Simple models for estimating local removals of timber in the northeast

Treesearch

David N. Larsen; David A. Gansner

1975-01-01

Provides a practical method of estimating subregional removals of timber and demonstrates its application to a typical problem. Stepwise multiple regression analysis is used to develop equations for estimating removals of softwood, hardwood, and all timber from selected characteristics of socioeconomic structure.

Needs of the Learning Effect on Instructional Website for Vocational High School Students

ERIC Educational Resources Information Center

Lo, Hung-Jen; Fu, Gwo-Liang; Chuang, Kuei-Chih

2013-01-01

The purpose of study was to understand the correlation between the needs of the learning effect on instructional website for the vocational high school students. Our research applied the statistic methods of product-moment correlation, stepwise regression, and structural equation method to analyze the questionnaire with the sample size of 377…

Advances in variable selection methods I: Causal selection methods versus stepwise regression and principal component analysis on data of known and unknown functional relationships

EPA Science Inventory

Hydrological predictions at a watershed scale are commonly based on extrapolation and upscaling of hydrological behavior at plot and hillslope scales. Yet, dominant hydrological drivers at a hillslope may not be as dominant at the watershed scale because of the heterogeneity of w...

Applications of modern statistical methods to analysis of data in physical science

NASA Astrophysics Data System (ADS)

Wicker, James Eric

Modern methods of statistical and computational analysis offer solutions to dilemmas confronting researchers in physical science. Although the ideas behind modern statistical and computational analysis methods were originally introduced in the 1970's, most scientists still rely on methods written during the early era of computing. These researchers, who analyze increasingly voluminous and multivariate data sets, need modern analysis methods to extract the best results from their studies. The first section of this work showcases applications of modern linear regression. Since the 1960's, many researchers in spectroscopy have used classical stepwise regression techniques to derive molecular constants. However, problems with thresholds of entry and exit for model variables plagues this analysis method. Other criticisms of this kind of stepwise procedure include its inefficient searching method, the order in which variables enter or leave the model and problems with overfitting data. We implement an information scoring technique that overcomes the assumptions inherent in the stepwise regression process to calculate molecular model parameters. We believe that this kind of information based model evaluation can be applied to more general analysis situations in physical science. The second section proposes new methods of multivariate cluster analysis. The K-means algorithm and the EM algorithm, introduced in the 1960's and 1970's respectively, formed the basis of multivariate cluster analysis methodology for many years. However, several shortcomings of these methods include strong dependence on initial seed values and inaccurate results when the data seriously depart from hypersphericity. We propose new cluster analysis methods based on genetic algorithms that overcomes the strong dependence on initial seed values. In addition, we propose a generalization of the Genetic K-means algorithm which can accurately identify clusters with complex hyperellipsoidal covariance structures. We then use this new algorithm in a genetic algorithm based Expectation-Maximization process that can accurately calculate parameters describing complex clusters in a mixture model routine. Using the accuracy of this GEM algorithm, we assign information scores to cluster calculations in order to best identify the number of mixture components in a multivariate data set. We will showcase how these algorithms can be used to process multivariate data from astronomical observations.

Depression and Related Problems in University Students

ERIC Educational Resources Information Center

Field, Tiffany; Diego, Miguel; Pelaez, Martha; Deeds, Osvelia; Delgado, Jeannette

2012-01-01

Method: Depression and related problems were studied in a sample of 283 university students. Results: The students with high depression scores also had high scores on anxiety, intrusive thoughts, controlling intrusive thoughts and sleep disturbances scales. A stepwise regression suggested that those problems contributed to a significant proportion…

Joint effect of unlinked genotypes: application to type 2 diabetes in the EPIC-Potsdam case-cohort study.

PubMed

Knüppel, Sven; Meidtner, Karina; Arregui, Maria; Holzhütter, Hermann-Georg; Boeing, Heiner

2015-07-01

Analyzing multiple single nucleotide polymorphisms (SNPs) is a promising approach to finding genetic effects beyond single-locus associations. We proposed the use of multilocus stepwise regression (MSR) to screen for allele combinations as a method to model joint effects, and compared the results with the often used genetic risk score (GRS), conventional stepwise selection, and the shrinkage method LASSO. In contrast to MSR, the GRS, conventional stepwise selection, and LASSO model each genotype by the risk allele doses. We reanalyzed 20 unlinked SNPs related to type 2 diabetes (T2D) in the EPIC-Potsdam case-cohort study (760 cases, 2193 noncases). No SNP-SNP interactions and no nonlinear effects were found. Two SNP combinations selected by MSR (Nagelkerke's R² = 0.050 and 0.048) included eight SNPs with mean allele combination frequency of 2%. GRS and stepwise selection selected nearly the same SNP combinations consisting of 12 and 13 SNPs (Nagelkerke's R² ranged from 0.020 to 0.029). LASSO showed similar results. The MSR method showed the best model fit measured by Nagelkerke's R² suggesting that further improvement may render this method a useful tool in genetic research. However, our comparison suggests that the GRS is a simple way to model genetic effects since it does not consider linkage, SNP-SNP interactions, and no non-linear effects. © 2015 John Wiley & Sons Ltd/University College London.

Juvenile Offender Recidivism: An Examination of Risk Factors

ERIC Educational Resources Information Center

Calley, Nancy G.

2012-01-01

One hundred and seventy three male juvenile offenders were followed two years postrelease from a residential treatment facility to assess recidivism and factors related to recidivism. The overall recidivism rate was 23.9%. Logistic regression with stepwise and backward variable selection methods was used to examine the relationship between…

A survey of variable selection methods in two Chinese epidemiology journals

PubMed Central

2010-01-01

Background Although much has been written on developing better procedures for variable selection, there is little research on how it is practiced in actual studies. This review surveys the variable selection methods reported in two high-ranking Chinese epidemiology journals. Methods Articles published in 2004, 2006, and 2008 in the Chinese Journal of Epidemiology and the Chinese Journal of Preventive Medicine were reviewed. Five categories of methods were identified whereby variables were selected using: A - bivariate analyses; B - multivariable analysis; e.g. stepwise or individual significance testing of model coefficients; C - first bivariate analyses, followed by multivariable analysis; D - bivariate analyses or multivariable analysis; and E - other criteria like prior knowledge or personal judgment. Results Among the 287 articles that reported using variable selection methods, 6%, 26%, 30%, 21%, and 17% were in categories A through E, respectively. One hundred sixty-three studies selected variables using bivariate analyses, 80% (130/163) via multiple significance testing at the 5% alpha-level. Of the 219 multivariable analyses, 97 (44%) used stepwise procedures, 89 (41%) tested individual regression coefficients, but 33 (15%) did not mention how variables were selected. Sixty percent (58/97) of the stepwise routines also did not specify the algorithm and/or significance levels. Conclusions The variable selection methods reported in the two journals were limited in variety, and details were often missing. Many studies still relied on problematic techniques like stepwise procedures and/or multiple testing of bivariate associations at the 0.05 alpha-level. These deficiencies should be rectified to safeguard the scientific validity of articles published in Chinese epidemiology journals. PMID:20920252

Nondestructive detection of zebra chip disease in potatoes using near-infrared spectroscopy

USDA-ARS?s Scientific Manuscript database

Near-Infrared (NIR) spectroscopy in the wavelength region from 900 nm to 2600 nm was evaluated as the basis for a rapid, non-destructive method for the detection of Zebra Chip disease in potatoes and the measurement of sugar concentrations in affected tubers. Using stepwise regression in conjunction...

CORRELATION PURSUIT: FORWARD STEPWISE VARIABLE SELECTION FOR INDEX MODELS

PubMed Central

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

2012-01-01

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

QSRR modeling for diverse drugs using different feature selection methods coupled with linear and nonlinear regressions.

PubMed

Goodarzi, Mohammad; Jensen, Richard; Vander Heyden, Yvan

2012-12-01

A Quantitative Structure-Retention Relationship (QSRR) is proposed to estimate the chromatographic retention of 83 diverse drugs on a Unisphere poly butadiene (PBD) column, using isocratic elutions at pH 11.7. Previous work has generated QSRR models for them using Classification And Regression Trees (CART). In this work, Ant Colony Optimization is used as a feature selection method to find the best molecular descriptors from a large pool. In addition, several other selection methods have been applied, such as Genetic Algorithms, Stepwise Regression and the Relief method, not only to evaluate Ant Colony Optimization as a feature selection method but also to investigate its ability to find the important descriptors in QSRR. Multiple Linear Regression (MLR) and Support Vector Machines (SVMs) were applied as linear and nonlinear regression methods, respectively, giving excellent correlation between the experimental, i.e. extrapolated to a mobile phase consisting of pure water, and predicted logarithms of the retention factors of the drugs (logk(w)). The overall best model was the SVM one built using descriptors selected by ACO. Copyright © 2012 Elsevier B.V. All rights reserved.

Causal correlation of foliar biochemical concentrations with AVIRIS spectra using forced entry linear regression

NASA Technical Reports Server (NTRS)

Dawson, Terence P.; Curran, Paul J.; Kupiec, John A.

1995-01-01

A major goal of airborne imaging spectrometry is to estimate the biochemical composition of vegetation canopies from reflectance spectra. Remotely-sensed estimates of foliar biochemical concentrations of forests would provide valuable indicators of ecosystem function at regional and eventually global scales. Empirical research has shown a relationship exists between the amount of radiation reflected from absorption features and the concentration of given biochemicals in leaves and canopies (Matson et al., 1994, Johnson et al., 1994). A technique commonly used to determine which wavelengths have the strongest correlation with the biochemical of interest is unguided (stepwise) multiple regression. Wavelengths are entered into a multivariate regression equation, in their order of importance, each contributing to the reduction of the variance in the measured biochemical concentration. A significant problem with the use of stepwise regression for determining the correlation between biochemical concentration and spectra is that of 'overfitting' as there are significantly more wavebands than biochemical measurements. This could result in the selection of wavebands which may be more accurately attributable to noise or canopy effects. In addition, there is a real problem of collinearity in that the individual biochemical concentrations may covary. A strong correlation between the reflectance at a given wavelength and the concentration of a biochemical of interest, therefore, may be due to the effect of another biochemical which is closely related. Furthermore, it is not always possible to account for potentially suitable waveband omissions in the stepwise selection procedure. This concern about the suitability of stepwise regression has been identified and acknowledged in a number of recent studies (Wessman et al., 1988, Curran, 1989, Curran et al., 1992, Peterson and Hubbard, 1992, Martine and Aber, 1994, Kupiec, 1994). These studies have pointed to the lack of a physical link between wavelengths chosen by stepwise regression and the biochemical of interest, and this in turn has cast doubts on the use of imaging spectrometry for the estimation of foliar biochemical concentrations at sites distant from the training sites. To investigate this problem, an analysis was conducted on the variation in canopy biochemical concentrations and reflectance spectra using forced entry linear regression.

Proton radius from electron scattering data

NASA Astrophysics Data System (ADS)

Higinbotham, Douglas W.; Kabir, Al Amin; Lin, Vincent; Meekins, David; Norum, Blaine; Sawatzky, Brad

2016-05-01

Background: The proton charge radius extracted from recent muonic hydrogen Lamb shift measurements is significantly smaller than that extracted from atomic hydrogen and electron scattering measurements. The discrepancy has become known as the proton radius puzzle. Purpose: In an attempt to understand the discrepancy, we review high-precision electron scattering results from Mainz, Jefferson Lab, Saskatoon, and Stanford. Methods: We make use of stepwise regression techniques using the F test as well as the Akaike information criterion to systematically determine the predictive variables to use for a given set and range of electron scattering data as well as to provide multivariate error estimates. Results: Starting with the precision, low four-momentum transfer (Q2) data from Mainz (1980) and Saskatoon (1974), we find that a stepwise regression of the Maclaurin series using the F test as well as the Akaike information criterion justify using a linear extrapolation which yields a value for the proton radius that is consistent with the result obtained from muonic hydrogen measurements. Applying the same Maclaurin series and statistical criteria to the 2014 Rosenbluth results on GE from Mainz, we again find that the stepwise regression tends to favor a radius consistent with the muonic hydrogen radius but produces results that are extremely sensitive to the range of data included in the fit. Making use of the high-Q2 data on GE to select functions which extrapolate to high Q2, we find that a Padé (N =M =1 ) statistical model works remarkably well, as does a dipole function with a 0.84 fm radius, GE(Q2) =(1+Q2/0.66 GeV2) -2 . Conclusions: Rigorous applications of stepwise regression techniques and multivariate error estimates result in the extraction of a proton charge radius that is consistent with the muonic hydrogen result of 0.84 fm; either from linear extrapolation of the extremely-low-Q2 data or by use of the Padé approximant for extrapolation using a larger range of data. Thus, based on a purely statistical analysis of electron scattering data, we conclude that the electron scattering results and the muonic hydrogen results are consistent. It is the atomic hydrogen results that are the outliers.

Predicting pork loin intramuscular fat using computer vision system.

PubMed

Liu, J-H; Sun, X; Young, J M; Bachmeier, L A; Newman, D J

2018-09-01

The objective of this study was to investigate the ability of computer vision system to predict pork intramuscular fat percentage (IMF%). Center-cut loin samples (n = 85) were trimmed of subcutaneous fat and connective tissue. Images were acquired and pixels were segregated to estimate image IMF% and 18 image color features for each image. Subjective IMF% was determined by a trained grader. Ether extract IMF% was calculated using ether extract method. Image color features and image IMF% were used as predictors for stepwise regression and support vector machine models. Results showed that subjective IMF% had a correlation of 0.81 with ether extract IMF% while the image IMF% had a 0.66 correlation with ether extract IMF%. Accuracy rates for regression models were 0.63 for stepwise and 0.75 for support vector machine. Although subjective IMF% has shown to have better prediction, results from computer vision system demonstrates the potential of being used as a tool in predicting pork IMF% in the future. Copyright © 2018 Elsevier Ltd. All rights reserved.

Spatial and temporal variability of reference evapotranspiration and influenced meteorological factors in the Jialing River Basin, China

NASA Astrophysics Data System (ADS)

Herath, Imali Kaushalya; Ye, Xuchun; Wang, Jianli; Bouraima, Abdel-Kabirou

2018-02-01

Reference evapotranspiration (ETr) is one of the important parameters in the hydrological cycle. The spatio-temporal variation of ETr and other meteorological parameters that influence ETr were investigated in the Jialing River Basin (JRB), China. The ETr was estimated using the CROPWAT 8.0 computer model based on the Penman-Montieth equation for the period 1964-2014. Mean temperature (MT), relative humidity (RH), sunshine duration (SD), and wind speed (WS) were the main input parameters of CROPWAT while 12 meteorological stations were evaluated. Linear regression and Mann-Kendall methods were applied to study the spatio-temporal trends while the inverse distance weighted (IDW) method was used to identify the spatial distribution of ETr. Stepwise regression and partial correlation methods were used to identify the meteorological variables that most significantly influenced the changes in ETr. The highest annual ETr was found in the northern part of the basin, whereas the lowest rate was recorded in the western part. In the autumn, the highest ETr was recorded in the southeast part of JRB. The annual ETr reflected neither significant increasing nor decreasing trends. Except for the summer, ETr is slightly increasing in other seasons. The MT significantly increased whereas SD and RH were significantly decreased during the 50-year period. Partial correlation and stepwise regression methods found that the impact of meteorological parameters on ETr varies on an annual and seasonal basis while SD, MT, and RH contributed to the changes of annual and seasonal ETr in the JRB.

[Key physical parameters of hawthorn leaf granules by stepwise regression analysis method].

PubMed

Jiang, Qie-Ying; Zeng, Rong-Gui; Li, Zhe; Luo, Juan; Zhao, Guo-Wei; Lv, Dan; Liao, Zheng-Gen

2017-05-01

The purpose of this study was to investigate the effect of key physical properties of hawthorn leaf granule on its dissolution behavior. Hawthorn leaves extract was utilized as a model drug. The extract was mixed with microcrystalline cellulose or starch with the same ratio by using different methods. Appropriate amount of lubricant and disintegrating agent was added into part of the mixed powder, and then the granules were prepared by using extrusion granulation and high shear granulation. The granules dissolution behavior was evaluated by using equilibrium dissolution quantity and dissolution rate constant of the hypericin as the indicators. Then the effect of physical properties on dissolution behavior was analyzed through the stepwise regression analysis method. The equilibrium dissolution quantity of hypericin and adsorption heat constant in hawthorn leaves were positively correlated with the monolayer adsorption capacity and negatively correlated with the moisture absorption rate constant. The dissolution rate constants were decreased with the increase of Hausner rate, monolayer adsorption capacity and adsorption heat constant, and were increased with the increase of Carr index and specific surface area. Adsorption heat constant, monolayer adsorption capacity, moisture absorption rate constant, Carr index and specific surface area were the key physical properties of hawthorn leaf granule to affect its dissolution behavior. Copyright© by the Chinese Pharmaceutical Association.

Illuminating Tradespace Decisions Using Efficient Experimental Space-Filling Designs for the Engineered Resilient System Architecture

DTIC Science & Technology

2015-06-30

7. Building Statistical Metamodels using Simulation Experimental Designs ............................................... 34 7.1. Statistical Design...system design drivers across several different domain models, our methodology uses statistical metamodeling to approximate the simulations’ behavior. A...output. We build metamodels using a number of statistical methods that include stepwise regression, boosted trees, neural nets, and bootstrap forest

Illuminating Tradespace Decisions Using Efficient Experimental Space-Filling Designs for the Engineered Resilient System Architecture

DTIC Science & Technology

2015-06-01

7. Building Statistical Metamodels using Simulation Experimental Designs ............................................... 34 7.1. Statistical Design...system design drivers across several different domain models, our methodology uses statistical metamodeling to approximate the simulations’ behavior. A...output. We build metamodels using a number of statistical methods that include stepwise regression, boosted trees, neural nets, and bootstrap forest

Comparison of bioavailable vanadium in alfalfa rhizosphere soil extracted by an improved BCR procedure and EDTA, HCl, and NaNO₃ single extractions in a pot experiment with V-Cd treatments.

