider two different problem set ups. In the first set up, we obtain variable fractional orders of one-dimensional FADE, considering both synthetic and field data. In the second set up, we identify constant fractional orders of two-dimensional FADE using synthetic data. We employ multi-resolution simulations using two-level and three-level Gaussian process regression models to construct the surrogates.

Anti-TNF levels in cord blood at birth are associated with anti-TNF type.

PubMed

Kanis, Shannon L; de Lima, Alison; van der Ent, Cokkie; Rizopoulos, Dimitris; van der Woude, C Janneke

2018-05-15

Pregnancy guidelines for women with Inflammatory Bowel Disease (IBD) provide recommendations regarding anti-TNF cessation during pregnancy, in order to limit fetal exposure. Although infliximab (IFX) leads to higher anti-TNF concentrations in cord blood than adalimumab (ADA), recommendations are similar. We aimed to demonstrate the effect of anti-TNF cessation during pregnancy on fetal exposure, for IFX and ADA separately. We conducted a prospective single center cohort study. Women with IBD, using IFX or ADA, were followed-up during pregnancy. In case of sustained disease remission, anti-TNF was stopped in the third trimester. At birth, anti-TNF concentration was measured in cord blood. A linear regression model was developed to demonstrate anti-TNF concentration in cord blood at birth. In addition, outcomes such as disease activity, pregnancy outcomes and 1-year health outcomes of infants were collected. We included 131 pregnancies that resulted in a live birth (73 IFX, 58 ADA). At birth, 94 cord blood samples were obtained (52 IFX, 42 ADA), showing significantly higher levels of IFX than ADA (p<0.0001). Anti-TNF type and stop week were used in the linear regression model. During the third trimester, IFX transportation over the placenta increases exponentially, however, ADA transportation is limited and increases in a linear fashion. Overall, health outcomes were comparable. Our linear regression model shows that ADA may be continued longer during pregnancy as transportation over the placenta is lower than IFX. This may reduce relapse risk of the mother without increasing fetal anti-TNF exposure.

A National Study of the Association between Food Environments and County-Level Health Outcomes

ERIC Educational Resources Information Center

Ahern, Melissa; Brown, Cheryl; Dukas, Stephen

2011-01-01

Purpose: This national, county-level study examines the relationship between food availability and access, and health outcomes (mortality, diabetes, and obesity rates) in both metro and non-metro areas. Methods: This is a secondary, cross-sectional analysis using Food Environment Atlas and CDC data. Linear regression models estimate relationships…

Seminal Plasma HIV-1 RNA Concentration Is Strongly Associated with Altered Levels of Seminal Plasma Interferon-γ, Interleukin-17, and Interleukin-5

PubMed Central

Hoffman, Jennifer C.; Anton, Peter A.; Baldwin, Gayle Cocita; Elliott, Julie; Anisman-Posner, Deborah; Tanner, Karen; Grogan, Tristan; Elashoff, David; Sugar, Catherine; Yang, Otto O.

2014-01-01

Abstract Seminal plasma HIV-1 RNA level is an important determinant of the risk of HIV-1 sexual transmission. We investigated potential associations between seminal plasma cytokine levels and viral concentration in the seminal plasma of HIV-1-infected men. This was a prospective, observational study of paired blood and semen samples from 18 HIV-1 chronically infected men off antiretroviral therapy. HIV-1 RNA levels and cytokine levels in seminal plasma and blood plasma were measured and analyzed using simple linear regressions to screen for associations between cytokines and seminal plasma HIV-1 levels. Forward stepwise regression was performed to construct the final multivariate model. The median HIV-1 RNA concentrations were 4.42 log10 copies/ml (IQR 2.98, 4.70) and 2.96 log10 copies/ml (IQR 2, 4.18) in blood and seminal plasma, respectively. In stepwise multivariate linear regression analysis, blood HIV-1 RNA level (p<0.0001) was most strongly associated with seminal plasma HIV-1 RNA level. After controlling for blood HIV-1 RNA level, seminal plasma HIV-1 RNA level was positively associated with interferon (IFN)-γ (p=0.03) and interleukin (IL)-17 (p=0.03) and negatively associated with IL-5 (p=0.0007) in seminal plasma. In addition to blood HIV-1 RNA level, cytokine profiles in the male genital tract are associated with HIV-1 RNA levels in semen. The Th1 and Th17 cytokines IFN-γ and IL-17 are associated with increased seminal plasma HIV-1 RNA, while the Th2 cytokine IL-5 is associated with decreased seminal plasma HIV-1 RNA. These results support the importance of genital tract immunomodulation in HIV-1 transmission. PMID:25209674

Mental ability and psychological work performance in Chinese workers.

PubMed

Zhong, Fei; Yano, Eiji; Lan, Yajia; Wang, Mianzhen; Wang, Zhiming; Wang, Xiaorong

2006-10-01

This study was to explore the relationship among mental ability, occupational stress, and psychological work performance in Chinese workers, and to identify relevant modifiers of mental ability and psychological work performance. Psychological Stress Intensity (PSI), psychological work performance, and mental ability (Mental Function Index, MFI) were determined among 485 Chinese workers (aged 33 to 62 yr, 65% of men) with varied work occupations. Occupational Stress Questionnaire (OSQ) and mental ability with 3 tests (including immediate memory, digit span, and cipher decoding) were used. The relationship between mental ability and psychological work performance was analyzed with multiple linear regression approach. PSI, MFI, or psychological work performance were significantly different among different work types and educational level groups (p<0.01). Multiple linear regression analysis showed that MFI was significantly related to gender, age, educational level, and work type. Higher MFI and lower PSI predicted a better psychological work performance, even after adjusted for gender, age, educational level, and work type. The study suggests that occupational stress and low mental ability are important predictors for poor psychological work performance, which is modified by both gender and educational level.

Dietary patterns, insulin sensitivity and adiposity in the multi-ethnic Insulin Resistance Atherosclerosis Study population.

PubMed

Liese, Angela D; Schulz, Mandy; Moore, Charity G; Mayer-Davis, Elizabeth J

2004-12-01

Epidemiological investigations increasingly employ dietary-pattern techniques to fully integrate dietary data. The present study evaluated the relationship of dietary patterns identified by cluster analysis with measures of insulin sensitivity (SI) and adiposity in the multi-ethnic, multi-centre Insulin Resistance Atherosclerosis Study (IRAS, 1992-94). Cross-sectional data from 980 middle-aged adults, of whom 67 % had normal and 33 % had impaired glucose tolerance, were analysed. Usual dietary intake was obtained by an interviewer-administered, validated food-frequency questionnaire. Outcomes included SI, fasting insulin (FI), BMI and waist circumference. The relationship of dietary patterns to log(SI+1), log(FI), BMI and waist circumference was modelled with multivariable linear regressions. Cluster analysis identified six distinct diet patterns--'dark bread', 'wine', 'fruits', 'low-frequency eaters', 'fries' and 'white bread'. The 'white bread' and the 'fries' patterns over-represented the Hispanic IRAS population predominantly from two centres, while the 'wine' and 'dark bread' groups were dominated by non-Hispanic whites. The dietary patterns were associated significantly with each of the outcomes first at the crude, clinical level (P<0.001). Furthermore, they were significantly associated with FI, BMI and waist circumference independent of age, sex, race or ethnicity, clinic, family history of diabetes, smoking and activity (P<0.004), whereas significance was lost for SI. Studying the total dietary behaviour via a pattern approach allowed us to focus both on the qualitative and quantitative dimensions of diet. The present study identified highly consistent associations of distinct dietary patterns with measures of insulin resistance and adiposity, which are risk factors for diabetes and heart disease.

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

PubMed

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

2018-06-01

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

Smooth individual level covariates adjustment in disease mapping.

PubMed

Huque, Md Hamidul; Anderson, Craig; Walton, Richard; Woolford, Samuel; Ryan, Louise

2018-05-01

Spatial models for disease mapping should ideally account for covariates measured both at individual and area levels. The newly available "indiCAR" model fits the popular conditional autoregresssive (CAR) model by accommodating both individual and group level covariates while adjusting for spatial correlation in the disease rates. This algorithm has been shown to be effective but assumes log-linear associations between individual level covariates and outcome. In many studies, the relationship between individual level covariates and the outcome may be non-log-linear, and methods to track such nonlinearity between individual level covariate and outcome in spatial regression modeling are not well developed. In this paper, we propose a new algorithm, smooth-indiCAR, to fit an extension to the popular conditional autoregresssive model that can accommodate both linear and nonlinear individual level covariate effects while adjusting for group level covariates and spatial correlation in the disease rates. In this formulation, the effect of a continuous individual level covariate is accommodated via penalized splines. We describe a two-step estimation procedure to obtain reliable estimates of individual and group level covariate effects where both individual and group level covariate effects are estimated separately. This distributed computing framework enhances its application in the Big Data domain with a large number of individual/group level covariates. We evaluate the performance of smooth-indiCAR through simulation. Our results indicate that the smooth-indiCAR method provides reliable estimates of all regression and random effect parameters. We illustrate our proposed methodology with an analysis of data on neutropenia admissions in New South Wales (NSW), Australia. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The Association of Menopausal Age and NT-proBrain Natriuretic Peptide: The Multi-Ethnic Study of Atherosclerosis

PubMed Central

Ebong, Imo A.; Watson, Karol E.; Goff, David C.; Bluemke, David A.; Srikanthan, Preethi; Horwich, Tamara; Bertoni, Alain G.

2014-01-01

Objective Menopausal age could affect the risk of developing cardiovascular disease (CVD). The purpose of this study was to investigate the associations of early menopause (menopause occurring before 45 years of age) and menopausal age with NT-pro brain natriuretic peptide (NT-proBNP), a potential risk marker of CVD and heart failure (HF). Methods Our cross-sectional study included 2275 postmenopausal women, aged 45–85 years, without clinical CVD (2000–2002), from the Multi-Ethnic Study of Atherosclerosis. Participants were classified as having or not having early menopause. NT-proBNP was log-transformed. Multivariable linear regression was used for analysis. Results There were 561 women with early menopause. The median NT-proBNP value was 79.0 (41.1–151.6) pg/ml for all participants with values of 83.4 (41.4–164.9) pg/ml and 78.0 (40.8–148.3) pg/ml for women with and without early menopause respectively. The mean (SD) age was 65 (10.1) and 65 (8.9) years for women with and without early menopause respectively. There were no significant interactions between menopausal age and ethnicity. In multivariable analysis, early menopause was associated with a 10.7% increase in NT-proBNP while each year increase in menopausal age was associated with a 0.7% decrease in NT-proBNP. Conclusion Early menopause is associated with greater NT-proBNP levels while each year increase in menopausal age is associated with lower NT-proBNP levels in postmenopausal women. PMID:25290536

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

PubMed

Khamanga, Sandile M; Walker, Roderick B

2011-01-15

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

Representational change and strategy use in children's number line estimation during the first years of primary school.

PubMed

White, Sonia L J; Szűcs, Dénes

2012-01-04

The objective of this study was to scrutinize number line estimation behaviors displayed by children in mathematics classrooms during the first three years of schooling. We extend existing research by not only mapping potential logarithmic-linear shifts but also provide a new perspective by studying in detail the estimation strategies of individual target digits within a number range familiar to children. Typically developing children (n = 67) from Years 1-3 completed a number-to-position numerical estimation task (0-20 number line). Estimation behaviors were first analyzed via logarithmic and linear regression modeling. Subsequently, using an analysis of variance we compared the estimation accuracy of each digit, thus identifying target digits that were estimated with the assistance of arithmetic strategy. Our results further confirm a developmental logarithmic-linear shift when utilizing regression modeling; however, uniquely we have identified that children employ variable strategies when completing numerical estimation, with levels of strategy advancing with development. In terms of the existing cognitive research, this strategy factor highlights the limitations of any regression modeling approach, or alternatively, it could underpin the developmental time course of the logarithmic-linear shift. Future studies need to systematically investigate this relationship and also consider the implications for educational practice.

Representational change and strategy use in children's number line estimation during the first years of primary school

PubMed Central

2012-01-01

Background The objective of this study was to scrutinize number line estimation behaviors displayed by children in mathematics classrooms during the first three years of schooling. We extend existing research by not only mapping potential logarithmic-linear shifts but also provide a new perspective by studying in detail the estimation strategies of individual target digits within a number range familiar to children. Methods Typically developing children (n = 67) from Years 1-3 completed a number-to-position numerical estimation task (0-20 number line). Estimation behaviors were first analyzed via logarithmic and linear regression modeling. Subsequently, using an analysis of variance we compared the estimation accuracy of each digit, thus identifying target digits that were estimated with the assistance of arithmetic strategy. Results Our results further confirm a developmental logarithmic-linear shift when utilizing regression modeling; however, uniquely we have identified that children employ variable strategies when completing numerical estimation, with levels of strategy advancing with development. Conclusion In terms of the existing cognitive research, this strategy factor highlights the limitations of any regression modeling approach, or alternatively, it could underpin the developmental time course of the logarithmic-linear shift. Future studies need to systematically investigate this relationship and also consider the implications for educational practice. PMID:22217191

Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling.

PubMed

Perdikaris, P; Raissi, M; Damianou, A; Lawrence, N D; Karniadakis, G E

2017-02-01

Multi-fidelity modelling enables accurate inference of quantities of interest by synergistically combining realizations of low-cost/low-fidelity models with a small set of high-fidelity observations. This is particularly effective when the low- and high-fidelity models exhibit strong correlations, and can lead to significant computational gains over approaches that solely rely on high-fidelity models. However, in many cases of practical interest, low-fidelity models can only be well correlated to their high-fidelity counterparts for a specific range of input parameters, and potentially return wrong trends and erroneous predictions if probed outside of their validity regime. Here we put forth a probabilistic framework based on Gaussian process regression and nonlinear autoregressive schemes that is capable of learning complex nonlinear and space-dependent cross-correlations between models of variable fidelity, and can effectively safeguard against low-fidelity models that provide wrong trends. This introduces a new class of multi-fidelity information fusion algorithms that provide a fundamental extension to the existing linear autoregressive methodologies, while still maintaining the same algorithmic complexity and overall computational cost. The performance of the proposed methods is tested in several benchmark problems involving both synthetic and real multi-fidelity datasets from computational fluid dynamics simulations.

3D Multi-segment foot kinematics in children: A developmental study in typically developing boys.

PubMed

Deschamps, Kevin; Staes, Filip; Peerlinck, Kathelijne; Van Geet, Christel; Hermans, Cedric; Matricali, Giovanni Arnoldo; Lobet, Sebastien

2017-02-01

The relationship between age and 3D rotations objectivized with multisegment foot models has not been quantified until now. The purpose of this study was therefore to investigate the relationship between age and multi-segment foot kinematics in a cross-sectional database. Barefoot multi-segment foot kinematics of thirty two typically developing boys, aged 6-20 years, were captured with the Rizzoli Multi-segment Foot Model. One-dimensional statistical parametric mapping linear regression was used to examine the relationship between age and 3D inter-segment rotations of the dominant leg during the full gait cycle. Age was significantly correlated with sagittal plane kinematics of the midfoot and the calcaneus-metatarsus inter-segment angle (p<0.0125). Age was also correlated with the transverse plane kinematics of the calcaneus-metatarsus angle (p<0.0001). Gait labs should consider age related differences and variability if optimal decision making is pursued. It remains unclear if this is of interest for all foot models, however, the current study highlights that this is of particular relevance for foot models which incorporate a separate midfoot segment. Copyright © 2016 Elsevier B.V. All rights reserved.

Multivariate functional response regression, with application to fluorescence spectroscopy in a cervical pre-cancer study.

PubMed

Zhu, Hongxiao; Morris, Jeffrey S; Wei, Fengrong; Cox, Dennis D

2017-07-01

Many scientific studies measure different types of high-dimensional signals or images from the same subject, producing multivariate functional data. These functional measurements carry different types of information about the scientific process, and a joint analysis that integrates information across them may provide new insights into the underlying mechanism for the phenomenon under study. Motivated by fluorescence spectroscopy data in a cervical pre-cancer study, a multivariate functional response regression model is proposed, which treats multivariate functional observations as responses and a common set of covariates as predictors. This novel modeling framework simultaneously accounts for correlations between functional variables and potential multi-level structures in data that are induced by experimental design. The model is fitted by performing a two-stage linear transformation-a basis expansion to each functional variable followed by principal component analysis for the concatenated basis coefficients. This transformation effectively reduces the intra-and inter-function correlations and facilitates fast and convenient calculation. A fully Bayesian approach is adopted to sample the model parameters in the transformed space, and posterior inference is performed after inverse-transforming the regression coefficients back to the original data domain. The proposed approach produces functional tests that flag local regions on the functional effects, while controlling the overall experiment-wise error rate or false discovery rate. It also enables functional discriminant analysis through posterior predictive calculation. Analysis of the fluorescence spectroscopy data reveals local regions with differential expressions across the pre-cancer and normal samples. These regions may serve as biomarkers for prognosis and disease assessment.

Digital Image Restoration Under a Regression Model - The Unconstrained, Linear Equality and Inequality Constrained Approaches

DTIC Science & Technology

1974-01-01

REGRESSION MODEL - THE UNCONSTRAINED, LINEAR EQUALITY AND INEQUALITY CONSTRAINED APPROACHES January 1974 Nelson Delfino d’Avila Mascarenha;? Image...Report 520 DIGITAL IMAGE RESTORATION UNDER A REGRESSION MODEL THE UNCONSTRAINED, LINEAR EQUALITY AND INEQUALITY CONSTRAINED APPROACHES January...a two- dimensional form adequately describes the linear model . A dis- cretization is performed by using quadrature methods. By trans

Multidimensionality of the relationship between social status and dietary patterns in early childhood: longitudinal results from the French EDEN mother-child cohort.

PubMed

Camara, Soumaïla; de Lauzon-Guillain, Blandine; Heude, Barbara; Charles, Marie-Aline; Botton, Jérémie; Plancoulaine, Sabine; Forhan, Anne; Saurel-Cubizolles, Marie-Josèphe; Dargent-Molina, Patricia; Lioret, Sandrine

2015-09-24

The association between socioeconomic position and diet in early childhood has mainly been addressed based on maternal education and household income. We aimed to assess the influence of a variety of social factors from different socio-ecological levels (parents, household and child-care) on multi-time point dietary patterns identified from 2 to 5 y. This study included 974 children from the French EDEN mother-child cohort. Two multi-time point dietary patterns were derived in a previous study: they correspond to consistent exposures to either core- or non-core foods across 2, 3 and 5 y and were labelled "Guidelines" and "Processed, fast-foods". The associations of various social factors collected during pregnancy (age, education level) or at 2-y follow-up (mother's single status, occupation, work commitments, household financial disadvantage, presence of older siblings and child-care arrangements) with each of the two dietary patterns, were assessed by multivariable linear regression analysis. The adherence to a diet close to "Guidelines" was positively and independently associated with both maternal and paternal education levels. The adherence to a diet consistently composed of processed and fast-foods was essentially linked with maternal variables (younger age and lower education level), household financial disadvantage, the presence of older sibling (s) and being cared for at home by someone other than the mother. Multiple social factors operating at different levels (parents, household, and child-care) were found to be associated with the diet of young children. Different independent predictors were found for each of the two longitudinal dietary patterns, suggesting distinct pathways of influence. Our findings further suggest that interventions promoting healthier dietary choices for young children should involve both parents and take into account not only household financial disadvantage but also maternal age, family size and options for child-care.

Differences in Natriuretic Peptide Levels by Race/Ethnicity (From the Multi-Ethnic Study of Atherosclerosis).

PubMed

Gupta, Deepak K; Daniels, Lori B; Cheng, Susan; deFilippi, Christopher R; Criqui, Michael H; Maisel, Alan S; Lima, Joao A; Bahrami, Hossein; Greenland, Philip; Cushman, Mary; Tracy, Russell; Siscovick, David; Bertoni, Alain G; Cannone, Valentina; Burnett, John C; Carr, John Jeffrey; Wang, Thomas J

2017-09-15

Natriuretic peptides (NP) are cardiac-derived hormones with favorable cardiometabolic actions. Low NP levels are associated with increased risks of hypertension and diabetes mellitus, conditions with variable prevalence by race and ethnicity. Heritable factors underlie a significant proportion of the interindividual variation in NP concentrations, but the specific influences of race and ancestry are unknown. In 5597 individuals (40% white, 24% black, 23% Hispanic, and 13% Chinese) without prevalent cardiovascular disease at baseline in the Multi-Ethnic Study of Atherosclerosis, multivariable linear regression and restricted cubic splines were used to estimate differences in serum N-terminal pro B-type natriuretic peptide (NT-proBNP) levels according to, ethnicity, and ancestry. Ancestry was determined using genetic ancestry informative markers. NT-proBNP concentrations differed significantly by race and ethnicity (black, median 43 pg/ml [interquartile range 17 to 94], Chinese 43 [17 to 90], Hispanic 53 [23 to 107], white 68 [34 to 136]; p = 0.0001). In multivariable models, NT-proBNP was 44% lower (95% confidence interval -48 to -40) in black and 46% lower (-50 to -41) in Chinese, compared with white individuals. Hispanic individuals had intermediate concentrations. Self-identified blacks and Hispanics were the most genetically admixed. Among self-identified black individuals, a 20% increase in genetic European ancestry was associated with 12% higher (1% to 23%) NT-proBNP. Among Hispanic individuals, genetic European and African ancestry were positively and negatively associated with NT-proBNP levels, respectively. In conclusion, NT-proBNP levels differ according to race and ethnicity, with the lowest concentrations in black and Chinese individuals. Racial and ethnic differences in NT-proBNP may have a genetic basis, with European and African ancestry associated with higher and lower NT-proBNP concentrations, respectively. Copyright © 2017 Elsevier Inc. All rights reserved.

Can change in high-density lipoprotein cholesterol levels reduce cardiovascular risk?

PubMed

Dean, Bonnie B; Borenstein, Jeff E; Henning, James M; Knight, Kevin; Merz, C Noel Bairey

2004-06-01

The cardiovascular risk reduction observed in many trials of lipid-lowering agents is greater than expected on the basis of observed low-density lipoprotein cholesterol (LDL-C) level reductions. Our objective was to explore the degree to which high-density lipoprotein cholesterol (HDL-C) level changes explain cardiovascular risk reduction. A systematic review identified trials of lipid-lowering agents reporting changes in HDL-C and LDL-C levels and the incidence of coronary heart disease (CHD). The observed relative risk reduction (RRR) in CHD morbidity and mortality rates was calculated. The expected RRR, given the treatment effect on total cholesterol level, was calculated for each trial with logistic regression coefficients from observational studies. The difference between observed and expected RRR was plotted against the change in HDL-C level, and a least-squares regression line was calculated. Fifty-one trials were identified. Nineteen statin trials addressed the association of HDL-C with CHD. Limited numbers of trials of other therapies precluded additional analyses. Among statin trials, therapy reduced total cholesterol levels as much as 32% and LDL-C levels as much as 45%. HDL-C level increases were <10%. Treatment effect on HDL-C levels was not a significant linear predictor of the difference in observed and expected CHD mortality rates, although we observed a trend in this direction (P =.08). Similarly, HDL-C effect was not a significant linear predictor of the difference between observed and expected RRRs for CHD morbidity (P =.20). Although a linear trend toward greater risk reduction was observed with greater effects on HDL-C, differences were not statistically significant. The narrow range of HDL-C level increases in the statin trials likely reduced our ability to detect a beneficial HDL-C effect, if present.

Estimating energy expenditure from heart rate in older adults: a case for calibration.

PubMed

Schrack, Jennifer A; Zipunnikov, Vadim; Goldsmith, Jeff; Bandeen-Roche, Karen; Crainiceanu, Ciprian M; Ferrucci, Luigi

2014-01-01

Accurate measurement of free-living energy expenditure is vital to understanding changes in energy metabolism with aging. The efficacy of heart rate as a surrogate for energy expenditure is rooted in the assumption of a linear function between heart rate and energy expenditure, but its validity and reliability in older adults remains unclear. To assess the validity and reliability of the linear function between heart rate and energy expenditure in older adults using different levels of calibration. Heart rate and energy expenditure were assessed across five levels of exertion in 290 adults participating in the Baltimore Longitudinal Study of Aging. Correlation and random effects regression analyses assessed the linearity of the relationship between heart rate and energy expenditure and cross-validation models assessed predictive performance. Heart rate and energy expenditure were highly correlated (r=0.98) and linear regardless of age or sex. Intra-person variability was low but inter-person variability was high, with substantial heterogeneity of the random intercept (s.d. =0.372) despite similar slopes. Cross-validation models indicated individual calibration data substantially improves accuracy predictions of energy expenditure from heart rate, reducing the potential for considerable measurement bias. Although using five calibration measures provided the greatest reduction in the standard deviation of prediction errors (1.08 kcals/min), substantial improvement was also noted with two (0.75 kcals/min). These findings indicate standard regression equations may be used to make population-level inferences when estimating energy expenditure from heart rate in older adults but caution should be exercised when making inferences at the individual level without proper calibration.

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

PubMed

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

2015-01-01

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

Analysis of Student and School Level Variables Related to Mathematics Self-Efficacy Level Based on PISA 2012 Results for China-Shanghai, Turkey, and Greece

ERIC Educational Resources Information Center

Usta, H. Gonca

2016-01-01

This study aims to analyze the student and school level variables that affect students' self-efficacy levels in mathematics in China-Shanghai, Turkey, and Greece based on PISA 2012 results. In line with this purpose, the hierarchical linear regression model (HLM) was employed. The interschool variability is estimated at approximately 17% in…

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

PubMed

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

2009-02-01

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

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

ERIC Educational Resources Information Center

Lamb, John H.

2007-01-01

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

A multi-criteria index for ecological evaluation of tropical agriculture in southeastern Mexico.

PubMed

Huerta, Esperanza; Kampichler, Christian; Ochoa-Gaona, Susana; De Jong, Ben; Hernandez-Daumas, Salvador; Geissen, Violette

2014-01-01

The aim of this study was to generate an easy to use index to evaluate the ecological state of agricultural land from a sustainability perspective. We selected environmental indicators, such as the use of organic soil amendments (green manure) versus chemical fertilizers, plant biodiversity (including crop associations), variables which characterize soil conservation of conventional agricultural systems, pesticide use, method and frequency of tillage. We monitored the ecological state of 52 agricultural plots to test the performance of the index. The variables were hierarchically aggregated with simple mathematical algorithms, if-then rules, and rule-based fuzzy models, yielding the final multi-criteria index with values from 0 (worst) to 1 (best conditions). We validated the model through independent evaluation by experts, and we obtained a linear regression with an r2 = 0.61 (p = 2.4e-06, d.f. = 49) between index output and the experts' evaluation.

A Multi-Criteria Index for Ecological Evaluation of Tropical Agriculture in Southeastern Mexico

PubMed Central

Huerta, Esperanza; Kampichler, Christian; Ochoa-Gaona, Susana; De Jong, Ben; Hernandez-Daumas, Salvador; Geissen, Violette

2014-01-01

The aim of this study was to generate an easy to use index to evaluate the ecological state of agricultural land from a sustainability perspective. We selected environmental indicators, such as the use of organic soil amendments (green manure) versus chemical fertilizers, plant biodiversity (including crop associations), variables which characterize soil conservation of conventional agricultural systems, pesticide use, method and frequency of tillage. We monitored the ecological state of 52 agricultural plots to test the performance of the index. The variables were hierarchically aggregated with simple mathematical algorithms, if-then rules, and rule-based fuzzy models, yielding the final multi-criteria index with values from 0 (worst) to 1 (best conditions). We validated the model through independent evaluation by experts, and we obtained a linear regression with an r2 = 0.61 (p = 2.4e-06, d.f. = 49) between index output and the experts’ evaluation. PMID:25405980

Impact of adiposity on cellular adhesion: The Multi-Ethnic Study of atherosclerosis (MESA).

PubMed

Christoph, Mary J; Allison, Matthew A; Pankow, James S; Decker, Paul A; Kirsch, Phillip S; Tsai, Michael Y; Sale, Michele M; de Andrade, Mariza; Sicotte, Hugues; Tang, Weihong; Hanson, Naomi Q; Berardi, Cecilia; Wassel, Christina L; Larson, Nicholas B; Bielinski, Suzette J

2016-01-01

At the cellular level, how excess adiposity promotes atherogenesis is not fully understood. One pathway involves secretion of adipokines that stimulate endothelial dysfunction through increased expression of adhesion molecules. However, the relationship of adiposity to adhesion molecules that promote atherosclerosis is largely unknown. Linear regression models were used to assess the sex-specific associations of soluble cellular adhesion molecules (sP- and sL-selectin, sICAM-1, sVCAM-1, and sHGF) and adiposity in 5,974 adults examined as part of the Multi-Ethnic Study of Atherosclerosis (MESA). Adiposity measures included body mass index (BMI), waist-to-hip-ratio (WHR), and computed tomography measures of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT). The mean age was 64 years and 52% were female. In multivariable models adjusting for traditional cardiovascular risk factors, sHGF was positively associated with BMI, WHR, and VAT in both males and females, and sP-selectin with WHR and VAT in males. sVCAM-1 was inversely associated with VAT in females only. Our results showed the relation of adiposity to soluble cellular adhesion proteins was similar across adiposity measures and for both sexes. However, the relationship between adiposity and sVCAM-1 and P-selectin may be modified by sex and the measure used to assess adiposity. © 2015 The Obesity Society.

Resting State Network Estimation in Individual Subjects

PubMed Central

Hacker, Carl D.; Laumann, Timothy O.; Szrama, Nicholas P.; Baldassarre, Antonello; Snyder, Abraham Z.

2014-01-01

Resting-state functional magnetic resonance imaging (fMRI) has been used to study brain networks associated with both normal and pathological cognitive function. The objective of this work is to reliably compute resting state network (RSN) topography in single participants. We trained a supervised classifier (multi-layer perceptron; MLP) to associate blood oxygen level dependent (BOLD) correlation maps corresponding to pre-defined seeds with specific RSN identities. Hard classification of maps obtained from a priori seeds was highly reliable across new participants. Interestingly, continuous estimates of RSN membership retained substantial residual error. This result is consistent with the view that RSNs are hierarchically organized, and therefore not fully separable into spatially independent components. After training on a priori seed-based maps, we propagated voxel-wise correlation maps through the MLP to produce estimates of RSN membership throughout the brain. The MLP generated RSN topography estimates in individuals consistent with previous studies, even in brain regions not represented in the training data. This method could be used in future studies to relate RSN topography to other measures of functional brain organization (e.g., task-evoked responses, stimulation mapping, and deficits associated with lesions) in individuals. The multi-layer perceptron was directly compared to two alternative voxel classification procedures, specifically, dual regression and linear discriminant analysis; the perceptron generated more spatially specific RSN maps than either alternative. PMID:23735260

Loneliness, Depression, and Inflammation: Evidence from the Multi-Ethnic Study of Atherosclerosis.

PubMed

Mezuk, Briana; Choi, Moon; DeSantis, Amy S; Rapp, Stephen R; Diez Roux, Ana V; Seeman, Teresa

2016-01-01

Both objective and subjective aspects of social isolation have been associated with alterations in immune markers relevant to multiple chronic diseases among older adults. However, these associations may be confounded by health status, and it is unclear whether these social factors are associated with immune functioning among relatively healthy adults. The goal of this study was to examine the associations between perceived loneliness and circulating levels of inflammatory markers among a diverse sample of adults. Data come from a subset of the Multi-Ethnic Study of Atherosclerosis (n = 441). Loneliness was measured by three items derived from the UCLA Loneliness Scale. The association between loneliness and C-reactive protein (CRP) and fibrinogen was assessed using multivariable linear regression analyses. Models were adjusted for demographic and health characteristics. Approximately 50% of participants reported that they hardly ever felt lonely and 17.2% felt highly lonely. Individuals who were unmarried/unpartnered or with higher depressive symptoms were more likely to report being highly lonely. There was no relationship between perceived loneliness and ln(CRP) (β = -0.051, p = 0.239) adjusting for demographic and health characteristics. Loneliness was inversely associated with ln(fibrinogen) (β = -0.091, p = 0.040), although the absolute magnitude of this relationship was small. These results indicate that loneliness is not positively associated with fibrinogen or CRP among relatively healthy middle-aged adults.