PubMed

Yang, Jie; Teng, Yanguo; Zuo, Rui; Song, Liuting

2015-06-01

The BCR sequential extraction procedure was compared with EDTA, HCl, and NaNO3 single extractions for evaluating vanadium bioavailability in alfalfa rhizosphere soil. The amounts of vanadium extracted by these methods were in the following order: BCR (bioavailable V) > EDTA ≈ HCl > NaNO3. Both correlation analysis and stepwise regression were adopted to illustrate the extractable vanadium between different reagents. The correlation coefficients between extracted vanadium and the vanadium contents in alfalfa roots were R NaNO3 = 0.948, R HCl = 0.902, R EDTA = 0.816, and R bioavailable V = 0.819. The stepwise multiple regression equation of the NaNO3 extraction was the most significant at a 95 % confidence interval. The influence of pH, total organic carbon, and cadmium content of soil to vanadium bioavailability were not definite. In summary, both the BCR sequential extraction and the single extraction methods were valid approaches for predicting vanadium bioavailability in alfalfa rhizosphere soil, especially the single extractions.

Stepwise group sparse regression (SGSR): gene-set-based pharmacogenomic predictive models with stepwise selection of functional priors.

PubMed

Jang, In Sock; Dienstmann, Rodrigo; Margolin, Adam A; Guinney, Justin

2015-01-01

Complex mechanisms involving genomic aberrations in numerous proteins and pathways are believed to be a key cause of many diseases such as cancer. With recent advances in genomics, elucidating the molecular basis of cancer at a patient level is now feasible, and has led to personalized treatment strategies whereby a patient is treated according to his or her genomic profile. However, there is growing recognition that existing treatment modalities are overly simplistic, and do not fully account for the deep genomic complexity associated with sensitivity or resistance to cancer therapies. To overcome these limitations, large-scale pharmacogenomic screens of cancer cell lines--in conjunction with modern statistical learning approaches--have been used to explore the genetic underpinnings of drug response. While these analyses have demonstrated the ability to infer genetic predictors of compound sensitivity, to date most modeling approaches have been data-driven, i.e. they do not explicitly incorporate domain-specific knowledge (priors) in the process of learning a model. While a purely data-driven approach offers an unbiased perspective of the data--and may yield unexpected or novel insights--this strategy introduces challenges for both model interpretability and accuracy. In this study, we propose a novel prior-incorporated sparse regression model in which the choice of informative predictor sets is carried out by knowledge-driven priors (gene sets) in a stepwise fashion. Under regularization in a linear regression model, our algorithm is able to incorporate prior biological knowledge across the predictive variables thereby improving the interpretability of the final model with no loss--and often an improvement--in predictive performance. We evaluate the performance of our algorithm compared to well-known regularization methods such as LASSO, Ridge and Elastic net regression in the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (Sanger) pharmacogenomics datasets, demonstrating that incorporation of the biological priors selected by our model confers improved predictability and interpretability, despite much fewer predictors, over existing state-of-the-art methods.

Prediction of troponin-T degradation using color image texture features in 10d aged beef longissimus steaks.

PubMed

Sun, X; Chen, K J; Berg, E P; Newman, D J; Schwartz, C A; Keller, W L; Maddock Carlin, K R

2014-02-01

The objective was to use digital color image texture features to predict troponin-T degradation in beef. Image texture features, including 88 gray level co-occurrence texture features, 81 two-dimension fast Fourier transformation texture features, and 48 Gabor wavelet filter texture features, were extracted from color images of beef strip steaks (longissimus dorsi, n = 102) aged for 10d obtained using a digital camera and additional lighting. Steaks were designated degraded or not-degraded based on troponin-T degradation determined on d 3 and d 10 postmortem by immunoblotting. Statistical analysis (STEPWISE regression model) and artificial neural network (support vector machine model, SVM) methods were designed to classify protein degradation. The d 3 and d 10 STEPWISE models were 94% and 86% accurate, respectively, while the d 3 and d 10 SVM models were 63% and 71%, respectively, in predicting protein degradation in aged meat. STEPWISE and SVM models based on image texture features show potential to predict troponin-T degradation in meat. © 2013.

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.

Prevalence of consistent condom use with various types of sex partners and associated factors among money boys in Changsha, China.

PubMed

Wang, Lian-Hong; Yan, Jin; Yang, Guo-Li; Long, Shuo; Yu, Yong; Wu, Xi-Lin

2015-04-01

Money boys with inconsistent condom use (less than 100% of the time) are at high risk of infection by human immunodeficiency virus (HIV) or sexually transmitted infection (STI), but relatively little research has examined their risk behaviors. We investigated the prevalence of consistent condom use (100% of the time) and associated factors among money boys. A cross-sectional study using a structured questionnaire was conducted among money boys in Changsha, China, between July 2012 and January 2013. Independent variables included socio-demographic data, substance abuse history, work characteristics, and self-reported HIV and STI history. Dependent variables included the consistent condom use with different types of sex partners. Among the participants, 82.4% used condoms consistently with male clients, 80.2% with male sex partners, and 77.1% with female sex partners in the past 3 months. A multiple stepwise logistic regression model identified four statistically significant factors associated with lower likelihoods of consistent condom use with male clients: age group, substance abuse, lack of an "employment" arrangement, and having no HIV test within the prior 6 months. In a similar model, only one factor associated significantly with lower likelihoods of consistent condom use with male sex partners was identified in multiple stepwise logistic regression analyses: having no HIV test within the prior six months. As for female sex partners, two significant variables were statistically significant in the multiple stepwise logistic regression analysis: having no HIV test within the prior 6 months and having STI history. Interventions which are linked with more realistic and acceptable HIV prevention methods are greatly warranted and should increase risk awareness and the behavior of consistent condom use in both commercial and personal relationship. © 2015 International Society for Sexual Medicine.

Proton radius from electron scattering data

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

Higinbotham, Douglas W.; Kabir, Al Amin; Lin, Vincent

Background: The proton charge radius extracted from recent muonic hydrogen Lamb shift measurements is significantly smaller than that extracted from atomic hydrogen and electron scattering measurements. The discrepancy has become known as the proton radius puzzle. Purpose: In an attempt to understand the discrepancy, we review high-precision electron scattering results from Mainz, Jefferson Lab, Saskatoon and Stanford. Methods: We make use of stepwise regression techniques using the F-test as well as the Akaike information criterion to systematically determine the predictive variables to use for a given set and range of electron scattering data as well as to provide multivariate errormore » estimates. Results: Starting with the precision, low four-momentum transfer (Q 2) data from Mainz (1980) and Saskatoon (1974), we find that a stepwise regression of the Maclaurin series using the F-test as well as the Akaike information criterion justify using a linear extrapolation which yields a value for the proton radius that is consistent with the result obtained from muonic hydrogen measurements. Applying the same Maclaurin series and statistical criteria to the 2014 Rosenbluth results on GE from Mainz, we again find that the stepwise regression tends to favor a radius consistent with the muonic hydrogen radius but produces results that are extremely sensitive to the range of data included in the fit. Making use of the high-Q 2 data on G E to select functions which extrapolate to high Q 2, we find that a Pad´e (N = M = 1) statistical model works remarkably well, as does a dipole function with a 0.84 fm radius, G E(Q 2) = (1 + Q 2/0.66 GeV 2) -2. Conclusions: Rigorous applications of stepwise regression techniques and multivariate error estimates result in the extraction of a proton charge radius that is consistent with the muonic hydrogen result of 0.84 fm; either from linear extrapolation of the extreme low-Q 2 data or by use of the Pad´e approximant for extrapolation using a larger range of data. Thus, based on a purely statistical analysis of electron scattering data, we conclude that the electron scattering result and the muonic hydrogen result are consistent. Lastly, it is the atomic hydrogen results that are the outliers.« less

Proton radius from electron scattering data

DOE PAGES

Higinbotham, Douglas W.; Kabir, Al Amin; Lin, Vincent; ...

2016-05-31

Background: The proton charge radius extracted from recent muonic hydrogen Lamb shift measurements is significantly smaller than that extracted from atomic hydrogen and electron scattering measurements. The discrepancy has become known as the proton radius puzzle. Purpose: In an attempt to understand the discrepancy, we review high-precision electron scattering results from Mainz, Jefferson Lab, Saskatoon and Stanford. Methods: We make use of stepwise regression techniques using the F-test as well as the Akaike information criterion to systematically determine the predictive variables to use for a given set and range of electron scattering data as well as to provide multivariate errormore » estimates. Results: Starting with the precision, low four-momentum transfer (Q 2) data from Mainz (1980) and Saskatoon (1974), we find that a stepwise regression of the Maclaurin series using the F-test as well as the Akaike information criterion justify using a linear extrapolation which yields a value for the proton radius that is consistent with the result obtained from muonic hydrogen measurements. Applying the same Maclaurin series and statistical criteria to the 2014 Rosenbluth results on GE from Mainz, we again find that the stepwise regression tends to favor a radius consistent with the muonic hydrogen radius but produces results that are extremely sensitive to the range of data included in the fit. Making use of the high-Q 2 data on G E to select functions which extrapolate to high Q 2, we find that a Pad´e (N = M = 1) statistical model works remarkably well, as does a dipole function with a 0.84 fm radius, G E(Q 2) = (1 + Q 2/0.66 GeV 2) -2. Conclusions: Rigorous applications of stepwise regression techniques and multivariate error estimates result in the extraction of a proton charge radius that is consistent with the muonic hydrogen result of 0.84 fm; either from linear extrapolation of the extreme low-Q 2 data or by use of the Pad´e approximant for extrapolation using a larger range of data. Thus, based on a purely statistical analysis of electron scattering data, we conclude that the electron scattering result and the muonic hydrogen result are consistent. Lastly, it is the atomic hydrogen results that are the outliers.« less

Development of a Bayesian model to estimate health care outcomes in the severely wounded

PubMed Central

Stojadinovic, Alexander; Eberhardt, John; Brown, Trevor S; Hawksworth, Jason S; Gage, Frederick; Tadaki, Douglas K; Forsberg, Jonathan A; Davis, Thomas A; Potter, Benjamin K; Dunne, James R; Elster, E A

2010-01-01

Background: Graphical probabilistic models have the ability to provide insights as to how clinical factors are conditionally related. These models can be used to help us understand factors influencing health care outcomes and resource utilization, and to estimate morbidity and clinical outcomes in trauma patient populations. Study design: Thirty-two combat casualties with severe extremity injuries enrolled in a prospective observational study were analyzed using step-wise machine-learned Bayesian belief network (BBN) and step-wise logistic regression (LR). Models were evaluated using 10-fold cross-validation to calculate area-under-the-curve (AUC) from receiver operating characteristics (ROC) curves. Results: Our BBN showed important associations between various factors in our data set that could not be developed using standard regression methods. Cross-validated ROC curve analysis showed that our BBN model was a robust representation of our data domain and that LR models trained on these findings were also robust: hospital-acquired infection (AUC: LR, 0.81; BBN, 0.79), intensive care unit length of stay (AUC: LR, 0.97; BBN, 0.81), and wound healing (AUC: LR, 0.91; BBN, 0.72) showed strong AUC. Conclusions: A BBN model can effectively represent clinical outcomes and biomarkers in patients hospitalized after severe wounding, and is confirmed by 10-fold cross-validation and further confirmed through logistic regression modeling. The method warrants further development and independent validation in other, more diverse patient populations. PMID:21197361

Determination of benzo[a]pyrene in cigarette mainstream smoke by using mid-infrared spectroscopy associated with a novel chemometric algorithm.

PubMed

Zhang, Yan; Zou, Hong-Yan; Shi, Pei; Yang, Qin; Tang, Li-Juan; Jiang, Jian-Hui; Wu, Hai-Long; Yu, Ru-Qin

2016-01-01

Determination of benzo[a]pyrene (BaP) in cigarette smoke can be very important for the tobacco quality control and the assessment of its harm to human health. In this study, mid-infrared spectroscopy (MIR) coupled to chemometric algorithm (DPSO-WPT-PLS), which was based on the wavelet packet transform (WPT), discrete particle swarm optimization algorithm (DPSO) and partial least squares regression (PLS), was used to quantify harmful ingredient benzo[a]pyrene in the cigarette mainstream smoke with promising result. Furthermore, the proposed method provided better performance compared to several other chemometric models, i.e., PLS, radial basis function-based PLS (RBF-PLS), PLS with stepwise regression variable selection (Stepwise-PLS) as well as WPT-PLS with informative wavelet coefficients selected by correlation coefficient test (rtest-WPT-PLS). It can be expected that the proposed strategy could become a new effective, rapid quantitative analysis technique in analyzing the harmful ingredient BaP in cigarette mainstream smoke. Copyright © 2015 Elsevier B.V. All rights reserved.

Retro-regression--another important multivariate regression improvement.

PubMed

Randić, M

2001-01-01

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

Stepwise versus Hierarchical Regression: Pros and Cons

ERIC Educational Resources Information Center

Lewis, Mitzi

2007-01-01

Multiple regression is commonly used in social and behavioral data analysis. In multiple regression contexts, researchers are very often interested in determining the "best" predictors in the analysis. This focus may stem from a need to identify those predictors that are supportive of theory. Alternatively, the researcher may simply be interested…

A Hybrid Approach of Stepwise Regression, Logistic Regression, Support Vector Machine, and Decision Tree for Forecasting Fraudulent Financial Statements

PubMed Central

Goo, Yeong-Jia James; Shen, Zone-De

2014-01-01

As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%. PMID:25302338

A hybrid approach of stepwise regression, logistic regression, support vector machine, and decision tree for forecasting fraudulent financial statements.

PubMed

Chen, Suduan; Goo, Yeong-Jia James; Shen, Zone-De

2014-01-01

As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%.

A stepwise model to predict monthly streamflow

NASA Astrophysics Data System (ADS)

Mahmood Al-Juboori, Anas; Guven, Aytac

2016-12-01

In this study, a stepwise model empowered with genetic programming is developed to predict the monthly flows of Hurman River in Turkey and Diyalah and Lesser Zab Rivers in Iraq. The model divides the monthly flow data to twelve intervals representing the number of months in a year. The flow of a month, t is considered as a function of the antecedent month's flow (t - 1) and it is predicted by multiplying the antecedent monthly flow by a constant value called K. The optimum value of K is obtained by a stepwise procedure which employs Gene Expression Programming (GEP) and Nonlinear Generalized Reduced Gradient Optimization (NGRGO) as alternative to traditional nonlinear regression technique. The degree of determination and root mean squared error are used to evaluate the performance of the proposed models. The results of the proposed model are compared with the conventional Markovian and Auto Regressive Integrated Moving Average (ARIMA) models based on observed monthly flow data. The comparison results based on five different statistic measures show that the proposed stepwise model performed better than Markovian model and ARIMA model. The R2 values of the proposed model range between 0.81 and 0.92 for the three rivers in this study.

A new model for estimating total body water from bioelectrical resistance

NASA Technical Reports Server (NTRS)

Siconolfi, S. F.; Kear, K. T.

1992-01-01

Estimation of total body water (T) from bioelectrical resistance (R) is commonly done by stepwise regression models with height squared over R, H(exp 2)/R, age, sex, and weight (W). Polynomials of H(exp 2)/R have not been included in these models. We examined the validity of a model with third order polynomials and W. Methods: T was measured with oxygen-18 labled water in 27 subjects. R at 50 kHz was obtained from electrodes placed on the hand and foot while subjects were in the supine position. A stepwise regression equation was developed with 13 subjects (age 31.5 plus or minus 6.2 years, T 38.2 plus or minus 6.6 L, W 65.2 plus or minus 12.0 kg). Correlations, standard error of estimates and mean differences were computed between T and estimated T's from the new (N) model and other models. Evaluations were completed with the remaining 14 subjects (age 32.4 plus or minus 6.3 years, T 40.3 plus or minus 8 L, W 70.2 plus or minus 12.3 kg) and two of its subgroups (high and low) Results: A regression equation was developed from the model. The only significant mean difference was between T and one of the earlier models. Conclusion: Third order polynomials in regression models may increase the accuracy of estimating total body water. Evaluating the model with a larger population is needed.

Association Between Smartphone Use and Musculoskeletal Discomfort in Adolescent Students.

PubMed

Yang, Shang-Yu; Chen, Ming-De; Huang, Yueh-Chu; Lin, Chung-Ying; Chang, Jer-Hao

2017-06-01

Despite the substantial increase in the number of adolescent smartphone users, few studies have investigated the behavioural effects of smartphone use on adolescent students as it relates to musculoskeletal discomfort. The purpose of this study was to explore the association between smartphone use and musculoskeletal discomfort in students at a Taiwanese junior college. We hypothesised that the duration of smartphone use would be associated with increased instances of musculoskeletal discomfort in these students. This cross-sectional study employed a convenience sampling method to recruit students from a junior college in southern Taiwan. All the students (n = 315) were asked to answer questionnaires on smartphone use. A descriptive analysis, stepwise regression, and logistic regression were used to examine specific components of smartphone use and their relationship to musculoskeletal discomfort. Nearly half of the participants experienced neck and shoulder discomfort. The stepwise regression results indicated that the number of body parts with discomfort (F = 6.009, p < 0.05) increased with hours spent using ancillary smartphone functions. The logistic regression analysis showed that the students who talked on the phone >3 h/day had a higher risk of upper back discomfort than did those who talked on the phone <1 h/day [odds ratio (OR) = 4.23, p < 0.05]. This study revealed that the relationship between smartphone use and musculoskeletal discomfort is related to the duration of smartphone ancillary function use. Moreover, hours spent talking on the phone was a predictor of upper back discomfort.

An investigation of correlation between pilot scanning behavior and workload using stepwise regression analysis

NASA Technical Reports Server (NTRS)

Waller, M. C.