Loneliness, Depression, and Inflammation: Evidence from the Multi-Ethnic Study of Atherosclerosis

PubMed Central

Mezuk, Briana; Choi, Moon; DeSantis, Amy S.; Rapp, Stephen R.; Diez Roux, Ana V.; Seeman, Teresa

2016-01-01

Objective Both objective and subjective aspects of social isolation have been associated with alterations in immune markers relevant to multiple chronic diseases among older adults. However, these associations may be confounded by health status, and it is unclear whether these social factors are associated with immune functioning among relatively healthy adults. The goal of this study was to examine the associations between perceived loneliness and circulating levels of inflammatory markers among a diverse sample of adults. Methods Data come from a subset of the Multi-Ethnic Study of Atherosclerosis (n = 441). Loneliness was measured by three items derived from the UCLA Loneliness Scale. The association between loneliness and C-reactive protein (CRP) and fibrinogen was assessed using multivariable linear regression analyses. Models were adjusted for demographic and health characteristics. Results Approximately 50% of participants reported that they hardly ever felt lonely and 17.2% felt highly lonely. Individuals who were unmarried/unpartnered or with higher depressive symptoms were more likely to report being highly lonely. There was no relationship between perceived loneliness and ln(CRP) (β = -0.051, p = 0.239) adjusting for demographic and health characteristics. Loneliness was inversely associated with ln(fibrinogen) (β = -0.091, p = 0.040), although the absolute magnitude of this relationship was small. Conclusion These results indicate that loneliness is not positively associated with fibrinogen or CRP among relatively healthy middle-aged adults. PMID:27367428

Personalized Metabolomics for Predicting Glucose Tolerance Changes in Sedentary Women After High-Intensity Interval Training

PubMed Central

Kuehnbaum, Naomi L.; Gillen, Jenna B.; Gibala, Martin J.; Britz-McKibbin, Philip

2014-01-01

High-intensity interval training (HIIT) offers a practical approach for enhancing cardiorespiratory fitness, however its role in improving glucose regulation among sedentary yet normoglycemic women remains unclear. Herein, multi-segment injection capillary electrophoresis-mass spectrometry is used as a high-throughput platform in metabolomics to assess dynamic responses of overweight/obese women (BMI > 25, n = 11) to standardized oral glucose tolerance tests (OGTTs) performed before and after a 6-week HIIT intervention. Various statistical methods were used to classify plasma metabolic signatures associated with post-prandial glucose and/or training status when using a repeated measures/cross-over study design. Branched-chain/aromatic amino acids and other intermediates of urea cycle and carnitine metabolism decreased over time in plasma after oral glucose loading. Adaptive exercise-induced changes to plasma thiol redox and orthinine status were measured for trained subjects while at rest in a fasting state. A multi-linear regression model was developed to predict changes in glucose tolerance based on a panel of plasma metabolites measured for naïve subjects in their untrained state. Since treatment outcomes to physical activity are variable between-subjects, prognostic markers offer a novel approach to screen for potential negative responders while designing lifestyle modifications that maximize the salutary benefits of exercise for diabetes prevention on an individual level. PMID:25164777

Deriving Hounsfield units using grey levels in cone beam computed tomography

PubMed Central

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

2010-01-01

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

Postmolar gestational trophoblastic neoplasia: beyond the traditional risk factors.

PubMed

Bakhtiyari, Mahmood; Mirzamoradi, Masoumeh; Kimyaiee, Parichehr; Aghaie, Abbas; Mansournia, Mohammd Ali; Ashrafi-Vand, Sepideh; Sarfjoo, Fatemeh Sadat

2015-09-01

To investigate the slope of linear regression of postevacuation serum hCG as an independent risk factor for postmolar gestational trophoblastic neoplasia (GTN). Multicenter retrospective cohort study. Academic referral health care centers. All subjects with confirmed hydatidiform mole and at least four measurements of β-hCG titer. None. Type and magnitude of the relationship between the slope of linear regression of β-hCG as a new risk factor and GTN using Bayesian logistic regression with penalized log-likelihood estimation. Among the high-risk and low-risk molar pregnancy cases, 11 (18.6%) and 19 cases (13.3%) had GTN, respectively. No significant relationship was found between the components of a high-risk pregnancy and GTN. The β-hCG return slope was higher in the spontaneous cure group. However, the initial level of this hormone in the first measurement was higher in the GTN group compared with in the spontaneous recovery group. The average time for diagnosing GTN in the high-risk molar pregnancy group was 2 weeks less than that of the low-risk molar pregnancy group. In addition to slope of linear regression of β-hCG (odds ratio [OR], 12.74, confidence interval [CI], 5.42-29.2), abortion history (OR, 2.53; 95% CI, 1.27-5.04) and large uterine height for gestational age (OR, 1.26; CI, 1.04-1.54) had the maximum effects on GTN outcome, respectively. The slope of linear regression of β-hCG was introduced as an independent risk factor, which could be used for clinical decision making based on records of β-hCG titer and subsequent prevention program. Copyright © 2015 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

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

Treesearch

Harold M. Rauscher

1983-01-01

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

Multi-Target Regression via Robust Low-Rank Learning.

PubMed

Zhen, Xiantong; Yu, Mengyang; He, Xiaofei; Li, Shuo

2018-02-01

Multi-target regression has recently regained great popularity due to its capability of simultaneously learning multiple relevant regression tasks and its wide applications in data mining, computer vision and medical image analysis, while great challenges arise from jointly handling inter-target correlations and input-output relationships. In this paper, we propose Multi-layer Multi-target Regression (MMR) which enables simultaneously modeling intrinsic inter-target correlations and nonlinear input-output relationships in a general framework via robust low-rank learning. Specifically, the MMR can explicitly encode inter-target correlations in a structure matrix by matrix elastic nets (MEN); the MMR can work in conjunction with the kernel trick to effectively disentangle highly complex nonlinear input-output relationships; the MMR can be efficiently solved by a new alternating optimization algorithm with guaranteed convergence. The MMR leverages the strength of kernel methods for nonlinear feature learning and the structural advantage of multi-layer learning architectures for inter-target correlation modeling. More importantly, it offers a new multi-layer learning paradigm for multi-target regression which is endowed with high generality, flexibility and expressive ability. Extensive experimental evaluation on 18 diverse real-world datasets demonstrates that our MMR can achieve consistently high performance and outperforms representative state-of-the-art algorithms, which shows its great effectiveness and generality for multivariate prediction.

Water requirements of canine athletes during multi-day exercise.

PubMed

Stephens-Brown, Lara; Davis, Michael

2018-03-23

Exercise increases water requirements, but there is little information regarding water loss in dogs performing multi-day exercise OBJECTIVES: Quantify the daily water turnover of working dogs during multi-day exercise and establish the suitability of SC administration of tracer to determine water turnover. Fifteen privately owned Labrador retrievers trained for explosive detection duties and 16 privately owned Alaskan Huskies conditioned for mid-distance racing. All dogs received 0.3 g D 2 O/kg body weight by IV infusion, gavage, or SC injection before the start of a multi-day exercise challenge. Explosive detection dogs conducted 5 days of simulated off-leash explosive detection activity. Alaskan sled dogs completed a mid-distance stage race totaling 222 km in 2 days. Total body water (TBW) and daily water turnover were calculated using both indicator dilution and elimination regression techniques. Total body water (% of body weight) varied from 60% ± 8.6% in minimally conditioned Labrador retrievers to 74% ± 4.5% in highly conditioned Labrador retrievers. Daily water turnover was as high as 45% of TBW during exercise in cold conditions. There was no effect of sex or speed on daily water turnover. There was good agreement between results calculated using the indicator dilution approach and those calculated using a semilog linear regression approach when indicator isotope was administered IV or SC. Water requirements are influenced primarily by the amount of work done. SC administration of isotope-labeled water offers a simple and accurate alternative method for metabolic studies. © 2018 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.

Single-chip source-free terahertz spectroscope across 0.04-0.99 THz: combining sub-wavelength near-field sensing and regression analysis.

PubMed

Wu, Xue; Sengupta, Kaushik

2018-03-19

This paper demonstrates a methodology to miniaturize THz spectroscopes into a single silicon chip by eliminating traditional solid-state architectural components such as complex tunable THz and optical sources, nonlinear mixing and amplifiers. The proposed method achieves this by extracting incident THz spectral signatures from the surface of an on-chip antenna itself. The information is sensed through the spectrally-sensitive 2D distribution of the impressed current surface under the THz incident field. By converting the antenna from a single-port to a massively multi-port architecture with integrated electronics and deep subwavelength sensing, THz spectral estimation is converted into a linear estimation problem. We employ rigorous regression techniques and analysis to demonstrate a single silicon chip system operating at room temperature across 0.04-0.99 THz with 10 MHz accuracy in spectrum estimation of THz tones across the entire spectrum.

Development and validation of multivariate calibration methods for simultaneous estimation of Paracetamol, Enalapril maleate and hydrochlorothiazide in pharmaceutical dosage form

NASA Astrophysics Data System (ADS)

Singh, Veena D.; Daharwal, Sanjay J.

2017-01-01

Three multivariate calibration spectrophotometric methods were developed for simultaneous estimation of Paracetamol (PARA), Enalapril maleate (ENM) and Hydrochlorothiazide (HCTZ) in tablet dosage form; namely multi-linear regression calibration (MLRC), trilinear regression calibration method (TLRC) and classical least square (CLS) method. The selectivity of the proposed methods were studied by analyzing the laboratory prepared ternary mixture and successfully applied in their combined dosage form. The proposed methods were validated as per ICH guidelines and good accuracy; precision and specificity were confirmed within the concentration range of 5-35 μg mL- 1, 5-40 μg mL- 1 and 5-40 μg mL- 1of PARA, HCTZ and ENM, respectively. The results were statistically compared with reported HPLC method. Thus, the proposed methods can be effectively useful for the routine quality control analysis of these drugs in commercial tablet dosage form.

Individual and Area Level Socioeconomic Status and Its Association with Cognitive Function and Cognitive Impairment (Low MMSE) among Community-Dwelling Elderly in Singapore.

PubMed

Wee, Liang En; Yeo, Wei Xin; Yang, Gui Rong; Hannan, Nazirul; Lim, Kenny; Chua, Christopher; Tan, Mae Yue; Fong, Nikki; Yeap, Amelia; Chen, Lionel; Koh, Gerald Choon-Huat; Shen, Han Ming

2012-01-01

Neighborhood socioeconomic status (SES) can affect cognitive function. We assessed cognitive function and cognitive impairment among community-dwelling elderly in a multi-ethnic urban low-SES Asian neighborhood and compared them with a higher-SES neighborhood. The study population involved all residents aged ≥60 years in two housing estates comprising owner-occupied housing (higher SES) and rental flats (low SES) in Singapore in 2012. Cognitive impairment was defined as <24 on the Mini Mental State Examination. Demographic/clinical details were collected via questionnaire. Multilevel linear regression was used to evaluate factors associated with cognitive function, while multilevel logistic regression determined predictors of cognitive impairment. Participation was 61.4% (558/909). Cognitive impairment was found in 26.2% (104/397) of residents in the low-SES community and in 16.1% (26/161) of residents in the higher-SES community. After adjusting for other sociodemographic variables, living in a low-SES community was independently associated with poorer cognitive function (β = -1.41, SD = 0.58, p < 0.01) and cognitive impairment (adjusted odds ratio 5.13, 95% CI 1.98-13.34). Among cognitively impaired elderly in the low-SES community, 96.2% (100/104) were newly detected. Living in a low-SES community is independently associated with cognitive impairment in an urban Asian society.

Measuring Multi-Joint Stiffness during Single Movements: Numerical Validation of a Novel Time-Frequency Approach

PubMed Central

Piovesan, Davide; Pierobon, Alberto; DiZio, Paul; Lackner, James R.

2012-01-01

This study presents and validates a Time-Frequency technique for measuring 2-dimensional multijoint arm stiffness throughout a single planar movement as well as during static posture. It is proposed as an alternative to current regressive methods which require numerous repetitions to obtain average stiffness on a small segment of the hand trajectory. The method is based on the analysis of the reassigned spectrogram of the arm's response to impulsive perturbations and can estimate arm stiffness on a trial-by-trial basis. Analytic and empirical methods are first derived and tested through modal analysis on synthetic data. The technique's accuracy and robustness are assessed by modeling the estimation of stiffness time profiles changing at different rates and affected by different noise levels. Our method obtains results comparable with two well-known regressive techniques. We also test how the technique can identify the viscoelastic component of non-linear and higher than second order systems with a non-parametrical approach. The technique proposed here is very impervious to noise and can be used easily for both postural and movement tasks. Estimations of stiffness profiles are possible with only one perturbation, making our method a useful tool for estimating limb stiffness during motor learning and adaptation tasks, and for understanding the modulation of stiffness in individuals with neurodegenerative diseases. PMID:22448233

Viscosity induced emission of red-emitting NLOphoric coumarin morpholine-thiazole hybrid styryl dyes as FMRs: Consolidated experimental and theoretical approach

NASA Astrophysics Data System (ADS)

Avhad, Kiran C.; Patil, Dinesh S.; Chitrambalam, S.; Sreenath, M. C.; Joe, I. Hubert; Sekar, Nagaiyan

2018-05-01

Four new coumarin hybrid styryl dyes are synthesized by condensing 4-(7-(diethylamino)-2-oxo-2H-chromen-3-yl)-2-morpholinothiazole-5-carbaldehyde with dicyanovinylene containing active methylene intermediates and their linear and non-linear optical properties are studied. The dye having dicyanovinylene-isophorone acceptor displayed a large Stokes shift of 3702-4795 cm-1 in non-polar to polar solvent respectively. The dyes exhibit a good charge transfer characteristics and positive emission solvatochromism (∼50 nm-72 nm) in non-polar to a polar solvent which is well supported by multi-linear regression analysis. Viscosity induced enhancement study in ethanol/polyethylene glycol-400 system shows 2.71-6.78 fold increase in emission intensity. The intra and twisted-intramolecular charge transfer (ICT-TICT) characteristics were established using emission solvatochromism, polarity plots, generalised Mullikan-Hush (GMH) analysis and optimized geometry. A dye having the highest charge transfer dipole moment relatively possess the maximum two-photon absorption cross-section area (KK-1 = 165-207 GM) which was established using theoretical two-level model. The NLO properties have been investigated employing solvatochromic and computational methods and were found to be directly proportional to the polarity of the solvent. Z-scan results reveal that the dyes KK-1 and KK-2 possesses reverse saturable kind of behaviour whereas KK-3 and KK-4 show saturable kind of behaviour. From the experimental and theoretical data, these coumarin thiazole hybrid dyes can be considered as promising candidates for FMR and NLOphores.

A Multilevel Study of Students' Motivations of Studying Accounting: Implications for Employers

ERIC Educational Resources Information Center

Law, Philip; Yuen, Desmond

2012-01-01

Purpose: The purpose of this study is to examine the influence of factors affecting students' choice of accounting as a study major in Hong Kong. Design/methodology/approach: Multinomial logistic regression and Hierarchical Generalized Linear Modeling (HGLM) are used to analyze the survey data for the level one and level two data, which is the…

Early Literacy Skills and English Language Learners: An Analysis of Students in a Title I School

ERIC Educational Resources Information Center

Ostayan, Jennifer R.

2016-01-01

This article examined student literacy assessments in light of students' levels of English language proficiency. The study supported the hypotheses that a student's level of language proficiency positively predicted their DIBELS Composite score at the beginning, middle, and end of kindergarten by utilizing a simple linear regression. An ANOVA…

BN-FLEMOps pluvial - A probabilistic multi-variable loss estimation model for pluvial floods

NASA Astrophysics Data System (ADS)

Roezer, V.; Kreibich, H.; Schroeter, K.; Doss-Gollin, J.; Lall, U.; Merz, B.

2017-12-01

Pluvial flood events, such as in Copenhagen (Denmark) in 2011, Beijing (China) in 2012 or Houston (USA) in 2016, have caused severe losses to urban dwellings in recent years. These floods are caused by storm events with high rainfall rates well above the design levels of urban drainage systems, which lead to inundation of streets and buildings. A projected increase in frequency and intensity of heavy rainfall events in many areas and an ongoing urbanization may increase pluvial flood losses in the future. For an efficient risk assessment and adaptation to pluvial floods, a quantification of the flood risk is needed. Few loss models have been developed particularly for pluvial floods. These models usually use simple waterlevel- or rainfall-loss functions and come with very high uncertainties. To account for these uncertainties and improve the loss estimation, we present a probabilistic multi-variable loss estimation model for pluvial floods based on empirical data. The model was developed in a two-step process using a machine learning approach and a comprehensive database comprising 783 records of direct building and content damage of private households. The data was gathered through surveys after four different pluvial flood events in Germany between 2005 and 2014. In a first step, linear and non-linear machine learning algorithms, such as tree-based and penalized regression models were used to identify the most important loss influencing factors among a set of 55 candidate variables. These variables comprise hydrological and hydraulic aspects, early warning, precaution, building characteristics and the socio-economic status of the household. In a second step, the most important loss influencing variables were used to derive a probabilistic multi-variable pluvial flood loss estimation model based on Bayesian Networks. Two different networks were tested: a score-based network learned from the data and a network based on expert knowledge. Loss predictions are made through Bayesian inference using Markov chain Monte Carlo (MCMC) sampling. With the ability to cope with incomplete information and use expert knowledge, as well as inherently providing quantitative uncertainty information, it is shown that loss models based on BNs are superior to deterministic approaches for pluvial flood risk assessment.

Predicting adult pulmonary ventilation volume and wearing complianceby on-board accelerometry during personal level exposure assessments

NASA Astrophysics Data System (ADS)

Rodes, C. E.; Chillrud, S. N.; Haskell, W. L.; Intille, S. S.; Albinali, F.; Rosenberger, M. E.

2012-09-01

BackgroundMetabolic functions typically increase with human activity, but optimal methods to characterize activity levels for real-time predictions of ventilation volume (l min-1) during exposure assessments have not been available. Could tiny, triaxial accelerometers be incorporated into personal level monitors to define periods of acceptable wearing compliance, and allow the exposures (μg m-3) to be extended to potential doses in μg min-1 kg-1 of body weight? ObjectivesIn a pilot effort, we tested: 1) whether appropriately-processed accelerometer data could be utilized to predict compliance and in linear regressions to predict ventilation volumes in real-time as an on-board component of personal level exposure sensor systems, and 2) whether locating the exposure monitors on the chest in the breathing zone, provided comparable accelerometric data to other locations more typically utilized (waist, thigh, wrist, etc.). MethodsPrototype exposure monitors from RTI International and Columbia University were worn on the chest by a pilot cohort of adults while conducting an array of scripted activities (all <10 METS), spanning common recumbent, sedentary, and ambulatory activity categories. Referee Wocket accelerometers that were placed at various body locations allowed comparison with the chest-located exposure sensor accelerometers. An Oxycon Mobile mask was used to measure oral-nasal ventilation volumes in-situ. For the subset of participants with complete data (n = 22), linear regressions were constructed (processed accelerometric variable versus ventilation rate) for each participant and exposure monitor type, and Pearson correlations computed to compare across scenarios. ResultsTriaxial accelerometer data were demonstrated to be adequately sensitive indicators for predicting exposure monitor wearing compliance. Strong linear correlations (R values from 0.77 to 0.99) were observed for all participants for both exposure sensor accelerometer variables against ventilation volume for recumbent, sedentary, and ambulatory activities with MET values ˜<6. The RTI monitors mean R value of 0.91 was slightly higher than the Columbia monitors mean of 0.86 due to utilizing a 20 Hz data rate instead of a slower 1 Hz rate. A nominal mean regression slope was computed for the RTI system across participants and showed a modest RSD of +/-36.6%. Comparison of the correlation values of the exposure monitors with the Wocket accelerometers at various body locations showed statistically identical regressions for all sensors at alternate hip, ankle, upper arm, thigh, and pocket locations, but not for the Wocket accelerometer located at the dominant side wrist location (R = 0.57; p = 0.016). ConclusionsEven with a modest number of adult volunteers, the consistency and linearity of regression slopes for all subjects were very good with excellent within-person Pearson correlations for the accelerometer versus ventilation volume data. Computing accelerometric standard deviations allowed good sensitivity for compliance assessments even for sedentary activities. These pilot findings supported the hypothesis that a common linear regression is likely to be usable for a wider range of adults to predict ventilation volumes from accelerometry data over a range of low to moderate energy level activities. The predicted volumes would then allow real-time estimates of potential dose, enabling more robust panel studies. The poorer correlation in predicting ventilation rate for an accelerometer located on the wrist suggested that this location should not be considered for predictions of ventilation volume.

Indirect synthesis of multi-degree of freedom transient systems. [linear programming for a kinematically linear system

NASA Technical Reports Server (NTRS)

Pilkey, W. D.; Chen, Y. H.

1974-01-01

An indirect synthesis method is used in the efficient optimal design of multi-degree of freedom, multi-design element, nonlinear, transient systems. A limiting performance analysis which requires linear programming for a kinematically linear system is presented. The system is selected using system identification methods such that the designed system responds as closely as possible to the limiting performance. The efficiency is a result of the method avoiding the repetitive systems analyses accompanying other numerical optimization methods.

Interior car noise created by textured pavement surfaces : final report.

DOT National Transportation Integrated Search

1975-01-01

Because of widespread concern about the effect of textured pavement surfaces on interior car noise, sound pressure levels (SPL) were measured inside a test vehicle as it traversed 21 pavements with various textures. A linear regression analysis run o...

Evaluating the Impacts of ICT Use: A Multi-Level Analysis with Hierarchical Linear Modeling

ERIC Educational Resources Information Center

Song, Hae-Deok; Kang, Taehoon

2012-01-01

The purpose of this study is to evaluate the impacts of ICT use on achievements by considering not only ICT use, but also the process and background variables that influence ICT use at both the student- and school-level. This study was conducted using data from the 2010 Survey of Seoul Education Longitudinal Research. A Hierarchical Linear…

SAND: an automated VLBI imaging and analysing pipeline - I. Stripping component trajectories

NASA Astrophysics Data System (ADS)

Zhang, M.; Collioud, A.; Charlot, P.

2018-02-01

We present our implementation of an automated very long baseline interferometry (VLBI) data-reduction pipeline that is dedicated to interferometric data imaging and analysis. The pipeline can handle massive VLBI data efficiently, which makes it an appropriate tool to investigate multi-epoch multiband VLBI data. Compared to traditional manual data reduction, our pipeline provides more objective results as less human interference is involved. The source extraction is carried out in the image plane, while deconvolution and model fitting are performed in both the image plane and the uv plane for parallel comparison. The output from the pipeline includes catalogues of CLEANed images and reconstructed models, polarization maps, proper motion estimates, core light curves and multiband spectra. We have developed a regression STRIP algorithm to automatically detect linear or non-linear patterns in the jet component trajectories. This algorithm offers an objective method to match jet components at different epochs and to determine their proper motions.

Conventional multi-slice computed tomography (CT) and cone-beam CT (CBCT) for computer-aided implant placement. Part II: reliability of mucosa-supported stereolithographic guides.

PubMed

Arisan, Volkan; Karabuda, Zihni Cüneyt; Pişkin, Bülent; Özdemir, Tayfun

2013-12-01

Deviations of implants that were placed by conventional computed tomography (CT)- or cone beam CT (CBCT)-derived mucosa-supported stereolithographic (SLA) surgical guides were analyzed in this study. Eleven patients were randomly scanned by a multi-slice CT (CT group) or a CBCT scanner (CBCT group). A total of 108 implants were planned on the software and placed using SLA guides. A new CT or CBCT scan was obtained and merged with the planning data to identify the deviations between the planned and placed implants. Results were analyzed by Mann-Whitney U test and multiple regressions (p < .05). Mean angular and linear deviations in the CT group were 3.30° (SD 0.36), and 0.75 (SD 0.32) and 0.80 mm (SD 0.35) at the implant shoulder and tip, respectively. In the CBCT group, mean angular and linear deviations were 3.47° (SD 0.37), and 0.81 (SD 0.32) and 0.87 mm (SD 0.32) at the implant shoulder and tip, respectively. No statistically significant differences were detected between the CT and CBCT groups (p = .169 and p = .551, p = .113 for angular and linear deviations, respectively). Implant placement via CT- or CBCT-derived mucosa-supported SLA guides yielded similar deviation values. Results should be confirmed on alternative CBCT scanners. © 2012 Wiley Periodicals, Inc.

Environmental factors and flow paths related to Escherichia coli concentrations at two beaches on Lake St. Clair, Michigan, 2002–2005

USGS Publications Warehouse

Holtschlag, David J.; Shively, Dawn; Whitman, Richard L.; Haack, Sheridan K.; Fogarty, Lisa R.

2008-01-01

Regression analyses and hydrodynamic modeling were used to identify environmental factors and flow paths associated with Escherichia coli (E. coli) concentrations at Memorial and Metropolitan Beaches on Lake St. Clair in Macomb County, Mich. Lake St. Clair is part of the binational waterway between the United States and Canada that connects Lake Huron with Lake Erie in the Great Lakes Basin. Linear regression, regression-tree, and logistic regression models were developed from E. coli concentration and ancillary environmental data. Linear regression models on log10 E. coli concentrations indicated that rainfall prior to sampling, water temperature, and turbidity were positively associated with bacteria concentrations at both beaches. Flow from Clinton River, changes in water levels, wind conditions, and log10 E. coli concentrations 2 days before or after the target bacteria concentrations were statistically significant at one or both beaches. In addition, various interaction terms were significant at Memorial Beach. Linear regression models for both beaches explained only about 30 percent of the variability in log10 E. coli concentrations. Regression-tree models were developed from data from both Memorial and Metropolitan Beaches but were found to have limited predictive capability in this study. The results indicate that too few observations were available to develop reliable regression-tree models. Linear logistic models were developed to estimate the probability of E. coli concentrations exceeding 300 most probable number (MPN) per 100 milliliters (mL). Rainfall amounts before bacteria sampling were positively associated with exceedance probabilities at both beaches. Flow of Clinton River, turbidity, and log10 E. coli concentrations measured before or after the target E. coli measurements were related to exceedances at one or both beaches. The linear logistic models were effective in estimating bacteria exceedances at both beaches. A receiver operating characteristic (ROC) analysis was used to determine cut points for maximizing the true positive rate prediction while minimizing the false positive rate. A two-dimensional hydrodynamic model was developed to simulate horizontal current patterns on Lake St. Clair in response to wind, flow, and water-level conditions at model boundaries. Simulated velocity fields were used to track hypothetical massless particles backward in time from the beaches along flow paths toward source areas. Reverse particle tracking for idealized steady-state conditions shows changes in expected flow paths and traveltimes with wind speeds and directions from 24 sectors. The results indicate that three to four sets of contiguous wind sectors have similar effects on flow paths in the vicinity of the beaches. In addition, reverse particle tracking was used for transient conditions to identify expected flow paths for 10 E. coli sampling events in 2004. These results demonstrate the ability to track hypothetical particles from the beaches, backward in time, to likely source areas. This ability, coupled with a greater frequency of bacteria sampling, may provide insight into changes in bacteria concentrations between source and sink areas.

Association between blood glucose level derived using the oral glucose tolerance test and glycated hemoglobin level.

PubMed

Kim, Hyoung Joo; Kim, Young Geon; Park, Jin Soo; Ahn, Young Hwan; Ha, Kyoung Hwa; Kim, Dae Jung

2016-05-01

Glycated hemoglobin (HbA1c) is widely used as a marker of glycemic control. Translation of the HbA1c level to an average blood glucose level is useful because the latter figure is easily understood by patients. We studied the association between blood glucose levels revealed by the oral glucose tolerance test (OGTT) and HbA1c levels in a Korean population. A total of 1,000 subjects aged 30 to 64 years from the Cardiovascular and Metabolic Diseases Etiology Research Center cohort were included. Fasting glucose levels, post-load glucose levels at 30, 60, and 120 minutes into the OGTT, and HbA1c levels were measured. Linear regression of HbA1c with mean blood glucose levels derived using the OGTT revealed a significant correlation between these measures (predicted mean glucose [mg/dL] = 49.4 × HbA1c [%] - 149.6; R (2) = 0.54, p < 0.001). Our linear regression equation was quite different from that of the Alc-Derived Average Glucose (ADAG) study and Diabetes Control and Complications Trial (DCCT) cohort. Discrepancies between our results and those of the ADAG study and DCCT cohort may be attributable to differences in the test methods used and the extent of insulin secretion. More studies are needed to evaluate the association between HbA1c and self monitoring blood glucose levels.

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

PubMed

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

2017-05-10

We described the time trend of acute myocardial infarction (AMI) from 1999 to 2013 in Tianjin incidence rate with Cochran-Armitage trend (CAT) test and linear regression analysis, and the results were compared. Based on actual population, CAT test had much stronger statistical power than linear regression analysis for both overall incidence trend and age specific incidence trend (Cochran-Armitage trend P value

The non-linear association between low-level lead exposure and maternal stress among pregnant women.

PubMed

Li, Shufang; Xu, Jian; Liu, Zhiwei; Yan, Chong-Huai

2017-03-01

Neuro-developmental impairments in the developing fetus due to exposure to low-level lead have been well documented. However, few studies have investigated the relation between maternal stress levels and low-level lead exposure among pregnant women. To investigate the relation between maternal blood lead and stress levels during index pregnancy. 1931 pregnant women (gestational week 28-36) were investigated using stratified-cluster-sampling in Shanghai in 2010. Maternal life event stress and emotional stress were assessed using "Life-Event-Stress-Scale-for-Pregnant-Women" (LESPW) and "Symptom-Checklist-90-Revised" (SCL-90-R), respectively. Maternal whole blood lead levels were determined, and other data on covariates were obtained from maternal interviews and medical records. Two piecewise linear regression models were applied to assess the relations between blood lead and stress levels using a data-driven approach according to spline smoothing fitting of the data. Maternal blood lead levels ranged from 0.80 to 14.84μg/dL, and the geometric mean was 3.97μg/dL. The P-values for the two piecewise linear models against the single linear regression models were 0.010, 0.003 and 0.017 for models predicting GSI, depression and anxiety symptom scores, respectively. When blood lead levels were below 2.57μg/dL, each unit increase in log10 transformed blood lead levels (μg/dL) was associated with about 18% increase in maternal GSI, depression and anxiety symptom scores (P GSI =0.013, P depression =0.002, P anxiety =0.019, respectively). However, no significant relation was found when blood lead levels were above 2.57μg/dL (all P-values>0.05). Our findings suggested a nonlinear relationship between blood lead and emotional stress levels among pregnant women. Emotional stress increased along with blood lead levels, and appeared to be plateaued when blood lead levels reached 2.57μg/dL. Copyright © 2016 Elsevier B.V. All rights reserved.