1976-01-01

An electro-optical device called an oculometer which tracks a subject's lookpoint as a time function has been used to collect data in a real-time simulation study of instrument landing system (ILS) approaches. The data describing the scanning behavior of a pilot during the instrument approaches have been analyzed by use of a stepwise regression analysis technique. A statistically significant correlation between pilot workload, as indicated by pilot ratings, and scanning behavior has been established. In addition, it was demonstrated that parameters derived from the scanning behavior data can be combined in a mathematical equation to provide a good representation of pilot workload.

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 chemerin levels are independently associated with quality of life in colorectal cancer survivors: A pilot study

PubMed Central

Lee, Jee-Yon; Lee, Mi-Kyung; Kim, Nam-Kyu; Chu, Sang-Hui; Lee, Duk-Chul; Lee, Hye-Sun

2017-01-01

Background Colorectal cancer (CRC) survivors are known to experience various symptoms that significantly affect their quality of life (QOL); therefore, it is important to identify clinical markers related with CRC survivor QOL. Here we investigated the relationship between serum chemerin levels, a newly identified proinflammatory adipokine, and QOL in CRC survivors. Methods A data of total of 110 CRC survivors were analysed in the study. Serum chemerin levels were measured with an enzyme immunoassay analyser. Functional Assessment of Cancer Therapy (FACT) scores were used as an indicator of QOL in CRC survivors. Results Weak but not negligible relationships were observed between serum chemerin levels and FACT-General (G) (r = -0.22, p<0.02), FACT-Colorectal cancer (C) (r = -0.23, p<0.02) and FACT-Fatigue (F) scores (r = -0.27, p<0.01) after adjusting for confounding factors. Both stepwise and enter method multiple linear regression analyses confirmed that serum chemerin levels were independently associated with FACT-G (stepwise: β = -0.15, p<0.01; enter: β = -0.12, p = 0.02), FACT-C (stepwise: β = -0.19, p<0.01; enter; β = -0.14, p = 0.02) and FACT-F scores (stepwise: β = -0.23, p<0.01; enter: β = -0.20, p<0.01). Conclusions Our results demonstrate a weak inverse relationship between serum chemerin and CRC survivor QOL. Although it is impossible to determine causality, our findings suggest that serum chemerin levels may have a significant association with CRC survivor QOL. Further prospective studies are required to confirm the clinical significance of our pilot study. PMID:28475614

Identification method of laser gyro error model under changing physical field

NASA Astrophysics Data System (ADS)

Wang, Qingqing; Niu, Zhenzhong

2018-04-01

In this paper, the influence mechanism of temperature, temperature changing rate and temperature gradient on the inertial devices is studied. The two-order model of zero bias and the three-order model of the calibration factor of lster gyro under temperature variation are deduced. The calibration scheme of temperature error is designed, and the experiment is carried out. Two methods of stepwise regression analysis and BP neural network are used to identify the parameters of the temperature error model, and the effectiveness of the two methods is proved by the temperature error compensation.

Associations between dietary and lifestyle risk factors and colorectal cancer in the Scottish population.

PubMed

Theodoratou, Evropi; Farrington, Susan M; Tenesa, Albert; McNeill, Geraldine; Cetnarskyj, Roseanne; Korakakis, Emmanouil; Din, Farhat V N; Porteous, Mary E; Dunlop, Malcolm G; Campbell, Harry

2014-01-01

Colorectal cancer (CRC) accounts for 9.7% of all cancer cases and for 8% of all cancer-related deaths. Established risk factors include personal or family history of CRC as well as lifestyle and dietary factors. We investigated the relationship between CRC and demographic, lifestyle, food and nutrient risk factors through a case-control study that included 2062 patients and 2776 controls from Scotland. Forward and backward stepwise regression was applied and the stability of the models was assessed in 1000 bootstrap samples. The variables that were automatically selected to be included by the forward or backward stepwise regression and whose selection was verified by bootstrap sampling in the current study were family history, dietary energy, 'high-energy snack foods', eggs, juice, sugar-sweetened beverages and white fish (associated with an increased CRC risk) and NSAIDs, coffee and magnesium (associated with a decreased CRC risk). Application of forward and backward stepwise regression in this CRC study identified some already established as well as some novel potential risk factors. Bootstrap findings suggest that examination of the stability of regression models by bootstrap sampling is useful in the interpretation of study findings. 'High-energy snack foods' and high-energy drinks (including sugar-sweetened beverages and fruit juices) as risk factors for CRC have not been reported previously and merit further investigation as such snacks and beverages are important contributors in European and North American diets.

Self-Concept and Participation in School Activities Reanalyzed.

ERIC Educational Resources Information Center

Winne, Philip H.; Walsh, John

1980-01-01

Yarworth and Gauthier (EJ 189 606) examined whether self-concept variables enhanced predictions about students' participation in school activities, using unstructured stepwise regression techniques. A reanalysis of their data using hierarchial regression models tested their hypothesis more appropriately, and uncovered multicollinearity and…

Assessment of plant species diversity based on hyperspectral indices at a fine scale.

PubMed

Peng, Yu; Fan, Min; Song, Jingyi; Cui, Tiantian; Li, Rui

2018-03-19

Fast and nondestructive approaches of measuring plant species diversity have been a subject of excessive scientific curiosity and disquiet to environmentalists and field ecologists worldwide. In this study, we measured the hyperspectral reflectances and plant species diversity indices at a fine scale (0.8 meter) in central Hunshandak Sandland of Inner Mongolia, China. The first-order derivative value (FD) at each waveband and 37 hyperspectral indices were used to assess plant species diversity. Results demonstrated that the stepwise linear regression of FD can accurately estimate the Simpson (R 2  = 0.83), Pielou (R 2  = 0.87) and Shannon-Wiener index (R 2  = 0.88). Stepwise linear regression of FD (R 2  = 0.81, R 2  = 0.82) and spectral vegetation indices (R 2  = 0.51, R 2  = 0.58) significantly predicted the Margalef and Gleason index. It was proposed that the Simpson, Pielou and Shannon-Wiener indices, which are widely used as plant species diversity indicators, can be precisely estimated through hyperspectral indices at a fine scale. This research promotes the development of methods for assessment of plant diversity using hyperspectral data.

Destination bedside: using research findings to visualize optimal unit layouts and health information technology in support of bedside care.

PubMed

Watkins, Nicholas; Kennedy, Mary; Lee, Nelson; O'Neill, Michael; Peavey, Erin; Ducharme, Maria; Padula, Cynthia

2012-05-01

This study explored the impact of unit design and healthcare information technology (HIT) on nursing workflow and patient-centered care (PCC). Healthcare information technology and unit layout-related predictors of nursing workflow and PCC were measured during a 3-phase study involving questionnaires and work sampling methods. Stepwise multiple linear regressions demonstrated several HIT and unit layout-related factors that impact nursing workflow and PCC.

Predicting psychopharmacological drug effects on actual driving performance (SDLP) from psychometric tests measuring driving-related skills.

PubMed

Verster, Joris C; Roth, Thomas

2012-03-01

There are various methods to examine driving ability. Comparisons between these methods and their relationship with actual on-road driving is often not determined. The objective of this study was to determine whether laboratory tests measuring driving-related skills could adequately predict on-the-road driving performance during normal traffic. Ninety-six healthy volunteers performed a standardized on-the-road driving test. Subjects were instructed to drive with a constant speed and steady lateral position within the right traffic lane. Standard deviation of lateral position (SDLP), i.e., the weaving of the car, was determined. The subjects also performed a psychometric test battery including the DSST, Sternberg memory scanning test, a tracking test, and a divided attention test. Difference scores from placebo for parameters of the psychometric tests and SDLP were computed and correlated with each other. A stepwise linear regression analysis determined the predictive validity of the laboratory test battery to SDLP. Stepwise regression analyses revealed that the combination of five parameters, hard tracking, tracking and reaction time of the divided attention test, and reaction time and percentage of errors of the Sternberg memory scanning test, together had a predictive validity of 33.4%. The psychometric tests in this test battery showed insufficient predictive validity to replace the on-the-road driving test during normal traffic.

[Associated factors in newborns with intrauterine growth retardation].

PubMed

Thompson-Chagoyán, Oscar C; Vega-Franco, Leopoldo

2008-01-01

To identify the risk factors implicated in the intrauterine growth retardation (IUGR) of neonates born in a social security institution. Case controls design study in 376 neonates: 188 with IUGR (weight < 10 percentile) and 188 without IUGR. When they born, information about 30 variables of risk for IUGR were obtained from mothers. Risk analysis and logistical regression (stepwise) were used. Odds ratios were significant for 12 of the variables. The model obtains by stepwise regression included: weight gain at pregnancy, prenatal care attendance, toxemia, chocolate ingestion, father's weight, and the environmental house. Must of the variables included in the model are related to socioeconomic disadvantages related to the risk of RCIU in the population.

Use of principal-component, correlation, and stepwise multiple-regression analyses to investigate selected physical and hydraulic properties of carbonate-rock aquifers

USGS Publications Warehouse

Brown, C. Erwin

1993-01-01

Correlation analysis in conjunction with principal-component and multiple-regression analyses were applied to laboratory chemical and petrographic data to assess the usefulness of these techniques in evaluating selected physical and hydraulic properties of carbonate-rock aquifers in central Pennsylvania. Correlation and principal-component analyses were used to establish relations and associations among variables, to determine dimensions of property variation of samples, and to filter the variables containing similar information. Principal-component and correlation analyses showed that porosity is related to other measured variables and that permeability is most related to porosity and grain size. Four principal components are found to be significant in explaining the variance of data. Stepwise multiple-regression analysis was used to see how well the measured variables could predict porosity and (or) permeability for this suite of rocks. The variation in permeability and porosity is not totally predicted by the other variables, but the regression is significant at the 5% significance level. ?? 1993.

Outcome modelling strategies in epidemiology: traditional methods and basic alternatives

PubMed Central

Greenland, Sander; Daniel, Rhian; Pearce, Neil

2016-01-01

Abstract Controlling for too many potential confounders can lead to or aggravate problems of data sparsity or multicollinearity, particularly when the number of covariates is large in relation to the study size. As a result, methods to reduce the number of modelled covariates are often deployed. We review several traditional modelling strategies, including stepwise regression and the ‘change-in-estimate’ (CIE) approach to deciding which potential confounders to include in an outcome-regression model for estimating effects of a targeted exposure. We discuss their shortcomings, and then provide some basic alternatives and refinements that do not require special macros or programming. Throughout, we assume the main goal is to derive the most accurate effect estimates obtainable from the data and commercial software. Allowing that most users must stay within standard software packages, this goal can be roughly approximated using basic methods to assess, and thereby minimize, mean squared error (MSE). PMID:27097747

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

Tang, Kunkun, E-mail: ktg@illinois.edu; Inria Bordeaux – Sud-Ouest, Team Cardamom, 200 avenue de la Vieille Tour, 33405 Talence; Congedo, Pietro M.

The Polynomial Dimensional Decomposition (PDD) is employed in this work for the global sensitivity analysis and uncertainty quantification (UQ) of stochastic systems subject to a moderate to large number of input random variables. Due to the intimate connection between the PDD and the Analysis of Variance (ANOVA) approaches, PDD is able to provide a simpler and more direct evaluation of the Sobol' sensitivity indices, when compared to the Polynomial Chaos expansion (PC). Unfortunately, the number of PDD terms grows exponentially with respect to the size of the input random vector, which makes the computational cost of standard methods unaffordable formore » real engineering applications. In order to address the problem of the curse of dimensionality, this work proposes essentially variance-based adaptive strategies aiming to build a cheap meta-model (i.e. surrogate model) by employing the sparse PDD approach with its coefficients computed by regression. Three levels of adaptivity are carried out in this paper: 1) the truncated dimensionality for ANOVA component functions, 2) the active dimension technique especially for second- and higher-order parameter interactions, and 3) the stepwise regression approach designed to retain only the most influential polynomials in the PDD expansion. During this adaptive procedure featuring stepwise regressions, the surrogate model representation keeps containing few terms, so that the cost to resolve repeatedly the linear systems of the least-squares regression problem is negligible. The size of the finally obtained sparse PDD representation is much smaller than the one of the full expansion, since only significant terms are eventually retained. Consequently, a much smaller number of calls to the deterministic model is required to compute the final PDD coefficients.« less

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

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

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

Hypnotism as a Function of Trance State Effects, Expectancy, and Suggestibility: An Italian Replication.

PubMed

Pekala, Ronald J; Baglio, Francesca; Cabinio, Monia; Lipari, Susanna; Baglio, Gisella; Mendozzi, Laura; Cecconi, Pietro; Pugnetti, Luigi; Sciaky, Riccardo

2017-01-01

Previous research using stepwise regression analyses found self-reported hypnotic depth (srHD) to be a function of suggestibility, trance state effects, and expectancy. This study sought to replicate and expand that research using a general state measure of hypnotic responsivity, the Phenomenology of Consciousness Inventory: Hypnotic Assessment Procedure (PCI-HAP). Ninety-five participants completed an Italian translation of the PCI-HAP, with srHD scores predicted from the PCI-HAP assessment items. The regression analysis replicated the previous research results. Additionally, stepwise regression analyses were able to predict the srHD score equally well using only the PCI dimension scores. These results not only replicated prior research but suggest how this methodology to assess hypnotic responsivity, when combined with more traditional neurophysiological and cognitive-behavioral methodologies, may allow for a more comprehensive understanding of that enigma called hypnosis.

Analysis of flight data from a High-Incidence Research Model by system identification methods

NASA Technical Reports Server (NTRS)

Batterson, James G.; Klein, Vladislav

1989-01-01

Data partitioning and modified stepwise regression were applied to recorded flight data from a Royal Aerospace Establishment high incidence research model. An aerodynamic model structure and corresponding stability and control derivatives were determined for angles of attack between 18 and 30 deg. Several nonlinearities in angles of attack and sideslip as well as a unique roll-dominated set of lateral modes were found. All flight estimated values were compared to available wind tunnel measurements.

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

USGS Publications Warehouse

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

2011-01-01

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

[Regional scale remote sensing-based yield estimation of winter wheat by using MODIS-NDVI data: a case study of Jining City in Shandong Province].

PubMed

Ren, Jianqiang; Chen, Zhongxin; Tang, Huajun

2006-12-01

Taking Jining City of Shandong Province, one of the most important winter wheat production regions in Huanghuaihai Plain as an example, the winter wheat yield was estimated by using the 250 m MODIS-NDVI data smoothed by Savitzky-Golay filter. The NDVI values between 0. 20 and 0. 80 were selected, and the sum of NDVI value for each county was calculated to build its relation with winter wheat yield. By using stepwise regression method, the linear regression model between NDVI and winter wheat yield was established, with the precision validated by the ground survey data. The results showed that the relative error of predicted yield was between -3.6% and 3.9%, suggesting that the method was relatively accurate and feasible.

Caries risk assessment in schoolchildren - a form based on Cariogram® software

PubMed Central

CABRAL, Renata Nunes; HILGERT, Leandro Augusto; FABER, Jorge; LEAL, Soraya Coelho

2014-01-01

Identifying caries risk factors is an important measure which contributes to best understanding of the cariogenic profile of the patient. The Cariogram® software provides this analysis, and protocols simplifying the method were suggested. Objectives The aim of this study was to determine whether a newly developed Caries Risk Assessment (CRA) form based on the Cariogram® software could classify schoolchildren according to their caries risk and to evaluate relationships between caries risk and the variables in the form. Material and Methods 150 schoolchildren aged 5 to 7 years old were included in this survey. Caries prevalence was obtained according to International Caries Detection and Assessment System (ICDAS) II. Information for filling in the form based on Cariogram® was collected clinically and from questionnaires sent to parents. Linear regression and a forward stepwise multiple regression model were applied to correlate the variables included in the form with the caries risk. Results Caries prevalence, in primary dentition, including enamel and dentine carious lesions was 98.6%, and 77.3% when only dentine lesions were considered. Eighty-six percent of the children were classified as at moderate caries risk. The forward stepwise multiple regression model result was significant (R2=0.904; p<0.00001), showing that the most significant factors influencing caries risk were caries experience, oral hygiene, frequency of food consumption, sugar consumption and fluoride sources. Conclusion The use of the form based on the Cariogram® software enabled classification of the schoolchildren at low, moderate and high caries risk. Caries experience, oral hygiene, frequency of food consumption, sugar consumption and fluoride sources are the variables that were shown to be highly correlated with caries risk. PMID:25466473

Nowcasting of Low-Visibility Procedure States with Ordered Logistic Regression at Vienna International Airport

NASA Astrophysics Data System (ADS)

Kneringer, Philipp; Dietz, Sebastian; Mayr, Georg J.; Zeileis, Achim

2017-04-01

Low-visibility conditions have a large impact on aviation safety and economic efficiency of airports and airlines. To support decision makers, we develop a statistical probabilistic nowcasting tool for the occurrence of capacity-reducing operations related to low visibility. The probabilities of four different low visibility classes are predicted with an ordered logistic regression model based on time series of meteorological point measurements. Potential predictor variables for the statistical models are visibility, humidity, temperature and wind measurements at several measurement sites. A stepwise variable selection method indicates that visibility and humidity measurements are the most important model inputs. The forecasts are tested with a 30 minute forecast interval up to two hours, which is a sufficient time span for tactical planning at Vienna Airport. The ordered logistic regression models outperform persistence and are competitive with human forecasters.

A statistical model for Windstorm Variability over the British Isles based on Large-scale Atmospheric and Oceanic Mechanisms

NASA Astrophysics Data System (ADS)

Kirchner-Bossi, Nicolas; Befort, Daniel J.; Wild, Simon B.; Ulbrich, Uwe; Leckebusch, Gregor C.

2016-04-01

Time-clustered winter storms are responsible for a majority of the wind-induced losses in Europe. Over last years, different atmospheric and oceanic large-scale mechanisms as the North Atlantic Oscillation (NAO) or the Meridional Overturning Circulation (MOC) have been proven to drive some significant portion of the windstorm variability over Europe. In this work we systematically investigate the influence of different large-scale natural variability modes: more than 20 indices related to those mechanisms with proven or potential influence on the windstorm frequency variability over Europe - mostly SST- or pressure-based - are derived by means of ECMWF ERA-20C reanalysis during the last century (1902-2009), and compared to the windstorm variability for the European winter (DJF). Windstorms are defined and tracked as in Leckebusch et al. (2008). The derived indices are then employed to develop a statistical procedure including a stepwise Multiple Linear Regression (MLR) and an Artificial Neural Network (ANN), aiming to hindcast the inter-annual (DJF) regional windstorm frequency variability in a case study for the British Isles. This case study reveals 13 indices with a statistically significant coupling with seasonal windstorm counts. The Scandinavian Pattern (SCA) showed the strongest correlation (0.61), followed by the NAO (0.48) and the Polar/Eurasia Pattern (0.46). The obtained indices (standard-normalised) are selected as predictors for a windstorm variability hindcast model applied for the British Isles. First, a stepwise linear regression is performed, to identify which mechanisms can explain windstorm variability best. Finally, the indices retained by the stepwise regression are used to develop a multlayer perceptron-based ANN that hindcasted seasonal windstorm frequency and clustering. Eight indices (SCA, NAO, EA, PDO, W.NAtl.SST, AMO (unsmoothed), EA/WR and Trop.N.Atl SST) are retained by the stepwise regression. Among them, SCA showed the highest linear coefficient, followed by SST in western Atlantic, AMO and NAO. The explanatory regression model (considering all time steps) provided a Coefficient of Determination (R^2) of 0.75. A predictive version of the linear model applying a leave-one-out cross-validation (LOOCV) shows an R2 of 0.56 and a relative RMSE of 4.67 counts/season. An ANN-based nonlinear hindcast model for the seasonal windstorm frequency is developed with the aim to improve the stepwise hindcast ability and thus better predict a time-clustered season over the case study. A 7 node-hidden layer perceptron is set, and the LOOCV procedure reveals a R2 of 0.71. In comparison to the stepwise MLR the RMSE is reduced a 20%. This work shows that for the British Isles case study, most of the interannual variability can be explained by certain large-scale mechanisms, considering also nonlinear effects (ANN). This allows to discern a time-clustered season from a non-clustered one - a key issue for applications e.g., in the (re)insurance industry.

Estimating extent of mortality associated with the Douglas-fir beetle in the Central and Northern Rockies

Treesearch

Jose F. Negron; Willis C. Schaupp; Kenneth E. Gibson; John Anhold; Dawn Hansen; Ralph Thier; Phil Mocettini

1999-01-01

Data collected from Douglas-fir stands infected by the Douglas-fir beetle in Wyoming, Montana, Idaho, and Utah, were used to develop models to estimate amount of mortality in terms of basal area killed. Models were built using stepwise linear regression and regression tree approaches. Linear regression models using initial Douglas-fir basal area were built for all...

Improving prediction of metal uptake by Chinese cabbage (Brassica pekinensis L.) based on a soil-plant stepwise analysis.

PubMed

Zhang, Sha; Song, Jing; Gao, Hui; Zhang, Qiang; Lv, Ming-Chao; Wang, Shuang; Liu, Gan; Pan, Yun-Yu; Christie, Peter; Sun, Wenjie

2016-11-01

It is crucial to develop predictive soil-plant transfer (SPT) models to derive the threshold values of toxic metals in contaminated arable soils. The present study was designed to examine the heavy metal uptake pattern and to improve the prediction of metal uptake by Chinese cabbage grown in agricultural soils with multiple contamination by Cd, Cu, Ni, Pb, and Zn. Pot experiments were performed with 25 historically contaminated soils to determine metal accumulation in different parts of Chinese cabbage. Different soil bioavailable metal fractions were determined using different extractants (0.43M HNO3, 0.01M CaCl2, 0.005M DTPA, and 0.01M LWMOAs), soil moisture samplers, and diffusive gradients in thin films (DGT), and the fractions were compared with shoot metal uptake using both direct and stepwise multiple regression analysis. The stepwise approach significantly improved the prediction of metal uptake by cabbage over the direct approach. Strongly pH dependent or nonlinear relationships were found for the adsorption of root surfaces and in root-shoot uptake processes. Metals were linearly translocated from the root surface to the root. Therefore, the nonlinearity of uptake pattern is an important explanation for the inadequacy of the direct approach in some cases. The stepwise approach offers an alternative and robust method to study the pattern of metal uptake by Chinese cabbage (Brassica pekinensis L.). Copyright © 2016. Published by Elsevier B.V.

The investigation of the relationship between probability of suicide and reasons for living in psychiatric inpatients

PubMed Central

Eskiyurt, Reyhan; Ozkan, Birgul

2017-01-01

Aim: This study was carried out to determine the reasons of the suicide probability and reasons for living of the inpatients hospitalized at the psychiatry clinic and to analyze the relationship between them. Materials and Methods: The sample of the study consisted of 192 patients who were hospitalized in psychiatric clinics between February and May 2016 and who agreed to participate in the study. In collecting data, personal information form, suicide probability scale (SPS), reasons for living inventory (RFL), and Beck's depression inventory (BDI) were used. Stepwise regression method was used to determine the factors that predict suicide probability. Results: In the study, as a result of analyses made, the median score on the SPS was found 76.0, the median score on the RFL was found 137.0, the median score on the BDI of the patients was found 13.5, and it was found that patients with a high probability of suicide had less reasons for living and that their depression levels were very high. As a result of stepwise regression analysis, it was determined that suicidal ideation, reasons for living, maltreatment, education level, age, and income status were the predictors of suicide probability (F = 61.125; P < 0.001). Discussion: It was found that the patients who hospitalized in the psychiatric clinic have high suicide probability and the reasons of living are strong predictors of suicide probability in accordance with the literature. PMID:29497185

Feet deformities are correlated with impaired balance and postural stability in seniors over 75

PubMed Central

Puszczalowska-Lizis, Ewa; Bujas, Przemyslaw; Omorczyk, Jaroslaw; Jandzis, Slawomir

2017-01-01

Objective Understanding the factors and mechanisms that determine balance in seniors appears vital in terms of their self-reliance and overall safety. The study aimed to determine the relationship between the features of feet structure and the indicators of postural stability in the elderly. Methods The study group comprised 80 seniors (41F, 39M; aged 75–85 years). CQ-ST podoscope and the CQ-Stab 2P two-platform posturograph were used as primary research tools. The data were analyzed based on Spearman’s rank correlation and forward stepwise regression. Results Analysis of forward stepwise regression identified the left foot length in females and Clarke’s angle of the left foot in men as significant and independent predictors of postural up to 30% of the variance of dependent variables. Conclusions Longer feet provide older women with better stability, whereas in men, the lowering of the longitudinal arch results in postural deterioration. In the elderly, the left lower limb shows greater activity in the stabilizing processes in the standing position than the right one. In gerontological rehabilitation special attention should be paid to the individually tailored, gender-specific treatment, with a view to enhancing overall safety and quality of seniors’ lives. PMID:28877185

Quantifying Parkinson's disease finger-tapping severity by extracting and synthesizing finger motion properties.

PubMed

Sano, Yuko; Kandori, Akihiko; Shima, Keisuke; Yamaguchi, Yuki; Tsuji, Toshio; Noda, Masafumi; Higashikawa, Fumiko; Yokoe, Masaru; Sakoda, Saburo

2016-06-01

We propose a novel index of Parkinson's disease (PD) finger-tapping severity, called "PDFTsi," for quantifying the severity of symptoms related to the finger tapping of PD patients with high accuracy. To validate the efficacy of PDFTsi, the finger-tapping movements of normal controls and PD patients were measured by using magnetic sensors, and 21 characteristics were extracted from the finger-tapping waveforms. To distinguish motor deterioration due to PD from that due to aging, the aging effect on finger tapping was removed from these characteristics. Principal component analysis (PCA) was applied to the age-normalized characteristics, and principal components that represented the motion properties of finger tapping were calculated. Multiple linear regression (MLR) with stepwise variable selection was applied to the principal components, and PDFTsi was calculated. The calculated PDFTsi indicates that PDFTsi has a high estimation ability, namely a mean square error of 0.45. The estimation ability of PDFTsi is higher than that of the alternative method, MLR with stepwise regression selection without PCA, namely a mean square error of 1.30. This result suggests that PDFTsi can quantify PD finger-tapping severity accurately. Furthermore, the result of interpreting a model for calculating PDFTsi indicated that motion wideness and rhythm disorder are important for estimating PD finger-tapping severity.

Patient satisfaction in Dental Healthcare Centers

PubMed Central

Ali, Dena A.

2016-01-01

Objectives: This study aimed to (1) measure the degree of patient satisfaction among the clinical and nonclinical dental services offered at specialty dental centers and (2) investigate the factors associated with the degree of overall satisfaction. Materials and Methods: Four hundred and ninety-seven participants from five dental centers were recruited for this study. Each participant completed a self-administered questionnaire to measure patient satisfaction with clinical and nonclinical dental services. Analysis of variance, t-tests, a general linear model, and stepwise regression analysis was applied. Results: The respondents were generally satisfied, but internal differences were observed. The exhibited highest satisfaction with the dentists’ performance, followed by the dental assistants’ services, and the lowest satisfaction with the center's physical appearance and accessibility. Females, participants with less than a bachelor's degree, and younger individuals were more satisfied with the clinical and nonclinical dental services. The stepwise regression analysis revealed that the coefficient of determination (R2) was 40.4%. The patient satisfaction with the performance of the dentists explained 42.6% of the overall satisfaction, whereas their satisfaction with the clinical setting explained 31.5% of the overall satisfaction. Conclusion: Additional improvements with regard to the accessibility and physical appearance of the dental centers are needed. In addition, interventions regarding accessibility, particularly when booking an appointment, are required. PMID:27403045

Adaptive surrogate modeling by ANOVA and sparse polynomial dimensional decomposition for global sensitivity analysis in fluid simulation

NASA Astrophysics Data System (ADS)

Tang, Kunkun; Congedo, Pietro M.; Abgrall, Rémi

2016-06-01

The Polynomial Dimensional Decomposition (PDD) is employed in this work for the global sensitivity analysis and uncertainty quantification (UQ) of stochastic systems subject to a moderate to large number of input random variables. Due to the intimate connection between the PDD and the Analysis of Variance (ANOVA) approaches, PDD is able to provide a simpler and more direct evaluation of the Sobol' sensitivity indices, when compared to the Polynomial Chaos expansion (PC). Unfortunately, the number of PDD terms grows exponentially with respect to the size of the input random vector, which makes the computational cost of standard methods unaffordable for real engineering applications. In order to address the problem of the curse of dimensionality, this work proposes essentially variance-based adaptive strategies aiming to build a cheap meta-model (i.e. surrogate model) by employing the sparse PDD approach with its coefficients computed by regression. Three levels of adaptivity are carried out in this paper: 1) the truncated dimensionality for ANOVA component functions, 2) the active dimension technique especially for second- and higher-order parameter interactions, and 3) the stepwise regression approach designed to retain only the most influential polynomials in the PDD expansion. During this adaptive procedure featuring stepwise regressions, the surrogate model representation keeps containing few terms, so that the cost to resolve repeatedly the linear systems of the least-squares regression problem is negligible. The size of the finally obtained sparse PDD representation is much smaller than the one of the full expansion, since only significant terms are eventually retained. Consequently, a much smaller number of calls to the deterministic model is required to compute the final PDD coefficients.

Influence of storage conditions on the stability of monomeric anthocyanins studied by reversed-phase high-performance liquid chromatography.

PubMed

Morais, Helena; Ramos, Cristina; Forgács, Esther; Cserháti, Tibor; Oliviera, José

2002-04-25

The effect of light, storage time and temperature on the decomposition rate of monomeric anthocyanin pigments extracted from skins of grape (Vitis vinifera var. Red globe) was determined by reversed-phase high-performance liquid chromatography (RP-HPLC). The impact of various storage conditions on the pigment stability was assessed by stepwise regression analysis. RP-HPLC separated well the five anthocyanins identified and proved the presence of other unidentified pigments at lower concentrations. Stepwise regression analysis confirmed that the overall decomposition rate of monomeric anthocyanins, peonidin-3-glucoside and malvidin-3-glucoside significantly depended on the time and temperature of storage, the effect of storage time being the most important. The presence or absence of light exerted a negligible impact on the decomposition rate.

Estimation of longitudinal stability and control derivatives for an icing research aircraft from flight data

NASA Technical Reports Server (NTRS)

Batterson, James G.; Omara, Thomas M.

1989-01-01

The results of applying a modified stepwise regression algorithm and a maximum likelihood algorithm to flight data from a twin-engine commuter-class icing research aircraft are presented. The results are in the form of body-axis stability and control derivatives related to the short-period, longitudinal motion of the aircraft. Data were analyzed for the baseline (uniced) and for the airplane with an artificial glaze ice shape attached to the leading edge of the horizontal tail. The results are discussed as to the accuracy of the derivative estimates and the difference between the derivative values found for the baseline and the iced airplane. Additional comparisons were made between the maximum likelihood results and the modified stepwise regression results with causes for any discrepancies postulated.

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.

Efficient least angle regression for identification of linear-in-the-parameters models

PubMed Central

Beach, Thomas H.; Rezgui, Yacine

2017-01-01

Least angle regression, as a promising model selection method, differentiates itself from conventional stepwise and stagewise methods, in that it is neither too greedy nor too slow. It is closely related to L1 norm optimization, which has the advantage of low prediction variance through sacrificing part of model bias property in order to enhance model generalization capability. In this paper, we propose an efficient least angle regression algorithm for model selection for a large class of linear-in-the-parameters models with the purpose of accelerating the model selection process. The entire algorithm works completely in a recursive manner, where the correlations between model terms and residuals, the evolving directions and other pertinent variables are derived explicitly and updated successively at every subset selection step. The model coefficients are only computed when the algorithm finishes. The direct involvement of matrix inversions is thereby relieved. A detailed computational complexity analysis indicates that the proposed algorithm possesses significant computational efficiency, compared with the original approach where the well-known efficient Cholesky decomposition is involved in solving least angle regression. Three artificial and real-world examples are employed to demonstrate the effectiveness, efficiency and numerical stability of the proposed algorithm. PMID:28293140

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

NASA Astrophysics Data System (ADS)

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

2016-06-01

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

Soil sail content estimation in the yellow river delta with satellite hyperspectral data

USGS Publications Warehouse

Weng, Yongling; Gong, Peng; Zhu, Zhi-Liang

2008-01-01

Soil salinization is one of the most common land degradation processes and is a severe environmental hazard. The primary objective of this study is to investigate the potential of predicting salt content in soils with hyperspectral data acquired with EO-1 Hyperion. Both partial least-squares regression (PLSR) and conventional multiple linear regression (MLR), such as stepwise regression (SWR), were tested as the prediction model. PLSR is commonly used to overcome the problem caused by high-dimensional and correlated predictors. Chemical analysis of 95 samples collected from the top layer of soils in the Yellow River delta area shows that salt content was high on average, and the dominant chemicals in the saline soil were NaCl and MgCl2. Multivariate models were established between soil contents and hyperspectral data. Our results indicate that the PLSR technique with laboratory spectral data has a strong prediction capacity. Spectral bands at 1487-1527, 1971-1991, 2032-2092, and 2163-2355 nm possessed large absolute values of regression coefficients, with the largest coefficient at 2203 nm. We obtained a root mean squared error (RMSE) for calibration (with 61 samples) of RMSEC = 0.753 (R2 = 0.893) and a root mean squared error for validation (with 30 samples) of RMSEV = 0.574. The prediction model was applied on a pixel-by-pixel basis to a Hyperion reflectance image to yield a quantitative surface distribution map of soil salt content. The result was validated successfully from 38 sampling points. We obtained an RMSE estimate of 1.037 (R2 = 0.784) for the soil salt content map derived by the PLSR model. The salinity map derived from the SWR model shows that the predicted value is higher than the true value. These results demonstrate that the PLSR method is a more suitable technique than stepwise regression for quantitative estimation of soil salt content in a large area. ?? 2008 CASI.

Comparison of machine-learning methods for above-ground biomass estimation based on Landsat imagery

NASA Astrophysics Data System (ADS)

Wu, Chaofan; Shen, Huanhuan; Shen, Aihua; Deng, Jinsong; Gan, Muye; Zhu, Jinxia; Xu, Hongwei; Wang, Ke

2016-07-01

Biomass is one significant biophysical parameter of a forest ecosystem, and accurate biomass estimation on the regional scale provides important information for carbon-cycle investigation and sustainable forest management. In this study, Landsat satellite imagery data combined with field-based measurements were integrated through comparisons of five regression approaches [stepwise linear regression, K-nearest neighbor, support vector regression, random forest (RF), and stochastic gradient boosting] with two different candidate variable strategies to implement the optimal spatial above-ground biomass (AGB) estimation. The results suggested that RF algorithm exhibited the best performance by 10-fold cross-validation with respect to R2 (0.63) and root-mean-square error (26.44 ton/ha). Consequently, the map of estimated AGB was generated with a mean value of 89.34 ton/ha in northwestern Zhejiang Province, China, with a similar pattern to the distribution mode of local forest species. This research indicates that machine-learning approaches associated with Landsat imagery provide an economical way for biomass estimation. Moreover, ensemble methods using all candidate variables, especially for Landsat images, provide an alternative for regional biomass simulation.

Modeling and forecasting US presidential election using learning algorithms

NASA Astrophysics Data System (ADS)

Zolghadr, Mohammad; Niaki, Seyed Armin Akhavan; Niaki, S. T. A.