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

PubMed

Jürgens, Julian H W; Schulz, Nadine; Wybranski, Christian; Seidensticker, Max; Streit, Sebastian; Brauner, Jan; Wohlgemuth, Walter A; Deuerling-Zheng, Yu; Ricke, Jens; Dudeck, Oliver

2015-02-01

The objective of this study was to compare the parameter maps of a new flat-panel detector application for time-resolved perfusion imaging in the angiography room (FD-CTP) with computed tomography perfusion (CTP) in an experimental tumor model. Twenty-four VX2 tumors were implanted into the hind legs of 12 rabbits. Three weeks later, FD-CTP (Artis zeego; Siemens) and CTP (SOMATOM Definition AS +; Siemens) were performed. The parameter maps for the FD-CTP were calculated using a prototype software, and those for the CTP were calculated with VPCT-body software on a dedicated syngo MultiModality Workplace. The parameters were compared using Pearson product-moment correlation coefficient and linear regression analysis. The Pearson product-moment correlation coefficient showed good correlation values for both the intratumoral blood volume of 0.848 (P < 0.01) and the blood flow of 0.698 (P < 0.01). The linear regression analysis of the perfusion between FD-CTP and CTP showed for the blood volume a regression equation y = 4.44x + 36.72 (P < 0.01) and for the blood flow y = 0.75x + 14.61 (P < 0.01). This preclinical study provides evidence that FD-CTP allows a time-resolved (dynamic) perfusion imaging of tumors similar to CTP, which provides the basis for clinical applications such as the assessment of tumor response to locoregional therapies directly in the angiography suite.

[Ultrasonic measurements of fetal thalamus, caudate nucleus and lenticular nucleus in prenatal diagnosis].

PubMed

Yang, Ruiqi; Wang, Fei; Zhang, Jialing; Zhu, Chonglei; Fan, Limei

2015-05-19

To establish the reference values of thalamus, caudate nucleus and lenticular nucleus diameters through fetal thalamic transverse section. A total of 265 fetuses at our hospital were randomly selected from November 2012 to August 2014. And the transverse and length diameters of thalamus, caudate nucleus and lenticular nucleus were measured. SPSS 19.0 statistical software was used to calculate the regression curve of fetal diameter changes and gestational weeks of pregnancy. P < 0.05 was considered as having statistical significance. The linear regression equation of fetal thalamic length diameter and gestational week was: Y = 0.051X+0.201, R = 0.876, linear regression equation of thalamic transverse diameter and fetal gestational week was: Y = 0.031X+0.229, R = 0.817, linear regression equation of fetal head of caudate nucleus length diameter and gestational age was: Y = 0.033X+0.101, R = 0.722, linear regression equation of fetal head of caudate nucleus transverse diameter and gestational week was: R = 0.025 - 0.046, R = 0.711, linear regression equation of fetal lentiform nucleus length diameter and gestational week was: Y = 0.046+0.229, R = 0.765, linear regression equation of fetal lentiform nucleus diameter and gestational week was: Y = 0.025 - 0.05, R = 0.772. Ultrasonic measurement of diameter of fetal thalamus caudate nucleus, and lenticular nucleus through thalamic transverse section is simple and convenient. And measurements increase with fetal gestational weeks and there is linear regression relationship between them.

Local Linear Regression for Data with AR Errors.

PubMed

Li, Runze; Li, Yan

2009-07-01

In many statistical applications, data are collected over time, and they are likely correlated. In this paper, we investigate how to incorporate the correlation information into the local linear regression. Under the assumption that the error process is an auto-regressive process, a new estimation procedure is proposed for the nonparametric regression by using local linear regression method and the profile least squares techniques. We further propose the SCAD penalized profile least squares method to determine the order of auto-regressive process. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed procedure, and to compare the performance of the proposed procedures with the existing one. From our empirical studies, the newly proposed procedures can dramatically improve the accuracy of naive local linear regression with working-independent error structure. We illustrate the proposed methodology by an analysis of real data set.

Ranking the Potential Yield of Salinity and Selenium from Subbasins in the Lower Gunnison River Basin Using Seasonal, Multi-parameter Regression Models

NASA Astrophysics Data System (ADS)

Linard, J.; Leib, K.; Colorado Water Science Center

2010-12-01

Elevated levels of salinity and dissolved selenium can detrimentally effect the quality of water where anthropogenic and natural uses are concerned. In areas, such as the lower Gunnison Basin of western Colorado, salinity and selenium are such a concern that control projects are implemented to limit their mobilization. To prioritize the locations in which control projects are implemented, multi-parameter regression models were developed to identify subbasins in the lower Gunnison River Basin that were most likely to have elevated salinity and dissolved selenium levels. The drainage area is about 5,900 mi2 and is underlain by Cretaceous marine shale, which is the most common source of salinity and dissolved selenium. To characterize the complex hydrologic and chemical processes governing constituent mobilization, geospatial variables representing 70 different environmental characteristics were correlated to mean seasonal (irrigation and nonirrigation seasons) salinity and selenium yields estimated at 154 sampling sites. The variables generally represented characteristics of the physical basin, precipitation, soil, geology, land use, and irrigation water delivery systems. Irrigation and nonirrigation seasons were selected due to documented effects of irrigation on constituent mobilization. Following a stepwise approach, combinations of the geospatial variables were used to develop four multi-parameter regression models. These models predicted salinity and selenium yield, within a 95 percent confidence range, at individual points in the Lower Gunnison Basin for irrigation and non-irrigation seasons. The corresponding subbasins were ranked according to their potential to yield salinity and selenium and rankings were used to prioritize areas that would most benefit from control projects.

Orthogonal Regression: A Teaching Perspective

ERIC Educational Resources Information Center

Carr, James R.

2012-01-01

A well-known approach to linear least squares regression is that which involves minimizing the sum of squared orthogonal projections of data points onto the best fit line. This form of regression is known as orthogonal regression, and the linear model that it yields is known as the major axis. A similar method, reduced major axis regression, is…

Is susceptibility to prenatal methylmercury exposure from fish consumption non-homogeneous? Tree-structured analysis for the Seychelles Child Development Study.

PubMed

Huang, Li-Shan; Myers, Gary J; Davidson, Philip W; Cox, Christopher; Xiao, Fenyuan; Thurston, Sally W; Cernichiari, Elsa; Shamlaye, Conrad F; Sloane-Reeves, Jean; Georger, Lesley; Clarkson, Thomas W

2007-11-01

Studies of the association between prenatal methylmercury exposure from maternal fish consumption during pregnancy and neurodevelopmental test scores in the Seychelles Child Development Study have found no consistent pattern of associations through age 9 years. The analyses for the most recent 9-year data examined the population effects of prenatal exposure, but did not address the possibility of non-homogeneous susceptibility. This paper presents a regression tree approach: covariate effects are treated non-linearly and non-additively and non-homogeneous effects of prenatal methylmercury exposure are permitted among the covariate clusters identified by the regression tree. The approach allows us to address whether children in the lower or higher ends of the developmental spectrum differ in susceptibility to subtle exposure effects. Of 21 endpoints available at age 9 years, we chose the Weschler Full Scale IQ and its associated covariates to construct the regression tree. The prenatal mercury effect in each of the nine resulting clusters was assessed linearly and non-homogeneously. In addition we reanalyzed five other 9-year endpoints that in the linear analysis had a two-tailed p-value <0.2 for the effect of prenatal exposure. In this analysis, motor proficiency and activity level improved significantly with increasing MeHg for 53% of the children who had an average home environment. Motor proficiency significantly decreased with increasing prenatal MeHg exposure in 7% of the children whose home environment was below average. The regression tree results support previous analyses of outcomes in this cohort. However, this analysis raises the intriguing possibility that an effect may be non-homogeneous among children with different backgrounds and IQ levels.

ESTIMATING GROUND LEVEL PM 2.5 IN THE EASTERN UNITED STATES USING SATELLITE REMOTE SENSING

EPA Science Inventory

An empirical model based on the regression between daily average final particle (PM2.5) concentrations and aerosol optical thickness (AOT) measurements from the Multi-angle Imaging SpectroRadiometer (MISR) was developed and tested using data from the eastern United States during ...

Optimal Design for Regression Discontinuity Studies with Clustering

ERIC Educational Resources Information Center

Rhoads, Christopher; Dye, Charles

2014-01-01

Recent years have seen an increased interest in quantitative educational research studies that use random assignment (RA) to evaluate the causal impacts of educational interventions (Angrist, 2004). The multi-level structure of the public education system in the United States often leads to experimental designs where naturally occurring clusters…

Practical Session: Simple Linear Regression

NASA Astrophysics Data System (ADS)

Clausel, M.; Grégoire, G.

2014-12-01

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

Circulating leptin and adiponectin are associated with insulin resistance in healthy postmenopausal women with hot flashes.

PubMed

Huang, Wan-Yu; Chang, Chia-Chu; Chen, Dar-Ren; Kor, Chew-Teng; Chen, Ting-Yu; Wu, Hung-Ming

2017-01-01

Hot flashes have been postulated to be linked to the development of metabolic disorders. This study aimed to evaluate the relationship between hot flashes, adipocyte-derived hormones, and insulin resistance in healthy, non-obese postmenopausal women. In this cross-sectional study, a total of 151 women aged 45-60 years were stratified into one of three groups according to hot-flash status over the past three months: never experienced hot flashes (Group N), mild-to-moderate hot flashes (Group M), and severe hot flashes (Group S). Variables measured in this study included clinical parameters, hot flash experience, fasting levels of circulating glucose, lipid profiles, plasma insulin, and adipocyte-derived hormones. Multiple linear regression analysis was used to evaluate the associations of hot flashes with adipocyte-derived hormones, and with insulin resistance. The study was performed in a hospital medical center. The mean (standard deviation) of body-mass index was 22.8(2.7) for Group N, 22.6(2.6) for Group M, and 23.5(2.4) for Group S, respectively. Women in Group S displayed statistically significantly higher levels of leptin, fasting glucose, and insulin, and lower levels of adiponectin than those in Groups M and N. Multivariate linear regression analysis revealed that hot-flash severity was significantly associated with higher leptin levels, lower adiponectin levels, and higher leptin-to-adiponectin ratio. Univariate linear regression analysis revealed that hot-flash severity was strongly associated with a higher HOMA-IR index (% difference, 58.03%; 95% confidence interval, 31.00-90.64; p < 0.001). The association between hot flashes and HOMA-IR index was attenuated after adjusting for leptin or adiponectin and was no longer significant after simultaneously adjusting for leptin and adiponectin. The present study provides evidence that hot flashes are associated with insulin resistance in postmenopausal women. It further suggests that hot flash association with insulin resistance is dependent on the combination of leptin and adiponectin variables.

Tolerance of ciliated protozoan Paramecium bursaria (Protozoa, Ciliophora) to ammonia and nitrites

NASA Astrophysics Data System (ADS)

Xu, Henglong; Song, Weibo; Lu, Lu; Alan, Warren

2005-09-01

The tolerance to ammonia and nitrites in freshwater ciliate Paramecium bursaria was measured in a conventional open system. The ciliate was exposed to different concentrations of ammonia and nitrites for 2h and 12h in order to determine the lethal concentrations. Linear regression analysis revealed that the 2h-LC50 value for ammonia was 95.94 mg/L and for nitrite 27.35 mg/L using probit scale method (with 95% confidence intervals). There was a linear correlation between the mortality probit scale and logarithmic concentration of ammonia which fit by a regression equation y=7.32 x 9.51 ( R 2=0.98; y, mortality probit scale; x, logarithmic concentration of ammonia), by which 2 h-LC50 value for ammonia was found to be 95.50 mg/L. A linear correlation between mortality probit scales and logarithmic concentration of nitrite is also followed the regression equation y=2.86 x+0.89 ( R 2=0.95; y, mortality probit scale; x, logarithmic concentration of nitrite). The regression analysis of toxicity curves showed that the linear correlation between exposed time of ammonia-N LC50 value and ammonia-N LC50 value followed the regression equation y=2 862.85 e -0.08 x ( R 2=0.95; y, duration of exposure to LC50 value; x, LC50 value), and that between exposed time of nitrite-N LC50 value and nitrite-N LC50 value followed the regression equation y=127.15 e -0.13 x ( R 2=0.91; y, exposed time of LC50 value; x, LC50 value). The results demonstrate that the tolerance to ammonia in P. bursaria is considerably higher than that of the larvae or juveniles of some metozoa, e.g. cultured prawns and oysters. In addition, ciliates, as bacterial predators, are likely to play a positive role in maintaining and improving water quality in aquatic environments with high-level ammonium, such as sewage treatment systems.

Integration of least angle regression with empirical Bayes for multi-locus genome-wide association studies

USDA-ARS?s Scientific Manuscript database

Multi-locus genome-wide association studies has become the state-of-the-art procedure to identify quantitative trait loci (QTL) associated with traits simultaneously. However, implementation of multi-locus model is still difficult. In this study, we integrated least angle regression with empirical B...

Morse Code, Scrabble, and the Alphabet

ERIC Educational Resources Information Center

Richardson, Mary; Gabrosek, John; Reischman, Diann; Curtiss, Phyliss

2004-01-01

In this paper we describe an interactive activity that illustrates simple linear regression. Students collect data and analyze it using simple linear regression techniques taught in an introductory applied statistics course. The activity is extended to illustrate checks for regression assumptions and regression diagnostics taught in an…

A comparison of two adaptive multivariate analysis methods (PLSR and ANN) for winter wheat yield forecasting using Landsat-8 OLI images

NASA Astrophysics Data System (ADS)

Chen, Pengfei; Jing, Qi

2017-02-01

An assumption that the non-linear method is more reasonable than the linear method when canopy reflectance is used to establish the yield prediction model was proposed and tested in this study. For this purpose, partial least squares regression (PLSR) and artificial neural networks (ANN), represented linear and non-linear analysis method, were applied and compared for wheat yield prediction. Multi-period Landsat-8 OLI images were collected at two different wheat growth stages, and a field campaign was conducted to obtain grain yields at selected sampling sites in 2014. The field data were divided into a calibration database and a testing database. Using calibration data, a cross-validation concept was introduced for the PLSR and ANN model construction to prevent over-fitting. All models were tested using the test data. The ANN yield-prediction model produced R2, RMSE and RMSE% values of 0.61, 979 kg ha-1, and 10.38%, respectively, in the testing phase, performing better than the PLSR yield-prediction model, which produced R2, RMSE, and RMSE% values of 0.39, 1211 kg ha-1, and 12.84%, respectively. Non-linear method was suggested as a better method for yield prediction.

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

Serum Vitamin D Levels and Markers of Severity of Childhood Asthma in Costa Rica

PubMed Central

Brehm, John M.; Celedón, Juan C.; Soto-Quiros, Manuel E.; Avila, Lydiana; Hunninghake, Gary M.; Forno, Erick; Laskey, Daniel; Sylvia, Jody S.; Hollis, Bruce W.; Weiss, Scott T.; Litonjua, Augusto A.

2009-01-01

Rationale: Maternal vitamin D intake during pregnancy has been inversely associated with asthma symptoms in early childhood. However, no study has examined the relationship between measured vitamin D levels and markers of asthma severity in childhood. Objectives: To determine the relationship between measured vitamin D levels and both markers of asthma severity and allergy in childhood. Methods: We examined the relation between 25-hydroxyvitamin D levels (the major circulating form of vitamin D) and markers of allergy and asthma severity in a cross-sectional study of 616 Costa Rican children between the ages of 6 and 14 years. Linear, logistic, and negative binomial regressions were used for the univariate and multivariate analyses. Measurements and Main Results: Of the 616 children with asthma, 175 (28%) had insufficient levels of vitamin D (<30 ng/ml). In multivariate linear regression models, vitamin D levels were significantly and inversely associated with total IgE and eosinophil count. In multivariate logistic regression models, a log10 unit increase in vitamin D levels was associated with reduced odds of any hospitalization in the previous year (odds ratio [OR], 0.05; 95% confidence interval [CI], 0.004–0.71; P = 0.03), any use of antiinflammatory medications in the previous year (OR, 0.18; 95% CI, 0.05–0.67; P = 0.01), and increased airway responsiveness (a ≤8.58-μmol provocative dose of methacholine producing a 20% fall in baseline FEV1 [OR, 0.15; 95% CI, 0.024–0.97; P = 0.05]). Conclusions: Our results suggest that vitamin D insufficiency is relatively frequent in an equatorial population of children with asthma. In these children, lower vitamin D levels are associated with increased markers of allergy and asthma severity. PMID:19179486

How quantitative measures unravel design principles in multi-stage phosphorylation cascades.

PubMed

Frey, Simone; Millat, Thomas; Hohmann, Stefan; Wolkenhauer, Olaf

2008-09-07

We investigate design principles of linear multi-stage phosphorylation cascades by using quantitative measures for signaling time, signal duration and signal amplitude. We compare alternative pathway structures by varying the number of phosphorylations and the length of the cascade. We show that a model for a weakly activated pathway does not reflect the biological context well, unless it is restricted to certain parameter combinations. Focusing therefore on a more general model, we compare alternative structures with respect to a multivariate optimization criterion. We test the hypothesis that the structure of a linear multi-stage phosphorylation cascade is the result of an optimization process aiming for a fast response, defined by the minimum of the product of signaling time and signal duration. It is then shown that certain pathway structures minimize this criterion. Several popular models of MAPK cascades form the basis of our study. These models represent different levels of approximation, which we compare and discuss with respect to the quantitative measures.

Depressive disorder in pregnant Latin women: does intimate partner violence matter?

PubMed

Fonseca-Machado, Mariana de Oliveira; Alves, Lisiane Camargo; Monteiro, Juliana Cristina Dos Santos; Stefanello, Juliana; Nakano, Ana Márcia Spanó; Haas, Vanderlei José; Gomes-Sponholz, Flávia

2015-05-01

To identify the association of antenatal depressive symptoms with intimate partner violence during the current pregnancy in Brazilian women. Intimate partner violence is an important risk factor for antenatal depression. To the authors' knowledge, there has been no study to date that assessed the association between intimate partner violence during pregnancy and antenatal depressive symptoms among Brazilian women. Cross-sectional study. Three hundred and fifty-eight pregnant women were enrolled in the study. The Edinburgh Postnatal Depression Scale and an adapted version of the instrument used in the World Health Organization Multi-country Study on Women's Health and Domestic Violence were used to measure antenatal depressive symptoms and psychological, physical and sexual acts of intimate partner violence during the current pregnancy respectively. Multiple logistic regression and multiple linear regression were used for data analysis. The prevalence of antenatal depressive symptoms, as determined by the cut-off score of 12 in the Edinburgh Postnatal Depression Scale, was 28·2% (101). Of the participants, 63 (17·6%) reported some type of intimate partner violence during pregnancy. Among them, 60 (95·2%) reported suffering psychological violence, 23 (36·5%) physical violence and one (1·6%) sexual violence. Multiple logistic regression and multiple linear regression indicated that antenatal depressive symptoms are extremely associated with intimate partner violence during pregnancy. Among Brazilian women, exposure to intimate partner violence during pregnancy increases the chances of experiencing antenatal depressive symptoms. Clinical nurses and nurses midwifes should pay attention to the particularities of Brazilian women, especially with regard to the occurrence of intimate partner violence, whose impacts on the mental health of this population are extremely significant, both during the gestational period and postpartum. © 2015 John Wiley & Sons Ltd.

Very-short-term wind power prediction by a hybrid model with single- and multi-step approaches

NASA Astrophysics Data System (ADS)

Mohammed, E.; Wang, S.; Yu, J.

2017-05-01

Very-short-term wind power prediction (VSTWPP) has played an essential role for the operation of electric power systems. This paper aims at improving and applying a hybrid method of VSTWPP based on historical data. The hybrid method is combined by multiple linear regressions and least square (MLR&LS), which is intended for reducing prediction errors. The predicted values are obtained through two sub-processes:1) transform the time-series data of actual wind power into the power ratio, and then predict the power ratio;2) use the predicted power ratio to predict the wind power. Besides, the proposed method can include two prediction approaches: single-step prediction (SSP) and multi-step prediction (MSP). WPP is tested comparatively by auto-regressive moving average (ARMA) model from the predicted values and errors. The validity of the proposed hybrid method is confirmed in terms of error analysis by using probability density function (PDF), mean absolute percent error (MAPE) and means square error (MSE). Meanwhile, comparison of the correlation coefficients between the actual values and the predicted values for different prediction times and window has confirmed that MSP approach by using the hybrid model is the most accurate while comparing to SSP approach and ARMA. The MLR&LS is accurate and promising for solving problems in WPP.

Testing a single regression coefficient in high dimensional linear models

PubMed Central

Zhong, Ping-Shou; Li, Runze; Wang, Hansheng; Tsai, Chih-Ling

2017-01-01

In linear regression models with high dimensional data, the classical z-test (or t-test) for testing the significance of each single regression coefficient is no longer applicable. This is mainly because the number of covariates exceeds the sample size. In this paper, we propose a simple and novel alternative by introducing the Correlated Predictors Screening (CPS) method to control for predictors that are highly correlated with the target covariate. Accordingly, the classical ordinary least squares approach can be employed to estimate the regression coefficient associated with the target covariate. In addition, we demonstrate that the resulting estimator is consistent and asymptotically normal even if the random errors are heteroscedastic. This enables us to apply the z-test to assess the significance of each covariate. Based on the p-value obtained from testing the significance of each covariate, we further conduct multiple hypothesis testing by controlling the false discovery rate at the nominal level. Then, we show that the multiple hypothesis testing achieves consistent model selection. Simulation studies and empirical examples are presented to illustrate the finite sample performance and the usefulness of the proposed method, respectively. PMID:28663668

Testing a single regression coefficient in high dimensional linear models.

PubMed

Lan, Wei; Zhong, Ping-Shou; Li, Runze; Wang, Hansheng; Tsai, Chih-Ling

2016-11-01

In linear regression models with high dimensional data, the classical z -test (or t -test) for testing the significance of each single regression coefficient is no longer applicable. This is mainly because the number of covariates exceeds the sample size. In this paper, we propose a simple and novel alternative by introducing the Correlated Predictors Screening (CPS) method to control for predictors that are highly correlated with the target covariate. Accordingly, the classical ordinary least squares approach can be employed to estimate the regression coefficient associated with the target covariate. In addition, we demonstrate that the resulting estimator is consistent and asymptotically normal even if the random errors are heteroscedastic. This enables us to apply the z -test to assess the significance of each covariate. Based on the p -value obtained from testing the significance of each covariate, we further conduct multiple hypothesis testing by controlling the false discovery rate at the nominal level. Then, we show that the multiple hypothesis testing achieves consistent model selection. Simulation studies and empirical examples are presented to illustrate the finite sample performance and the usefulness of the proposed method, respectively.

Empirical Modeling of Microbial Indicators at a South Carolina Beach

EPA Science Inventory

Public concerns about water quality at beaches have prompted the development of multiple linear regression and other models that can be used to "nowcast" levels of bacterial indicators. Hydrometeorological and biogeochemical data from summer, 2009 were used to develop empirical m...

Mathematics Readiness of First-Year University Students

ERIC Educational Resources Information Center

Atuahene, Francis; Russell, Tammy A.

2016-01-01

The majority of high school students, particularly underrepresented minorities (URMs) from low socioeconomic backgrounds are graduating from high school less prepared academically for advanced-level college mathematics. Using 2009 and 2010 course enrollment data, several statistical analyses (multiple linear regression, Cochran Mantel Haenszel…

Monitoring Springs in the Mojave Desert Using Landsat Time Series Analysis

NASA Technical Reports Server (NTRS)

Potter, Christopher S.

2018-01-01

The purpose of this study, based on Landsat satellite data was to characterize variations and trends over 30 consecutive years (1985-2016) in perennial vegetation green cover at over 400 confirmed Mojave Desert spring locations. These springs were surveyed between in 2015 and 2016 on lands managed in California by the U.S. Bureau of Land Management (BLM) and on several land trusts within the Barstow, Needles, and Ridgecrest BLM Field Offices. The normalized difference vegetation index (NDVI) from July Landsat images was computed at each spring location and a trend model was first fit to the multi-year NDVI time series using least squares linear regression.Â

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

PubMed

Marill, Keith A

2004-01-01

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

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

PubMed

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

2017-09-29

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

Reversed inverse regression for the univariate linear calibration and its statistical properties derived using a new methodology

NASA Astrophysics Data System (ADS)

Kang, Pilsang; Koo, Changhoi; Roh, Hokyu

2017-11-01

Since simple linear regression theory was established at the beginning of the 1900s, it has been used in a variety of fields. Unfortunately, it cannot be used directly for calibration. In practical calibrations, the observed measurements (the inputs) are subject to errors, and hence they vary, thus violating the assumption that the inputs are fixed. Therefore, in the case of calibration, the regression line fitted using the method of least squares is not consistent with the statistical properties of simple linear regression as already established based on this assumption. To resolve this problem, "classical regression" and "inverse regression" have been proposed. However, they do not completely resolve the problem. As a fundamental solution, we introduce "reversed inverse regression" along with a new methodology for deriving its statistical properties. In this study, the statistical properties of this regression are derived using the "error propagation rule" and the "method of simultaneous error equations" and are compared with those of the existing regression approaches. The accuracy of the statistical properties thus derived is investigated in a simulation study. We conclude that the newly proposed regression and methodology constitute the complete regression approach for univariate linear calibrations.

Parts-Per-Billion Mass Measurement Accuracy Achieved through the Combination of Multiple Linear Regression and Automatic Gain Control in a Fourier Transform Ion Cyclotron Resonance Mass Spectrometer

PubMed Central

Williams, D. Keith; Muddiman, David C.

2008-01-01

Fourier transform ion cyclotron resonance mass spectrometry has the ability to achieve unprecedented mass measurement accuracy (MMA); MMA is one of the most significant attributes of mass spectrometric measurements as it affords extraordinary molecular specificity. However, due to space-charge effects, the achievable MMA significantly depends on the total number of ions trapped in the ICR cell for a particular measurement. Even through the use of automatic gain control (AGC), the total ion population is not constant between spectra. Multiple linear regression calibration in conjunction with AGC is utilized in these experiments to formally account for the differences in total ion population in the ICR cell between the external calibration spectra and experimental spectra. This ability allows for the extension of dynamic range of the instrument while allowing mean MMA values to remain less than 1 ppm. In addition, multiple linear regression calibration is used to account for both differences in total ion population in the ICR cell as well as relative ion abundance of a given species, which also affords mean MMA values at the parts-per-billion level. PMID:17539605

The Effect of Information Level on Human-Agent Interaction for Route Planning

DTIC Science & Technology

2015-12-01

13 Fig. 4 Experiment 1 shows regression results for time spent at DP predicting posttest trust group membership for the high LOI...decision time by pretest trust group membership. Bars denote standard error (SE). DT at DP was evaluated to see if it predicted posttest trust... group . Linear regression indicated that DT at DP was not a significant predictor of posttest trust for the Low or the Medium LOI conditions; however, it

Motor Nerve Conduction Velocity In Postmenopausal Women with Peripheral Neuropathy.

PubMed

Singh, Akanksha; Asif, Naiyer; Singh, Paras Nath; Hossain, Mohd Mobarak

2016-12-01

The post-menopausal phase is characterized by a decline in the serum oestrogen and progesterone levels. This phase is also associated with higher incidence of peripheral neuropathy. To explore the relationship between the peripheral motor nerve status and serum oestrogen and progesterone levels through assessment of Motor Nerve Conduction Velocity (MNCV) in post-menopausal women with peripheral neuropathy. This cross-sectional study was conducted at Jawaharlal Nehru Medical College during 2011-2013. The study included 30 post-menopausal women with peripheral neuropathy (age: 51.4±7.9) and 30 post-menopausal women without peripheral neuropathy (control) (age: 52.5±4.9). They were compared for MNCV in median, ulnar and common peroneal nerves and serum levels of oestrogen and progesterone estimated through enzyme immunoassays. To study the relationship between hormone levels and MNCV, a stepwise linear regression analysis was done. The post-menopausal women with peripheral neuropathy had significantly lower MNCV and serum oestrogen and progesterone levels as compared to control subjects. Stepwise linear regression analysis showed oestrogen with main effect on MNCV. The findings of the present study suggest that while the post-menopausal age group is at a greater risk of peripheral neuropathy, it is the decline in the serum estrogen levels which is critical in the development of peripheral neuropathy.

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

PubMed

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

2012-01-01

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

Serum Folate Shows an Inverse Association with Blood Pressure in a Cohort of Chinese Women of Childbearing Age: A Cross-Sectional Study

PubMed Central

Shen, Minxue; Tan, Hongzhuan; Zhou, Shujin; Retnakaran, Ravi; Smith, Graeme N.; Davidge, Sandra T.; Trasler, Jacquetta; Walker, Mark C.; Wen, Shi Wu

2016-01-01

Background It has been reported that higher folate intake from food and supplementation is associated with decreased blood pressure (BP). The association between serum folate concentration and BP has been examined in few studies. We aim to examine the association between serum folate and BP levels in a cohort of young Chinese women. Methods We used the baseline data from a pre-conception cohort of women of childbearing age in Liuyang, China, for this study. Demographic data were collected by structured interview. Serum folate concentration was measured by immunoassay, and homocysteine, blood glucose, triglyceride and total cholesterol were measured through standardized clinical procedures. Multiple linear regression and principal component regression model were applied in the analysis. Results A total of 1,532 healthy normotensive non-pregnant women were included in the final analysis. The mean concentration of serum folate was 7.5 ± 5.4 nmol/L and 55% of the women presented with folate deficiency (< 6.8 nmol/L). Multiple linear regression and principal component regression showed that serum folate levels were inversely associated with systolic and diastolic BP, after adjusting for demographic, anthropometric, and biochemical factors. Conclusions Serum folate is inversely associated with BP in non-pregnant women of childbearing age with high prevalence of folate deficiency. PMID:27182603

Female married illiteracy as the most important continual determinant of total fertility rate among districts of Empowered Action Group States of India: Evidence from Annual Health Survey 2011-12.

PubMed

Kumar, Rajesh; Dogra, Vishal; Rani, Khushbu; Sahu, Kanti

2017-01-01

District level determinants of total fertility rate in Empowered Action Group states of India can help in ongoing population stabilization programs in India. Present study intends to assess the role of district level determinants in predicting total fertility rate among districts of the Empowered Action Group states of India. Data from Annual Health Survey (2011-12) was analysed using STATA and R software packages. Multiple linear regression models were built and evaluated using Akaike Information Criterion. For further understanding, recursive partitioning was used to prepare a regression tree. Female married illiteracy positively associated with total fertility rate and explained more than half (53%) of variance. Under multiple linear regression model, married illiteracy, infant mortality rate, Ante natal care registration, household size, median age of live birth and sex ratio explained 70% of total variance in total fertility rate. In regression tree, female married illiteracy was the root node and splits at 42% determined TFR <= 2.7. The next left side branch was again married illiteracy with splits at 23% to determine TFR <= 2.1. We conclude that female married illiteracy is one of the most important determinants explaining total fertility rate among the districts of an Empowered Action Group states. Focus on female literacy is required to stabilize the population growth in long run.

Quality of life in breast cancer patients--a quantile regression analysis.

PubMed

Pourhoseingholi, Mohamad Amin; Safaee, Azadeh; Moghimi-Dehkordi, Bijan; Zeighami, Bahram; Faghihzadeh, Soghrat; Tabatabaee, Hamid Reza; Pourhoseingholi, Asma

2008-01-01

Quality of life study has an important role in health care especially in chronic diseases, in clinical judgment and in medical resources supplying. Statistical tools like linear regression are widely used to assess the predictors of quality of life. But when the response is not normal the results are misleading. The aim of this study is to determine the predictors of quality of life in breast cancer patients, using quantile regression model and compare to linear regression. A cross-sectional study conducted on 119 breast cancer patients that admitted and treated in chemotherapy ward of Namazi hospital in Shiraz. We used QLQ-C30 questionnaire to assessment quality of life in these patients. A quantile regression was employed to assess the assocciated factors and the results were compared to linear regression. All analysis carried out using SAS. The mean score for the global health status for breast cancer patients was 64.92+/-11.42. Linear regression showed that only grade of tumor, occupational status, menopausal status, financial difficulties and dyspnea were statistically significant. In spite of linear regression, financial difficulties were not significant in quantile regression analysis and dyspnea was only significant for first quartile. Also emotion functioning and duration of disease statistically predicted the QOL score in the third quartile. The results have demonstrated that using quantile regression leads to better interpretation and richer inference about predictors of the breast cancer patient quality of life.

Interpretation of commonly used statistical regression models.

PubMed

Kasza, Jessica; Wolfe, Rory

2014-01-01

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

Associations between the workplace-effort in psychosocial risk management and the employee-rating of the psychosocial work environment - a multilevel study of 7565 employees in 1013 workplaces.

PubMed

Thorsen, Sannie Vester; Madsen, Ida Elisabeth Huitfeldt; Flyvholm, Mari-Ann; Hasle, Peter

2017-07-01

This study examined the association between the workplace-effort in psychosocial risk management and later employee-rating of the psychosocial work environment. The study is based on data from two questionnaire surveys - one including 1013 workplaces and one including 7565 employees from these workplaces. The association was analyzed using multi-level linear regression. The association for five different trade-groups and for five different psychosocial work environment domains was examined. Limited but statistically significant better employee-ratings of the psychosocial work environment in the respective domains were observed among Danish workplaces that prioritized "development possibilities for employees," "recognition of employees," "employees influence on own work tasks," good "communication at the workplace," and "help to prevent work overload." Danish workplaces with a high effort in psychosocial risk management in the preceding year had a small but significantly more positive rating of the psychosocial work environment by the employees. However, future studies are needed to establish the causality of the associations.

Immobilization of Cellulase from Bacillus subtilis UniMAP-KB01 on Multi-walled Carbon Nanotubes for Biofuel Production

NASA Astrophysics Data System (ADS)

Naresh, Sandrasekaran; Hoong Shuit, Siew; Kunasundari, Balakrishnan; Hoo Peng, Yong; Qi, Hwa Ng; Teoh, Yi Peng

2018-03-01

Bacillus subtilis UniMAP-KB01, a cellulase producer was isolated from Malaysian mangrove soil. Through morphological identification it was observed that the B. subtilis appears to be in rod shaped and identified as a gram positive bacterium. Growth profile of isolated B. subtilis was established by measuring optical density (OD) at 600 nm for every 1 hour intervals. Polymath software was employed to plot the growth profile and the non-linear plot established gave the precision value of linear regression, R2 of 0.9602, root mean square deviation (RMSD) of 0.0176 and variance of 0.0025. The hydrolysis capacity testing revealed the cellulolytic index of 2.83 ± 0.46 after stained with Gram’s Iodine. The harvested crude enzyme after 24 hours incubation in carboxymethylcellulose (CMC) broth at 45°C and 100 RPM, was tested for enzyme activity. Through Filter Paper Assay (FPA), the cellulase activity was calculated to be 0.05 U/mL. The hydrolysis capacity testing and FPA shown an acceptable value for thermophilic bacterial enzyme activity. Thus, this isolated strain reasoned to be potential for producing thermostable cellulase which will be immobilized onto multi-walled carbon nanotubes and the cellulolytic activity will be characterized for biofuel production.

Dosing algorithm for warfarin using CYP2C9 and VKORC1 genotyping from a multi-ethnic population: comparison with other equations.

PubMed

Wu, Alan H B; Wang, Ping; Smith, Andrew; Haller, Christine; Drake, Katherine; Linder, Mark; Valdes, Roland

2008-02-01

Polymorphism in the genes for cytochrome (CYP)2C9 and the vitamin K epoxide reductase complex subunit 1 (VKORC1) affect the pharmacokinetics and pharmacodynamics of warfarin. We developed and validated a warfarin-dosing algorithm for a multi-ethnic population that predicts the best dose for stable anticoagulation, and compared its performance against other regression equations. We determined the allele and haplotype frequencies of genes for CYP2C9 and VKORC1 on 167 Caucasian, African-American, Asian and Hispanic patients on warfarin. On a subset where complete data were available (n=92), we developed a dosing equation that predicts the actual dose needed to maintain target anticoagulation using demographic variables and genotypes. This regression was validated against an independent group of subjects. We also applied our data to five other published warfarin-dosing equations. The allele frequency for CYP2C9*2 and *3 and the A allele for VKORC1 3673 was similar to previously published reports. For Caucasians and Asians, VKORC1 SNPs were in Hardy-Weinberg linkage equilibrium. Some VKORC1 SNPs among the African-American population and one SNP among Hispanics were not in equilibrium. The linear regression of predicted versus actual warfarin dose produced r-values of 0.71 for the training set and 0.67 for the validation set. The regression coefficient improved (to r=0.78 and 0.75, respectively) when rare genotypes were eliminated or when the 7566 VKORC1 genotype was added to the model. All of the regression models tested produced a similar degree of correlation. The exclusion of rare genotypes that are more associated with certain ethnicities improved the model. Minor improvements in algorithms can be observed with the inclusion of ethnicity and more CYP2C9 and VKORC1 SNPs as variables. Major improvements will likely require the identification of new gene associations with warfarin dosing.

Serum Iron Level Is Associated with Time to Antibiotics in Cystic Fibrosis.

PubMed

Gifford, Alex H; Dorman, Dana B; Moulton, Lisa A; Helm, Jennifer E; Griffin, Mary M; MacKenzie, Todd A

2015-12-01

Serum levels of hepcidin-25, a peptide hormone that reduces blood iron content, are elevated when patients with cystic fibrosis (CF) develop pulmonary exacerbation (PEx). Because hepcidin-25 is unavailable as a clinical laboratory test, we questioned whether a one-time serum iron level was associated with the subsequent number of days until PEx, as defined by the need to receive systemic antibiotics (ABX) for health deterioration. Clinical, biochemical, and microbiological parameters were simultaneously checked in 54 adults with CF. Charts were reviewed to determine when they first experienced a PEx after these parameters were assessed. Time to ABX was compared in subgroups with and without specific attributes. Multivariate linear regression was used to identify parameters that significantly explained variation in time to ABX. In univariate analyses, time to ABX was significantly shorter in subjects with Aspergillus-positive sputum cultures and CF-related diabetes. Multivariate linear regression models demonstrated that shorter time to ABX was associated with younger age, lower serum iron level, and Aspergillus sputum culture positivity. Serum iron, age, and Aspergillus sputum culture positivity are factors associated with shorter time to subsequent PEx in CF adults. © 2015 Wiley Periodicals, Inc.

Use of probabilistic weights to enhance linear regression myoelectric control

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

Emulation of the MBM-MEDUSA model: exploring the sea level and the basin-to-shelf transfer influence on the system dynamics

NASA Astrophysics Data System (ADS)

Ermakov, Ilya; Crucifix, Michel; Munhoven, Guy

2013-04-01

Complex climate models require high computational burden. However, computational limitations may be avoided by using emulators. In this work we present several approaches for dynamical emulation (also called metamodelling) of the Multi-Box Model (MBM) coupled to the Model of Early Diagenesis in the Upper Sediment A (MEDUSA) that simulates the carbon cycle of the ocean and atmosphere [1]. We consider two experiments performed on the MBM-MEDUSA that explore the Basin-to-Shelf Transfer (BST) dynamics. In both experiments the sea level is varied according to a paleo sea level reconstruction. Such experiments are interesting because the BST is an important cause of the CO2 variation and the dynamics is potentially nonlinear. The output that we are interested in is the variation of the carbon dioxide partial pressure in the atmosphere over the Pleistocene. The first experiment considers that the BST is fixed constant during the simulation. In the second experiment the BST is interactively adjusted according to the sea level, since the sea level is the primary control of the growth and decay of coral reefs and other shelf carbon reservoirs. The main aim of the present contribution is to create a metamodel of the MBM-MEDUSA using the Dynamic Emulation Modelling methodology [2] and compare the results obtained using linear and non-linear methods. The first step in the emulation methodology used in this work is to identify the structure of the metamodel. In order to select an optimal approach for emulation we compare the results of identification obtained by the simple linear and more complex nonlinear models. In order to identify the metamodel in the first experiment the simple linear regression and the least-squares method is sufficient to obtain a 99,9% fit between the temporal outputs of the model and the metamodel. For the second experiment the MBM's output is highly nonlinear. In this case we apply nonlinear models, such as, NARX, Hammerstein model, and an 'ad-hoc' switching model. After the identification we perform the parameter mapping using spline interpolation and validate the emulator on a new set of parameters. References: [1] G. Munhoven, "Glacial-interglacial rain ratio changes: Implications for atmospheric CO2 and ocean-sediment interaction," Deep-Sea Res Pt II, vol. 54, pp. 722-746, 2007. [2] A. Castelletti et al., "A general framework for Dynamic Emulation Modelling in environmental problems," Environ Modell Softw, vol. 34, pp. 5-18, 2012.

The association of longitudinal trend of fasting plasma glucose with retinal microvasculature in people without established diabetes.

PubMed

Hu, Yin; Niu, Yong; Wang, Dandan; Wang, Ying; Holden, Brien A; He, Mingguang

2015-01-22

Structural changes of retinal vasculature, such as altered retinal vascular calibers, are considered as early signs of systemic vascular damage. We examined the associations of 5-year mean level, longitudinal trend, and fluctuation in fasting plasma glucose (FPG) with retinal vascular caliber in people without established diabetes. A prospective study was conducted in a cohort of Chinese people age ≥40 years in Guangzhou, southern China. The FPG was measured at baseline in 2008 and annually until 2012. In 2012, retinal vascular caliber was assessed using standard fundus photographs and validated software. A total of 3645 baseline nondiabetic participants with baseline and follow-up data on FPG for 3 or more visits was included for statistical analysis. The associations of retinal vascular caliber with 5-year mean FPG level, longitudinal FPG trend (slope of linear regression-FPG), and fluctuation (standard deviation and root mean square error of FPG) were analyzed using multivariable linear regression analyses. Multivariate regression models adjusted for baseline FPG and other potential confounders showed that a 10% annual increase in FPG was associated independently with a 2.65-μm narrowing in retinal arterioles (P = 0.008) and a 3.47-μm widening in venules (P = 0. 0.004). Associations with mean FPG level and fluctuation were not statistically significant. Annual rising trend in FPG, but not its mean level or fluctuation, is associated with altered retinal vasculature in nondiabetic people. Copyright 2015 The Association for Research in Vision and Ophthalmology, Inc.

A Multi-Level Simultaneous Analysis of How Student and School Characteristics Are Related to Students' English Language Achievement

ERIC Educational Resources Information Center

Güvendir, Emre

2015-01-01

This study examines how student and school characteristics are related to Turkish students' English language achievement in Evaluation of Student Achievement Test (ÖBBS) of 2009. The participants of the study involve 43707 ninth year students who were required to take ÖBBS in 2009. For data analysis two level hierarchical linear modeling was…

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

Mercury, lead and cadmium in human milk in relation to diet, lifestyle habits and sociodemographic variables in Madrid (Spain).

PubMed

García-Esquinas, Esther; Pérez-Gómez, Beatriz; Fernández, Mario Antonio; Pérez-Meixeira, Ana María; Gil, Elisa; de Paz, Concha; Iriso, Andrés; Sanz, Juan Carlos; Astray, Jenaro; Cisneros, Margot; de Santos, Amparo; Asensio, Angel; García-Sagredo, José Miguel; García, José Frutos; Vioque, Jesus; Pollán, Marina; López-Abente, Gonzalo; González, Maria José; Martínez, Mercedes; Bohigas, Pedro Arias; Pastor, Roberto; Aragonés, Nuria

2011-09-01

Although breastfeeding is the ideal way of nurturing infants, it can be a source of exposure to toxicants. This study reports the concentration of Hg, Pb and Cd in breast milk from a sample of women drawn from the general population of the Madrid Region, and explores the association between metal levels and socio-demographic factors, lifestyle habits, diet and environmental exposures, including tobacco smoke, exposure at home and occupational exposures. Breast milk was obtained from 100 women (20 mL) at around the third week postpartum. Pb, Cd and Hg levels were determined using Atomic Absorption Spectrometry. Metal levels were log-transformed due to non-normal distribution. Their association with the variables collected by questionnaire was assessed using linear regression models. Separate models were fitted for Hg, Pb and Cd, using univariate linear regression in a first step. Secondly, multivariate linear regression models were adjusted introducing potential confounders specific for each metal. Finally, a test for trend was performed in order to evaluate possible dose-response relationships between metal levels and changes in variables categories. Geometric mean Hg, Pb and Cd content in milk were 0.53 μg L(-1), 15.56 μg L(-1), and 1.31 μg L(-1), respectively. Decreases in Hg levels in older women and in those with a previous history of pregnancies and lactations suggested clearance of this metal over lifetime, though differences were not statistically significant, probably due to limited sample size. Lead concentrations increased with greater exposure to motor vehicle traffic and higher potato consumption. Increased Cd levels were associated with type of lactation and tended to increase with tobacco smoking. Surveillance for the presence of heavy metals in human milk is needed. Smoking and dietary habits are the main factors linked to heavy metal levels in breast milk. Our results reinforce the need to strengthen national food safety programs and to further promote avoidance of unhealthy behaviors such as smoking during pregnancy. Copyright © 2011 Elsevier Ltd. All rights reserved.

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.

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

NASA Astrophysics Data System (ADS)

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

2016-07-01

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

Association between Serum Uric Acid Level and Carotid Atherosclerosis in Chinese Individuals Aged 75 Years or Older: A Hospital-Based Case-Control Study.

PubMed

Feng, L; Hua, C; Sun, H; Qin, L-Y; Niu, P-P; Guo, Z-N; Yang, Y

2018-01-01

To investigate the association between serum uric acid level and the presence and progression of carotid atherosclerosis in Chinese individuals aged 75 years or older. Case-control study. In a teaching hospital. Five hundred and sixty-four elderlies (75 years or above) who underwent general health screening in our hospital were enrolled. The detailed carotid ultrasound results, physical examination information, medical history, and laboratory test results including serum uric acid level were recorded, these data were used to analyze the relationship between serum uric acid level and carotid atherosclerosis. Then, subjects who underwent the second carotid ultrasound 1.5-2 years later were further identified to analyzed the relationship between serum uric acid and the progression of carotid atherosclerosis. A total of 564 subjects were included, carotid plaque was found in 482 (85.5%) individuals. Logistic regression showed that subjects with elevated serum uric acid (expressed per 1 standard deviation change) had significantly higher incidence of carotid plaque (odds ratio, 1.37; 95% confidence interval, 1.07-1.75; P= 0.012) after controlling for other factors. A total of 236 subjects underwent the follow-up carotid ultrasound. Linear regression showed that serum uric acid level (expressed per 1 standard deviation change; 1 standard deviation = 95.5 μmol/L) was significantly associated with percentage of change of plaque score (P = 0.008). Multivariable linear regression showed that 1 standard deviation increase in serum uric acid levels was expected to increase 0.448% of plaque score (P = 0.023). The elevated serum uric acid level may be independently and significantly associated with the presence and progression of carotid atherosclerosis in Chinese individuals aged 75 years or older.

Improving medium-range ensemble streamflow forecasts through statistical post-processing

NASA Astrophysics Data System (ADS)

Mendoza, Pablo; Wood, Andy; Clark, Elizabeth; Nijssen, Bart; Clark, Martyn; Ramos, Maria-Helena; Nowak, Kenneth; Arnold, Jeffrey

2017-04-01

Probabilistic hydrologic forecasts are a powerful source of information for decision-making in water resources operations. A common approach is the hydrologic model-based generation of streamflow forecast ensembles, which can be implemented to account for different sources of uncertainties - e.g., from initial hydrologic conditions (IHCs), weather forecasts, and hydrologic model structure and parameters. In practice, hydrologic ensemble forecasts typically have biases and spread errors stemming from errors in the aforementioned elements, resulting in a degradation of probabilistic properties. In this work, we compare several statistical post-processing techniques applied to medium-range ensemble streamflow forecasts obtained with the System for Hydromet Applications, Research and Prediction (SHARP). SHARP is a fully automated prediction system for the assessment and demonstration of short-term to seasonal streamflow forecasting applications, developed by the National Center for Atmospheric Research, University of Washington, U.S. Army Corps of Engineers, and U.S. Bureau of Reclamation. The suite of post-processing techniques includes linear blending, quantile mapping, extended logistic regression, quantile regression, ensemble analogs, and the generalized linear model post-processor (GLMPP). We assess and compare these techniques using multi-year hindcasts in several river basins in the western US. This presentation discusses preliminary findings about the effectiveness of the techniques for improving probabilistic skill, reliability, discrimination, sharpness and resolution.

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

Bayesian Travel Time Inversion adopting Gaussian Process Regression

NASA Astrophysics Data System (ADS)

Mauerberger, S.; Holschneider, M.

2017-12-01

A major application in seismology is the determination of seismic velocity models. Travel time measurements are putting an integral constraint on the velocity between source and receiver. We provide insight into travel time inversion from a correlation-based Bayesian point of view. Therefore, the concept of Gaussian process regression is adopted to estimate a velocity model. The non-linear travel time integral is approximated by a 1st order Taylor expansion. A heuristic covariance describes correlations amongst observations and a priori model. That approach enables us to assess a proxy of the Bayesian posterior distribution at ordinary computational costs. No multi dimensional numeric integration nor excessive sampling is necessary. Instead of stacking the data, we suggest to progressively build the posterior distribution. Incorporating only a single evidence at a time accounts for the deficit of linearization. As a result, the most probable model is given by the posterior mean whereas uncertainties are described by the posterior covariance.As a proof of concept, a synthetic purely 1d model is addressed. Therefore a single source accompanied by multiple receivers is considered on top of a model comprising a discontinuity. We consider travel times of both phases - direct and reflected wave - corrupted by noise. Left and right of the interface are assumed independent where the squared exponential kernel serves as covariance.

Assessing the Causality between Blood Pressure and Retinal Vascular Caliber through Mendelian Randomisation

NASA Astrophysics Data System (ADS)

Li, Ling-Jun; Liao, Jiemin; Cheung, Carol Yim-Lui; Ikram, M. Kamran; Shyong, Tai E.; Wong, Tien-Yin; Cheng, Ching-Yu

2016-02-01

We aimed to determine the association between blood pressure (BP) and retinal vascular caliber changes that were free from confounders and reverse causation by using Mendelian randomisation. A total of 6528 participants from a multi-ethnic cohort (Chinese, Malays, and Indians) in Singapore were included in this study. Retinal arteriolar and venular caliber was measured by a semi-automated computer program. Genotyping was done using Illumina 610-quad chips. Meta-analysis of association between BP, and retinal arteriolar and venular caliber across three ethnic groups was performed both in conventional linear regression and Mendelian randomisation framework with a genetic risk score of BP as an instrumental variable. In multiple linear regression models, each 10 mm Hg increase in systolic BP, diastolic BP, and mean arterial BP (MAP) was associated with significant decreases in retinal arteriolar caliber of a 1.4, 3.0, and 2.6 μm, and significant decreases in retinal venular caliber of a 0.6, 0.7, and 0.9 μm, respectively. In a Mendelian randomisation model, only associations between DBP and MAP and retinal arteriolar narrowing remained yet its significance was greatly reduced. Our data showed weak evidence of a causal relationship between elevated BP and retinal arteriolar narrowing.

Emotional exhaustion and cognitive performance in apparently healthy teachers: a longitudinal multi-source study.

PubMed

Feuerhahn, Nicolas; Stamov-Roßnagel, Christian; Wolfram, Maren; Bellingrath, Silja; Kudielka, Brigitte M

2013-10-01

We investigate how emotional exhaustion (EE), the core component of burnout, relates to cognitive performance, job performance and health. Cognitive performance was assessed by self-rated cognitive stress symptoms, self-rated and peer-rated cognitive impairments in everyday tasks and a neuropsychological test of learning and memory (LGT-3); job performance and physical health were gauged by self-reports. Cross-sectional linear regression analyses in a sample of 100 teachers confirm that EE is negatively related to cognitive performance as assessed by self-rating and peer-rating as well as neuropsychological testing (all p < .05). Longitudinal linear regression analyses confirm similar trends (p < .10) for self-rated and peer-rated cognitive performance. Executive control deficits might explain impaired cognitive performance in EE. In longitudinal analyses, EE also significantly predicts physical health. Contrary to our expectations, EE does not affect job performance. When reversed causation is tested, none of the outcome variables at Time 1 predict EE at Time 2. This speaks against cognitive dysfunctioning serving as a vulnerability factor for exhaustion. In sum, results underpin the negative consequences of EE for cognitive performance and health, which are relevant for individuals and organizations alike. In this way, findings might contribute to the understanding of the burnout syndrome. Copyright © 2012 John Wiley & Sons, Ltd.

Simplified large African carnivore density estimators from track indices.

PubMed

Winterbach, Christiaan W; Ferreira, Sam M; Funston, Paul J; Somers, Michael J

2016-01-01

The range, population size and trend of large carnivores are important parameters to assess their status globally and to plan conservation strategies. One can use linear models to assess population size and trends of large carnivores from track-based surveys on suitable substrates. The conventional approach of a linear model with intercept may not intercept at zero, but may fit the data better than linear model through the origin. We assess whether a linear regression through the origin is more appropriate than a linear regression with intercept to model large African carnivore densities and track indices. We did simple linear regression with intercept analysis and simple linear regression through the origin and used the confidence interval for ß in the linear model y  =  αx  + ß, Standard Error of Estimate, Mean Squares Residual and Akaike Information Criteria to evaluate the models. The Lion on Clay and Low Density on Sand models with intercept were not significant ( P  > 0.05). The other four models with intercept and the six models thorough origin were all significant ( P  < 0.05). The models using linear regression with intercept all included zero in the confidence interval for ß and the null hypothesis that ß = 0 could not be rejected. All models showed that the linear model through the origin provided a better fit than the linear model with intercept, as indicated by the Standard Error of Estimate and Mean Square Residuals. Akaike Information Criteria showed that linear models through the origin were better and that none of the linear models with intercept had substantial support. Our results showed that linear regression through the origin is justified over the more typical linear regression with intercept for all models we tested. A general model can be used to estimate large carnivore densities from track densities across species and study areas. The formula observed track density = 3.26 × carnivore density can be used to estimate densities of large African carnivores using track counts on sandy substrates in areas where carnivore densities are 0.27 carnivores/100 km 2 or higher. To improve the current models, we need independent data to validate the models and data to test for non-linear relationship between track indices and true density at low densities.

[From clinical judgment to linear regression model.

PubMed

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

2013-01-01

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

Discrimination and Acculturative Stress among First-Generation Dominicans

ERIC Educational Resources Information Center

Dawson, Beverly Araujo; Panchanadeswaran, Subadra

2010-01-01

The present study examined the relationship between discriminatory experiences and acculturative stress levels among a sample of 283 Dominican immigrants. Findings from a linear regression analysis revealed that experiences of daily racial discrimination and major racist events were significant predictors of acculturative stress after controlling…

Prevalence and correlates of cognitive impairment in kidney transplant recipients.

PubMed

Gupta, Aditi; Mahnken, Jonathan D; Johnson, David K; Thomas, Tashra S; Subramaniam, Dipti; Polshak, Tyler; Gani, Imran; John Chen, G; Burns, Jeffrey M; Sarnak, Mark J

2017-05-12

There is a high prevalence of cognitive impairment in dialysis patients. The prevalence of cognitive impairment after kidney transplantation is unknown. Study Design: Cross-sectional study. Single center study of prevalent kidney transplant recipients from a transplant clinic in a large academic center. Assessment of cognition using the Montreal Cognitive Assessment (MoCA). Demographic and clinical variables associated with cognitive impairment were also examined. Outcomes and Measurements: a) Prevalence of cognitive impairment defined by a MoCA score of <26. b) Multivariable linear and logistic regression to examine the association of demographic and clinical factors with cognitive impairment. Data from 226 patients were analyzed. Mean (SD) age was 54 (13.4) years, 73% were white, 60% were male, 37% had diabetes, 58% had an education level of college or above, and the mean (SD) time since kidney transplant was 3.4 (4.1) years. The prevalence of cognitive impairment was 58.0%. Multivariable linear regression demonstrated that older age, male gender and absence of diabetes were associated with lower MoCA scores (p < 0.01 for all). Estimated glomerular filtration rate (eGFR) was not associated with level of cognition. The logistic regression analysis confirmed the association of older age with cognitive impairment. Cognitive impairment is common in prevalent kidney transplant recipients, at a younger age compared to general population, and is associated with certain demographic variables, but not level of eGFR.

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

PubMed

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

2005-04-15

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

The natural history of children with joint hypermobility syndrome and Ehlers-Danlos hypermobility type: a longitudinal cohort study.

PubMed

Scheper, Mark C; Nicholson, Lesley L; Adams, Roger D; Tofts, Louise; Pacey, Verity

2017-12-01

The objective of the manuscript was to describe the natural history of complaints and disability in children diagnosed with joint hypermobility syndrome (JHS)/Ehlers-Danlos-hypermobility type (EDS-HT) and to identify the constructs that underlie functional decline. One hundred and one JHS/EDS-HT children were observed over 3 years and assessed at three time points on the following: functional impairments, quality of life, connective tissue laxity, muscle function, postural control and musculoskeletal and multi-systemic complaints. Cluster analysis was performed to identify subgroups in severity. Clinical profiles were determined for these subgroups, and differences were assessed by multivariate analysis of covariance. Mixed linear regression models were used to determine the subsequent trajectories. Finally, an exploratory factor analysis was used to uncover the underlying constructs of functional impairment. Three clusters of children were identified in terms of functional impairment: mild, moderately and severely affected. Functional impairment at baseline was predictive of worsening trajectories in terms of reduced walking distance and decreased quality of life (P ⩽ 0.05) over 3 years. Multiple interactions between the secondary outcomes were observed, with four underlying constructs identified. All four constructs (multi-systemic effects, pain, fatigue and loss of postural control) contributed significantly to disability (P ⩽ 0.046). Children diagnosed with JHS/EDS-HT who have a high incidence of multi-systemic complaints (particularly, orthostatic intolerance, urinary incontinence and diarrhoea) and poor postural control in addition to high levels of pain and fatigue at baseline are most likely to have a deteriorating trajectory of functional impairment and, accordingly, warrant clinical prioritization. © The Author 2017. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com

INNOVATIVE INSTRUMENTATION AND ANALYSIS OF THE TEMPERATURE MEASUREMENT FOR HIGH TEMPERATURE GASIFICATION

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

Seong W. Lee

2004-10-01

The systematic tests of the gasifier simulator on the clean thermocouple were completed in this reporting period. Within the systematic tests on the clean thermocouple, five (5) factors were considered as the experimental parameters including air flow rate, water flow rate, fine dust particle amount, ammonia addition and high/low frequency device (electric motor). The fractional factorial design method was used in the experiment design with sixteen (16) data sets of readings. Analysis of Variances (ANOVA) was applied to the results from systematic tests. The ANOVA results show that the un-balanced motor vibration frequency did not have the significant impact onmore » the temperature changes in the gasifier simulator. For the fine dust particles testing, the amount of fine dust particles has significant impact to the temperature measurements in the gasifier simulator. The effects of the air and water on the temperature measurements show the same results as reported in the previous report. The ammonia concentration was included as an experimental parameter for the reducing environment in this reporting period. The ammonia concentration does not seem to be a significant factor on the temperature changes. The linear regression analysis was applied to the temperature reading with five (5) factors. The accuracy of the linear regression is relatively low, which is less than 10% accuracy. Nonlinear regression was also conducted to the temperature reading with the same factors. Since the experiments were designed in two (2) levels, the nonlinear regression is not very effective with the dataset (16 readings). An extra central point test was conducted. With the data of the center point testing, the accuracy of the nonlinear regression is much better than the linear regression.« less

Fourier transform infrared reflectance spectra of latent fingerprints: a biometric gauge for the age of an individual.

PubMed

Hemmila, April; McGill, Jim; Ritter, David

2008-03-01

To determine if changes in fingerprint infrared spectra linear with age can be found, partial least squares (PLS1) regression of 155 fingerprint infrared spectra against the person's age was constructed. The regression produced a linear model of age as a function of spectrum with a root mean square error of calibration of less than 4 years, showing an inflection at about 25 years of age. The spectral ranges emphasized by the regression do not correspond to the highest concentration constituents of the fingerprints. Separate linear regression models for old and young people can be constructed with even more statistical rigor. The success of the regression demonstrates that a combination of constituents can be found that changes linearly with age, with a significant shift around puberty.

Study on accumulated crystallization characteristics of amorphous Ge2Sb2Te5 induced by multi-pulsed laser irradiations with different fluences

NASA Astrophysics Data System (ADS)

Fan, T.; Liu, F. R.; Li, W. Q.; Guo, J. C.; Wang, Y. H.; Sun, N. X.; Liu, F.

2018-07-01

Accumulated crystallization characteristics of amorphous Ge2Sb2Te5 (a-GST) films induced by multi-pulsed laser irradiations with different fluences were investigated by x-ray diffraction (XRD), Raman spectroscopy and spectrophotometer. Solid-state transformation was performed at low fluence (LF, 30.5 mJ cm‑2), whereas melting-cooling transformation dominated at medium and high fluence (MF, 45.7 and HF, 61 mJ cm‑2). Solid-state transformation induced by subsequent LF pulses promoted the growth and coalescence of grains, linearly increasing the average grain size, accordingly causing blue-shifts of the Raman spectral peaks. For MF/HF pulse irradiated films, the relatively high laser fluence increased the melting depth and reduced the volume fraction of the crystalline state induced by individual pulses, thereby increasing the threshold of laser pulse numbers for XRD detectable crystallization. However, the remelting depth induced by subsequent MF/HF laser pulse progressively decreased. The remelting-recrystallization process refined grain sizes, which improved the red-shifts of Raman spectral peaks. Moreover, optical contrast increased dramatically compared to single laser irradiation and five-level storage could be realized for a linear increase of optical contrast. The present study is fundamental for realizing the potential of multi-level devices.

Linearity versus Nonlinearity of Offspring-Parent Regression: An Experimental Study of Drosophila Melanogaster

PubMed Central

Gimelfarb, A.; Willis, J. H.

1994-01-01

An experiment was conducted to investigate the offspring-parent regression for three quantitative traits (weight, abdominal bristles and wing length) in Drosophila melanogaster. Linear and polynomial models were fitted for the regressions of a character in offspring on both parents. It is demonstrated that responses by the characters to selection predicted by the nonlinear regressions may differ substantially from those predicted by the linear regressions. This is true even, and especially, if selection is weak. The realized heritability for a character under selection is shown to be determined not only by the offspring-parent regression but also by the distribution of the character and by the form and strength of selection. PMID:7828818

Use of linear regression models to determine influence factors on the concentration levels of radon in occupied houses

NASA Astrophysics Data System (ADS)

Buermeyer, Jonas; Gundlach, Matthias; Grund, Anna-Lisa; Grimm, Volker; Spizyn, Alexander; Breckow, Joachim

2016-09-01

This work is part of the analysis of the effects of constructional energy-saving measures to radon concentration levels in dwellings performed on behalf of the German Federal Office for Radiation Protection. In parallel to radon measurements for five buildings, both meteorological data outside the buildings and the indoor climate factors were recorded. In order to access effects of inhabited buildings, the amount of carbon dioxide (CO2) was measured. For a statistical linear regression model, the data of one object was chosen as an example. Three dummy variables were extracted from the process of the CO2 concentration to provide information on the usage and ventilation of the room. The analysis revealed a highly autoregressive model for the radon concentration with additional influence by the natural environmental factors. The autoregression implies a strong dependency on a radon source since it reflects a backward dependency in time. At this point of the investigation, it cannot be determined whether the influence by outside factors affects the source of radon or the habitant’s ventilation behavior resulting in variation of the occurring concentration levels. In any case, the regression analysis might provide further information that would help to distinguish these effects. In the next step, the influence factors will be weighted according to their impact on the concentration levels. This might lead to a model that enables the prediction of radon concentration levels based on the measurement of CO2 in combination with environmental parameters, as well as the development of advices for ventilation.