2017-09-01

The primary objective of this research is to obtain an accurate forecasting model for the US presidential election. To identify a reliable model, artificial neural networks (ANN) and support vector regression (SVR) models are compared based on some specified performance measures. Moreover, six independent variables such as GDP, unemployment rate, the president's approval rate, and others are considered in a stepwise regression to identify significant variables. The president's approval rate is identified as the most significant variable, based on which eight other variables are identified and considered in the model development. Preprocessing methods are applied to prepare the data for the learning algorithms. The proposed procedure significantly increases the accuracy of the model by 50%. The learning algorithms (ANN and SVR) proved to be superior to linear regression based on each method's calculated performance measures. The SVR model is identified as the most accurate model among the other models as this model successfully predicted the outcome of the election in the last three elections (2004, 2008, and 2012). The proposed approach significantly increases the accuracy of the forecast.

Improving the Accuracy of Mapping Urban Vegetation Carbon Density by Combining Shadow Remove, Spectral Unmixing Analysis and Spatial Modeling

NASA Astrophysics Data System (ADS)

Qie, G.; Wang, G.; Wang, M.

2016-12-01

Mixed pixels and shadows due to buildings in urban areas impede accurate estimation and mapping of city vegetation carbon density. In most of previous studies, these factors are often ignored, which thus result in underestimation of city vegetation carbon density. In this study we presented an integrated methodology to improve the accuracy of mapping city vegetation carbon density. Firstly, we applied a linear shadow remove analysis (LSRA) on remotely sensed Landsat 8 images to reduce the shadow effects on carbon estimation. Secondly, we integrated a linear spectral unmixing analysis (LSUA) with a linear stepwise regression (LSR), a logistic model-based stepwise regression (LMSR) and k-Nearest Neighbors (kNN), and utilized and compared the integrated models on shadow-removed images to map vegetation carbon density. This methodology was examined in Shenzhen City of Southeast China. A data set from a total of 175 sample plots measured in 2013 and 2014 was used to train the models. The independent variables statistically significantly contributing to improving the fit of the models to the data and reducing the sum of squared errors were selected from a total of 608 variables derived from different image band combinations and transformations. The vegetation fraction from LSUA was then added into the models as an important independent variable. The estimates obtained were evaluated using a cross-validation method. Our results showed that higher accuracies were obtained from the integrated models compared with the ones using traditional methods which ignore the effects of mixed pixels and shadows. This study indicates that the integrated method has great potential on improving the accuracy of urban vegetation carbon density estimation. Key words: Urban vegetation carbon, shadow, spectral unmixing, spatial modeling, Landsat 8 images

A Comparative Investigation of the Combined Effects of Pre-Processing, Wavelength Selection, and Regression Methods on Near-Infrared Calibration Model Performance.

PubMed

Wan, Jian; Chen, Yi-Chieh; Morris, A Julian; Thennadil, Suresh N

2017-07-01

Near-infrared (NIR) spectroscopy is being widely used in various fields ranging from pharmaceutics to the food industry for analyzing chemical and physical properties of the substances concerned. Its advantages over other analytical techniques include available physical interpretation of spectral data, nondestructive nature and high speed of measurements, and little or no need for sample preparation. The successful application of NIR spectroscopy relies on three main aspects: pre-processing of spectral data to eliminate nonlinear variations due to temperature, light scattering effects and many others, selection of those wavelengths that contribute useful information, and identification of suitable calibration models using linear/nonlinear regression . Several methods have been developed for each of these three aspects and many comparative studies of different methods exist for an individual aspect or some combinations. However, there is still a lack of comparative studies for the interactions among these three aspects, which can shed light on what role each aspect plays in the calibration and how to combine various methods of each aspect together to obtain the best calibration model. This paper aims to provide such a comparative study based on four benchmark data sets using three typical pre-processing methods, namely, orthogonal signal correction (OSC), extended multiplicative signal correction (EMSC) and optical path-length estimation and correction (OPLEC); two existing wavelength selection methods, namely, stepwise forward selection (SFS) and genetic algorithm optimization combined with partial least squares regression for spectral data (GAPLSSP); four popular regression methods, namely, partial least squares (PLS), least absolute shrinkage and selection operator (LASSO), least squares support vector machine (LS-SVM), and Gaussian process regression (GPR). The comparative study indicates that, in general, pre-processing of spectral data can play a significant role in the calibration while wavelength selection plays a marginal role and the combination of certain pre-processing, wavelength selection, and nonlinear regression methods can achieve superior performance over traditional linear regression-based calibration.

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

PubMed

Kolasa-Wiecek, Alicja

2015-04-01

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

Cytopathologic differential diagnosis of low-grade urothelial carcinoma and reactive urothelial proliferation in bladder washings: a logistic regression analysis.

PubMed

Cakir, Ebru; Kucuk, Ulku; Pala, Emel Ebru; Sezer, Ozlem; Ekin, Rahmi Gokhan; Cakmak, Ozgur

2017-05-01

Conventional cytomorphologic assessment is the first step to establish an accurate diagnosis in urinary cytology. In cytologic preparations, the separation of low-grade urothelial carcinoma (LGUC) from reactive urothelial proliferation (RUP) can be exceedingly difficult. The bladder washing cytologies of 32 LGUC and 29 RUP were reviewed. The cytologic slides were examined for the presence or absence of the 28 cytologic features. The cytologic criteria showing statistical significance in LGUC were increased numbers of monotonous single (non-umbrella) cells, three-dimensional cellular papillary clusters without fibrovascular cores, irregular bordered clusters, atypical single cells, irregular nuclear overlap, cytoplasmic homogeneity, increased N/C ratio, pleomorphism, nuclear border irregularity, nuclear eccentricity, elongated nuclei, and hyperchromasia (p ˂ 0.05), and the cytologic criteria showing statistical significance in RUP were inflammatory background, mixture of small and large urothelial cells, loose monolayer aggregates, and vacuolated cytoplasm (p ˂ 0.05). When these variables were subjected to a stepwise logistic regression analysis, four features were selected to distinguish LGUC from RUP: increased numbers of monotonous single (non-umbrella) cells, increased nuclear cytoplasmic ratio, hyperchromasia, and presence of small and large urothelial cells (p = 0.0001). By this logistic model of the 32 cases with proven LGUC, the stepwise logistic regression analysis correctly predicted 31 (96.9%) patients with this diagnosis, and of the 29 patients with RUP, the logistic model correctly predicted 26 (89.7%) patients as having this disease. There are several cytologic features to separate LGUC from RUP. Stepwise logistic regression analysis is a valuable tool for determining the most useful cytologic criteria to distinguish these entities. © 2017 APMIS. Published by John Wiley & Sons Ltd.

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.

Stepwise Analysis of Differential Item Functioning Based on Multiple-Group Partial Credit Model.

ERIC Educational Resources Information Center

Muraki, Eiji

1999-01-01

Extended an Item Response Theory (IRT) method for detection of differential item functioning to the partial credit model and applied the method to simulated data using a stepwise procedure. Then applied the stepwise DIF analysis based on the multiple-group partial credit model to writing trend data from the National Assessment of Educational…

Stochastic optimal operation of reservoirs based on copula functions

NASA Astrophysics Data System (ADS)

Lei, Xiao-hui; Tan, Qiao-feng; Wang, Xu; Wang, Hao; Wen, Xin; Wang, Chao; Zhang, Jing-wen

2018-02-01

Stochastic dynamic programming (SDP) has been widely used to derive operating policies for reservoirs considering streamflow uncertainties. In SDP, there is a need to calculate the transition probability matrix more accurately and efficiently in order to improve the economic benefit of reservoir operation. In this study, we proposed a stochastic optimization model for hydropower generation reservoirs, in which 1) the transition probability matrix was calculated based on copula functions; and 2) the value function of the last period was calculated by stepwise iteration. Firstly, the marginal distribution of stochastic inflow in each period was built and the joint distributions of adjacent periods were obtained using the three members of the Archimedean copulas, based on which the conditional probability formula was derived. Then, the value in the last period was calculated by a simple recursive equation with the proposed stepwise iteration method and the value function was fitted with a linear regression model. These improvements were incorporated into the classic SDP and applied to the case study in Ertan reservoir, China. The results show that the transition probability matrix can be more easily and accurately obtained by the proposed copula function based method than conventional methods based on the observed or synthetic streamflow series, and the reservoir operation benefit can also be increased.

Impact of statistical learning methods on the predictive power of multivariate normal tissue complication probability models.

PubMed

Xu, Cheng-Jian; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A; van't Veld, Aart A

2012-03-15

To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended. Copyright © 2012 Elsevier Inc. All rights reserved.

Actual and estimated costs of disposable materials used during surgical procedures.

PubMed

Toyabe, Shin-Ichi; Cao, Pengyu; Kurashima, Sachiko; Nakayama, Yukiko; Ishii, Yuko; Hosoyama, Noriko; Akazawa, Kouhei

2005-07-01

It is difficult to estimate precisely the costs of disposable materials used during surgical operations. To evaluate the actual costs of disposable materials, we calculated the actual costs of disposable materials used in 59 operations by taking account of costs of all disposable materials used for each operation. The costs of the disposable materials varied significantly from operation to operation (US$ 38-4230 per operation), and the median [25-percentile and 75-percentile] of the sum total of disposable material costs of a single operation was found to be US$ 686 [205 and 993]. Multiple regression analysis with a stepwise regression method showed that costs of disposable materials significantly correlated only with operation time (p<0.001). Based on the results, we propose a simple method for estimating costs of disposable materials by measuring operation time, and we found that the method gives reliable results. Since costs of disposable materials used during surgical operations are considerable, precise estimation of the costs is essential for hospital cost accounting. Our method should be useful for planning hospital administration strategies.

Causal Methods for Observational Research: A Primer.

PubMed

Almasi-Hashiani, Amir; Nedjat, Saharnaz; Mansournia, Mohammad Ali

2018-04-01

The goal of many observational studies is to estimate the causal effect of an exposure on an outcome after adjustment for confounders, but there are still some serious errors in adjusting confounders in clinical journals. Standard regression modeling (e.g., ordinary logistic regression) fails to estimate the average effect of exposure in total population in the presence of interaction between exposure and covariates, and also cannot adjust for time-varying confounding appropriately. Moreover, stepwise algorithms of the selection of confounders based on P values may miss important confounders and lead to bias in effect estimates. Causal methods overcome these limitations. We illustrate three causal methods including inverse-probability-of-treatment-weighting (IPTW) and parametric g-formula, with an emphasis on a clever combination of these 2 methods: targeted maximum likelihood estimation (TMLE) which enjoys a double-robust property against bias. © 2018 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Simple Patchy-Based Simulators Used to Explore Pondscape Systematic Dynamics

PubMed Central

Fang, Wei-Ta; Chou, Jui-Yu; Lu, Shiau-Yun

2014-01-01

Thousands of farm ponds disappeared on the tableland in Taoyuan County, Taiwan since 1920s. The number of farm ponds that have disappeared is 1,895 (37%), 2,667 ponds remain (52%), and only 537 (11%) new ponds were created within a 757 km2 area in Taoyuan, Taiwan between 1926 and 1960. In this study, a geographic information system (GIS) and logistic stepwise regression model were used to detect pond-loss rates and to understand the driving forces behind pondscape changes. The logistic stepwise regression model was used to develop a series of relationships between pondscapes affected by intrinsic driving forces (patch size, perimeter, and patch shape) and external driving forces (distance from the edge of the ponds to the edges of roads, rivers, and canals). The authors concluded that the loss of ponds was caused by pond intrinsic factors, such as pond perimeter; a large perimeter increases the chances of pond loss, but also increases the possibility of creating new ponds. However, a large perimeter is closely associated with circular shapes (lower value of the mean pond-patch fractal dimension [MPFD]), which characterize the majority of newly created ponds. The method used in this study might be helpful to those seeking to protect this unique landscape by enabling the monitoring of patch-loss problems by using simple patchy-based simulators. PMID:24466281

Dietary acculturation and body composition predict American Hmong children's blood pressure.

PubMed

Smith, Chery; Franzen-Castle, Lisa

2012-01-01

Determine how dietary acculturation, anthropometric measures (height, weight, circumferences, and skinfolds), body mass index (BMI), and waist hip ratios (WHRs) are associated with blood pressure (BP) measures in Hmong children living in Minnesota. Acculturation was measured using responses to questions regarding language usage, social connections, and diet. Dietary assessment was completed using the multiple-pass 24-h dietary recall method on two different days. Anthropometric and BP measurement were taken using standard procedures, and BMI and WHR were calculated. Data analyses included descriptive statistics, ANOVA, and stepwise regression analyses. Using stepwise regression analysis, hip circumference (HC) predicted boys' systolic (S)BP (R(2) = 0.55). For girls' SBP, mid-upper arm circumference, WHR, low calcium consumption, and height percentile jointly explained 41% of the total variation. Mid upper arm circumference (MAC) and carbohydrate consumption predicted 35% of the variance for boys' diastolic (D)BP, and HC, dairy consumption, and calcium intake predicted 31% of the total variance for girls' DBP. Responses to dietary acculturation questions revealed between group differences for breakfast with half of the younger Born-Thailand/Laos (Born-T/L) consuming mostly Hmong food, while at dinner Born-US consumed a mixed diet and Born-T/L were more likely to consume Hmong food. Dietary acculturation and body composition predict Hmong children's BP. Copyright © 2012 Wiley Periodicals, Inc.

The investigation of the relationship between probability of suicide and reasons for living in psychiatric inpatients.

PubMed

Eskiyurt, Reyhan; Ozkan, Birgul

2017-01-01

This study was carried out to determine the reasons of the suicide probability and reasons for living of the inpatients hospitalized at the psychiatry clinic and to analyze the relationship between them. The sample of the study consisted of 192 patients who were hospitalized in psychiatric clinics between February and May 2016 and who agreed to participate in the study. In collecting data, personal information form, suicide probability scale (SPS), reasons for living inventory (RFL), and Beck's depression inventory (BDI) were used. Stepwise regression method was used to determine the factors that predict suicide probability. In the study, as a result of analyses made, the median score on the SPS was found 76.0, the median score on the RFL was found 137.0, the median score on the BDI of the patients was found 13.5, and it was found that patients with a high probability of suicide had less reasons for living and that their depression levels were very high. As a result of stepwise regression analysis, it was determined that suicidal ideation, reasons for living, maltreatment, education level, age, and income status were the predictors of suicide probability ( F = 61.125; P < 0.001). It was found that the patients who hospitalized in the psychiatric clinic have high suicide probability and the reasons of living are strong predictors of suicide probability in accordance with the literature.

Socio-economic variables influencing mean age at marriage in Karnataka and Kerala.

PubMed

Prakasam, C P; Upadhyay, R B

1985-01-01

"In this paper an attempt was made to study the influence of certain socio-economic variables on the male and the female age at marriage in Karnataka and Kerala [India] for the year 1971. Step-wise regression method has been used to select the predictor variables influencing mean age at marriage. The results reveal that percent female literate...and percent female in labour force...are found to influence female mean age at marriage in Kerala, while the variables for Karnataka were percent female literate..., percent male literate..., and percent urban male population...." excerpt

Quantifying prosthetic gait deviation using simple outcome measures

PubMed Central

Kark, Lauren; Odell, Ross; McIntosh, Andrew S; Simmons, Anne

2016-01-01

AIM: To develop a subset of simple outcome measures to quantify prosthetic gait deviation without needing three-dimensional gait analysis (3DGA). METHODS: Eight unilateral, transfemoral amputees and 12 unilateral, transtibial amputees were recruited. Twenty-eight able-bodied controls were recruited. All participants underwent 3DGA, the timed-up-and-go test and the six-minute walk test (6MWT). The lower-limb amputees also completed the Prosthesis Evaluation Questionnaire. Results from 3DGA were summarised using the gait deviation index (GDI), which was subsequently regressed, using stepwise regression, against the other measures. RESULTS: Step-length (SL), self-selected walking speed (SSWS) and the distance walked during the 6MWT (6MWD) were significantly correlated with GDI. The 6MWD was the strongest, single predictor of the GDI, followed by SL and SSWS. The predictive ability of the regression equations were improved following inclusion of self-report data related to mobility and prosthetic utility. CONCLUSION: This study offers a practicable alternative to quantifying kinematic deviation without the need to conduct complete 3DGA. PMID:27335814

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

PubMed

Li, Zhenghua; Cheng, Fansheng; Xia, Zhining

2011-01-01

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

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

NASA Technical Reports Server (NTRS)

Barrett, C. A.

1985-01-01

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

Power Relative to Body Mass Best Predicts Change in Core Temperature During Exercise-Heat Stress.

PubMed

Gibson, Oliver R; Willmott, Ashley G B; James, Carl A; Hayes, Mark; Maxwell, Neil S

2017-02-01

Gibson, OR, Willmott, AGB, James, CA, Hayes, M, and Maxwell, NS. Power relative to body mass best predicts change in core temperature during exercise-heat stress. J Strength Cond Res 31(2): 403-414, 2017-Controlling internal temperature is crucial when prescribing exercise-heat stress, particularly during interventions designed to induce thermoregulatory adaptations. This study aimed to determine the relationship between the rate of rectal temperature (Trec) increase, and various methods for prescribing exercise-heat stress, to identify the most efficient method of prescribing isothermic heat acclimation (HA) training. Thirty-five men cycled in hot conditions (40° C, 39% R.H.) for 29 ± 2 minutes. Subjects exercised at 60 ± 9% V[Combining Dot Above]O2peak, with methods for prescribing exercise retrospectively observed for each participant. Pearson product moment correlations were calculated for each prescriptive variable against the rate of change in Trec (° C·h), with stepwise multiple regressions performed on statistically significant variables (p ≤ 0.05). Linear regression identified the predicted intensity required to increase Trec by 1.0-2.0° C between 20- and 45-minute periods and the duration taken to increase Trec by 1.5° C in response to incremental intensities to guide prescription. Significant (p ≤ 0.05) relationships with the rate of change in Trec were observed for prescriptions based on relative power (W·kg; r = 0.764), power (%Powermax; r = 0.679), rating of perceived exertion (RPE) (r = 0.577), V[Combining Dot Above]O2 (%V[Combining Dot Above]O2peak; r = 0.562), heart rate (HR) (%HRmax; r = 0.534), and thermal sensation (r = 0.311). Stepwise multiple regressions observed relative power and RPE as variables to improve the model (r = 0.791), with no improvement after inclusion of any anthropometric variable. Prescription of exercise under heat stress using power (W·kg or %Powermax) has the strongest relationship with the rate of change in Trec with no additional requirement to correct for body composition within a normal range. Practitioners should therefore prescribe exercise intensity using relative power during isothermic HA training to increase Trec efficiently and maximize adaptation.