A linearly frequency-swept high-speed-rate multi-wavelength laser for optical coherence tomography

NASA Astrophysics Data System (ADS)

Wang, Qiyu; Wang, Zhaoying; Yuan, Quan; Ma, Rui; Du, Tao; Yang, Tianxin

2017-02-01

We proposed and demonstrated a linearly frequency-swept multi-wavelength laser source for optical coherence tomography (OCT) eliminating the need of wavenumber space resampling in the postprocessing progress. The source consists of a multi-wavelength fiber laser source (MFS) and an optical sweeping loop. In this novel laser source, an equally spaced multi-wavelength laser is swept simultaneously by a certain step each time in the frequency domain in the optical sweeping loop. The sweeping step is determined by radio frequency (RF) signal which can be precisely controlled. Thus the sweeping behavior strictly maintains a linear relationship between time and frequency. We experimentally achieved linear time-frequency sweeping at a sweeping rate of 400 kHz with our laser source.

Accounting for the decrease of photosystem photochemical efficiency with increasing irradiance to estimate quantum yield of leaf photosynthesis.

PubMed

Yin, Xinyou; Belay, Daniel W; van der Putten, Peter E L; Struik, Paul C

2014-12-01

Maximum quantum yield for leaf CO2 assimilation under limiting light conditions (Φ CO2LL) is commonly estimated as the slope of the linear regression of net photosynthetic rate against absorbed irradiance over a range of low-irradiance conditions. Methodological errors associated with this estimation have often been attributed either to light absorptance by non-photosynthetic pigments or to some data points being beyond the linear range of the irradiance response, both causing an underestimation of Φ CO2LL. We demonstrate here that a decrease in photosystem (PS) photochemical efficiency with increasing irradiance, even at very low levels, is another source of error that causes a systematic underestimation of Φ CO2LL. A model method accounting for this error was developed, and was used to estimate Φ CO2LL from simultaneous measurements of gas exchange and chlorophyll fluorescence on leaves using various combinations of species, CO2, O2, or leaf temperature levels. The conventional linear regression method under-estimated Φ CO2LL by ca. 10-15%. Differences in the estimated Φ CO2LL among measurement conditions were generally accounted for by different levels of photorespiration as described by the Farquhar-von Caemmerer-Berry model. However, our data revealed that the temperature dependence of PSII photochemical efficiency under low light was an additional factor that should be accounted for in the model.

The Oklahoma's Promise Program: A National Model to Promote College Persistence

ERIC Educational Resources Information Center

Mendoza, Pilar; Mendez, Jesse P.

2013-01-01

Using a multi-method approach involving fixed effects and logistic regressions, this study examined the effect of the Oklahoma's Promise Program on student persistence in relation to the Pell and Stafford federal programs and according to socio-economic characteristics and class level. The Oklahoma's Promise is a hybrid state program that pays…

An Analysis of Terrestrial and Aquatic Environmental Controls of Riverine Dissolved Organic Carbon in the Conterminous United States

DOE PAGES

Yang, Qichun; Zhang, Xuesong; Xu, Xingya; ...

2017-05-29

Riverine carbon cycling is an important, but insufficiently investigated component of the global carbon cycle. Analyses of environmental controls on riverine carbon cycling are critical for improved understanding of mechanisms regulating carbon processing and storage along the terrestrial-aquatic continuum. Here, we compile and analyze riverine dissolved organic carbon (DOC) concentration data from 1402 United States Geological Survey (USGS) gauge stations to examine the spatial variability and environmental controls of DOC concentrations in the United States (U.S.) surface waters. DOC concentrations exhibit high spatial variability, with an average of 6.42 ± 6.47 mg C/ L (Mean ± Standard Deviation). In general,more » high DOC concentrations occur in the Upper Mississippi River basin and the Southeastern U.S., while low concentrations are mainly distributed in the Western U.S. Single-factor analysis indicates that slope of drainage areas, wetlands, forests, percentage of first-order streams, and instream nutrients (such as nitrogen and phosphorus) pronouncedly influence DOC concentrations, but the explanatory power of each bivariate model is lower than 35%. Analyses based on the general multi-linear regression models suggest DOC concentrations are jointly impacted by multiple factors. Soil properties mainly show positive correlations with DOC concentrations; forest and shrub lands have positive correlations with DOC concentrations, but urban area and croplands demonstrate negative impacts; total instream phosphorus and dam density correlate positively with DOC concentrations. Notably, the relative importance of these environmental controls varies substantially across major U.S. water resource regions. In addition, DOC concentrations and environmental controls also show significant variability from small streams to large rivers, which may be caused by changing carbon sources and removal rates by river orders. In sum, our results reveal that general multi-linear regression analysis of twenty one terrestrial and aquatic environmental factors can partially explain (56%) the DOC concentration variation. In conclusion, this study highlights the complexity of the interactions among these environmental factors in determining DOC concentrations, thus calls for processes-based, non-linear methodologies to constrain uncertainties in riverine DOC cycling.« less

Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control.

PubMed

Hahne, J M; Biessmann, F; Jiang, N; Rehbaum, H; Farina, D; Meinecke, F C; Muller, K-R; Parra, L C

2014-03-01

In recent years the number of active controllable joints in electrically powered hand-prostheses has increased significantly. However, the control strategies for these devices in current clinical use are inadequate as they require separate and sequential control of each degree-of-freedom (DoF). In this study we systematically compare linear and nonlinear regression techniques for an independent, simultaneous and proportional myoelectric control of wrist movements with two DoF. These techniques include linear regression, mixture of linear experts (ME), multilayer-perceptron, and kernel ridge regression (KRR). They are investigated offline with electro-myographic signals acquired from ten able-bodied subjects and one person with congenital upper limb deficiency. The control accuracy is reported as a function of the number of electrodes and the amount and diversity of training data providing guidance for the requirements in clinical practice. The results showed that KRR, a nonparametric statistical learning method, outperformed the other methods. However, simple transformations in the feature space could linearize the problem, so that linear models could achieve similar performance as KRR at much lower computational costs. Especially ME, a physiologically inspired extension of linear regression represents a promising candidate for the next generation of prosthetic devices.

Inexact fuzzy-stochastic mixed-integer programming approach for long-term planning of waste management--Part A: methodology.

PubMed

Guo, P; Huang, G H

2009-01-01

In this study, an inexact fuzzy chance-constrained two-stage mixed-integer linear programming (IFCTIP) approach is proposed for supporting long-term planning of waste-management systems under multiple uncertainties in the City of Regina, Canada. The method improves upon the existing inexact two-stage programming and mixed-integer linear programming techniques by incorporating uncertainties expressed as multiple uncertainties of intervals and dual probability distributions within a general optimization framework. The developed method can provide an effective linkage between the predefined environmental policies and the associated economic implications. Four special characteristics of the proposed method make it unique compared with other optimization techniques that deal with uncertainties. Firstly, it provides a linkage to predefined policies that have to be respected when a modeling effort is undertaken; secondly, it is useful for tackling uncertainties presented as intervals, probabilities, fuzzy sets and their incorporation; thirdly, it facilitates dynamic analysis for decisions of facility-expansion planning and waste-flow allocation within a multi-facility, multi-period, multi-level, and multi-option context; fourthly, the penalties are exercised with recourse against any infeasibility, which permits in-depth analyses of various policy scenarios that are associated with different levels of economic consequences when the promised solid waste-generation rates are violated. In a companion paper, the developed method is applied to a real case for the long-term planning of waste management in the City of Regina, Canada.

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…

An Expert System for the Evaluation of Cost Models

DTIC Science & Technology

1990-09-01

contrast to the condition of equal error variance, called homoscedasticity. (Reference: Applied Linear Regression Models by John Neter - page 423...normal. (Reference: Applied Linear Regression Models by John Neter - page 125) Click Here to continue -> Autocorrelation Click Here for the index - Index...over time. Error terms correlated over time are said to be autocorrelated or serially correlated. (REFERENCE: Applied Linear Regression Models by John

Prostate specific antigen velocity as a measure of the natural history of prostate cancer: defining a 'rapid riser' subset.

PubMed

Nam, R K; Klotz, L H; Jewett, M A; Danjoux, C; Trachtenberg, J

1998-01-01

To study the rate of change in prostate specific antigen (PSA velocity) in patients with prostate cancer initially managed by 'watchful waiting'. Serial PSA levels were determined in 141 patients with prostate cancer confirmed by biopsy, who were initially managed expectantly and enrolled between May 1990 and December 1995. Sixty-seven patients eventually underwent surgery (mean age 59 years) because they chose it (the decision for surgery was not based on PSA velocity). A cohort of 74 patients remained on 'watchful waiting' (mean age 69 years). Linear regression and logarithmic transformations were used to segregate those patients who showed a rapid rise, defined as a > 50% rise in PSA per year (or a doubling time of < 2 years) and designated 'rapid risers'. An initial analysis based on a minimum of two PSA values showed that 31% were rapid risers. Only 15% of patients with more than three serial PSA determinations over > or = 6 months showed a rapid rise in PSA level. There was no advantage of log-linear analysis over linear regression models. Three serial PSA determinations over > or = 6 months in patients with clinically localized prostate cancer identifies a subset (15%) of patients with a rapidly rising PSA level. Shorter PSA surveillance with fewer PSA values may falsely identify patients with rapid rises in PSA level. However, further follow-up is required to determine if a rapid rise in PSA level identifies a subset of patients with an aggressive biological phenotype who are either still curable or who have already progressed to incurability through metastatic disease.

Indoor-to-outdoor particle concentration ratio model for human exposure analysis

NASA Astrophysics Data System (ADS)

Lee, Jae Young; Ryu, Sung Hee; Lee, Gwangjae; Bae, Gwi-Nam

2016-02-01

This study presents an indoor-to-outdoor particle concentration ratio (IOR) model for improved estimates of indoor exposure levels. This model is useful in epidemiological studies with large population, because sampling indoor pollutants in all participants' house is often necessary but impractical. As a part of a study examining the association between air pollutants and atopic dermatitis in children, 16 parents agreed to measure the indoor and outdoor PM10 and PM2.5 concentrations at their homes for 48 h. Correlation analysis and multi-step multivariate linear regression analysis was performed to develop the IOR model. Temperature and floor level were found to be powerful predictors of the IOR. Despite the simplicity of the model, it demonstrated high accuracy in terms of the root mean square error (RMSE). Especially for long-term IOR estimations, the RMSE was as low as 0.064 and 0.063 for PM10 and PM2.5, respectively. When using a prediction model in an epidemiological study, understanding the consequence of the modeling error and justifying the use of the model is very important. In the last section, this paper discussed the impact of the modeling error and developed a novel methodology to justify the use of the model.

Is It the Intervention or the Students? Using Linear Regression to Control for Student Characteristics in Undergraduate STEM Education Research

PubMed Central

Theobald, Roddy; Freeman, Scott

2014-01-01

Although researchers in undergraduate science, technology, engineering, and mathematics education are currently using several methods to analyze learning gains from pre- and posttest data, the most commonly used approaches have significant shortcomings. Chief among these is the inability to distinguish whether differences in learning gains are due to the effect of an instructional intervention or to differences in student characteristics when students cannot be assigned to control and treatment groups at random. Using pre- and posttest scores from an introductory biology course, we illustrate how the methods currently in wide use can lead to erroneous conclusions, and how multiple linear regression offers an effective framework for distinguishing the impact of an instructional intervention from the impact of student characteristics on test score gains. In general, we recommend that researchers always use student-level regression models that control for possible differences in student ability and preparation to estimate the effect of any nonrandomized instructional intervention on student performance. PMID:24591502

Is it the intervention or the students? using linear regression to control for student characteristics in undergraduate STEM education research.

PubMed

Theobald, Roddy; Freeman, Scott

2014-01-01

Although researchers in undergraduate science, technology, engineering, and mathematics education are currently using several methods to analyze learning gains from pre- and posttest data, the most commonly used approaches have significant shortcomings. Chief among these is the inability to distinguish whether differences in learning gains are due to the effect of an instructional intervention or to differences in student characteristics when students cannot be assigned to control and treatment groups at random. Using pre- and posttest scores from an introductory biology course, we illustrate how the methods currently in wide use can lead to erroneous conclusions, and how multiple linear regression offers an effective framework for distinguishing the impact of an instructional intervention from the impact of student characteristics on test score gains. In general, we recommend that researchers always use student-level regression models that control for possible differences in student ability and preparation to estimate the effect of any nonrandomized instructional intervention on student performance.

Mild asthma and chronic bronchitis seem to influence functional exercise capacity: a multi-case control study.

PubMed

Marcon, Alessandro; Girardi, Paolo; Ferrari, Marcello; Olivieri, Mario; Accordini, Simone; Bombieri, Cristina; Bortolami, Oscar; Braggion, Marco; Cappa, Veronica; Cazzoletti, Lucia; Locatelli, Francesca; Nicolis, Morena; Perbellini, Luigi; Sembeni, Silvia; Verlato, Giuseppe; Zanolin, Maria Elisabetta; de Marco, Roberto

2013-01-01

In the Genes Environment Interaction in Respiratory Diseases population-based multi-case control study, we investigated whether asthma, chronic bronchitis (CB) and rhinitis were associated with a reduced 6-minute walk distance (6MWD), and whether the 6MWD determinants were similar for subjects with/without respiratory diseases. Cases of asthma (n = 360), CB (n = 120), rhinitis (n = 203) and controls (no respiratory diseases: n = 302) were recruited. The variation in the 6MWD across the groups was analyzed by ANCOVA, adjusting for gender, age, height, weight and comorbidity. The 6MWD determinants were studied by linear regression, and heterogeneity across the cases and controls was investigated. The 6MWD differed across cases and controls (p = 0.01). It was shorter for cases of asthma (-17.1, 95% CI -28.3 to -5.8 m) and CB (-20.7, 95% CI: -36.6 to -4.8 m) than for controls (604 ± 68 m on average), but not for cases of rhinitis. The negative association between age and the 6MWD was significant for cases of CB, but not for the other groups (p = 0.001). Even at the level of severity found in the general population, asthma and CB could influence the 6MWD, which seems to reflect the functional exercise level for daily physical activities. The negative association between ageing and the 6MWD was particularly strong in subjects with CB. Our report adds to the mounting evidence that CB is not a trivial condition, especially in the ageing adult population, and it supports the importance of monitoring functional capacity and of physical reconditioning in mild asthma. Copyright © 2013 S. Karger AG, Basel.

Factors associated with sexual and reproductive health stigma among adolescent girls in Ghana.

PubMed

Hall, Kelli Stidham; Morhe, Emmanuel; Manu, Abubakar; Harris, Lisa H; Ela, Elizabeth; Loll, Dana; Kolenic, Giselle; Dozier, Jessica L; Challa, Sneha; Zochowski, Melissa K; Boakye, Andrew; Adanu, Richard; Dalton, Vanessa K

2018-01-01

Using our previously developed and tested Adolescent Sexual and Reproductive Health (SRH) Stigma Scale, we investigated factors associated with perceived SRH stigma among adolescent girls in Ghana. We drew upon data from our survey study of 1,063 females 15-24yrs recruited from community- and clinic-based sites in two Ghanaian cities. Our Adolescent SRH Stigma Scale comprised 20 items and 3 sub-scales (Internalized, Enacted, Lay Attitudes) to measure stigma occurring with sexual activity, contraceptive use, pregnancy, abortion and family planning service use. We assessed relationships between a comprehensive set of demographic, health and social factors and SRH Stigma with multi-level multivariable linear regression models. In unadjusted bivariate analyses, compared to their counterparts, SRH stigma scores were higher among girls who were younger, Accra residents, Muslim, still in/dropped out of secondary school, unemployed, reporting excellent/very good health, not in a relationship, not sexually experienced, never received family planning services, never used contraception, but had been pregnant (all p-values <0.05). In multivariable models, higher SRH stigma scores were associated with history of pregnancy (β = 1.53, CI = 0.51,2.56) and excellent/very good self-rated health (β = 0.89, CI = 0.20,1.58), while lower stigma scores were associated with older age (β = -0.17, 95%CI = -0.24,-0.09), higher educational attainment (β = -1.22, CI = -1.82,-0.63), and sexual intercourse experience (β = -1.32, CI = -2.10,-0.55). Findings provide insight into factors contributing to SRH stigma among this young Ghanaian female sample. Further research disentangling the complex interrelationships between SRH stigma, health, and social context is needed to guide multi-level interventions to address SRH stigma and its causes and consequences for adolescents worldwide.

Factors associated with sexual and reproductive health stigma among adolescent girls in Ghana

PubMed Central

Morhe, Emmanuel; Manu, Abubakar; Harris, Lisa H.; Ela, Elizabeth; Loll, Dana; Kolenic, Giselle; Dozier, Jessica L.; Challa, Sneha; Zochowski, Melissa K.; Boakye, Andrew; Adanu, Richard; Dalton, Vanessa K.

2018-01-01

Objective Using our previously developed and tested Adolescent Sexual and Reproductive Health (SRH) Stigma Scale, we investigated factors associated with perceived SRH stigma among adolescent girls in Ghana. Methods We drew upon data from our survey study of 1,063 females 15-24yrs recruited from community- and clinic-based sites in two Ghanaian cities. Our Adolescent SRH Stigma Scale comprised 20 items and 3 sub-scales (Internalized, Enacted, Lay Attitudes) to measure stigma occurring with sexual activity, contraceptive use, pregnancy, abortion and family planning service use. We assessed relationships between a comprehensive set of demographic, health and social factors and SRH Stigma with multi-level multivariable linear regression models. Results In unadjusted bivariate analyses, compared to their counterparts, SRH stigma scores were higher among girls who were younger, Accra residents, Muslim, still in/dropped out of secondary school, unemployed, reporting excellent/very good health, not in a relationship, not sexually experienced, never received family planning services, never used contraception, but had been pregnant (all p-values <0.05). In multivariable models, higher SRH stigma scores were associated with history of pregnancy (β = 1.53, CI = 0.51,2.56) and excellent/very good self-rated health (β = 0.89, CI = 0.20,1.58), while lower stigma scores were associated with older age (β = -0.17, 95%CI = -0.24,-0.09), higher educational attainment (β = -1.22, CI = -1.82,-0.63), and sexual intercourse experience (β = -1.32, CI = -2.10,-0.55). Conclusions Findings provide insight into factors contributing to SRH stigma among this young Ghanaian female sample. Further research disentangling the complex interrelationships between SRH stigma, health, and social context is needed to guide multi-level interventions to address SRH stigma and its causes and consequences for adolescents worldwide. PMID:29608595

Garden greenery and the health of older people in residential care facilities: a multi-level cross-sectional study.

PubMed

Dahlkvist, Eva; Hartig, Terry; Nilsson, Annika; Högberg, Hans; Skovdahl, Kirsti; Engström, Maria

2016-09-01

To test the relationship between greenery in gardens at residential facilities for older people and the self-perceived health of residents, mediated by experiences of being away and fascination when in the garden and the frequency of visitation there. To examine how these indirect effects vary with the number of physical barriers to visiting the garden. Many older people in residential facilities suffer from complex health problems. Access to a green outdoor environment may enable psychological distance, engage effortless attention, encourage more frequent visitation and promote resident health. A multi-level, cross-sectional, correlational design. Questionnaires were administered June-August, 2011 to convenience samples of residents at 72 facilities for older people with complex healthcare needs. One to 10 eligible residents were sampled during self-motivated garden visits at each facility (n = 290). They reported on their garden experiences and health. Facility staff reported on objective garden characteristics and barriers to access. A serial mediation model was tested with multiple linear regression analysis. The total indirect effect of greenery on self-perceived health was positive and significant. Garden greenery appears to affect health by enhancing a sense of being away, affording possibilities to experience the outdoor environment as interesting and encouraging visitation. Among residents in homes with multiple barriers, only fascination mediated the relationship between greenery and self-perceived health. Ample greenery in outdoor space at residential facilities for older people appears to promote experiences of being away and fascination, more frequent visitation and better health. © 2016 The Authors. Journal of Advanced Nursing Published by John Wiley & Sons Ltd.

The Neighborhood Social Environment and Objective Measures of Sleep in the Multi-Ethnic Study of Atherosclerosis.

PubMed

Johnson, Dayna A; Simonelli, Guido; Moore, Kari; Billings, Martha; Mujahid, Mahasin S; Rueschman, Michael; Kawachi, Ichiro; Redline, Susan; Diez Roux, Ana V; Patel, Sanjay R

2017-01-01

To investigate cross-sectional associations of neighborhood social environment (social cohesion, safety) with objective measures of sleep duration, timing, and disturbances. A racially/ethnically diverse population of men and women (N = 1949) aged 54 to 93 years participating in the Multi-Ethnic Study of Atherosclerosis Sleep and Neighborhood Ancillary studies. Participants underwent 1-week actigraphy between 2010 and 2013. Measures of sleep duration, timing, and disruption were averaged over all days. Neighborhood characteristics were assessed via questionnaires administered to participants and an independent sample within the same neighborhood and aggregated at the neighborhood (census tract, N = 783) level using empirical Bayes estimation. Multilevel linear regression models were used to assess the association between the neighborhood social environment and each sleep outcome. Neighborhood social environment characterized by higher levels of social cohesion and safety were associated with longer sleep duration and earlier sleep midpoint. Each 1 standard deviation higher neighborhood social environment score was associated with 6.1 minutes longer [95% confidence interval (CI): 2.0, 10.2] sleep duration and 6.4 minutes earlier (CI: 2.2, 10.6) sleep midpoint after adjustment for age, sex, race, socioeconomic status, and marital status. These associations persisted after adjustment for other risk factors. Neighborhood social factors were not associated with sleep efficiency or sleep fragmentation index. A more favorable neighborhood social environment is associated with longer objectively measured sleep duration and earlier sleep timing. Intervening on the neighborhood environment may improve sleep and subsequent health outcomes. © Sleep Research Society 2017. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

Developing indicators for European birds

PubMed Central

Gregory, Richard D; van Strien, Arco; Vorisek, Petr; Gmelig Meyling, Adriaan W; Noble, David G; Foppen, Ruud P.B; Gibbons, David W

2005-01-01

The global pledge to deliver ‘a significant reduction in the current rate of biodiversity loss by 2010’ is echoed in a number of regional and national level targets. There is broad consensus, however, that in the absence of conservation action, biodiversity will continue to be lost at a rate unprecedented in the recent era. Remarkably, we lack a basic system to measure progress towards these targets and, in particular, we lack standard measures of biodiversity and procedures to construct and assess summary statistics. Here, we develop a simple classification of biodiversity indicators to assist their development and clarify purpose. We use European birds, as example taxa, to show how robust indicators can be constructed and how they can be interpreted. We have developed statistical methods to calculate supranational, multi-species indices using population data from national annual breeding bird surveys in Europe. Skilled volunteers using standardized field methods undertake data collection where methods and survey designs differ slightly across countries. Survey plots tend to be widely distributed at a national level, covering many bird species and habitats with reasonable representation. National species' indices are calculated using log-linear regression, which allows for plot turnover. Supranational species' indices are constructed by combining the national species' indices weighted by national population sizes of each species. Supranational, multi-species indicators are calculated by averaging the resulting indices. We show that common farmland birds in Europe have declined steeply over the last two decades, whereas woodland birds have not. Evidence elsewhere shows that the main driver of farmland bird declines is increased agricultural intensification. We argue that the farmland bird indicator is a useful surrogate for trends in other elements of biodiversity in this habitat. PMID:15814345

Albumin, a marker for post-operative myocardial damage in cardiac surgery.

PubMed

van Beek, Dianne E C; van der Horst, Iwan C C; de Geus, A Fred; Mariani, Massimo A; Scheeren, Thomas W L

2018-06-06

Low serum albumin (SA) is a prognostic factor for poor outcome after cardiac surgery. The aim of this study was to estimate the association between pre-operative SA, early post-operative SA and postoperative myocardial injury. This single center cohort study included adult patients undergoing cardiac surgery during 4 consecutive years. Postoperative myocardial damage was defined by calculating the area under the curve (AUC) of troponin (Tn) values during the first 72 h after surgery and its association with SA analyzed using linear regression and with multivariable linear regression to account for patient related and procedural confounders. The association between SA and the secondary outcomes (peri-operative myocardial infarction [PMI], requiring ventilation >24 h, rhythm disturbances, 30-day mortality) was studied using (multivariable) log binomial regression analysis. In total 2757 patients were included. The mean pre-operative SA was 29 ± 13 g/l and the mean post-operative SA was 26 ± 6 g/l. Post-operative SA levels (on average 26 min after surgery) were inversely associated with postoperative myocardial damage in both univariable analysis (regression coefficient - 0.019, 95%CI -0.022/-0.015, p < 0.005) and after adjustment for patient related and surgical confounders (regression coefficient - 0.014 [95% CI -0.020/-0.008], p < 0.0005). Post-operative albumin levels were significantly correlated with the amount of postoperative myocardial damage in patients undergoing cardiac surgery independent of typical confounders. Copyright © 2018. Published by Elsevier Inc.

Assessment of mechanical properties of isolated bovine intervertebral discs from multi-parametric magnetic resonance imaging.

PubMed

Recuerda, Maximilien; Périé, Delphine; Gilbert, Guillaume; Beaudoin, Gilles

2012-10-12

The treatment planning of spine pathologies requires information on the rigidity and permeability of the intervertebral discs (IVDs). Magnetic resonance imaging (MRI) offers great potential as a sensitive and non-invasive technique for describing the mechanical properties of IVDs. However, the literature reported small correlation coefficients between mechanical properties and MRI parameters. Our hypothesis is that the compressive modulus and the permeability of the IVD can be predicted by a linear combination of MRI parameters. Sixty IVDs were harvested from bovine tails, and randomly separated in four groups (in-situ, digested-6h, digested-18h, digested-24h). Multi-parametric MRI acquisitions were used to quantify the relaxation times T1 and T2, the magnetization transfer ratio MTR, the apparent diffusion coefficient ADC and the fractional anisotropy FA. Unconfined compression, confined compression and direct permeability measurements were performed to quantify the compressive moduli and the hydraulic permeabilities. Differences between groups were evaluated from a one way ANOVA. Multi linear regressions were performed between dependent mechanical properties and independent MRI parameters to verify our hypothesis. A principal component analysis was used to convert the set of possibly correlated variables into a set of linearly uncorrelated variables. Agglomerative Hierarchical Clustering was performed on the 3 principal components. Multilinear regressions showed that 45 to 80% of the Young's modulus E, the aggregate modulus in absence of deformation HA0, the radial permeability kr and the axial permeability in absence of deformation k0 can be explained by the MRI parameters within both the nucleus pulposus and the annulus pulposus. The principal component analysis reduced our variables to two principal components with a cumulative variability of 52-65%, which increased to 70-82% when considering the third principal component. The dendograms showed a natural division into four clusters for the nucleus pulposus and into three or four clusters for the annulus fibrosus. The compressive moduli and the permeabilities of isolated IVDs can be assessed mostly by MT and diffusion sequences. However, the relationships have to be improved with the inclusion of MRI parameters more sensitive to IVD degeneration. Before the use of this technique to quantify the mechanical properties of IVDs in vivo on patients suffering from various diseases, the relationships have to be defined for each degeneration state of the tissue that mimics the pathology. Our MRI protocol associated to principal component analysis and agglomerative hierarchical clustering are promising tools to classify the degenerated intervertebral discs and further find biomarkers and predictive factors of the evolution of the pathologies.

Mathematical model for the contribution of individual organs to non-zero y-intercepts in single and multi-compartment linear models of whole-body energy expenditure.

PubMed

Kaiyala, Karl J

2014-01-01

Mathematical models for the dependence of energy expenditure (EE) on body mass and composition are essential tools in metabolic phenotyping. EE scales over broad ranges of body mass as a non-linear allometric function. When considered within restricted ranges of body mass, however, allometric EE curves exhibit 'local linearity.' Indeed, modern EE analysis makes extensive use of linear models. Such models typically involve one or two body mass compartments (e.g., fat free mass and fat mass). Importantly, linear EE models typically involve a non-zero (usually positive) y-intercept term of uncertain origin, a recurring theme in discussions of EE analysis and a source of confounding in traditional ratio-based EE normalization. Emerging linear model approaches quantify whole-body resting EE (REE) in terms of individual organ masses (e.g., liver, kidneys, heart, brain). Proponents of individual organ REE modeling hypothesize that multi-organ linear models may eliminate non-zero y-intercepts. This could have advantages in adjusting REE for body mass and composition. Studies reveal that individual organ REE is an allometric function of total body mass. I exploit first-order Taylor linearization of individual organ REEs to model the manner in which individual organs contribute to whole-body REE and to the non-zero y-intercept in linear REE models. The model predicts that REE analysis at the individual organ-tissue level will not eliminate intercept terms. I demonstrate that the parameters of a linear EE equation can be transformed into the parameters of the underlying 'latent' allometric equation. This permits estimates of the allometric scaling of EE in a diverse variety of physiological states that are not represented in the allometric EE literature but are well represented by published linear EE analyses.

Missing-value estimation using linear and non-linear regression with Bayesian gene selection.

PubMed

Zhou, Xiaobo; Wang, Xiaodong; Dougherty, Edward R

2003-11-22

Data from microarray experiments are usually in the form of large matrices of expression levels of genes under different experimental conditions. Owing to various reasons, there are frequently missing values. Estimating these missing values is important because they affect downstream analysis, such as clustering, classification and network design. Several methods of missing-value estimation are in use. The problem has two parts: (1) selection of genes for estimation and (2) design of an estimation rule. We propose Bayesian variable selection to obtain genes to be used for estimation, and employ both linear and nonlinear regression for the estimation rule itself. Fast implementation issues for these methods are discussed, including the use of QR decomposition for parameter estimation. The proposed methods are tested on data sets arising from hereditary breast cancer and small round blue-cell tumors. The results compare very favorably with currently used methods based on the normalized root-mean-square error. The appendix is available from http://gspsnap.tamu.edu/gspweb/zxb/missing_zxb/ (user: gspweb; passwd: gsplab).

Wired: Energy Drinks, Jock Identity, Masculine Norms, and Risk Taking

ERIC Educational Resources Information Center

Miller, Kathleen E.

2008-01-01

Objective: The author examined gendered links among sport-related identity, endorsement of conventional masculine norms, risk taking, and energy-drink consumption. Participants: The author surveyed 795 undergraduate students enrolled in introductory-level courses at a public university. Methods: The author conducted linear regression analyses of…

Multivariate decoding of brain images using ordinal regression.

PubMed

Doyle, O M; Ashburner, J; Zelaya, F O; Williams, S C R; Mehta, M A; Marquand, A F

2013-11-01

Neuroimaging data are increasingly being used to predict potential outcomes or groupings, such as clinical severity, drug dose response, and transitional illness states. In these examples, the variable (target) we want to predict is ordinal in nature. Conventional classification schemes assume that the targets are nominal and hence ignore their ranked nature, whereas parametric and/or non-parametric regression models enforce a metric notion of distance between classes. Here, we propose a novel, alternative multivariate approach that overcomes these limitations - whole brain probabilistic ordinal regression using a Gaussian process framework. We applied this technique to two data sets of pharmacological neuroimaging data from healthy volunteers. The first study was designed to investigate the effect of ketamine on brain activity and its subsequent modulation with two compounds - lamotrigine and risperidone. The second study investigates the effect of scopolamine on cerebral blood flow and its modulation using donepezil. We compared ordinal regression to multi-class classification schemes and metric regression. Considering the modulation of ketamine with lamotrigine, we found that ordinal regression significantly outperformed multi-class classification and metric regression in terms of accuracy and mean absolute error. However, for risperidone ordinal regression significantly outperformed metric regression but performed similarly to multi-class classification both in terms of accuracy and mean absolute error. For the scopolamine data set, ordinal regression was found to outperform both multi-class and metric regression techniques considering the regional cerebral blood flow in the anterior cingulate cortex. Ordinal regression was thus the only method that performed well in all cases. Our results indicate the potential of an ordinal regression approach for neuroimaging data while providing a fully probabilistic framework with elegant approaches for model selection. Copyright © 2013. Published by Elsevier Inc.

Compound Identification Using Penalized Linear Regression on Metabolomics

PubMed Central

Liu, Ruiqi; Wu, Dongfeng; Zhang, Xiang; Kim, Seongho

2014-01-01

Compound identification is often achieved by matching the experimental mass spectra to the mass spectra stored in a reference library based on mass spectral similarity. Because the number of compounds in the reference library is much larger than the range of mass-to-charge ratio (m/z) values so that the data become high dimensional data suffering from singularity. For this reason, penalized linear regressions such as ridge regression and the lasso are used instead of the ordinary least squares regression. Furthermore, two-step approaches using the dot product and Pearson’s correlation along with the penalized linear regression are proposed in this study. PMID:27212894

The Role of Family Environment in Depressive Symptoms among University Students: A Large Sample Survey in China

PubMed Central

Yang, Yanjie; Chen, Lu; Qiu, Xiaohui; Qiao, Zhengxue; Zhou, Jiawei; Pan, Hui; Ban, Bo; Zhu, Xiongzhao; He, Jincai; Ding, Yongqing; Bai, Bing

2015-01-01

Objective To explore the relationship between family environment and depressive symptoms and to evaluate the influence of hard and soft family environmental factors on depression levels in a large sample of university students in China. Methods A multi-stage stratified sampling procedure was used to select 6,000 participants. The response rate was 88.8%, with 5,329 students completing the Beck Depression Inventory (BDI) and the Family Environment Scale Chinese Version (FES-CV), which was adapted for the Chinese population. Differences between the groups were tested for significance by the Student’s t-test; ANOVA was used to test continuous variables. The relationship between soft family environmental factors and BDI were tested by Pearson correlation analysis. Hierarchical linear regression analysis was conducted to model the effects of hard environmental factors and soft environmental factors on depression in university students. Results A total of 11.8% of students scored above the threshold of moderate depression(BDI≧14). Hard family environmental factors such as parent relationship, family economic status, level of parental literacy and non-intact family structure were associated with depressive symptoms. The soft family environmental factors—conflict and control—were positively associated with depression, while cohesion was negatively related to depressive symptom after controlling for other important associates of depression. Hierarchical regression analysis indicated that the soft family environment correlates more strongly with depression than the hard family environment. Conclusions Soft family environmental factors—especially cohesion, conflict and control—appeared to play an important role in the occurrence of depressive symptoms. These findings underline the significance of the family environment as a source of risk factors for depression among university students in China and suggest that family-based interventions and improvement are very important to reduce depression among university students. PMID:26629694

The Role of Family Environment in Depressive Symptoms among University Students: A Large Sample Survey in China.

PubMed

Yu, Yunmiao; Yang, Xiuxian; Yang, Yanjie; Chen, Lu; Qiu, Xiaohui; Qiao, Zhengxue; Zhou, Jiawei; Pan, Hui; Ban, Bo; Zhu, Xiongzhao; He, Jincai; Ding, Yongqing; Bai, Bing

2015-01-01

To explore the relationship between family environment and depressive symptoms and to evaluate the influence of hard and soft family environmental factors on depression levels in a large sample of university students in China. A multi-stage stratified sampling procedure was used to select 6,000 participants. The response rate was 88.8%, with 5,329 students completing the Beck Depression Inventory (BDI) and the Family Environment Scale Chinese Version (FES-CV), which was adapted for the Chinese population. Differences between the groups were tested for significance by the Student's t-test; ANOVA was used to test continuous variables. The relationship between soft family environmental factors and BDI were tested by Pearson correlation analysis. Hierarchical linear regression analysis was conducted to model the effects of hard environmental factors and soft environmental factors on depression in university students. A total of 11.8% of students scored above the threshold of moderate depression (BDI≧14). Hard family environmental factors such as parent relationship, family economic status, level of parental literacy and non-intact family structure were associated with depressive symptoms. The soft family environmental factors--conflict and control--were positively associated with depression, while cohesion was negatively related to depressive symptom after controlling for other important associates of depression. Hierarchical regression analysis indicated that the soft family environment correlates more strongly with depression than the hard family environment. Soft family environmental factors--especially cohesion, conflict and control--appeared to play an important role in the occurrence of depressive symptoms. These findings underline the significance of the family environment as a source of risk factors for depression among university students in China and suggest that family-based interventions and improvement are very important to reduce depression among university students.

Longitudinal study of the socio-demographic determinants of changes in body weight and waist circumference in a multi-ethnic Asian population.

PubMed

Ong, S K; Fong, C W; Ma, S; Lee, J; Heng, D; Deurenberg-Yap, M; Low, Y-L; Tan, M; Lim, W-Y; Tai, E S

2009-11-01

To examine the changes in weight and waist circumference of adult Singaporeans between 1998 and 2005-2007, and the associations of these changes with demographic and socio-economic factors. A prospective study, which followed up participants aged 18-69 years from the 1998 National Health Survey. Analysis was performed on data from 2483 individuals (53% of original sample) who returned for follow-up in 2005-2007. Body weight and waist circumference were measured both at baseline and follow-up. Logistic regression was used to examine factors associated with being overweight and obese at baseline. Linear regression was used to examine changes in weight and waist circumference over time. The variables examined were age, gender, ethnicity, marital status, educational level, housing and employment status, smoking, alcohol consumption and sports activities. Mean weight for the population increased over the follow-up period by 1.48 kg (s.d.=4.95) and mean waist circumference increased by 3.32 cm (s.d.=7.92). Cross-sectionally, those who were overweight or obese were more likely to be Malays or Indians, married, homemakers and have lower educational level. Prospectively, individuals who gained the most weight were younger, more likely to be ethnic minority groups and have the lowest body mass index (BMI) at baseline. They also appeared to be of higher socio-economic status (SES) based on housing type. These associations were statistically significant even after adjusting for other variables. Obesity prevention should start early in the younger age. Preventive programs need to reach out to Malay and Indian ethnic groups and those with higher SES. These findings should be used in designing messaging of preventive strategies.

A global, cross cultural study examining the relationship between employee health risk status and work performance metrics.

PubMed

Howarth, Ana; Quesada, Jose; Mills, Peter R

2017-01-01

Health risk assessments (HRA) are used by many organisations as a basis for developing relevant and targeted employee health and well-being interventions. However, many HRA's have a western-centric focus and therefore it is unclear whether the results can be directly extrapolated to those from non-western countries. More information regarding the differences in the associations between country status and health risks is needed along with a more global perspective of employee health risk factors and well-being overall. Therefore we aimed to i) quantify and compare associations for a number of health risk factors based on country status, and then ii) explore which characteristics can aid better prediction of well-being levels and in turn workplace productivity globally. Online employee HRA data collected from 254 multi-national companies, for the years 2013 through 2016 was analysed (n = 117,274). Multiple linear regression models were fitted, adjusting for age and gender, to quantify associations between country status and health risk factors. Separate regression models were used to assess the prediction of well-being measures related to productivity. On average, the developing countries were comprised of younger individuals with lower obesity rates and markedly higher job satisfaction compared to their developed country counterparts. However, they also reported higher levels of anxiety and depression, a greater number of health risks and lower job effectiveness. Assessment of key factors related to productivity found that region of residency was the biggest predictor of presenteeism and poor pain management was the biggest predictor of absenteeism. Clear differences in health risks exist between employees from developed and developing countries and these should be considered when addressing well-being and productivity in the global workforce.

Walk Score® and Transit Score® and Walking in the Multi-Ethnic Study of Atherosclerosis

PubMed Central

Hirsch, Jana A.; Moore, Kari A.; Evenson, Kelly R.; Rodriguez, Daniel A; Diez Roux, Ana V.

2013-01-01

Background Walk Score® and Transit Score® are open-source measures of the neighborhood built environment to support walking (“walkability”) and access to transportation. Purpose To investigate associations of Street Smart Walk Score and Transit Score with self-reported transport and leisure walking using data from a large multi-city and diverse population-based sample of adults. Methods Data from a sample of 4552 residents of Baltimore MD; Chicago IL; Forsyth County NC; Los Angeles CA; New York NY; and St. Paul MN from the Multi-Ethnic Study of Atherosclerosis (2010–2012) were linked to Walk Score and Transit Score (collected in 2012). Logistic and linear regression models estimated ORs of not walking and mean differences in minutes walked, respectively, associated with continuous and categoric Walk Score and Transit Score. All analyses were conducted in 2012. Results After adjustment for site, key sociodemographic, and health variables, a higher Walk Score was associated with lower odds of not walking for transport and more minutes/week of transport walking. Compared to those in a “walker’s paradise,” lower categories of Walk Score were associated with a linear increase in odds of not transport walking and a decline in minutes of leisure walking. An increase in Transit Score was associated with lower odds of not transport walking or leisure walking, and additional minutes/week of leisure walking. Conclusions Walk Score and Transit Score appear to be useful as measures of walkability in analyses of neighborhood effects. PMID:23867022

Serum 25-hydroxyvitamin D level is associated with myopia in the Korea national health and nutrition examination survey.

PubMed

Kwon, Jin-Woo; Choi, Jin A; La, Tae Yoon

2016-11-01

The aim of this article was to assess the associations of serum 25-hydroxyvitamin D [25(OH)D] and daily sun exposure time with myopia in Korean adults.This study is based on the Korea National Health and Nutrition Examination Survey (KNHANES) of Korean adults in 2010-2012; multiple logistic regression analyses were performed to examine the associations of serum 25(OH)D levels and daily sun exposure time with myopia, defined as spherical equivalent ≤-0.5D, after adjustment for age, sex, household income, body mass index (BMI), exercise, intraocular pressure (IOP), and education level. Also, multiple linear regression analyses were performed to examine the relationship between serum 25(OH)D levels with spherical equivalent after adjustment for daily sun exposure time in addition to the confounding factors above.Between the nonmyopic and myopic groups, spherical equivalent, age, IOP, BMI, waist circumference, education level, household income, and area of residence differed significantly (all P < 0.05). Compared with subjects with daily sun exposure time <2 hour, subjects with sun exposure time ≥2 to <5 hour, and those with sun exposure time ≥5 hour had significantly less myopia (P < 0.001). In addition, compared with subjects were categorized into quartiles of serum 25(OH)D, the higher quartiles had gradually lower prevalences of myopia after adjustment for confounding factors (P < 0.001). In multiple linear regression analyses, spherical equivalent was significantly associated with serum 25(OH)D concentration after adjustment for confounding factors (P = 0.002).Low serum 25(OH)D levels and shorter daily sun exposure time may be independently associated with a high prevalence of myopia in Korean adults. These data suggest a direct role for vitamin D in the development of myopia.

A General Sparse Tensor Framework for Electronic Structure Theory

DOE PAGES

Manzer, Samuel; Epifanovsky, Evgeny; Krylov, Anna I.; ...

2017-01-24

Linear-scaling algorithms must be developed in order to extend the domain of applicability of electronic structure theory to molecules of any desired size. But, the increasing complexity of modern linear-scaling methods makes code development and maintenance a significant challenge. A major contributor to this difficulty is the lack of robust software abstractions for handling block-sparse tensor operations. We therefore report the development of a highly efficient symbolic block-sparse tensor library in order to provide access to high-level software constructs to treat such problems. Our implementation supports arbitrary multi-dimensional sparsity in all input and output tensors. We then avoid cumbersome machine-generatedmore » code by implementing all functionality as a high-level symbolic C++ language library and demonstrate that our implementation attains very high performance for linear-scaling sparse tensor contractions.« less

Machine learning and linear regression models to predict catchment-level base cation weathering rates across the southern Appalachian Mountain region, USA

Treesearch

Nicholas A. Povak; Paul F. Hessburg; Todd C. McDonnell; Keith M. Reynolds; Timothy J. Sullivan; R. Brion Salter; Bernard J. Crosby

2014-01-01

Accurate estimates of soil mineral weathering are required for regional critical load (CL) modeling to identify ecosystems at risk of the deleterious effects from acidification. Within a correlative modeling framework, we used modeled catchment-level base cation weathering (BCw) as the response variable to identify key environmental correlates and predict a continuous...

Multi-objective control of nonlinear boiler-turbine dynamics with actuator magnitude and rate constraints.

PubMed

Chen, Pang-Chia

2013-01-01

This paper investigates multi-objective controller design approaches for nonlinear boiler-turbine dynamics subject to actuator magnitude and rate constraints. System nonlinearity is handled by a suitable linear parameter varying system representation with drum pressure as the system varying parameter. Variation of the drum pressure is represented by suitable norm-bounded uncertainty and affine dependence on system matrices. Based on linear matrix inequality algorithms, the magnitude and rate constraints on the actuator and the deviations of fluid density and water level are formulated while the tracking abilities on the drum pressure and power output are optimized. Variation ranges of drum pressure and magnitude tracking commands are used as controller design parameters, determined according to the boiler-turbine's operation range. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Control Variate Selection for Multiresponse Simulation.

DTIC Science & Technology

1987-05-01

M. H. Knuter, Applied Linear Regression Mfodels, Richard D. Erwin, Inc., Homewood, Illinois, 1983. Neuts, Marcel F., Probability, Allyn and Bacon...1982. Neter, J., V. Wasserman, and M. H. Knuter, Applied Linear Regression .fodels, Richard D. Erwin, Inc., Homewood, Illinois, 1983. Neuts, Marcel F...Aspects of J%,ultivariate Statistical Theory, John Wiley and Sons, New York, New York, 1982. dY Neter, J., W. Wasserman, and M. H. Knuter, Applied Linear Regression Mfodels

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

ERIC Educational Resources Information Center

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

2011-01-01

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

High correlations between MRI brain volume measurements based on NeuroQuant® and FreeSurfer.

PubMed

Ross, David E; Ochs, Alfred L; Tate, David F; Tokac, Umit; Seabaugh, John; Abildskov, Tracy J; Bigler, Erin D

2018-05-30

NeuroQuant ® (NQ) and FreeSurfer (FS) are commonly used computer-automated programs for measuring MRI brain volume. Previously they were reported to have high intermethod reliabilities but often large intermethod effect size differences. We hypothesized that linear transformations could be used to reduce the large effect sizes. This study was an extension of our previously reported study. We performed NQ and FS brain volume measurements on 60 subjects (including normal controls, patients with traumatic brain injury, and patients with Alzheimer's disease). We used two statistical approaches in parallel to develop methods for transforming FS volumes into NQ volumes: traditional linear regression, and Bayesian linear regression. For both methods, we used regression analyses to develop linear transformations of the FS volumes to make them more similar to the NQ volumes. The FS-to-NQ transformations based on traditional linear regression resulted in effect sizes which were small to moderate. The transformations based on Bayesian linear regression resulted in all effect sizes being trivially small. To our knowledge, this is the first report describing a method for transforming FS to NQ data so as to achieve high reliability and low effect size differences. Machine learning methods like Bayesian regression may be more useful than traditional methods. Copyright © 2018 Elsevier B.V. All rights reserved.

Aircraft Anomaly Detection Using Performance Models Trained on Fleet Data

NASA Technical Reports Server (NTRS)

Gorinevsky, Dimitry; Matthews, Bryan L.; Martin, Rodney

2012-01-01

This paper describes an application of data mining technology called Distributed Fleet Monitoring (DFM) to Flight Operational Quality Assurance (FOQA) data collected from a fleet of commercial aircraft. DFM transforms the data into aircraft performance models, flight-to-flight trends, and individual flight anomalies by fitting a multi-level regression model to the data. The model represents aircraft flight performance and takes into account fixed effects: flight-to-flight and vehicle-to-vehicle variability. The regression parameters include aerodynamic coefficients and other aircraft performance parameters that are usually identified by aircraft manufacturers in flight tests. Using DFM, the multi-terabyte FOQA data set with half-million flights was processed in a few hours. The anomalies found include wrong values of competed variables, (e.g., aircraft weight), sensor failures and baises, failures, biases, and trends in flight actuators. These anomalies were missed by the existing airline monitoring of FOQA data exceedances.

Quantile Regression in the Study of Developmental Sciences

PubMed Central

Petscher, Yaacov; Logan, Jessica A. R.

2014-01-01

Linear regression analysis is one of the most common techniques applied in developmental research, but only allows for an estimate of the average relations between the predictor(s) and the outcome. This study describes quantile regression, which provides estimates of the relations between the predictor(s) and outcome, but across multiple points of the outcome’s distribution. Using data from the High School and Beyond and U.S. Sustained Effects Study databases, quantile regression is demonstrated and contrasted with linear regression when considering models with: (a) one continuous predictor, (b) one dichotomous predictor, (c) a continuous and a dichotomous predictor, and (d) a longitudinal application. Results from each example exhibited the differential inferences which may be drawn using linear or quantile regression. PMID:24329596

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

PubMed Central

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

2018-01-01

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

Satellite remote sensing of fine particulate air pollutants over Indian mega cities

NASA Astrophysics Data System (ADS)

Sreekanth, V.; Mahesh, B.; Niranjan, K.

2017-11-01

In the backdrop of the need for high spatio-temporal resolution data on PM2.5 mass concentrations for health and epidemiological studies over India, empirical relations between Aerosol Optical Depth (AOD) and PM2.5 mass concentrations are established over five Indian mega cities. These relations are sought to predict the surface PM2.5 mass concentrations from high resolution columnar AOD datasets. Current study utilizes multi-city public domain PM2.5 data (from US Consulate and Embassy's air monitoring program) and MODIS AOD, spanning for almost four years. PM2.5 is found to be positively correlated with AOD. Station-wise linear regression analysis has shown spatially varying regression coefficients. Similar analysis has been repeated by eliminating data from the elevated aerosol prone seasons, which has improved the correlation coefficient. The impact of the day to day variability in the local meteorological conditions on the AOD-PM2.5 relationship has been explored by performing a multiple regression analysis. A cross-validation approach for the multiple regression analysis considering three years of data as training dataset and one-year data as validation dataset yielded an R value of ∼0.63. The study was concluded by discussing the factors which can improve the relationship.

Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification

NASA Astrophysics Data System (ADS)

Cao, Faxian; Yang, Zhijing; Ren, Jinchang; Ling, Wing-Kuen; Zhao, Huimin; Marshall, Stephen

2017-12-01

Although the sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constrained its applications for hyperspectral image (HSI) classification. In order to tackle these two drawbacks, an extreme sparse multinomial logistic regression (ESMLR) is proposed for effective classification of HSI. First, the HSI dataset is projected to a new feature space with randomly generated weight and bias. Second, an optimization model is established by the Lagrange multiplier method and the dual principle to automatically determine a good initial regressor for SMLR via minimizing the training error and the regressor value. Furthermore, the extended multi-attribute profiles (EMAPs) are utilized for extracting both the spectral and spatial features. A combinational linear multiple features learning (MFL) method is proposed to further enhance the features extracted by ESMLR and EMAPs. Finally, the logistic regression via the variable splitting and the augmented Lagrangian (LORSAL) is adopted in the proposed framework for reducing the computational time. Experiments are conducted on two well-known HSI datasets, namely the Indian Pines dataset and the Pavia University dataset, which have shown the fast and robust performance of the proposed ESMLR framework.

Multi-Model Assessment of the Factors Driving Stratospheric Ozone Evolution Over the 21st Century

NASA Technical Reports Server (NTRS)

Oman, L. D.; Plummer, D. A.; Waugh, D. W.; Austin, J.; Scinocca, J.; Douglass, A. R.; Salawitch, R. J.; Canty, T.; Akiyoshi, H.; Bekki, S.;

A SEMIPARAMETRIC BAYESIAN MODEL FOR CIRCULAR-LINEAR REGRESSION

EPA Science Inventory

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

Vitamin D is associated with testosterone and hypogonadism in Chinese men: Results from a cross-sectional SPECT-China study.

PubMed

Wang, Ningjian; Han, Bing; Li, Qin; Chen, Yi; Chen, Yingchao; Xia, Fangzhen; Lin, Dongping; Jensen, Michael D; Lu, Yingli

2015-07-16

To date, no study has explored the association between androgen levels and 25-hydroxyvitamin D (25(OH)D) levels in Chinese men. We aimed to investigate the relationship between 25(OH)D levels and total and free testosterone (T), sex hormone binding globulin (SHBG), estradiol, and hypogonadism in Chinese men. Our data, which were based on the population, were collected from 16 sites in East China. There were 2,854 men enrolled in the study, with a mean (SD) age of 53.0 (13.5) years. Hypogonadism was defined as total T <11.3 nmol/L or free T <22.56 pmol/L. The 25(OH)D, follicle-stimulating hormone, luteinizing hormone, total T, estradiol and SHBG were measured using chemiluminescence and free T by enzyme-linked immune-sorbent assay. The associations between 25(OH)D and reproductive hormones and hypogonadism were analyzed using linear regression and binary logistic regression analyses, respectively. A total of 713 (25.0 %) men had hypogonadism with significantly lower 25(OH)D levels but greater BMI and HOMA-IR. Using linear regression, after fully adjusting for age, residence area, economic status, smoking, BMI, HOMA-IR, diabetes and systolic pressure, 25(OH)D was associated with total T and estradiol (P < 0.05). In the logistic regression analyses, increased quartiles of 25(OH)D were associated with significantly decreased odds ratios of hypogonadism (P for trend <0.01). This association, which was considerably attenuated by BMI and HOMA-IR, persisted in the fully adjusted model (P for trend <0.01) in which for the lowest compared with the highest quartile of 25(OH)D, the odds ratio of hypogonadism was 1.50 (95 % CI, 1.14, 1.97). A lower vitamin D level was associated with a higher prevalence of hypogonadism in Chinese men. This association might, in part, be explained by adiposity and insulin resistance and warrants additional investigation.

Enhancing oil removal from water by immobilizing multi-wall carbon nanotubes on the surface of polyurethane foam.

PubMed

Keshavarz, Alireza; Zilouei, Hamid; Abdolmaleki, Amir; Asadinezhad, Ahmad

2015-07-01

A surface modification method was carried out to enhance the light crude oil sorption capacity of polyurethane foam (PUF) through immobilization of multi-walled carbon nanotube (MWCNT) on the foam surface at various concentrations. The developed sorbent was characterized using scanning electron microscopy, Fourier transform infrared spectroscopy, thermogravimetric analysis, and tensile elongation test. The results obtained from thermogravimetric and tensile elongation tests showed the improvement of thermal and mechanical resistance of surface-modified foam. The experimental data also revealed that the immobilization of MWCNT on PUF surface enhanced the sorption capacity of light crude oil and reduced water sorption. The highest oil removal capacity was obtained for 1 wt% MWCNT on PUF surface which was 21.44% enhancement in light crude oil sorption compared to the blank PUF. The reusability of surface modified PUF was determined through four cycles of chemical regeneration using petroleum ether. The adsorption of light crude oil with 30 g initial mass showed that 85.45% of the initial oil sorption capacity of this modified sorbent was remained after four regeneration cycles. Equilibrium isotherms for adsorption of oil were analyzed by the Freundlich, Langmuir, Temkin, and Redlich-Peterson models through linear and non-linear regression methods. Results of equilibrium revealed that Langmuir isotherm is the best fitting model and non-linear method is a more accurate way to predict the parameters involved in the isotherms. The overall findings suggested the promising potentials of the developed sorbent in order to be efficiently used in large-scale oil spill cleanup. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

PubMed

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

2018-01-01

Plasma very low-density lipoprotein (VLDL) can be used to select for low body fat or abdominal fat (AF) in broilers, but its correlation with AF is limited. We investigated whether any other biochemical indicator can be used in combination with VLDL for a better selective effect. Nineteen plasma biochemical indicators were measured in male chickens from the Northeast Agricultural University broiler lines divergently selected for AF content (NEAUHLF) in the fed state at 46 and 48 d of age. The average concentration of every parameter for the 2 d was used for statistical analysis. Levels of these 19 plasma biochemical parameters were compared between the lean and fat lines. The phenotypic correlations between these plasma biochemical indicators and AF traits were analyzed. Then, multiple linear regression models were constructed to select the best model used for selecting against AF content. and the heritabilities of plasma indicators contained in the best models were estimated. The results showed that 11 plasma biochemical indicators (triglycerides, total bile acid, total protein, globulin, albumin/globulin, aspartate transaminase, alanine transaminase, gamma-glutamyl transpeptidase, uric acid, creatinine, and VLDL) differed significantly between the lean and fat lines (P < 0.01), and correlated significantly with AF traits (P < 0.05). The best multiple linear regression models based on albumin/globulin, VLDL, triglycerides, globulin, total bile acid, and uric acid, had higher R2 (0.73) than the model based only on VLDL (0.21). The plasma parameters included in the best models had moderate heritability estimates (0.21 ≤ h2 ≤ 0.43). These results indicate that these multiple linear regression models can be used to select for lean broiler chickens. © 2017 Poultry Science Association Inc.

Multi -risk assessment at a national level in Georgia

NASA Astrophysics Data System (ADS)

Tsereteli, Nino; Varazanashvili, Otar; Amiranashvili, Avtandil; Tsereteli, Emili; Elizbarashvili, Elizbar; Saluqvadze, Manana; Dolodze, Jemal

2013-04-01

Work presented here was initiated by national GNSF project " Reducing natural disasters multiple risk: a positive factor for Georgia development " and two international projects: NATO SFP 983038 "Seismic hazard and Rusk assessment for Southern Caucasus-eastern Turkey Energy Corridors" and EMME " Earthquake Model for Middle east Region". Methodology for estimation of "general" vulnerability, hazards and multiple risk to natural hazards (namely, earthquakes, landslides, snow avalanches, flash floods, mudflows, drought, hurricanes, frost, hail) where developed for Georgia. The electronic detailed databases of natural disasters were created. These databases contain the parameters of hazardous phenomena that caused natural disasters. The magnitude and intensity scale of the mentioned disasters are reviewed and the new magnitude and intensity scales are suggested for disasters for which the corresponding formalization is not yet performed. The associated economic losses were evaluated and presented in monetary terms for these hazards. Based on the hazard inventory, an approach was developed that allowed for the calculation of an overall vulnerability value for each individual hazard type, using the Gross Domestic Product per unit area (applied to population) as the indicator for elements at risk exposed. The correlation between estimated economic losses, physical exposure and the magnitude for each of the six types of hazards has been investigated in detail by using multiple linear regression analysis. Economic losses for all past events and historical vulnerability were estimated. Finally, the spatial distribution of general vulnerability was assessed, and the expected maximum economic loss was calculated as well as a multi-risk map was set-up.

Using Anisotropic 3D Minkowski Functionals for Trabecular Bone Characterization and Biomechanical Strength Prediction in Proximal Femur Specimens

PubMed Central

Nagarajan, Mahesh B.; De, Titas; Lochmüller, Eva-Maria; Eckstein, Felix; Wismüller, Axel

2017-01-01

The ability of Anisotropic Minkowski Functionals (AMFs) to capture local anisotropy while evaluating topological properties of the underlying gray-level structures has been previously demonstrated. We evaluate the ability of this approach to characterize local structure properties of trabecular bone micro-architecture in ex vivo proximal femur specimens, as visualized on multi-detector CT, for purposes of biomechanical bone strength prediction. To this end, volumetric AMFs were computed locally for each voxel of volumes of interest (VOI) extracted from the femoral head of 146 specimens. The local anisotropy captured by such AMFs was quantified using a fractional anisotropy measure; the magnitude and direction of anisotropy at every pixel was stored in histograms that served as a feature vectors that characterized the VOIs. A linear multi-regression analysis algorithm was used to predict the failure load (FL) from the feature sets; the predicted FL was compared to the true FL determined through biomechanical testing. The prediction performance was measured by the root mean square error (RMSE) for each feature set. The best prediction performance was obtained from the fractional anisotropy histogram of AMF Euler Characteristic (RMSE = 1.01 ± 0.13), which was significantly better than MDCT-derived mean BMD (RMSE = 1.12 ± 0.16, p<0.05). We conclude that such anisotropic Minkowski Functionals can capture valuable information regarding regional trabecular bone quality and contribute to improved bone strength prediction, which is important for improving the clinical assessment of osteoporotic fracture risk. PMID:29170581

Pseudo second order kinetics and pseudo isotherms for malachite green onto activated carbon: comparison of linear and non-linear regression methods.

PubMed

Kumar, K Vasanth; Sivanesan, S

2006-08-25

Pseudo second order kinetic expressions of Ho, Sobkowsk and Czerwinski, Blanachard et al. and Ritchie were fitted to the experimental kinetic data of malachite green onto activated carbon by non-linear and linear method. Non-linear method was found to be a better way of obtaining the parameters involved in the second order rate kinetic expressions. Both linear and non-linear regression showed that the Sobkowsk and Czerwinski and Ritchie's pseudo second order model were the same. Non-linear regression analysis showed that both Blanachard et al. and Ho have similar ideas on the pseudo second order model but with different assumptions. The best fit of experimental data in Ho's pseudo second order expression by linear and non-linear regression method showed that Ho pseudo second order model was a better kinetic expression when compared to other pseudo second order kinetic expressions. The amount of dye adsorbed at equilibrium, q(e), was predicted from Ho pseudo second order expression and were fitted to the Langmuir, Freundlich and Redlich Peterson expressions by both linear and non-linear method to obtain the pseudo isotherms. The best fitting pseudo isotherm was found to be the Langmuir and Redlich Peterson isotherm. Redlich Peterson is a special case of Langmuir when the constant g equals unity.

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

PubMed

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

2010-01-01

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

Conjunctive management of multi-reservoir network system and groundwater system

NASA Astrophysics Data System (ADS)

Mani, A.; Tsai, F. T. C.

2015-12-01

This study develops a successive mixed-integer linear fractional programming (successive MILFP) method to conjunctively manage water resources provided by a multi-reservoir network system and a groundwater system. The conjunctive management objectives are to maximize groundwater withdrawals and maximize reservoir storages while satisfying water demands and raising groundwater level to a target level. The decision variables in the management problem are reservoir releases and spills, network flows and groundwater pumping rates. Using the fractional programming approach, the objective function is defined as a ratio of total groundwater withdraws to total reservoir storage deficits from the maximum storages. Maximizing this ratio function tends to maximizing groundwater use and minimizing surface water use. This study introduces a conditional constraint on groundwater head in order to sustain aquifers from overpumping: if current groundwater level is less than a target level, groundwater head at the next time period has to be raised; otherwise, it is allowed to decrease up to a certain extent. This conditional constraint is formulated into a set of mixed binary nonlinear constraints and results in a mixed-integer nonlinear fractional programming (MINLFP) problem. To solve the MINLFP problem, we first use the response matrix approach to linearize groundwater head with respect to pumping rate and reduce the problem to an MILFP problem. Using the Charnes-Cooper transformation, the MILFP is transformed to an equivalent mixed-integer linear programming (MILP). The solution of the MILP is successively updated by updating the response matrix in every iteration. The study uses IBM CPLEX to solve the MILP problem. The methodology is applied to water resources management in northern Louisiana. This conjunctive management approach aims to recover the declining groundwater level of the stressed Sparta aquifer by using surface water from a network of four reservoirs as an alternative source of supply.