The use of machine learning for the identification of peripheral artery disease and future mortality risk.

PubMed

Ross, Elsie Gyang; Shah, Nigam H; Dalman, Ronald L; Nead, Kevin T; Cooke, John P; Leeper, Nicholas J

2016-11-01

A key aspect of the precision medicine effort is the development of informatics tools that can analyze and interpret "big data" sets in an automated and adaptive fashion while providing accurate and actionable clinical information. The aims of this study were to develop machine learning algorithms for the identification of disease and the prognostication of mortality risk and to determine whether such models perform better than classical statistical analyses. Focusing on peripheral artery disease (PAD), patient data were derived from a prospective, observational study of 1755 patients who presented for elective coronary angiography. We employed multiple supervised machine learning algorithms and used diverse clinical, demographic, imaging, and genomic information in a hypothesis-free manner to build models that could identify patients with PAD and predict future mortality. Comparison was made to standard stepwise linear regression models. Our machine-learned models outperformed stepwise logistic regression models both for the identification of patients with PAD (area under the curve, 0.87 vs 0.76, respectively; P = .03) and for the prediction of future mortality (area under the curve, 0.76 vs 0.65, respectively; P = .10). Both machine-learned models were markedly better calibrated than the stepwise logistic regression models, thus providing more accurate disease and mortality risk estimates. Machine learning approaches can produce more accurate disease classification and prediction models. These tools may prove clinically useful for the automated identification of patients with highly morbid diseases for which aggressive risk factor management can improve outcomes. Copyright © 2016 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Identification of International Classification of Functioning, Disability and Health categories for patients with peripheral arterial disease.

PubMed

Vyskocil, Erich; Gruther, Wolfgang; Steiner, Irene; Schuhfried, Othmar

2014-07-01

Disease-specific categories of the International Classification of Functioning, Disability and Health have not yet been described for patients with chronic peripheral arterial obstructive disease (PAD). The authors examined the relationship between the categories of the Brief Core Sets for ischemic heart diseases with the Peripheral Artery Questionnaire and the ankle-brachial index to determine which International Classification of Functioning, Disability and Health categories are most relevant for patients with PAD. This is a retrospective cohort study including 77 patients with verified PAD. Statistical analyses of the relationship between International Classification of Functioning, Disability and Health categories as independent variables and the endpoints Peripheral Artery Questionnaire or ankle-brachial index were carried out by simple and stepwise linear regression models adjusting for age, sex, and leg (left vs. right). The stepwise linear regression model with the ankle-brachial index as dependent variable revealed a significant effect of the variables blood vessel functions and muscle endurance functions. Calculating a stepwise linear regression model with the Peripheral Artery Questionnaire as dependent variable, a significant effect of age, emotional functions, energy and drive functions, carrying out daily routine, as well as walking could be observed. This study identifies International Classification of Functioning, Disability and Health categories in the Brief Core Sets for ischemic heart diseases that show a significant effect on the ankle-brachial index and the Peripheral Artery Questionnaire score in patients with PAD. These categories provide fundamental information on functioning of patients with PAD and patient-centered outcomes for rehabilitation interventions.

Clinical utility of the AlphaFIM® instrument in stroke rehabilitation.

PubMed

Lo, Alexander; Tahair, Nicola; Sharp, Shelley; Bayley, Mark T

2012-02-01

The AlphaFIM instrument is an assessment tool designed to facilitate discharge planning of stroke patients from acute care, by extrapolating overall functional status from performance in six key Functional Independence Measure (FIM) instrument items. To determine whether acute care AlphaFIM rating is correlated to stroke rehabilitation outcomes. In this prospective observational study, data were analyzed from 891 patients referred for inpatient stroke rehabilitation through an Internet-based referral system. Simple linear and stepwise regression models determined correlations between rehabilitation-ready AlphaFIM rating and rehabilitation outcomes (admission and discharge FIM ratings, FIM gain, FIM efficiency, and length of stay). Covariates including demographic data, stroke characteristics, medical history, cognitive deficits, and activity tolerance were included in the stepwise regressions. The AlphaFIM instrument was significant in predicting admission and discharge FIM ratings at rehabilitation (adjusted R² 0.40 and 0.28, respectively; P < 0.0001) and was weakly correlated with FIM gain and length of stay (adjusted R² 0.04 and 0.09, respectively; P < 0.0001), but not FIM efficiency. AlphaFIM rating was inversely related to FIM gain. Age, bowel incontinence, left hemiparesis, and previous infarcts were negative predictors of discharge FIM rating on stepwise regression. Intact executive function and physical activity tolerance of 30 to 60 mins were predictors of FIM gain. The AlphaFIM instrument is a valuable tool for triaging stroke patients from acute care to rehabilitation and predicts functional status at discharge from rehabilitation. Patients with low AlphaFIM ratings have the potential to make significant functional gains and should not be denied admission to inpatient rehabilitation programs.

Emission and distribution of phosphine in paddy fields and its relationship with greenhouse gases.

PubMed

Chen, Weiyi; Niu, Xiaojun; An, Shaorong; Sheng, Hong; Tang, Zhenghua; Yang, Zhiquan; Gu, Xiaohong

2017-12-01

Phosphine (PH 3 ), as a gaseous phosphide, plays an important role in the phosphorus cycle in ecosystems. In this study, the emission and distribution of phosphine, carbon dioxide (CO 2 ) and methane (CH 4 ) in paddy fields were investigated to speculate the future potential impacts of enhanced greenhouse effect on phosphorus cycle involved in phosphine by the method of Pearson correlation analysis and multiple linear regression analysis. During the whole period of rice growth, there was a significant positive correlation between CO 2 emission flux and PH 3 emission flux (r=0.592, p=0.026, n=14). Similarly, a significant positive correlation of emission flux was also observed between CH 4 and PH 3 (r=0.563, p=0.036, n=14). The linear regression relationship was determined as [PH 3 ] flux =0.007[CO 2 ] flux +0.063[CH 4 ] flux -4.638. No significant differences were observed for all values of matrix-bound phosphine (MBP), soil carbon dioxide (SCO 2 ), and soil methane (SCH 4 ) in paddy soils. However, there was a significant positive correlation between MBP and SCO 2 at heading, flowering and ripening stage. The correlation coefficients were 0.909, 0.890 and 0.827, respectively. In vertical distribution, MBP had the analogical variation trend with SCO 2 and SCH 4 . Through Pearson correlation analysis and multiple stepwise linear regression analysis, pH, redox potential (Eh), total phosphorus (TP) and acid phosphatase (ACP) were identified as the principal factors affecting MBP levels, with correlative rankings of Eh>pH>TP>ACP. The multiple stepwise regression model ([MBP]=0.456∗[ACP]+0.235∗[TP]-1.458∗[Eh]-36.547∗[pH]+352.298) was obtained. The findings in this study hold great reference values to the global biogeochemical cycling of phosphorus in the future. Copyright © 2017 Elsevier B.V. All rights reserved.

A Randomized-Controlled Trial Examining the Effects of Reflexology on Anxiety of Patients Undergoing Coronary Angiography

PubMed Central

Molavi Vardanjani, Mehdi; Masoudi Alavi, Negin; Razavi, Narges Sadat; Aghajani, Mohammad; Azizi-Fini, Esmail; Vaghefi, Seied Morteza

2013-01-01

Background: The anxiety reduction before coronary angiography has clinical advantages and is one of the objectives of nursing. Reflexology is a non-invasive method that has been used in several clinical situations. Applying reflexology might have effect on the reduction of anxiety before coronary angiography. Objectives: The aim of this randomized clinical trial was to investigate the effect of reflexology on anxiety among patients undergoing coronary angiography. Patients and Methods: This trial was conducted in Shahid Beheshti Hospital, in Kashan, Iran. One hundred male patients who were undergoing coronary angiography were randomly enrolled into intervention and placebo groups. The intervention protocol was included 30 minutes of general foot massage and the stimulation of three reflex points including solar plexus, pituitary gland, and heart. The placebo group only received the general foot massage. Spielbergers state trait anxiety inventory was used to assess the anxiety experienced by patients. Data was analyzed using Man-Witney, Wilcoxon and Chi-square tests. The stepwise multiple regressions used to analyze the variables that are involved in anxiety reduction. Results: The mean range of anxiety decreased from 53.24 to 45.24 in reflexology group which represented 8 score reduction (P = 0.0001). The reduction in anxiety was 5.9 score in placebo group which was also significant (P = 0.0001). The anxiety reduction was significantly higher in reflexology group (P = 0.014). The stepwise multiple regression analysis showed that doing reflexology can explain the 7.5% of anxiety reduction which made a significant model. Conclusions: Reflexology can decrease the anxiety level before coronary angiography. Therefore, reflexology before coronary angiography is recommended. PMID:25414869

Development of a food frequency questionnaire for Sri Lankan adults

PubMed Central

2012-01-01

Background Food Frequency Questionnaires (FFQs) are commonly used in epidemiologic studies to assess long-term nutritional exposure. Because of wide variations in dietary habits in different countries, a FFQ must be developed to suit the specific population. Sri Lanka is undergoing nutritional transition and diet-related chronic diseases are emerging as an important health problem. Currently, no FFQ has been developed for Sri Lankan adults. In this study, we developed a FFQ to assess the regular dietary intake of Sri Lankan adults. Methods A nationally representative sample of 600 adults was selected by a multi-stage random cluster sampling technique and dietary intake was assessed by random 24-h dietary recall. Nutrient analysis of the FFQ required the selection of foods, development of recipes and application of these to cooked foods to develop a nutrient database. We constructed a comprehensive food list with the units of measurement. A stepwise regression method was used to identify foods contributing to a cumulative 90% of variance to total energy and macronutrients. In addition, a series of photographs were included. Results We obtained dietary data from 482 participants and 312 different food items were recorded. Nutritionists grouped similar food items which resulted in a total of 178 items. After performing step-wise multiple regression, 93 foods explained 90% of the variance for total energy intake, carbohydrates, protein, total fat and dietary fibre. Finally, 90 food items and 12 photographs were selected. Conclusion We developed a FFQ and the related nutrient composition database for Sri Lankan adults. Culturally specific dietary tools are central to capturing the role of diet in risk for chronic disease in Sri Lanka. The next step will involve the verification of FFQ reproducibility and validity. PMID:22937734

Lifestyle and health-related quality of life: A cross-sectional study among civil servants in China

PubMed Central

2012-01-01

Background Health-related quality of life (HRQoL) has been increasingly acknowledged as a valid and appropriate indicator of public health and chronic morbidity. However, limited research was conducted among Chinese civil servants owing to the different lifestyle. The aim of the study was to evaluate the HRQoL among Chinese civil servants and to identify factors might be associated with their HRQoL. Methods A cross-sectional study was conducted to investigate HRQoL of 15,000 civil servants in China using stratified random sampling methods. Independent-Samples t-Test, one-way ANOVA, and multiple stepwise regression were used to analyse the influencing factors and the HRQoL of the civil servants. Results A univariate analysis showed that there were significant differences among physical component summary (PCS), mental component summary (MCS), and TS between lifestyle factors, such as smoking, drinking alcohol, having breakfast, sleep time, physical exercise, work time, operating computers, and sedentariness (P < 0.05). Multiple stepwise regressions showed that there were significant differences among TS between lifestyle factors, such as breakfast, sleep time, physical exercise, operating computers, sedentariness, work time, and drinking (P < 0.05). Conclusion In this study, using Short Form 36 items (SF-36), we assessed the association of HRQoL with lifestyle factors, including smoking, drinking alcohol, having breakfast, sleep time, physical exercise, work time, operating computers, and sedentariness in China. The performance of the questionnaire in the large-scale survey is satisfactory and provides a large picture of the HRQoL status in Chinese civil servants. Our results indicate that lifestyle factors such as smoking, drinking alcohol, having breakfast, sleep time, physical exercise, work time, operating computers, and sedentariness affect the HRQoL of civil servants in China. PMID:22559315

A robust and efficient stepwise regression method for building sparse polynomial chaos expansions

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

Abraham, Simon, E-mail: Simon.Abraham@ulb.ac.be; Raisee, Mehrdad; Ghorbaniasl, Ghader

2017-03-01

Polynomial Chaos (PC) expansions are widely used in various engineering fields for quantifying uncertainties arising from uncertain parameters. The computational cost of classical PC solution schemes is unaffordable as the number of deterministic simulations to be calculated grows dramatically with the number of stochastic dimension. This considerably restricts the practical use of PC at the industrial level. A common approach to address such problems is to make use of sparse PC expansions. This paper presents a non-intrusive regression-based method for building sparse PC expansions. The most important PC contributions are detected sequentially through an automatic search procedure. The variable selectionmore » criterion is based on efficient tools relevant to probabilistic method. Two benchmark analytical functions are used to validate the proposed algorithm. The computational efficiency of the method is then illustrated by a more realistic CFD application, consisting of the non-deterministic flow around a transonic airfoil subject to geometrical uncertainties. To assess the performance of the developed methodology, a detailed comparison is made with the well established LAR-based selection technique. The results show that the developed sparse regression technique is able to identify the most significant PC contributions describing the problem. Moreover, the most important stochastic features are captured at a reduced computational cost compared to the LAR method. The results also demonstrate the superior robustness of the method by repeating the analyses using random experimental designs.« less

Stigma Resistance in Stable Schizophrenia: The Relative Contributions of Stereotype Endorsement, Self-Reflection, Self-Esteem, and Coping Styles

PubMed Central

Kao, Yu-Chen; Chang, Hsin-An; Tzeng, Nian-Sheng; Yeh, Chin-Bin; Loh, Ching-Hui

2017-01-01

Objective: Stigma resistance (SR) has recently emerged as a prominent aspect of research on recovery from schizophrenia, partly because studies have suggested that the development of stigma-resisting beliefs may help individuals lead a fulfilling life and recover from their mental illness. The present study assessed the relationship between personal SR ability and prediction variables such as self-stigma, self-esteem, self-reflection, coping styles, and psychotic symptomatology. Method: We performed an exploratory cross-sectional study of 170 community-dwelling patients with schizophrenia. Self-stigma, self-esteem, self-reflection, coping skills, and SR were assessed through self-report. Psychotic symptom severity was rated by the interviewers. Factors showing significant association in univariate analyses were included in a stepwise backward regression model. Results: Stepwise regressions revealed that acceptance of stereotypes of mental illness, self-esteem, self-reflection, and only 2 adaptive coping strategies (positive reinterpretation and religious coping) were significant predictors of SR. The prediction model accounted for 27.1% of the variance in the SR subscale score in our sample. Conclusions: Greater reflective capacity, greater self-esteem, greater preferences for positive reinterpretation and religious coping, and fewer endorsements of the stereotypes of mental illness may be key factors that relate to higher levels of SR. These factors are potentially modifiable in tailored interventions, and such modification may produce considerable improvements in the SR of the investigated population. This study has implications for psychosocial rehabilitation and emerging views of recovery from mental illness. PMID:28884606

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

PubMed

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

2013-01-01

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

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

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

This study examines the relationship between air pollution, measured as concentration of suspended particulates in the atmosphere, and infant mortality due to pneumonia in the metropolitan area of Rio de Janeiro. Multiple linear regression (progressive or stepwise method) was used to analyze infant mortality due to pneumonia, diarrhea, and all causes in 1980, by geographic area, income level, and degree of contamination. While the variable proportion of families with income equivalent to more than two minimum wages was included in the regressions corresponding to the three types of infant mortality, the average contamination index had a statistically significant coefficient (bmore » = 0.2208; t = 2.670; P = 0.0137) only in the case of mortality due to pneumonia. This would suggest a biological association, but, as in any ecological study, such conclusions should be viewed with caution. The authors believe that air quality indicators are essential to consider in studies of acute respiratory infections in developing countries.« less

Odontological approach to sexual dimorphism in southeastern France.

PubMed

Lladeres, Emilie; Saliba-Serre, Bérengère; Sastre, Julien; Foti, Bruno; Tardivo, Delphine; Adalian, Pascal

2013-01-01

The aim of this study was to establish a prediction formula to allow for the determination of sex among the southeastern French population using dental measurements. The sample consisted of 105 individuals (57 males and 48 females, aged between 18 and 25 years). Dental measurements were calculated using Euclidean distances, in three-dimensional space, from point coordinates obtained by a Microscribe. A multiple logistic regression analysis was performed to establish the prediction formula. Among 12 selected dental distances, a stepwise logistic regression analysis highlighted the two most significant discriminate predictors of sex: one located at the mandible and the other at the maxilla. A cutpoint was proposed to prediction of true sex. The prediction formula was then tested on a validation sample (20 males and 34 females, aged between 18 and 62 years and with a history of orthodontics or restorative care) to evaluate the accuracy of the method. © 2012 American Academy of Forensic Sciences.

Greater association of peak neuromuscular performance with cortical bone geometry, bone mass and bone strength than bone density: A study in 417 older women.

PubMed

Belavý, Daniel L; Armbrecht, Gabriele; Blenk, Tilo; Bock, Oliver; Börst, Hendrikje; Kocakaya, Emine; Luhn, Franziska; Rantalainen, Timo; Rawer, Rainer; Tomasius, Frederike; Willnecker, Johannes; Felsenberg, Dieter

2016-02-01

We evaluated which aspects of neuromuscular performance are associated with bone mass, density, strength and geometry. 417 women aged 60-94years were examined. Countermovement jump, sit-to-stand test, grip strength, forearm and calf muscle cross-sectional area, areal bone mineral content and density (aBMC and aBMD) at the hip and lumbar spine via dual X-ray absorptiometry, and measures of volumetric vBMC and vBMD, bone geometry and section modulus at 4% and 66% of radius length and 4%, 38% and 66% of tibia length via peripheral quantitative computed tomography were performed. The first principal component of the neuromuscular variables was calculated to generate a summary neuromuscular variable. Percentage of total variance in bone parameters explained by the neuromuscular parameters was calculated. Step-wise regression was also performed. At all pQCT bone sites (radius, ulna, tibia, fibula), a greater percentage of total variance in measures of bone mass, cortical geometry and/or bone strength was explained by peak neuromuscular performance than for vBMD. Sit-to-stand performance did not relate strongly to bone parameters. No obvious differential in the explanatory power of neuromuscular performance was seen for DXA aBMC versus aBMD. In step-wise regression, bone mass, cortical morphology, and/or strength remained significant in relation to the first principal component of the neuromuscular variables. In no case was vBMD positively related to neuromuscular performance in the final step-wise regression models. Peak neuromuscular performance has a stronger relationship with leg and forearm bone mass and cortical geometry as well as proximal forearm section modulus than with vBMD. Copyright © 2015 Elsevier Inc. All rights reserved.

A model for predicting sulcus-to-sulcus diameter in posterior chamber phakic intraocular lens candidates: correlation between ocular biometric parameters.

PubMed

Ghoreishi, Mohammad; Abdi-Shahshahani, Mehdi; Peyman, Alireza; Pourazizi, Mohsen

2018-02-21

The aim of this study was to determine the correlation between ocular biometric parameters and sulcus-to-sulcus (STS) diameter. This was a cross-sectional study of preoperative ocular biometry data of patients who were candidates for phakic intraocular lens (IOL) surgery. Subjects underwent ocular biometry analysis, including refraction error evaluation using an autorefractor and Orbscan topography for white-to-white (WTW) corneal diameter and measurement. Pentacam was used to perform WTW corneal diameter and measurements of minimum and maximum keratometry (K). Measurements of STS and angle-to-angle (ATA) were obtained using a 50-MHz B-mode ultrasound device. Anterior optical coherence tomography was performed for anterior chamber depth measurement. Pearson's correlation test and stepwise linear regression analysis were used to find a model to predict STS. Fifty-eight eyes of 58 patients were enrolled. Mean age ± standard deviation of sample was 28.95 ± 6.04 years. The Pearson's correlation coefficient between STS with WTW, ATA, mean K was 0.383, 0.492, and - 0.353, respectively, which was statistically significant (all P < 0.001). Using stepwise linear regression analysis, there is a statistically significant association between STS with WTW (P = 0.011) and mean K (P = 0.025). The standardized coefficient was 0.323 and - 0.284 for WTW and mean K, respectively. The stepwise linear regression analysis equation was: (STS = 9.549 + 0.518 WTW - 0.083 mean K). Based on our result, given the correlation of STS with WTW and mean K and potential of direct and essay measurement of WTW and mean K, it seems that current IOL sizing protocols could be estimating with WTW and mean K.

Aerobic Fitness Does Not Contribute to Prediction of Orthostatic Intolerance

NASA Technical Reports Server (NTRS)

Convertino, Victor A.; Sather, Tom M.; Goldwater, Danielle J.; Alford, William R.

1986-01-01

Several investigations have suggested that orthostatic tolerance may be inversely related to aerobic fitness (VO (sub 2max)). To test this hypothesis, 18 males (age 29 to 51 yr) underwent both treadmill VO(sub 2max) determination and graded lower body negative pressures (LBNP) exposure to tolerance. VO(2max) was measured during the last minute of a Bruce treadmill protocol. LBNP was terminated based on pre-syncopal symptoms and LBNP tolerance (peak LBNP) was expressed as the cumulative product of LBNP and time (torr-min). Changes in heart rate, stroke volume cardiac output, blood pressure and impedance rheographic indices of mid-thigh-leg initial accumulation were measured at rest and during the final minute of LBNP. For all 18 subjects, mean (plus or minus SE) fluid accumulation index and leg venous compliance index at peak LBNP were 139 plus or minus 3.9 plus or minus 0.4 ml-torr-min(exp -2) x 10(exp 3), respectively. Pearson product-moment correlations and step-wise linear regression were used to investigate relationships with peak LBNP. Variables associated with endurance training, such as VO(sub 2max) and percent body fat were not found to correlate significantly (P is less than 0.05) with peak LBNP and did not add sufficiently to the prediction of peak LBNP to be included in the step-wise regression model. The step-wise regression model included only fluid accumulation index leg venous compliance index, and blood volume and resulted in a squared multiple correlation coefficient of 0.978. These data do not support the hypothesis that orthostatic tolerance as measured by LBNP is lower in individuals with high aerobic fitness.

Epidemiological analysis of factors influencing rate of progress in Echinococcus granulosus control in New Zealand.

PubMed Central

Burridge, M. J.; Schwabe, C. W.

1977-01-01

The factors influencing the rate of progress in Echinococcus granulosus control in New Zealand were analysed by hydatid control area using stepwise multiple regression techniques. The results indicated that the rate of progress was related positively to initial E. granulosus prevalence in dogs and the efficiency with which local authorities implemented national control policy, and negatively to the Maori proportion in the local population and the number of dogs per owner. Problems in analysis of the New Zealand data are discussed and improved methods of monitoring progress in hydatid disease control programmes are described. Images Fig. 1 PMID:265340

Dynamic contrast-enhanced perfusion area-detector CT assessed with various mathematical models: Its capability for therapeutic outcome prediction for non-small cell lung cancer patients with chemoradiotherapy as compared with that of FDG-PET/CT.

PubMed

Ohno, Yoshiharu; Fujisawa, Yasuko; Koyama, Hisanobu; Kishida, Yuji; Seki, Shinichiro; Sugihara, Naoki; Yoshikawa, Takeshi

2017-01-01

To directly compare the capability of dynamic first-pass contrast-enhanced (CE-) perfusion area-detector CT (ADCT) and PET/CT for early prediction of treatment response, disease progression and overall survival of non-small cell carcinoma (NSCLC) patients treated with chemoradiotherapy. Fifty-three consecutive Stage IIIB NSCLC patients who had undergone PET/CT, dynamic first-pass CE-perfusion ADCT, chemoradiotherapy, and follow-up examination were enrolled in this study. They were divided into two groups: 1) complete or partial response (CR+PR) and 2) stable or progressive disease (SD+PD). Pulmonary arterial and systemic arterial perfusions and total perfusion were assessed at targeted lesions with the dual-input maximum slope method, permeability surface and distribution volume with the Patlak plot method, tumor perfusion with the single-input maximum slope method, and SUV max , and results were averaged to determine final values for each patient. Next, step-wise regression analysis was used to determine which indices were the most useful for predicting therapeutic effect. Finally, overall survival of responders and non-responders assessed by using the indices that had a significant effect on prediction of therapeutic outcome was statistically compared. The step-wise regression test showed that therapeutic effect (r 2 =0.63, p=0.01) was significantly affected by the following three factors in order of magnitude of impact: systemic arterial perfusion, total perfusion, and SUV max . Mean overall survival showed a significant difference for total perfusion (p=0.003) and systemic arterial perfusion (p=0.04). Dynamic first-pass CE-perfusion ADCT as well as PET/CT are useful for treatment response prediction in NSCLC patients treated with chemoradiotherapy. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Relationships between presenteeism and work-related musculoskeletal disorders among physical therapists in the Republic of Korea.

PubMed

Bae, Young-Hyeon

2017-12-14

This study investigated the relationship between presenteeism and work-related musculoskeletal disorders (WMSDs) among physical therapists (PTs) in the Republic of Korea. Questionnaires were given to 600 PTs in the Republic of Korea. General and occupational characteristics and the prevalence of presenteeism and absenteeism were self-reported on the questionnaire. Stepwise regression analyses were used to evaluate the effects of presenteeism and other variables on general and occupational characteristics. Of the 490 PTs who responded, 399 (81.4%) reported at least one WMSD. There was a low incidence rate of absenteeism, but work impairment scores indicate there was a high incidence of presenteeism. In the stepwise regression analyses, the incidence of WMSDs was highest in cases of presenteeism. The results of this study demonstrate that there is a high incidence rate of WMSDs in Republic of Korean PTs, that WMSDs are related to presenteeism and that PTs demonstrate high presenteeism and low absenteeism.

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

PubMed

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

2007-11-05

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

Robust Mosaicking of Stereo Digital Elevation Models from the Ames Stereo Pipeline

NASA Technical Reports Server (NTRS)

Kim, Tae Min; Moratto, Zachary M.; Nefian, Ara Victor

2010-01-01

Robust estimation method is proposed to combine multiple observations and create consistent, accurate, dense Digital Elevation Models (DEMs) from lunar orbital imagery. The NASA Ames Intelligent Robotics Group (IRG) aims to produce higher-quality terrain reconstructions of the Moon from Apollo Metric Camera (AMC) data than is currently possible. In particular, IRG makes use of a stereo vision process, the Ames Stereo Pipeline (ASP), to automatically generate DEMs from consecutive AMC image pairs. However, the DEMs currently produced by the ASP often contain errors and inconsistencies due to image noise, shadows, etc. The proposed method addresses this problem by making use of multiple observations and by considering their goodness of fit to improve both the accuracy and robustness of the estimate. The stepwise regression method is applied to estimate the relaxed weight of each observation.

Predictive value of grade point average (GPA), Medical College Admission Test (MCAT), internal examinations (Block) and National Board of Medical Examiners (NBME) scores on Medical Council of Canada qualifying examination part I (MCCQE-1) scores.

PubMed

Roy, Banibrata; Ripstein, Ira; Perry, Kyle; Cohen, Barry

2016-01-01

To determine whether the pre-medical Grade Point Average (GPA), Medical College Admission Test (MCAT), Internal examinations (Block) and National Board of Medical Examiners (NBME) scores are correlated with and predict the Medical Council of Canada Qualifying Examination Part I (MCCQE-1) scores. Data from 392 admitted students in the graduating classes of 2010-2013 at University of Manitoba (UofM), College of Medicine was considered. Pearson's correlation to assess the strength of the relationship, multiple linear regression to estimate MCCQE-1 score and stepwise linear regression to investigate the amount of variance were employed. Complete data from 367 (94%) students were studied. The MCCQE-1 had a moderate-to-large positive correlation with NBME scores and Block scores but a low correlation with GPA and MCAT scores. The multiple linear regression model gives a good estimate of the MCCQE-1 (R2 =0.604). Stepwise regression analysis demonstrated that 59.2% of the variation in the MCCQE-1 was accounted for by the NBME, but only 1.9% by the Block exams, and negligible variation came from the GPA and the MCAT. Amongst all the examinations used at UofM, the NBME is most closely correlated with MCCQE-1.

Optimizing separate phase light hydrocarbon recovery from contaminated unconfined aquifers

NASA Astrophysics Data System (ADS)

Cooper, Grant S.; Peralta, Richard C.; Kaluarachchi, Jagath J.

A modeling approach is presented that optimizes separate phase recovery of light non-aqueous phase liquids (LNAPL) for a single dual-extraction well in a homogeneous, isotropic unconfined aquifer. A simulation/regression/optimization (S/R/O) model is developed to predict, analyze, and optimize the oil recovery process. The approach combines detailed simulation, nonlinear regression, and optimization. The S/R/O model utilizes nonlinear regression equations describing system response to time-varying water pumping and oil skimming. Regression equations are developed for residual oil volume and free oil volume. The S/R/O model determines optimized time-varying (stepwise) pumping rates which minimize residual oil volume and maximize free oil recovery while causing free oil volume to decrease a specified amount. This S/R/O modeling approach implicitly immobilizes the free product plume by reversing the water table gradient while achieving containment. Application to a simple representative problem illustrates the S/R/O model utility for problem analysis and remediation design. When compared with the best steady pumping strategies, the optimal stepwise pumping strategy improves free oil recovery by 11.5% and reduces the amount of residual oil left in the system due to pumping by 15%. The S/R/O model approach offers promise for enhancing the design of free phase LNAPL recovery systems and to help in making cost-effective operation and management decisions for hydrogeologists, engineers, and regulators.

Random Survival Forest in practice: a method for modelling complex metabolomics data in time to event analysis.

PubMed

Dietrich, Stefan; Floegel, Anna; Troll, Martina; Kühn, Tilman; Rathmann, Wolfgang; Peters, Anette; Sookthai, Disorn; von Bergen, Martin; Kaaks, Rudolf; Adamski, Jerzy; Prehn, Cornelia; Boeing, Heiner; Schulze, Matthias B; Illig, Thomas; Pischon, Tobias; Knüppel, Sven; Wang-Sattler, Rui; Drogan, Dagmar

2016-10-01

The application of metabolomics in prospective cohort studies is statistically challenging. Given the importance of appropriate statistical methods for selection of disease-associated metabolites in highly correlated complex data, we combined random survival forest (RSF) with an automated backward elimination procedure that addresses such issues. Our RSF approach was illustrated with data from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study, with concentrations of 127 serum metabolites as exposure variables and time to development of type 2 diabetes mellitus (T2D) as outcome variable. Out of this data set, Cox regression with a stepwise selection method was recently published. Replication of methodical comparison (RSF and Cox regression) was conducted in two independent cohorts. Finally, the R-code for implementing the metabolite selection procedure into the RSF-syntax is provided. The application of the RSF approach in EPIC-Potsdam resulted in the identification of 16 incident T2D-associated metabolites which slightly improved prediction of T2D when used in addition to traditional T2D risk factors and also when used together with classical biomarkers. The identified metabolites partly agreed with previous findings using Cox regression, though RSF selected a higher number of highly correlated metabolites. The RSF method appeared to be a promising approach for identification of disease-associated variables in complex data with time to event as outcome. The demonstrated RSF approach provides comparable findings as the generally used Cox regression, but also addresses the problem of multicollinearity and is suitable for high-dimensional data. © The Author 2016; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.

Analyzing Teaching Performance of Instructors Using Data Mining Techniques

ERIC Educational Resources Information Center

Mardikyan, Sona; Badur, Bertain

2011-01-01

Student evaluations to measure the teaching effectiveness of instructor's are very frequently applied in higher education for many years. This study investigates the factors associated with the assessment of instructors teaching performance using two different data mining techniques; stepwise regression and decision trees. The data collected…

DEVELOPMENT OF RESIDENTIAL WOOD COMSUMPTION ESTIMATION MODELS

EPA Science Inventory

The report gives data on the distribution and usage of firewood, obtained from a pool of household wood use surveys. ased on a series of regression models developed using the STEPWISE procedure in the SAS statistical package, two variables appear to be most predictive of wood use...

[Establishment of cervical vertebral skeletal maturation of female children in Shanghai].

PubMed

Sun, Yan; Chen, Rong-jing; Yu, Quan; Fan, Li; Chen, Wei; Shen, Gang

2009-06-01

To establish a method for quantitatively evaluating skeletal maturation of cervical vertebrae of female children in Shanghai. The samples were selected from lateral cephalometric radiographs of 240 Shanghai girls, aged 8 to 15 years. The parameters were measured to indicate the morphological changes of the third (C3) and fourth (C4) vertebrae in width, height and the depth of the inferior curvature. The independent-sample t test and stepwise multiple regression analysis were used to estimate the growth status and the ratios of C3, C4 cervical vertebrae by SPSS 15.0 software package. The physical and morphological contour of C3, C4 cervical vertebrae increased proportionately with the increment of age. The regression formula for indicating cervical vertebral skeletal age of female children in Shanghai was expressed by the equation Y= -5.696+8.010 AH3/AP3+6.654 AH3/H3+6.045AH4/PH4 (r=0.912). The regression formula resulted from morphological measurements quantitatively indicates the skeletal maturation of cervical vertebrae of female children in Shanghai.

Psychological factors influence the gastroesophageal reflux disease (GERD) and their effect on quality of life among firefighters in South Korea

PubMed Central

Jang, Seung-Ho; Ryu, Han-Seung; Choi, Suck-Chei; Lee, Sang-Yeol

2016-01-01

Objectives The purpose of this study was to examine psychosocial factors related to gastroesophageal reflux disease (GERD) and their effects on quality of life (QOL) in firefighters. Methods Data were collected from 1217 firefighters in a Korean province. We measured psychological symptoms using the scale. In order to observe the influence of the high-risk group on occupational stress, we conduct logistic multiple linear regression. The correlation between psychological factors and QOL was also analyzed and performed a hierarchical regression analysis. Results GERD was observed in 32.2% of subjects. Subjects with GERD showed higher depressive symptom, anxiety and occupational stress scores, and lower self-esteem and QOL scores relative to those observed in GERD – negative subject. GERD risk was higher for the following occupational stress subcategories: job demand, lack of reward, interpersonal conflict, and occupational climate. The stepwise regression analysis showed that depressive symptoms, occupational stress, self-esteem, and anxiety were the best predictors of QOL. Conclusions The results suggest that psychological and medical approaches should be combined in GERD assessment. PMID:27691373

Sparse Logistic Regression for Diagnosis of Liver Fibrosis in Rat by Using SCAD-Penalized Likelihood

PubMed Central

Yan, Fang-Rong; Lin, Jin-Guan; Liu, Yu

2011-01-01

The objective of the present study is to find out the quantitative relationship between progression of liver fibrosis and the levels of certain serum markers using mathematic model. We provide the sparse logistic regression by using smoothly clipped absolute deviation (SCAD) penalized function to diagnose the liver fibrosis in rats. Not only does it give a sparse solution with high accuracy, it also provides the users with the precise probabilities of classification with the class information. In the simulative case and the experiment case, the proposed method is comparable to the stepwise linear discriminant analysis (SLDA) and the sparse logistic regression with least absolute shrinkage and selection operator (LASSO) penalty, by using receiver operating characteristic (ROC) with bayesian bootstrap estimating area under the curve (AUC) diagnostic sensitivity for selected variable. Results show that the new approach provides a good correlation between the serum marker levels and the liver fibrosis induced by thioacetamide (TAA) in rats. Meanwhile, this approach might also be used in predicting the development of liver cirrhosis. PMID:21716672

Prediction of adult height in girls: the Beunen-Malina-Freitas method.