The Effects of Academic Optimism on Elementary Reading Achievement

ERIC Educational Resources Information Center

Bevel, Raymona K.; Mitchell, Roxanne M.

2012-01-01

Purpose: The purpose of this paper is to explore the relationship between academic optimism (AO) and elementary reading achievement (RA). Design/methodology/approach: Using correlation and hierarchical linear regression, the authors examined school-level effects of AO on fifth grade reading achievement in 29 elementary schools in Alabama.…

USE OF WEIBULL FUNCTION FOR NON-LINEAR ANALYSIS OF EFFECTS OF LOW LEVELS OF SIMULATED HERBICIDE DRIFT ON PLANTS

EPA Science Inventory

We compared two regression models, which are based on the Weibull and probit functions, for the analysis of pesticide toxicity data from laboratory studies on Illinois crop and native plant species. Both mathematical models are continuous, differentiable, strictly positive, and...

The impact of article length on the number of future citations: a bibliometric analysis of general medicine journals.

PubMed

Falagas, Matthew E; Zarkali, Angeliki; Karageorgopoulos, Drosos E; Bardakas, Vangelis; Mavros, Michael N

2013-01-01

The number of citations received is considered an index of study quality and impact. We aimed to examine the factors associated with the number of citations of published articles, focusing on the article length. Original human studies published in the first trimester of 2006 in 5 major General Medicine journals were analyzed with regard to the number of authors and of author-affiliated institutions, title and abstract word count, article length (number of print pages), number of bibliographic references, study design, and 2006 journal impact factor (JIF). A multiple linear regression model was employed to identify the variables independently associated with the number of article citations received through January 2012. On univariate analysis the JIF, number of authors, article length, study design (interventional/observational and prospective/retrospective), title and abstract word count, number of author-affiliated institutions, and number of references were all associated with the number of citations received. On multivariate analysis with the logarithm of citations as the dependent variable, only article length [regression coefficient: 14.64 (95% confidence intervals: (5.76-23.50)] and JIF [3.37 (1.80-4.948)] independently predicted the number of citations. The variance of citations explained by these parameters was 51.2%. In a sample of articles published in major General Medicine journals, in addition to journal impact factors, article length and number of authors independently predicted the number of citations. This may reflect a higher complexity level and quality of longer and multi-authored studies.

Female married illiteracy as the most important continual determinant of total fertility rate among districts of Empowered Action Group States of India: Evidence from Annual Health Survey 2011–12

PubMed Central

Kumar, Rajesh; Dogra, Vishal; Rani, Khushbu; Sahu, Kanti

2017-01-01

Background: District level determinants of total fertility rate in Empowered Action Group states of India can help in ongoing population stabilization programs in India. Objective: Present study intends to assess the role of district level determinants in predicting total fertility rate among districts of the Empowered Action Group states of India. Material and Methods: Data from Annual Health Survey (2011-12) was analysed using STATA and R software packages. Multiple linear regression models were built and evaluated using Akaike Information Criterion. For further understanding, recursive partitioning was used to prepare a regression tree. Results: Female married illiteracy positively associated with total fertility rate and explained more than half (53%) of variance. Under multiple linear regression model, married illiteracy, infant mortality rate, Ante natal care registration, household size, median age of live birth and sex ratio explained 70% of total variance in total fertility rate. In regression tree, female married illiteracy was the root node and splits at 42% determined TFR <= 2.7. The next left side branch was again married illiteracy with splits at 23% to determine TFR <= 2.1. Conclusion: We conclude that female married illiteracy is one of the most important determinants explaining total fertility rate among the districts of an Empowered Action Group states. Focus on female literacy is required to stabilize the population growth in long run. PMID:29416999

Comparison of Neural Network and Linear Regression Models in Statistically Predicting Mental and Physical Health Status of Breast Cancer Survivors

DTIC Science & Technology

2015-07-15

Long-term effects on cancer survivors’ quality of life of physical training versus physical training combined with cognitive-behavioral therapy ...COMPARISON OF NEURAL NETWORK AND LINEAR REGRESSION MODELS IN STATISTICALLY PREDICTING MENTAL AND PHYSICAL HEALTH STATUS OF BREAST...34Comparison of Neural Network and Linear Regression Models in Statistically Predicting Mental and Physical Health Status of Breast Cancer Survivors

Prediction of the Main Engine Power of a New Container Ship at the Preliminary Design Stage

NASA Astrophysics Data System (ADS)

Cepowski, Tomasz

2017-06-01

The paper presents mathematical relationships that allow us to forecast the estimated main engine power of new container ships, based on data concerning vessels built in 2005-2015. The presented approximations allow us to estimate the engine power based on the length between perpendiculars and the number of containers the ship will carry. The approximations were developed using simple linear regression and multivariate linear regression analysis. The presented relations have practical application for estimation of container ship engine power needed in preliminary parametric design of the ship. It follows from the above that the use of multiple linear regression to predict the main engine power of a container ship brings more accurate solutions than simple linear regression.

Three essays on multi-level optimization models and applications

NASA Astrophysics Data System (ADS)

Rahdar, Mohammad

The general form of a multi-level mathematical programming problem is a set of nested optimization problems, in which each level controls a series of decision variables independently. However, the value of decision variables may also impact the objective function of other levels. A two-level model is called a bilevel model and can be considered as a Stackelberg game with a leader and a follower. The leader anticipates the response of the follower and optimizes its objective function, and then the follower reacts to the leader's action. The multi-level decision-making model has many real-world applications such as government decisions, energy policies, market economy, network design, etc. However, there is a lack of capable algorithms to solve medium and large scale these types of problems. The dissertation is devoted to both theoretical research and applications of multi-level mathematical programming models, which consists of three parts, each in a paper format. The first part studies the renewable energy portfolio under two major renewable energy policies. The potential competition for biomass for the growth of the renewable energy portfolio in the United States and other interactions between two policies over the next twenty years are investigated. This problem mainly has two levels of decision makers: the government/policy makers and biofuel producers/electricity generators/farmers. We focus on the lower-level problem to predict the amount of capacity expansions, fuel production, and power generation. In the second part, we address uncertainty over demand and lead time in a multi-stage mathematical programming problem. We propose a two-stage tri-level optimization model in the concept of rolling horizon approach to reducing the dimensionality of the multi-stage problem. In the third part of the dissertation, we introduce a new branch and bound algorithm to solve bilevel linear programming problems. The total time is reduced by solving a smaller relaxation problem in each node and decreasing the number of iterations. Computational experiments show that the proposed algorithm is faster than the existing ones.

The Effect of Organizational Citizenship Behaviours of Primary School Teachers on Their Burnout

ERIC Educational Resources Information Center

Inandi, Yusuf; Buyukozkan, Ayse Sezin

2013-01-01

It was examined in this study whether organizational citizenship behaviours of primary school teachers predict the level of their burnout. Correlation and multi regression analysis were used for this. Survey model was used in this descriptive study. Data were collected from 1699 primary school teachers working in Mersin. Maslach Burnout Inventory…

English Phonological Awareness in Bilinguals: A Cross-Linguistic Study of Tamil, Malay and Chinese English-Language Learners

ERIC Educational Resources Information Center

Dixon, L. Quentin; Chuang, Hui-Kai; Quiroz, Blanca

2012-01-01

To test the lexical restructuring hypothesis among bilingual English-language learners, English phonological awareness (PA), English vocabulary and ethnic language vocabulary (Mandarin Chinese, Malay or Tamil) were assessed among 284 kindergarteners (168 Chinese, 71 Malays and 45 Tamils) in Singapore. A multi-level regression analysis showed that…

Solvent-free synthesis, spectral correlations and antimicrobial activities of some aryl E 2-propen-1-ones

NASA Astrophysics Data System (ADS)

Sathiyamoorthi, K.; Mala, V.; Sakthinathan, S. P.; Kamalakkannan, D.; Suresh, R.; Vanangamudi, G.; Thirunarayanan, G.

2013-08-01

Totally 38 aryl E 2-propen-1-ones including nine substituted styryl 4-iodophenyl ketones have been synthesised using solvent-free SiO2-H3PO4 catalyzed Aldol condensation between respective methyl ketones and substituted benzaldehydes under microwave irradiation. The yields of the ketones are more than 80%. The synthesised chalcones were characterized by their analytical, physical and spectroscopic data. The spectral frequencies of synthesised substituted styryl 4-iodophenyl ketones have been correlated with Hammett substituent constants, F and R parameters using single and multi-linear regression analysis. The antimicrobial activities of 4-iodophenyl chalcones have been studied using Bauer-Kirby method.

Quantifying Main Trends in Lysozyme Nucleation: The Effect of Precipitant Concentration and Impurities

NASA Technical Reports Server (NTRS)

Burke, Michael W.; Judge, Russell A.; Pusey, Marc L.; Rose, M. Franklin (Technical Monitor)

2000-01-01

Full factorial experiment design incorporating multi-linear regression analysis of the experimental data allows the main trends and effects to be quickly identified while using only a limited number of experiments. These techniques were used to identify the effect of precipitant concentration and the presence of an impurity, the physiological lysozyme dimer, on the nucleation rate and crystal dimensions of the tetragonal form of chicken egg white lysozyme. Increasing precipitant concentration was found to decrease crystal numbers, the magnitude of this effect also depending on the supersaturation. The presence of the dimer generally increased nucleation. The crystal axial ratio decreased with increasing precipitant concentration independent of impurity.

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

Circulating fibrinogen but not D-dimer level is associated with vital exhaustion in school teachers.

PubMed

Kudielka, Brigitte M; Bellingrath, Silja; von Känel, Roland

2008-07-01

Meta-analyses have established elevated fibrinogen and D-dimer levels in the circulation as biological risk factors for the development and progression of coronary artery disease (CAD). Here, we investigated whether vital exhaustion (VE), a known psychosocial risk factor for CAD, is associated with fibrinogen and D-dimer levels in a sample of apparently healthy school teachers. The teaching profession has been proposed as a potentially high stressful occupation due to enhanced psychosocial stress at the workplace. Plasma fibrinogen and D-dimer levels were measured in 150 middle-aged male and female teachers derived from the first year of the Trier-Teacher-Stress-Study. Log-transformed levels were analyzed using linear regression. Results yielded a significant association between VE and fibrinogen (p = 0.02) but not D-dimer controlling for relevant covariates. Further investigation of possible interaction effects resulted in a significant association between fibrinogen and the interaction term "VE x gender" (p = 0.05). In a secondary analysis, we reran linear regression models for males and females separately. Gender-specific results revealed that the association between fibrinogen and VE remained significant in males but not females. In sum, the present data support the notion that fibrinogen levels are positively related to VE. Elevated fibrinogen might be one biological pathway by which chronic work stress may impact on teachers' cardiovascular health in the long run.

Diurnal salivary cortisol and regression status in MECP2 Duplication syndrome

PubMed Central

Peters, Sarika U.; Byiers, Breanne J.; Symons, Frank J.

2015-01-01

MECP2 duplication syndrome is an X-linked genomic disorder that is characterized by infantile hypotonia, intellectual disability, and recurrent respiratory infections. Regression affects a subset of individuals, and the etiology of regression has yet to be examined. In this study, alterations in the hypothalamus-pituitary-adrenal axis, including diurnal patterns in salivary cortisol, were examined in four males with MECP2 duplication syndrome who had regression, and four males with the same syndrome without regression (ages 3–22 years). Individuals who had experienced regression do not exhibit typical diurnal cortisol rhythms, and their profiles were flatter through the day. In contrast, individuals with MECP2 duplication syndrome who had not experienced regression showed more typical patterns of higher cortisol levels in the morning with linear decreases throughout the day. This study is the first to suggest a link between atypical diurnal cortisol rhythms and regression status in MECP2 duplication syndrome, and may have implications for treatment. PMID:25999300

Estimation of Standard Error of Regression Effects in Latent Regression Models Using Binder's Linearization. Research Report. ETS RR-07-09

ERIC Educational Resources Information Center

Li, Deping; Oranje, Andreas

2007-01-01

Two versions of a general method for approximating standard error of regression effect estimates within an IRT-based latent regression model are compared. The general method is based on Binder's (1983) approach, accounting for complex samples and finite populations by Taylor series linearization. In contrast, the current National Assessment of…

Bottom-Up Analysis of Single-Case Research Designs

ERIC Educational Resources Information Center

Parker, Richard I.; Vannest, Kimberly J.

2012-01-01

This paper defines and promotes the qualities of a "bottom-up" approach to single-case research (SCR) data analysis. Although "top-down" models, for example, multi-level or hierarchical linear models, are gaining momentum and have much to offer, interventionists should be cautious about analyses that are not easily understood, are not governed by…

A multi-criteria evaluation system for marine litter pollution based on statistical analyses of OSPAR beach litter monitoring time series.

PubMed

Schulz, Marcus; Neumann, Daniel; Fleet, David M; Matthies, Michael

2013-12-01

During the last decades, marine pollution with anthropogenic litter has become a worldwide major environmental concern. Standardized monitoring of litter since 2001 on 78 beaches selected within the framework of the Convention for the Protection of the Marine Environment of the North-East Atlantic (OSPAR) has been used to identify temporal trends of marine litter. Based on statistical analyses of this dataset a two-part multi-criteria evaluation system for beach litter pollution of the North-East Atlantic and the North Sea is proposed. Canonical correlation analyses, linear regression analyses, and non-parametric analyses of variance were used to identify different temporal trends. A classification of beaches was derived from cluster analyses and served to define different states of beach quality according to abundances of 17 input variables. The evaluation system is easily applicable and relies on the above-mentioned classification and on significant temporal trends implied by significant rank correlations. Copyright © 2013 Elsevier Ltd. All rights reserved.

Prevalence of health literacy and its correlates among patients with type II diabetes in Kuwait: A population based study.

PubMed

Hussein, Shaimaa H; Almajran, Abdullah; Albatineh, Ahmed N

2018-05-03

The purpose of this study is to estimate the prevalence of health literacy among patients with type II diabetes and investigate its association with several covariates. No studies were conducted in the Arabian Gulf region characterizing such factors for this population. A cross sectional study was implemented in which 359 type II diabetes patients were recruited from diabetes centers across Kuwait. Health literacy was measured by STOFHLA. Multivariate linear regression was applied to investigate the relationship between health literacy and several covariates. About 44.5% had inadequate, 19.5% marginal, and 35.5% adequate health literacy. Patients with inadequate health literacy were more likely to be older, females, widowed, low education, with income less than 500 KD/month. Multivariate linear regression indicated residence, nationality, education level, and age were significantly associated with health literacy. Adding marital status and gender, hierarchical linear regression revealed that 43.4% of the variability was accounted for. Inadequate health literacy is high in Kuwait. Interventions should be implemented to improve health literacy. This will reduce the prevalence of diabetes-related complications, produce better diabetes outcomes, and improve patients' quality-of-life. Health literacy should be an integral part to health promotion and chronic diseases' management programs in Kuwait. Copyright © 2018 Elsevier B.V. All rights reserved.

On the equivalence of case-crossover and time series methods in environmental epidemiology.

PubMed

Lu, Yun; Zeger, Scott L

2007-04-01

The case-crossover design was introduced in epidemiology 15 years ago as a method for studying the effects of a risk factor on a health event using only cases. The idea is to compare a case's exposure immediately prior to or during the case-defining event with that same person's exposure at otherwise similar "reference" times. An alternative approach to the analysis of daily exposure and case-only data is time series analysis. Here, log-linear regression models express the expected total number of events on each day as a function of the exposure level and potential confounding variables. In time series analyses of air pollution, smooth functions of time and weather are the main confounders. Time series and case-crossover methods are often viewed as competing methods. In this paper, we show that case-crossover using conditional logistic regression is a special case of time series analysis when there is a common exposure such as in air pollution studies. This equivalence provides computational convenience for case-crossover analyses and a better understanding of time series models. Time series log-linear regression accounts for overdispersion of the Poisson variance, while case-crossover analyses typically do not. This equivalence also permits model checking for case-crossover data using standard log-linear model diagnostics.

Cancer Patients Enrolled in a Smoking Cessation Clinical Trial: Characteristics and Correlates of Smoking Rate and Nicotine Dependence.

PubMed

Miele, Andrew; Thompson, Morgan; Jao, Nancy C; Kalhan, Ravi; Leone, Frank; Hogarth, Lee; Hitsman, Brian; Schnoll, Robert

2018-01-01

A substantial proportion of cancer patients continue to smoke after their diagnosis but few studies have evaluated correlates of nicotine dependence and smoking rate in this population, which could help guide smoking cessation interventions. This study evaluated correlates of smoking rate and nicotine dependence among 207 cancer patients. A cross-sectional analysis using multiple linear regression evaluated disease, demographic, affective, and tobacco-seeking correlates of smoking rate and nicotine dependence. Smoking rate was assessed using a timeline follow-back method. The Fagerström Test for Nicotine Dependence measured levels of nicotine dependence. A multiple linear regression predicting nicotine dependence showed an association with smoking to alleviate a sense of addiction from the Reasons for Smoking scale and tobacco-seeking behavior from the concurrent choice task ( p < .05), but not with affect measured by the HADS and PANAS ( p > .05). Multiple linear regression predicting prequit showed an association with smoking to alleviate addiction ( p < .05). ANOVA showed that Caucasian participants reported greater rates of smoking compared to other races. The results suggest that behavioral smoking cessation interventions that focus on helping patients to manage tobacco-seeking behavior, rather than mood management interventions, could help cancer patients quit smoking.

Regression assumptions in clinical psychology research practice-a systematic review of common misconceptions.

PubMed

Ernst, Anja F; Albers, Casper J

2017-01-01

Misconceptions about the assumptions behind the standard linear regression model are widespread and dangerous. These lead to using linear regression when inappropriate, and to employing alternative procedures with less statistical power when unnecessary. Our systematic literature review investigated employment and reporting of assumption checks in twelve clinical psychology journals. Findings indicate that normality of the variables themselves, rather than of the errors, was wrongfully held for a necessary assumption in 4% of papers that use regression. Furthermore, 92% of all papers using linear regression were unclear about their assumption checks, violating APA-recommendations. This paper appeals for a heightened awareness for and increased transparency in the reporting of statistical assumption checking.

Regression assumptions in clinical psychology research practice—a systematic review of common misconceptions

PubMed Central

Ernst, Anja F.

2017-01-01

Misconceptions about the assumptions behind the standard linear regression model are widespread and dangerous. These lead to using linear regression when inappropriate, and to employing alternative procedures with less statistical power when unnecessary. Our systematic literature review investigated employment and reporting of assumption checks in twelve clinical psychology journals. Findings indicate that normality of the variables themselves, rather than of the errors, was wrongfully held for a necessary assumption in 4% of papers that use regression. Furthermore, 92% of all papers using linear regression were unclear about their assumption checks, violating APA-recommendations. This paper appeals for a heightened awareness for and increased transparency in the reporting of statistical assumption checking. PMID:28533971

Meta-regression analysis of the effect of trans fatty acids on low-density lipoprotein cholesterol.

PubMed

Allen, Bruce C; Vincent, Melissa J; Liska, DeAnn; Haber, Lynne T

2016-12-01

We conducted a meta-regression of controlled clinical trial data to investigate quantitatively the relationship between dietary intake of industrial trans fatty acids (iTFA) and increased low-density lipoprotein cholesterol (LDL-C). Previous regression analyses included insufficient data to determine the nature of the dose response in the low-dose region and have nonetheless assumed a linear relationship between iTFA intake and LDL-C levels. This work contributes to the previous work by 1) including additional studies examining low-dose intake (identified using an evidence mapping procedure); 2) investigating a range of curve shapes, including both linear and nonlinear models; and 3) using Bayesian meta-regression to combine results across trials. We found that, contrary to previous assumptions, the linear model does not acceptably fit the data, while the nonlinear, S-shaped Hill model fits the data well. Based on a conservative estimate of the degree of intra-individual variability in LDL-C (0.1 mmoL/L), as an estimate of a change in LDL-C that is not adverse, a change in iTFA intake of 2.2% of energy intake (%en) (corresponding to a total iTFA intake of 2.2-2.9%en) does not cause adverse effects on LDL-C. The iTFA intake associated with this change in LDL-C is substantially higher than the average iTFA intake (0.5%en). Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Rapid Multi-Tracer PET Tumor Imaging With F-FDG and Secondary Shorter-Lived Tracers.

PubMed

Black, Noel F; McJames, Scott; Kadrmas, Dan J

2009-10-01

Rapid multi-tracer PET, where two to three PET tracers are rapidly scanned with staggered injections, can recover certain imaging measures for each tracer based on differences in tracer kinetics and decay. We previously showed that single-tracer imaging measures can be recovered to a certain extent from rapid dual-tracer (62)Cu - PTSM (blood flow) + (62)Cu - ATSM (hypoxia) tumor imaging. In this work, the feasibility of rapidly imaging (18)F-FDG plus one or two of these shorter-lived secondary tracers was evaluated in the same tumor model. Dynamic PET imaging was performed in four dogs with pre-existing tumors, and the raw scan data was combined to emulate 60 minute long dual- and triple-tracer scans, using the single-tracer scans as gold standards. The multi-tracer data were processed for static (SUV) and kinetic (K(1), K(net)) endpoints for each tracer, followed by linear regression analysis of multi-tracer versus single-tracer results. Static and quantitative dynamic imaging measures of FDG were both accurately recovered from the multi-tracer scans, closely matching the single-tracer FDG standards (R > 0.99). Quantitative blood flow information, as measured by PTSM K(1) and SUV, was also accurately recovered from the multi-tracer scans (R = 0.97). Recovery of ATSM kinetic parameters proved more difficult, though the ATSM SUV was reasonably well recovered (R = 0.92). We conclude that certain additional information from one to two shorter-lived PET tracers may be measured in a rapid multi-tracer scan alongside FDG without compromising the assessment of glucose metabolism. Such additional and complementary information has the potential to improve tumor characterization in vivo, warranting further investigation of rapid multi-tracer techniques.

Rapid Multi-Tracer PET Tumor Imaging With 18F-FDG and Secondary Shorter-Lived Tracers

PubMed Central

Black, Noel F.; McJames, Scott; Kadrmas, Dan J.

2009-01-01

Rapid multi-tracer PET, where two to three PET tracers are rapidly scanned with staggered injections, can recover certain imaging measures for each tracer based on differences in tracer kinetics and decay. We previously showed that single-tracer imaging measures can be recovered to a certain extent from rapid dual-tracer 62Cu – PTSM (blood flow) + 62Cu — ATSM (hypoxia) tumor imaging. In this work, the feasibility of rapidly imaging 18F-FDG plus one or two of these shorter-lived secondary tracers was evaluated in the same tumor model. Dynamic PET imaging was performed in four dogs with pre-existing tumors, and the raw scan data was combined to emulate 60 minute long dual- and triple-tracer scans, using the single-tracer scans as gold standards. The multi-tracer data were processed for static (SUV) and kinetic (K1, Knet) endpoints for each tracer, followed by linear regression analysis of multi-tracer versus single-tracer results. Static and quantitative dynamic imaging measures of FDG were both accurately recovered from the multi-tracer scans, closely matching the single-tracer FDG standards (R > 0.99). Quantitative blood flow information, as measured by PTSM K1 and SUV, was also accurately recovered from the multi-tracer scans (R = 0.97). Recovery of ATSM kinetic parameters proved more difficult, though the ATSM SUV was reasonably well recovered (R = 0.92). We conclude that certain additional information from one to two shorter-lived PET tracers may be measured in a rapid multi-tracer scan alongside FDG without compromising the assessment of glucose metabolism. Such additional and complementary information has the potential to improve tumor characterization in vivo, warranting further investigation of rapid multi-tracer techniques. PMID:20046800

Estimating linear temporal trends from aggregated environmental monitoring data

USGS Publications Warehouse

Erickson, Richard A.; Gray, Brian R.; Eager, Eric A.

2017-01-01

Trend estimates are often used as part of environmental monitoring programs. These trends inform managers (e.g., are desired species increasing or undesired species decreasing?). Data collected from environmental monitoring programs is often aggregated (i.e., averaged), which confounds sampling and process variation. State-space models allow sampling variation and process variations to be separated. We used simulated time-series to compare linear trend estimations from three state-space models, a simple linear regression model, and an auto-regressive model. We also compared the performance of these five models to estimate trends from a long term monitoring program. We specifically estimated trends for two species of fish and four species of aquatic vegetation from the Upper Mississippi River system. We found that the simple linear regression had the best performance of all the given models because it was best able to recover parameters and had consistent numerical convergence. Conversely, the simple linear regression did the worst job estimating populations in a given year. The state-space models did not estimate trends well, but estimated population sizes best when the models converged. We found that a simple linear regression performed better than more complex autoregression and state-space models when used to analyze aggregated environmental monitoring data.

Abnormal dynamics of language in schizophrenia.

PubMed

Stephane, Massoud; Kuskowski, Michael; Gundel, Jeanette

2014-05-30

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

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

NASA Astrophysics Data System (ADS)

Ravansalar, Masoud; Rajaee, Taher; Kisi, Ozgur

2017-06-01

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

Explicit criteria for prioritization of cataract surgery

PubMed Central

Ma Quintana, José; Escobar, Antonio; Bilbao, Amaia

2006-01-01

Background Consensus techniques have been used previously to create explicit criteria to prioritize cataract extraction; however, the appropriateness of the intervention was not included explicitly in previous studies. We developed a prioritization tool for cataract extraction according to the RAND method. Methods Criteria were developed using a modified Delphi panel judgment process. A panel of 11 ophthalmologists was assembled. Ratings were analyzed regarding the level of agreement among panelists. We studied the effect of all variables on the final panel score using general linear and logistic regression models. Priority scoring systems were developed by means of optimal scaling and general linear models. The explicit criteria developed were summarized by means of regression tree analysis. Results Eight variables were considered to create the indications. Of the 310 indications that the panel evaluated, 22.6% were considered high priority, 52.3% intermediate priority, and 25.2% low priority. Agreement was reached for 31.9% of the indications and disagreement for 0.3%. Logistic regression and general linear models showed that the preoperative visual acuity of the cataractous eye, visual function, and anticipated visual acuity postoperatively were the most influential variables. Alternative and simple scoring systems were obtained by optimal scaling and general linear models where the previous variables were also the most important. The decision tree also shows the importance of the previous variables and the appropriateness of the intervention. Conclusion Our results showed acceptable validity as an evaluation and management tool for prioritizing cataract extraction. It also provides easy algorithms for use in clinical practice. PMID:16512893

Comparing The Effectiveness of a90/95 Calculations (Preprint)

DTIC Science & Technology

2006-09-01

Nachtsheim, John Neter, William Li, Applied Linear Statistical Models , 5th ed., McGraw-Hill/Irwin, 2005 5. Mood, Graybill and Boes, Introduction...curves is based on methods that are only valid for ordinary linear regression. Requirements for a valid Ordinary Least-Squares Regression Model There... linear . For example is a linear model ; is not. 2. Uniform variance (homoscedasticity

Application of glas laser altimetry to detect elevation changes in East Antarctica

NASA Astrophysics Data System (ADS)

Scaioni, M.; Tong, X.; Li, R.

2013-10-01

In this paper the use of ICESat/GLAS laser altimeter for estimating multi-temporal elevation changes on polar ice sheets is afforded. Due to non-overlapping laser spots during repeat passes, interpolation methods are required to make comparisons. After reviewing the main methods described in the literature (crossover point analysis, cross-track DEM projection, space-temporal regressions), the last one has been chosen for its capability of providing more elevation change rate measurements. The standard implementation of the space-temporal linear regression technique has been revisited and improved to better cope with outliers and to check the estimability of model's parameters. GLAS data over the PANDA route in East Antarctica have been used for testing. Obtained results have been quite meaningful from a physical point of view, confirming the trend reported by the literature of a constant snow accumulation in the area during the two past decades, unlike the most part of the continent that has been losing mass.

Intimate partner violence and anxiety disorders in pregnancy: the importance of vocational training of the nursing staff in facing them1

PubMed Central

Fonseca-Machado, Mariana de Oliveira; Monteiro, Juliana Cristina dos Santos; Haas, Vanderlei José; Abrão, Ana Cristina Freitas de Vilhena; Gomes-Sponholz, Flávia

2015-01-01

Objective: to identify the relationship between posttraumatic stress disorder, trait and state anxiety, and intimate partner violence during pregnancy. Method: observational, cross-sectional study developed with 358 pregnant women. The Posttraumatic Stress Disorder Checklist - Civilian Version was used, as well as the State-Trait Anxiety Inventory and an adapted version of the instrument used in the World Health Organization Multi-country Study on Women's Health and Domestic Violence. Results: after adjusting to the multiple logistic regression model, intimate partner violence, occurred during pregnancy, was associated with the indication of posttraumatic stress disorder. The adjusted multiple linear regression models showed that the victims of violence, in the current pregnancy, had higher symptom scores of trait and state anxiety than non-victims. Conclusion: recognizing the intimate partner violence as a clinically relevant and identifiable risk factor for the occurrence of anxiety disorders during pregnancy can be a first step in the prevention thereof. PMID:26487135

The comparison between several robust ridge regression estimators in the presence of multicollinearity and multiple outliers

NASA Astrophysics Data System (ADS)

Zahari, Siti Meriam; Ramli, Norazan Mohamed; Moktar, Balkiah; Zainol, Mohammad Said

2014-09-01

In the presence of multicollinearity and multiple outliers, statistical inference of linear regression model using ordinary least squares (OLS) estimators would be severely affected and produces misleading results. To overcome this, many approaches have been investigated. These include robust methods which were reported to be less sensitive to the presence of outliers. In addition, ridge regression technique was employed to tackle multicollinearity problem. In order to mitigate both problems, a combination of ridge regression and robust methods was discussed in this study. The superiority of this approach was examined when simultaneous presence of multicollinearity and multiple outliers occurred in multiple linear regression. This study aimed to look at the performance of several well-known robust estimators; M, MM, RIDGE and robust ridge regression estimators, namely Weighted Ridge M-estimator (WRM), Weighted Ridge MM (WRMM), Ridge MM (RMM), in such a situation. Results of the study showed that in the presence of simultaneous multicollinearity and multiple outliers (in both x and y-direction), the RMM and RIDGE are more or less similar in terms of superiority over the other estimators, regardless of the number of observation, level of collinearity and percentage of outliers used. However, when outliers occurred in only single direction (y-direction), the WRMM estimator is the most superior among the robust ridge regression estimators, by producing the least variance. In conclusion, the robust ridge regression is the best alternative as compared to robust and conventional least squares estimators when dealing with simultaneous presence of multicollinearity and outliers.

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.

Estimation of continuous multi-DOF finger joint kinematics from surface EMG using a multi-output Gaussian Process.

PubMed

Ngeo, Jimson; Tamei, Tomoya; Shibata, Tomohiro

2014-01-01

Surface electromyographic (EMG) signals have often been used in estimating upper and lower limb dynamics and kinematics for the purpose of controlling robotic devices such as robot prosthesis and finger exoskeletons. However, in estimating multiple and a high number of degrees-of-freedom (DOF) kinematics from EMG, output DOFs are usually estimated independently. In this study, we estimate finger joint kinematics from EMG signals using a multi-output convolved Gaussian Process (Multi-output Full GP) that considers dependencies between outputs. We show that estimation of finger joints from muscle activation inputs can be improved by using a regression model that considers inherent coupling or correlation within the hand and finger joints. We also provide a comparison of estimation performance between different regression methods, such as Artificial Neural Networks (ANN) which is used by many of the related studies. We show that using a multi-output GP gives improved estimation compared to multi-output ANN and even dedicated or independent regression models.

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

PubMed

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

2018-02-01

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

Association of comorbid mental health symptoms and physical health conditions with employee productivity.

PubMed

Parker, Kristin M; Wilson, Mark G; Vandenberg, Robert J; DeJoy, David M; Orpinas, Pamela

2009-10-01

This study tests the hypothesis that employees with comorbid physical health conditions and mental health symptoms are less productive than other employees. Self-reported health status and productivity measures were collected from 1723 employees of a national retail organization. chi2, analysis of variance, and linear contrast analyses were conducted to evaluate whether health status groups differed on productivity measures. Multivariate linear regression and multinomial logistic regression analyses were conducted to analyze how predictive health status was of productivity. Those with comorbidities were significantly less productive on all productivity measures compared with all other health status groups and those with only physical health conditions or mental health symptoms. Health status also significantly predicted levels of employee productivity. These findings provide evidence for the relationship between health statuses and productivity, which has potential programmatic implications.

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

DTIC Science & Technology

2017-10-01

ENGINEERING CENTER GRAIN EVALUATION SOFTWARE TO NUMERICALLY PREDICT LINEAR BURN REGRESSION FOR SOLID PROPELLANT GRAIN GEOMETRIES Brian...author(s) and should not be construed as an official Department of the Army position, policy, or decision, unless so designated by other documentation...U.S. ARMY ARMAMENT RESEARCH, DEVELOPMENT AND ENGINEERING CENTER GRAIN EVALUATION SOFTWARE TO NUMERICALLY PREDICT LINEAR BURN REGRESSION FOR SOLID

Paternal mental health and socioemotional and behavioral development in their children.

PubMed

Kvalevaag, Anne Lise; Ramchandani, Paul G; Hove, Oddbjørn; Assmus, Jörg; Eberhard-Gran, Malin; Biringer, Eva

2013-02-01

To examine the association between symptoms of psychological distress in expectant fathers and socioemotional and behavioral outcomes in their children at age 36 months. The current study is based on data from the Norwegian Mother and Child Cohort Study on 31 663 children. Information about fathers' mental health was obtained by self-report (Hopkins Symptom Checklist) in week 17 or 18 of gestation. Information about mothers' pre- and postnatal mental health and children's socioemotional and behavioral development at 36 months of age was obtained from parent-report questionnaires. Linear multiple regression and logistic regression models were performed while controlling for demographics, lifestyle variables, and mothers' mental health. Three percent of the fathers had high levels of psychological distress. Using linear regression models, we found a small positive association between fathers' psychological distress and children's behavioral difficulties, B = 0.19 (95% confidence interval [CI] = 0.15-0.23); emotional difficulties, B = 0.22 (95% CI = 0.18-0.26); and social functioning, B = 0.12 (95% CI = 0.07-0.16). The associations did not change when adjusted for relevant confounders. Children whose fathers had high levels of psychological distress had higher levels of emotional and behavioral problems. This study suggests that some risk of future child emotional, behavioral, and social problems can be identified during pregnancy. The findings are of importance for clinicians and policy makers in their planning of health care in the perinatal period because this represents a significant opportunity for preventive intervention.

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

Linear regression in astronomy. II

NASA Technical Reports Server (NTRS)

Feigelson, Eric D.; Babu, Gutti J.

1992-01-01

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

A piecewise regression approach for determining biologically relevant hydraulic thresholds for the protection of fish at river infrastructure

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

Boys, Craig A.; Robinson, Wayne; Miller, Brett

2016-05-13

Barotrauma injury can occur when fish are exposed to rapid decompression during downstream passage through river infrastructure. A piecewise regression approach was used to objectively quantify barotrauma injury thresholds in two physoclistous species (Murray cod Maccullochella peelii and silver perch Bidyanus bidyanus) following simulated infrastructure passage in barometric chambers. The probability of injuries such as swim bladder rupture; exophthalmia; and haemorrhage and emphysema in various organs increased as the ratio between the lowest exposure pressure and the acclimation pressure (ratio of pressure change RPCE/A) fell. The relationship was typically non-linear and piecewise regression was able to quantify thresholds in RPCE/Amore » that once exceeded resulted in a substantial increase in barotrauma injury. Thresholds differed among injury types and between species but by applying a multi-species precautionary principle, the maintenance of exposure pressures at river infrastructure above 70% of acclimation pressure (RPCE/A of 0.7) should sufficiently protect downstream migrating juveniles of these two physoclistous species. These findings have important implications for determining the risk posed by current infrastructures and informing the design and operation of new ones.« less

Caloric sweetener consumption and dyslipidemia among US adults.

PubMed

Welsh, Jean A; Sharma, Andrea; Abramson, Jerome L; Vaccarino, Viola; Gillespie, Cathleen; Vos, Miriam B

2010-04-21

Dietary carbohydrates have been associated with dyslipidemia, a lipid profile known to increase cardiovascular disease risk. Added sugars (caloric sweeteners used as ingredients in processed or prepared foods) are an increasing and potentially modifiable component in the US diet. No known studies have examined the association between the consumption of added sugars and lipid measures. To assess the association between consumption of added sugars and blood lipid levels in US adults. Cross-sectional study among US adults (n = 6113) from the National Health and Nutrition Examination Survey (NHANES) 1999-2006. Respondents were grouped by intake of added sugars using limits specified in dietary recommendations (< 5% [reference group], 5%-<10%, 10%-<17.5%, 17.5%-<25%, and > or = 25% of total calories). Linear regression was used to estimate adjusted mean lipid levels. Logistic regression was used to determine adjusted odds ratios of dyslipidemia. Interactions between added sugars and sex were evaluated. Adjusted mean high-density lipoprotein cholesterol (HDL-C), geometric mean triglycerides, and mean low-density lipoprotein cholesterol (LDL-C) levels and adjusted odds ratios of dyslipidemia, including low HDL-C levels (< 40 mg/dL for men; < 50 mg/dL for women), high triglyceride levels (> or = 150 mg/dL), high LDL-C levels (> or = 130 mg/dL), or high ratio of triglycerides to HDL-C (> 3.8). Results were weighted to be representative of the US population. A mean of 15.8% of consumed calories was from added sugars. Among participants consuming less than 5%, 5% to less than 17.5%, 17.5% to less than 25%, and 25% or greater of total energy as added sugars, adjusted mean HDL-C levels were, respectively, 58.7, 57.5, 53.7, 51.0, and 47.7 mg/dL (P < .001 for linear trend), geometric mean triglyceride levels were 105, 102, 111, 113, and 114 mg/dL (P < .001 for linear trend), and LDL-C levels modified by sex were 116, 115, 118, 121, and 123 mg/dL among women (P = .047 for linear trend). There were no significant trends in LDL-C levels among men. Among higher consumers (> or = 10% added sugars) the odds of low HDL-C levels were 50% to more than 300% greater compared with the reference group (< 5% added sugars). In this study, there was a statistically significant correlation between dietary added sugars and blood lipid levels among US adults.

Circulating leptin and adiponectin are associated with insulin resistance in healthy postmenopausal women with hot flashes

PubMed Central

Huang, Wan-Yu; Chang, Chia-Chu; Chen, Dar-Ren; Kor, Chew-Teng; Chen, Ting-Yu; Wu, Hung-Ming

2017-01-01

Introduction Hot flashes have been postulated to be linked to the development of metabolic disorders. This study aimed to evaluate the relationship between hot flashes, adipocyte-derived hormones, and insulin resistance in healthy, non-obese postmenopausal women. Participants and design In this cross-sectional study, a total of 151 women aged 45–60 years were stratified into one of three groups according to hot-flash status over the past three months: never experienced hot flashes (Group N), mild-to-moderate hot flashes (Group M), and severe hot flashes (Group S). Variables measured in this study included clinical parameters, hot flash experience, fasting levels of circulating glucose, lipid profiles, plasma insulin, and adipocyte-derived hormones. Multiple linear regression analysis was used to evaluate the associations of hot flashes with adipocyte-derived hormones, and with insulin resistance. Settings The study was performed in a hospital medical center. Results The mean (standard deviation) of body-mass index was 22.8(2.7) for Group N, 22.6(2.6) for Group M, and 23.5(2.4) for Group S, respectively. Women in Group S displayed statistically significantly higher levels of leptin, fasting glucose, and insulin, and lower levels of adiponectin than those in Groups M and N. Multivariate linear regression analysis revealed that hot-flash severity was significantly associated with higher leptin levels, lower adiponectin levels, and higher leptin-to-adiponectin ratio. Univariate linear regression analysis revealed that hot-flash severity was strongly associated with a higher HOMA-IR index (% difference, 58.03%; 95% confidence interval, 31.00–90.64; p < 0.001). The association between hot flashes and HOMA-IR index was attenuated after adjusting for leptin or adiponectin and was no longer significant after simultaneously adjusting for leptin and adiponectin. Conclusion The present study provides evidence that hot flashes are associated with insulin resistance in postmenopausal women. It further suggests that hot flash association with insulin resistance is dependent on the combination of leptin and adiponectin variables. PMID:28448547

Controller design for a class of nonlinear MIMO coupled system using multiple models and second level adaptation.

PubMed

Pandey, Vinay Kumar; Kar, Indrani; Mahanta, Chitralekha

2017-07-01

In this paper, an adaptive control method using multiple models with second level adaptation is proposed for a class of nonlinear multi-input multi-output (MIMO) coupled systems. Multiple estimation models are used to tune the unknown parameters at the first level. The second level adaptation provides a single parameter vector for the controller. A feedback linearization technique is used to design a state feedback control. The efficacy of the designed controller is validated by conducting real time experiment on a laboratory setup of twin rotor MIMO system (TRMS). The TRMS setup is discussed in detail and the experiments were performed for regulation and tracking problem for pitch and yaw control using different reference signals. An Extended Kalman Filter (EKF) has been used to observe the unavailable states of the TRMS. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Improved score statistics for meta-analysis in single-variant and gene-level association studies.

PubMed

Yang, Jingjing; Chen, Sai; Abecasis, Gonçalo

2018-06-01

Meta-analysis is now an essential tool for genetic association studies, allowing them to combine large studies and greatly accelerating the pace of genetic discovery. Although the standard meta-analysis methods perform equivalently as the more cumbersome joint analysis under ideal settings, they result in substantial power loss under unbalanced settings with various case-control ratios. Here, we investigate the power loss problem by the standard meta-analysis methods for unbalanced studies, and further propose novel meta-analysis methods performing equivalently to the joint analysis under both balanced and unbalanced settings. We derive improved meta-score-statistics that can accurately approximate the joint-score-statistics with combined individual-level data, for both linear and logistic regression models, with and without covariates. In addition, we propose a novel approach to adjust for population stratification by correcting for known population structures through minor allele frequencies. In the simulated gene-level association studies under unbalanced settings, our method recovered up to 85% power loss caused by the standard methods. We further showed the power gain of our methods in gene-level tests with 26 unbalanced studies of age-related macular degeneration . In addition, we took the meta-analysis of three unbalanced studies of type 2 diabetes as an example to discuss the challenges of meta-analyzing multi-ethnic samples. In summary, our improved meta-score-statistics with corrections for population stratification can be used to construct both single-variant and gene-level association studies, providing a useful framework for ensuring well-powered, convenient, cross-study analyses. © 2018 WILEY PERIODICALS, INC.

A Constrained Linear Estimator for Multiple Regression

ERIC Educational Resources Information Center

Davis-Stober, Clintin P.; Dana, Jason; Budescu, David V.

2010-01-01

"Improper linear models" (see Dawes, Am. Psychol. 34:571-582, "1979"), such as equal weighting, have garnered interest as alternatives to standard regression models. We analyze the general circumstances under which these models perform well by recasting a class of "improper" linear models as "proper" statistical models with a single predictor. We…

Limits of detection and decision. Part 3

NASA Astrophysics Data System (ADS)

Voigtman, E.

2008-02-01

It has been shown that the MARLAP (Multi-Agency Radiological Laboratory Analytical Protocols) for estimating the Currie detection limit, which is based on 'critical values of the non-centrality parameter of the non-central t distribution', is intrinsically biased, even if no calibration curve or regression is used. This completed the refutation of the method, begun in Part 2. With the field cleared of obstructions, the true theory underlying Currie's limits of decision, detection and quantification, as they apply in a simple linear chemical measurement system (CMS) having heteroscedastic, Gaussian measurement noise and using weighted least squares (WLS) processing, was then derived. Extensive Monte Carlo simulations were performed, on 900 million independent calibration curves, for linear, "hockey stick" and quadratic noise precision models (NPMs). With errorless NPM parameters, all the simulation results were found to be in excellent agreement with the derived theoretical expressions. Even with as much as 30% noise on all of the relevant NPM parameters, the worst absolute errors in rates of false positives and false negatives, was only 0.3%.

Hyperspectral sensing of heavy metals in soil and vegetation: Feasibility and challenges

NASA Astrophysics Data System (ADS)

Wang, Fenghe; Gao, Jay; Zha, Yong

2018-02-01

Remote sensing of heavy metal contamination of soils has been widely studied. These studies concentrate heavily on the hyperspectral reflectance of typical metals in soils and in plants measured either in situ or in the laboratory. The most used wavebands lie within the visible-near infrared portion of the spectrum, especially the red edge. In comparison, mid- and far-infrared wavelengths are used far less frequently. Hyperspectral data are optimized to suppress noises and enhance the signal of the targeted metals through spectral derivatives and vegetation indexing. It is found that only subtle disparity exists in spectral responses for some metals at a sufficiently high content level. Not all metals have their own unique spectral response. Their detection has to rely on their co-variation with the spectrally responsive metals or organic matter in the soils. The closeness of the correlation dictates the accuracy of prediction. Without any theoretical grounding, this correlation is site-specific. Various analytical methods, including stepwise multi-linear regression, partial least squares regression, and neural networks have been used to model metal content level from the identified spectrally sensitive bands and/or their transformed indices. Both the model and the explanatory variables vary with the metal under detection and the area from which in situ samples are collected. Despite the amply demonstrated feasibility of estimating several metals by a large number of authors, only a few have succeeded in mapping the spatial distribution of metals from HyMAP, HJ-1A and Hyperion images to a satisfactory accuracy using complex algorithms and after taking environmental variables into account. The large number of reported failures testifies the difficulty in the detection of heavy metals in soils and plants, especially when their concentration level is low. The reasons or factors responsible for the success or failure have not been systematically analyzed, including the minimal spectral resolution required.

On the design of classifiers for crop inventories

NASA Technical Reports Server (NTRS)

Heydorn, R. P.; Takacs, H. C.

1986-01-01

Crop proportion estimators that use classifications of satellite data to correct, in an additive way, a given estimate acquired from ground observations are discussed. A linear version of these estimators is optimal, in terms of minimum variance, when the regression of the ground observations onto the satellite observations in linear. When this regression is not linear, but the reverse regression (satellite observations onto ground observations) is linear, the estimator is suboptimal but still has certain appealing variance properties. In this paper expressions are derived for those regressions which relate the intercepts and slopes to conditional classification probabilities. These expressions are then used to discuss the question of classifier designs that can lead to low-variance crop proportion estimates. Variance expressions for these estimates in terms of classifier omission and commission errors are also derived.

Influence of Teachers' Personal Health Behaviors on Operationalizing Obesity Prevention Policy in Head Start Preschools: A Project of the Children's Healthy Living Program (CHL).

PubMed

Esquivel, Monica Kazlausky; Nigg, Claudio R; Fialkowski, Marie K; Braun, Kathryn L; Li, Fenfang; Novotny, Rachel

2016-05-01

To quantify the Head Start (HS) teacher mediating and moderating influence on the effect of a wellness policy intervention. Intervention trial within a larger randomized community trial. HS preschools in Hawaii. Twenty-three HS classrooms located within 2 previously randomized communities. Seven-month multi-component intervention with policy changes to food served and service style, initiatives for employee wellness, classroom activities for preschoolers promoting physical activity (PA) and healthy eating, and training and technical assistance. The Environment and Policy Assessment and Observation (EPAO) classroom scores and teacher questionnaires assessing on knowledge, beliefs, priorities, and misconceptions around child nutrition and changes in personal health behaviors and status were the main outcome measures. Paired t tests and linear regression analysis tested the intervention effects on the classroom and mediating and moderating effects of the teacher variables on the classroom environment. General linear model test showed greater intervention effect on the EPAO score where teachers reported higher than average improvements in their own health status and behaviors (estimate [SE] = -2.47 (0.78), P < .05). Strategies to improve teacher health status and behaviors included in a multi-component policy intervention aimed at child obesity prevention may produce a greater effect on classroom environments. Copyright © 2016 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved.

Multi-timescale sediment responses across a human impacted river-estuary system

NASA Astrophysics Data System (ADS)

Chen, Yining; Chen, Nengwang; Li, Yan; Hong, Huasheng

2018-05-01

Hydrological processes regulating sediment transport from land to sea have been widely studied. However, anthropogenic factors controlling the river flow-sediment regime and subsequent response of the estuary are still poorly understood. Here we conducted a multi-timescale analysis on flow and sediment discharges during the period 1967-2014 for the two tributaries of the Jiulong River in Southeast China. The long-term flow-sediment relationship remained linear in the North River throughout the period, while the linearity showed a remarkable change after 1995 in the West River, largely due to construction of dams and reservoirs in the upland watershed. Over short timescales, rainstorm events caused the changes of suspended sediment concentration (SSC) in the rivers. Regression analysis using synchronous SSC data in a wet season (2009) revealed a delayed response (average 5 days) of the estuary to river input, and a box-model analysis established a quantitative relationship to further describe the response of the estuary to the river sediment input over multiple timescales. The short-term response is determined by both the vertical SSC-salinity changes and the sediment trapping rate in the estuary. However, over the long term, the reduction of riverine sediment yield increased marine sediments trapped into the estuary. The results of this study indicate that human activities (e.g., dams) have substantially altered sediment delivery patterns and river-estuary interactions at multiple timescales.

Abdominal girth and vertebral column length can adjust spinal anesthesia for lower limb surgery, a prospective, observational study.

PubMed

Zhou, Qing-he; Zhu, Bo; Wei, Chang-na; Yan, Min

2016-03-24

Studies have shown that abdominal girth and vertebral column length have high predictive value for spinal spread after administering a dose of plain bupivacaine. we designed a study to identify the specific correlations between abdominal girth, vertebral column length and a 0.5% dosage of plain bupivacaine, which should provide a minimum upper block level (T12) and a suitable upper block level (T10) for lower limb surgeries. A suitable dose of 0.5% plain bupivacaine was administered intrathecally between the L3 and L4 vertebrae for lower limb surgeries. If the upper cephalad spread of the patient by loss of pinprick discrimination was T12 or T10, the patient was enrolled in this study. Five patient variables and intrathecal plain bupivacaine dose were recorded. Linear regression and multiple regression analyses were performed. Totals of 111 patients and 121 patients who lost pinprick discrimination at T12 and T10, respectively, were analyzed in this study. Linear regression analysis showed that only abdominal girth and plain bupivacaine dose were strongly correlated (r =-0.827 for T12, r = -0.806 for T10; both p < 0.0001). Multiple linear regression analysis showed that both abdominal girth and vertebral column length were the key determinants of plain bupivacaine dose (both p < 0.0001). R(2) was 0.874 and 0.860 for the loss of pinprick discrimination at T12 and T10, respectively. Our data indicated that vertebral column length and abdominal girth were strongly correlated with the dosage of intrathecal plain bupivacaine for the loss of pinprick discrimination at T12 and T10. The two regression equations were YT12 = 3.547 + 0.045X1-0.044X2 and YT10 = 3.848 + 0.047X1- 0.046X2 (Y, 0.5% plain bupivacaine volume; X1, vertebral column length;and X 2, abdominal girth), which can accurately predict the minimum and suitable intrathecal bupivacaine dose for lower limb surgery to a great extent, separately.

TerraFERMA: Harnessing Advanced Computational Libraries in Earth Science

NASA Astrophysics Data System (ADS)

Wilson, C. R.; Spiegelman, M.; van Keken, P.

2012-12-01

Many important problems in Earth sciences can be described by non-linear coupled systems of partial differential equations. These "multi-physics" problems include thermo-chemical convection in Earth and planetary interiors, interactions of fluids and magmas with the Earth's mantle and crust and coupled flow of water and ice. These problems are of interest to a large community of researchers but are complicated to model and understand. Much of this complexity stems from the nature of multi-physics where small changes in the coupling between variables or constitutive relations can lead to radical changes in behavior, which in turn affect critical computational choices such as discretizations, solvers and preconditioners. To make progress in understanding such coupled systems requires a computational framework where multi-physics problems can be described at a high-level while maintaining the flexibility to easily modify the solution algorithm. Fortunately, recent advances in computational science provide a basis for implementing such a framework. Here we present the Transparent Finite Element Rapid Model Assembler (TerraFERMA), which leverages several advanced open-source libraries for core functionality. FEniCS (fenicsproject.org) provides a high level language for describing the weak forms of coupled systems of equations, and an automatic code generator that produces finite element assembly code. PETSc (www.mcs.anl.gov/petsc) provides a wide range of scalable linear and non-linear solvers that can be composed into effective multi-physics preconditioners. SPuD (amcg.ese.ic.ac.uk/Spud) is an application neutral options system that provides both human and machine-readable interfaces based on a single xml schema. Our software integrates these libraries and provides the user with a framework for exploring multi-physics problems. A single options file fully describes the problem, including all equations, coefficients and solver options. Custom compiled applications are generated from this file but share an infrastructure for services common to all models, e.g. diagnostics, checkpointing and global non-linear convergence monitoring. This maximizes code reusability, reliability and longevity ensuring that scientific results and the methods used to acquire them are transparent and reproducible. TerraFERMA has been tested against many published geodynamic benchmarks including 2D/3D thermal convection problems, the subduction zone benchmarks and benchmarks for magmatic solitary waves. It is currently being used in the investigation of reactive cracking phenomena with applications to carbon sequestration, but we will principally discuss its use in modeling the migration of fluids in subduction zones. Subduction zones require an understanding of the highly nonlinear interactions of fluids with solids and thus provide an excellent scientific driver for the development of multi-physics software.

Delaying cognitive and physical decline through multidomain interventions for residents with mild-to-moderate dementia in dementia care units in Taiwan: A prospective cohort study.

PubMed

Liang, Chih-Kuang; Chou, Ming-Yueh; Chen, Liang-Yu; Wang, Kuei-Yu; Lin, Shih-Yi; Chen, Liang-Kung; Lin, Yu-Te; Liu, Tsung-Yun; Loh, Ching-Hui

2017-04-01

To develop experimental multi-domain interventions for older people with mild-to-moderate dementia, and to evaluate the effect of delaying cognitive and physical decline, and improvement or prevention of geriatric syndromes during 1-year follow up. Participants aged 65 years and older with mild-to-moderate dementia (clinical dementia rating [CDR] 1 or 2) were grouped as intervention in Jia-Li Veterans Home and usual care model in the community (Memory clinic). All residents in Jia-Li Veterans Home received comprehensive intervention, including Multi-disciplinary team consultation and intervention, Multi-component non-pharmacological management, geriatric syndromes survey and intervention by CGA, and a dementia friendly medical Green channel Approach (2MCGA). The decline of cognitive and physical function are determined by the change of Mini-Mental State Examination score, CDR and the sum of CDR box, as well as activities of daily living based on the Barthel Index. We also screened geriatric syndromes at baseline and 1 year later. Participants in the intervention group were older and had a lower educational level, lower body mass index, poor baseline activities of daily living function, lower visual impairment, and higher rates of hearing impairment, polypharmacy and risk of malnutrition. The residents receiving 2MCGA had lower baseline Mini-Mental State Examination scores, and higher CDR. For residents in Jia-Li Veterans Home, all cognitive measurements except Mini-Mental State Examination were significantly associated with delaying the decline of cognition after analyzing by multiple linear regression, and multivariate logistic regression also showed that patients living in the community was independently associated with a higher odds ratio for activities of daily living decline (3.180, 95% CI 1.384-7.308, P = 0.006). There are also more improvement in their baseline geriatric syndromes and suffered less from new geriatric syndromes, including falls, urinary incontinence, and risk of malnutrition. The 2MCGA intervention shows strong delays in the decline of cognition and physical function for older residents with mild-to-moderate dementia. Furthermore, this strategy can also improve or prevent the onset of new geriatric syndromes, especially fall episodes, urinary incontinence and risk of malnutrition. Geriatr Gerontol Int 2017; 17 (Suppl. 1): 36-43. © 2017 Japan Geriatrics Society.

Consumption of non-cow's milk beverages and serum vitamin D levels in early childhood.

PubMed

Lee, Grace J; Birken, Catherine S; Parkin, Patricia C; Lebovic, Gerald; Chen, Yang; L'Abbé, Mary R; Maguire, Jonathon L

2014-11-18

Vitamin D fortification of non-cow's milk beverages is voluntary in North America. The effect of consuming non-cow's milk beverages on serum 25-hydroxyvitamin D levels in children is unclear. We studied the association between non-cow's milk consumption and 25-hydroxyvitamin D levels in healthy preschool-aged children. We also explored whether cow's milk consumption modified this association and analyzed the association between daily non-cow's milk and cow's milk consumption. In this cross-sectional study, we recruited children 1-6 years of age attending routinely scheduled well-child visits. Survey responses, and anthropometric and laboratory measurements were collected. The association between non-cow's milk consumption and 25-hydroxyvitamin D levels was tested using multiple linear regression and logistic regression. Cow's milk consumption was explored as an effect modifier using an interaction term. The association between daily intake of non-cow's milk and cow's milk was explored using multiple linear regression. A total of 2831 children were included. The interaction between non-cow's milk and cow's milk consumption was statistically significant (p = 0.03). Drinking non-cow's milk beverages was associated with a 4.2-nmol/L decrease in 25-hydroxyvitamin D level per 250-mL cup consumed among children who also drank cow's milk (p = 0.008). Children who drank only non-cow's milk were at higher risk of having a 25-hydroxyvitamin D level below 50 nmol/L than children who drank only cow's milk (odds ratio 2.7, 95% confidence interval 1.6 to 4.7). Consumption of non-cow's milk beverages was associated with decreased serum 25-hydroxyvitamin D levels in early childhood. This association was modified by cow's milk consumption, which suggests a trade-off between consumption of cow's milk fortified with higher levels of vitamin D and non-cow's milk with lower vitamin D content. © 2014 Canadian Medical Association or its licensors.

Linear Modeling and Evaluation of Controls on Flow Response in Western Post-Fire Watersheds

NASA Astrophysics Data System (ADS)

Saxe, S.; Hogue, T. S.; Hay, L.

2015-12-01

This research investigates the impact of wildfires on watershed flow regimes throughout the western United States, specifically focusing on evaluation of fire events within specified subregions and determination of the impact of climate and geophysical variables in post-fire flow response. Fire events were collected through federal and state-level databases and streamflow data were collected from U.S. Geological Survey stream gages. 263 watersheds were identified with at least 10 years of continuous pre-fire daily streamflow records and 5 years of continuous post-fire daily flow records. For each watershed, percent changes in runoff ratio (RO), annual seven day low-flows (7Q2) and annual seven day high-flows (7Q10) were calculated from pre- to post-fire. Numerous independent variables were identified for each watershed and fire event, including topographic, land cover, climate, burn severity, and soils data. The national watersheds were divided into five regions through K-clustering and a lasso linear regression model, applying the Leave-One-Out calibration method, was calculated for each region. Nash-Sutcliffe Efficiency (NSE) was used to determine the accuracy of the resulting models. The regions encompassing the United States along and west of the Rocky Mountains, excluding the coastal watersheds, produced the most accurate linear models. The Pacific coast region models produced poor and inconsistent results, indicating that the regions need to be further subdivided. Presently, RO and HF response variables appear to be more easily modeled than LF. Results of linear regression modeling showed varying importance of watershed and fire event variables, with conflicting correlation between land cover types and soil types by region. The addition of further independent variables and constriction of current variables based on correlation indicators is ongoing and should allow for more accurate linear regression modeling.

Vitamin D level and its association with adiposity among multi-ethnic adults in Kuala Lumpur, Malaysia: a cross sectional study.

PubMed

Shafinaz, I S; Moy, F M

2016-03-07

Vitamin D deficiency is highly prevalent in both temperate as well as tropical countries. Obesity is one of the factors contributing to vitamin D deficiency. As our country has a high prevalence of overweight and obesity, we aimed to study serum 25-hydroxyvitamin D (25(OH)D) level and its association with adiposity using various adiposity indicators; and to study other risk factors that affect serum 25(OH)D level among multi-ethnic adults in Kuala Lumpur, Malaysia. This was a cross sectional study conducted with a multistage sampling. All permanent teachers working in government secondary schools in Kuala Lumpur were invited for the study. The data collection included serum 25(OH)D, Parathyroid Hormone (PTH), body fat percentage, waist circumference, body mass index (BMI) and blood pressure. Demographic characteristics, sun avoidance, sun exposure and physical activity were enquired from the participants using a self-administered questionnaire. The data was analyzed using a complex sample analysis. A total of 858 participants were recruited. Majority of them were Malays, females and had tertiary education. The overall prevalence of vitamin D deficiency (<20 ng/ml) was 67.4 %. Indian participants (80.9 %) had the highest proportion of vitamin D deficiency, followed by Malays (75.6 %), others (44.9 %) and Chinese (25.1 %). There was a significant negative association between serum 25(OH)D level with BMI (β = -0.23) and body fat percentage (β = -0.14). In the multivariate linear regression analysis, Malays, Indians and females (p < 0.001); higher BMI and larger waist circumference (p < 0.05) were significantly associated with lower serum 25(OH)D level. The full model explained 32.8 % of the variation between participants in the serum 25(OH)D level. The two most influential factors affecting serum 25(OH)D level were ethnicity and gender. The prevalence of vitamin D deficiency among our participants was high. Adiposity was associated with serum 25(OH)D level. Skin pigmentation and gender based behaviours were more dominant in contributing to serum 25(OH)D level. Health education should be targeted in weight management, gender based behaviours on sun exposure, as skin pigmentation is non-modifiable.

Lichen-based critical loads for atmospheric nitrogen deposition in Western Oregon and Washington forests, USA

Treesearch

Linda H. Geiser; Sarah E. Jovan; Doug A. Glavich; Matthew K. Porter

2010-01-01

Critical loads (CLs) define maximum atmospheric deposition levels apparently preventative of ecosystem harm. We present first nitrogen CLs for northwestern North America's maritime forests. Using multiple linear regression, we related epiphytic-macrolichen community composition to: 1) wet deposition from the National Atmospheric Deposition Program, 2) wet, dry,...

Identifying Predictors of Physics Item Difficulty: A Linear Regression Approach

ERIC Educational Resources Information Center

Mesic, Vanes; Muratovic, Hasnija

2011-01-01

Large-scale assessments of student achievement in physics are often approached with an intention to discriminate students based on the attained level of their physics competencies. Therefore, for purposes of test design, it is important that items display an acceptable discriminatory behavior. To that end, it is recommended to avoid extraordinary…

Variables Affecting Proficiency in English as a Second Language

ERIC Educational Resources Information Center

Santana, Josefina C.; García-Santillán, Arturo; Escalera-Chávez, Milka Elena

2017-01-01

This study explores different variables leading to proficiency in English as a second language. Level of English on a placement exam taken upon entering a private university in Mexico was correlated to several variables. Additionally, participants (N = 218) were asked their perception of their own proficiency. A linear regression and a one-factor…

Reading Cooperatively or Independently? Study on ELL Student Reading Development

ERIC Educational Resources Information Center

Liu, Siping; Wang, Jian

2015-01-01

This study examines the effectiveness of cooperative reading teaching activities and independent reading activities for English language learner (ELL) students at 4th grade level. Based on simple linear regression and correlational analyses of data collected from two large data bases, PIRLS and NAEP, the study found that cooperative reading…