PubMed

Beunen, Gaston P; Malina, Robert M; Freitas, Duarte L; Thomis, Martine A; Maia, José A; Claessens, Albrecht L; Gouveia, Elvio R; Maes, Hermine H; Lefevre, Johan

2011-12-01

The purpose of this study was to validate and cross-validate the Beunen-Malina-Freitas method for non-invasive prediction of adult height in girls. A sample of 420 girls aged 10-15 years from the Madeira Growth Study were measured at yearly intervals and then 8 years later. Anthropometric dimensions (lengths, breadths, circumferences, and skinfolds) were measured; skeletal age was assessed using the Tanner-Whitehouse 3 method and menarcheal status (present or absent) was recorded. Adult height was measured and predicted using stepwise, forward, and maximum R (2) regression techniques. Multiple correlations, mean differences, standard errors of prediction, and error boundaries were calculated. A sample of the Leuven Longitudinal Twin Study was used to cross-validate the regressions. Age-specific coefficients of determination (R (2)) between predicted and measured adult height varied between 0.57 and 0.96, while standard errors of prediction varied between 1.1 and 3.9 cm. The cross-validation confirmed the validity of the Beunen-Malina-Freitas method in girls aged 12-15 years, but at lower ages the cross-validation was less consistent. We conclude that the Beunen-Malina-Freitas method is valid for the prediction of adult height in girls aged 12-15 years. It is applicable to European populations or populations of European ancestry.

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.

Linking land cover and water quality in New York City's water supply watersheds.

PubMed

Mehaffey, M H; Nash, M S; Wade, T G; Ebert, D W; Jones, K B; Rager, A

2005-08-01

The Catskill/Delaware reservoirs supply 90% of New York City's drinking water. The City has implemented a series of watershed protection measures, including land acquisition, aimed at preserving water quality in the Catskill/Delaware watersheds. The objective of this study was to examine how relationships between landscape and surface water measurements change between years. Thirty-two drainage areas delineated from surface water sample points (total nitrogen, total phosphorus, and fecal coliform bacteria concentrations) were used in step-wise regression analyses to test landscape and surface-water quality relationships. Two measurements of land use, percent agriculture and percent urban development, were positively related to water quality and consistently present in all regression models. Together these two land uses explained 25 to 75% of the regression model variation. However, the contribution of agriculture to water quality condition showed a decreasing trend with time as overall agricultural land cover decreased. Results from this study demonstrate that relationships between land cover and surface water concentrations of total nitrogen, total phosphorus, and fecal coliform bacteria counts over a large area can be evaluated using a relatively simple geographic information system method. Land managers may find this method useful for targeting resources in relation to a particular water quality concern, focusing best management efforts, and maximizing benefits to water quality with minimal costs.

An Exploring Model of Intelligence and Personality in Different Culture

ERIC Educational Resources Information Center

Wu, Yufeng; Qian, Guoying

2005-01-01

Middle school subjects of 13-21 years (from 4 nationalities) were used for studying the relationship between progressive cognition and personality characteristics by Raven's Standard Progressive Matrices and Eysenk's Personality Questionnaire. The results showed: (1) the correlation and stepwise regression were completely identical: P score was…

Switchgrass biomass composition traits and their effects on its digestion by ruminants and bioconversion to ethanol

USDA-ARS?s Scientific Manuscript database

Six generations of divergent breeding in switchgrass (Panicum virgatum L.) for forage in vitro digestibility (IVDMD) resulted in significant changes in 20 biomass composition traits. Stepwise multi-regression was used to determine which of the 20 composition traits had largest significant effects on...

Spatial Representation in Blind Children. 3: Effects of Individual Differences.

ERIC Educational Resources Information Center

Fletcher, Janet F.

1981-01-01

Data from a study of spatial representation in blind children were subjected to two stepwise regression analyses to determine the relationships between several subject related variables and responses to "map" (cognitive map) and "route" (sequential memory) questions about the position of furniture in a recently explored room. (Author/SBH)

Knowing When to Retire: The First Step towards Financial Planning in Malaysia

ERIC Educational Resources Information Center

Kock, Tan Hoe; Yoong, Folk Jee

2011-01-01

This article draws upon expected retirement age cohorts as a main determinant to financial planning preparation in Malaysia. The return rate was 55% from 600 questionnaires distributed. Five hypotheses were analyzed using hierarchical and stepwise regression analysis. The results revealed that expected retirement age cohort variables made…

Logistic LASSO regression for the diagnosis of breast cancer using clinical demographic data and the BI-RADS lexicon for ultrasonography.

PubMed

Kim, Sun Mi; Kim, Yongdai; Jeong, Kuhwan; Jeong, Heeyeong; Kim, Jiyoung

2018-01-01

The aim of this study was to compare the performance of image analysis for predicting breast cancer using two distinct regression models and to evaluate the usefulness of incorporating clinical and demographic data (CDD) into the image analysis in order to improve the diagnosis of breast cancer. This study included 139 solid masses from 139 patients who underwent a ultrasonography-guided core biopsy and had available CDD between June 2009 and April 2010. Three breast radiologists retrospectively reviewed 139 breast masses and described each lesion using the Breast Imaging Reporting and Data System (BI-RADS) lexicon. We applied and compared two regression methods-stepwise logistic (SL) regression and logistic least absolute shrinkage and selection operator (LASSO) regression-in which the BI-RADS descriptors and CDD were used as covariates. We investigated the performances of these regression methods and the agreement of radiologists in terms of test misclassification error and the area under the curve (AUC) of the tests. Logistic LASSO regression was superior (P<0.05) to SL regression, regardless of whether CDD was included in the covariates, in terms of test misclassification errors (0.234 vs. 0.253, without CDD; 0.196 vs. 0.258, with CDD) and AUC (0.785 vs. 0.759, without CDD; 0.873 vs. 0.735, with CDD). However, it was inferior (P<0.05) to the agreement of three radiologists in terms of test misclassification errors (0.234 vs. 0.168, without CDD; 0.196 vs. 0.088, with CDD) and the AUC without CDD (0.785 vs. 0.844, P<0.001), but was comparable to the AUC with CDD (0.873 vs. 0.880, P=0.141). Logistic LASSO regression based on BI-RADS descriptors and CDD showed better performance than SL in predicting the presence of breast cancer. The use of CDD as a supplement to the BI-RADS descriptors significantly improved the prediction of breast cancer using logistic LASSO regression.

A comparative study on the level of satisfaction among regular and contractual health-care workers in a Northern city of India

PubMed Central

Dixit, Jyoti; Goel, Sonu; Sharma, Vijaylakshmi

2017-01-01

Introduction: Job satisfaction greatly determines the productivity and efficiency of human resources for health. The current study aims to assess the level of satisfaction and factors influencing the job satisfaction among regular and contractual health-care workers. Materials and Methods: A cross-sectional quantitative study was conducted from January to June 2015 among health care workers (n = 354) at all levels of public health-care facilities of Chandigarh. The correlation between variables with overall level of satisfaction was computed for regular and contractual health-care workers. Stepwise multiple linear regression was done to elucidate the major factors influencing job satisfaction. Results: Majority of the regular health-care staff was highly satisfied (86.9%) as compared to contractual staff (10.5%), which however was moderately satisfied (55.9%). Stepwise regression model showed that work-related matters (β = 1.370, P < 0.01), organizational facilities (β = 1.586, P < 0.01), privileges attached to the job (β = 0.530, P < 0.01), attention to the suggestions (β = 0.515, P < 0.01), chance of promotion (β = 0.703, P < 0.01), and human resource issues (β = 1.0721, P < 0.01) are strong predictors of overall satisfaction level. Conclusion: Under the National Rural Health Mission, contract appointments have improved the overall availability of health-care staff at all levels of public health facilities. However, there are concerns regarding their level of motivation with various aspects related to the job, which need to be urgently addressed so as to improve the effectiveness and efficiency of health services. PMID:29302557

The relationship between personality traits and sexual self-esteem and its components

PubMed Central

Firoozi, Mahbobe; Azmoude, Elham; Asgharipoor, Negar

2016-01-01

Background: Women's sexual self-esteem is one of the most important factors that affect women's sexual satisfaction and their sexual anxiety. Various aspects of sexual life are blended with the entire personality. Determining the relationship between personality traits and self-concept aspects such as sexual self-esteem leads to better understanding of sexual behavior in people with different personality traits and helps in identifying the psychological variables affecting their sexual performance. The aim this study was to determine the relationship between personality traits and sexual self-esteem. Materials and Methods: This correlation study was performed on 127 married women who referred to selected health care centers of Mashhad in 2014–2015. Data collection tools included NEO personality inventory dimensions and Zeanah and Schwarz sexual self-esteem questionnaire. Data were analyzed through Pearson correlation coefficient test and stepwise regression model. Results: The results of Pearson correlation test showed a significant relationship between neuroticism personality dimension (r = −0.414), extroversion (r = 0.363), agreeableness (r = 0.420), and conscientiousness (r = 0.364) with sexual self-esteem (P < 0.05). The relationship between openness with sexual self-esteem was not significant (P > 0.05). In addition, based on the results of the stepwise regression model, three dimensions of agreeableness, neuroticism, and extraversion could predict 27% of the women's sexual self-esteem variance. Conclusions: The results showed a correlation between women's personality characteristics and their sexual self-esteem. Paying attention to personality characteristics may be important to identify at-risk group or the women having low sexual self-esteem in premarital and family counseling. PMID:27186198

Bisphosphonates and Bone Fractures in Long-term Kidney Transplant Recipients

PubMed Central

Conley, Emily; Muth, Brenda; Samaniego, Millie; Lotfi, Mary; Voss, Barbara; Armbrust, Mike; Pirsch, John; Djamali, Arjang

2013-01-01

Background There is little information on the role of bisphosphonates and bone mineral density (BMD) measurements for the follow-up and management of bone loss and fractures in long-term kidney transplant recipients. Methods To address this question, we retrospectively studied 554 patients who had two BMD measurements after the first year posttransplant and compared outcomes in patients treated, or not with bisphosphonates between the two BMD assessments. Kaplan-Meier survival and stepwise Cox regression analyses were performed to examine fracture-free survival rates and the risk-factors associated with fractures. Results The average time (±SE) between transplant and the first BMD was 1.2±0.05 years. The time interval between the two BMD measurements was 2.5±0.05 years. There were 239 and 315 patients in the no-bisphosphonate and bisphosphonate groups, respectively. Treatment was associated with significant preservation of bone loss at the femoral neck (HR 1.56, 95% CI 1.21-2.06, P=0.0007). However, there was no association between bone loss at the femoral neck and fractures regardless of bisphosphonate therapy. Stepwise Cox regression analyses showed that type-1 diabetes, baseline femoral neck T-score, interleukin-2 receptor blockade, and proteinuria (HR 2.02, 0.69, 0.4, 1.23 respectively, P<0.01), but not bisphosphonates, were associated with the risk of fracture. Conclusions Bisphosphonates may prevent bone loss in long-term kidney transplant recipients. However, these data suggest a limited role for the initiation of therapy after the first posttransplant year to prevent fractures. PMID:18645484

A randomized-controlled trial examining the effects of reflexology on anxiety of patients undergoing coronary angiography.

PubMed

Molavi Vardanjani, Mehdi; Masoudi Alavi, Negin; Razavi, Narges Sadat; Aghajani, Mohammad; Azizi-Fini, Esmail; Vaghefi, Seied Morteza

2013-09-01

The anxiety reduction before coronary angiography has clinical advantages and is one of the objectives of nursing. Reflexology is a non-invasive method that has been used in several clinical situations. Applying reflexology might have effect on the reduction of anxiety before coronary angiography. The aim of this randomized clinical trial was to investigate the effect of reflexology on anxiety among patients undergoing coronary angiography. This trial was conducted in Shahid Beheshti Hospital, in Kashan, Iran. One hundred male patients who were undergoing coronary angiography were randomly enrolled into intervention and placebo groups. The intervention protocol was included 30 minutes of general foot massage and the stimulation of three reflex points including solar plexus, pituitary gland, and heart. The placebo group only received the general foot massage. Spielbergers state trait anxiety inventory was used to assess the anxiety experienced by patients. Data was analyzed using Man-Witney, Wilcoxon and Chi-square tests. The stepwise multiple regressions used to analyze the variables that are involved in anxiety reduction. The mean range of anxiety decreased from 53.24 to 45.24 in reflexology group which represented 8 score reduction (P = 0.0001). The reduction in anxiety was 5.9 score in placebo group which was also significant (P = 0.0001). The anxiety reduction was significantly higher in reflexology group (P = 0.014). The stepwise multiple regression analysis showed that doing reflexology can explain the 7.5% of anxiety reduction which made a significant model. Reflexology can decrease the anxiety level before coronary angiography. Therefore, reflexology before coronary angiography is recommended.

Job satisfaction of primary care physicians in Switzerland: an observational study.

PubMed

Goetz, Katja; Jossen, Marianne; Szecsenyi, Joachim; Rosemann, Thomas; Hahn, Karolin; Hess, Sigrid

2016-10-01

Job satisfaction of physicians is an important issue for performance of a health care system. The aim of the study was to evaluate the job satisfaction of primary care physicians in Switzerland and to explore associations between overall job satisfaction, individual characteristics and satisfaction with aspects of work within the practice separated by gender. This cross-sectional study was based on a job satisfaction survey. Data were collected from 176 primary care physicians working in 91 primary care practices. Job satisfaction was measured with the 10-item Warr-Cook-Wall job satisfaction scale. Stepwise linear regression analysis was performed for physicians separated by gender. The response rate was 92.6%. Primary care physicians reported the highest level of satisfaction with 'freedom of working method' (mean = 6.45) and the lowest satisfaction for 'hours of work' (mean = 5.38) and 'income' (mean = 5.49). Moreover, some aspects of job satisfaction were rated higher by female physicians than male physicians. Within the stepwise regression analysis, the aspect 'opportunity to use abilities' (β = 0.644) showed the highest association to overall job satisfaction for male physicians while for female physicians it was income (β = 0.733). The presented results contribute to an understanding of factors that influence levels of satisfaction of female and male physicians. Therefore, research and intervention about job satisfaction should consider gender as well as the stereotypes that come along with these social roles. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Readiness of adolescents to use genetically modified organisms according to their knowledge and emotional attitude towards GMOs.

PubMed

Lachowski, Stanisław; Jurkiewicz, Anna; Choina, Piotr; Florek-Łuszczki, Magdalena; Buczaj, Agnieszka; Goździewska, Małgorzata

2017-06-07

Agriculture based on genetically modified organisms plays an increasingly important role in feeding the world population, which is evidenced by a considerable growth in the size of land under genetically modified crops (GM). Uncertainty and controversy around GM products are mainly due to the lack of accurate and reliable information, and lack of knowledge concerning the essence of genetic modifications, and the effect of GM food on the human organism, and consequently, a negative emotional attitude towards what is unknown. The objective of the presented study was to discover to what extent knowledge and the emotional attitude of adolescents towards genetically modified organisms is related with acceptance of growing genetically modified plants or breeding GM animals on own farm or allotment garden, and the purchase and consumption of GM food, as well as the use of GMOs in medicine. The study was conducted by the method of a diagnostic survey using a questionnaire designed by the author, which covered a group of 500 adolescents completing secondary school on the level of maturity examination. The collected material was subjected to statistical analysis. Research hypotheses were verified using chi-square test (χ ² ), t-Student test, and stepwise regression analysis. Stepwise regression analysis showed that the readiness of adolescents to use genetically modified organisms as food or for the production of pharmaceuticals, the production of GM plants or animals on own farm, depends on an emotional-evaluative attitude towards GMOs, and the level of knowledge concerning the essence of genetic modifications.

The role of multicollinearity in landslide susceptibility assessment by means of Binary Logistic Regression: comparison between VIF and AIC stepwise selection

NASA Astrophysics Data System (ADS)

Cama, Mariaelena; Cristi Nicu, Ionut; Conoscenti, Christian; Quénéhervé, Geraldine; Maerker, Michael

2016-04-01

Landslide susceptibility can be defined as the likelihood of a landslide occurring in a given area on the basis of local terrain conditions. In the last decades many research focused on its evaluation by means of stochastic approaches under the assumption that 'the past is the key to the future' which means that if a model is able to reproduce a known landslide spatial distribution, it will be able to predict the future locations of new (i.e. unknown) slope failures. Among the various stochastic approaches, Binary Logistic Regression (BLR) is one of the most used because it calculates the susceptibility in probabilistic terms and its results are easily interpretable from a geomorphological point of view. However, very often not much importance is given to multicollinearity assessment whose effect is that the coefficient estimates are unstable, with opposite sign and therefore difficult to interpret. Therefore, it should be evaluated every time in order to make a model whose results are geomorphologically correct. In this study the effects of multicollinearity in the predictive performance and robustness of landslide susceptibility models are analyzed. In particular, the multicollinearity is estimated by means of Variation Inflation Index (VIF) which is also used as selection criterion for the independent variables (VIF Stepwise Selection) and compared to the more commonly used AIC Stepwise Selection. The robustness of the results is evaluated through 100 replicates of the dataset. The study area selected to perform this analysis is the Moldavian Plateau where landslides are among the most frequent geomorphological processes. This area has an increasing trend of urbanization and a very high potential regarding the cultural heritage, being the place of discovery of the largest settlement belonging to the Cucuteni Culture from Eastern Europe (that led to the development of the great complex Cucuteni-Tripyllia). Therefore, identifying the areas susceptible to landslides may lead to a better understanding and mitigation for government, local authorities and stakeholders to plan the economic activities, minimize the damages costs, environmental and cultural heritage protection. The results show that although the VIF Stepwise selection allows a more stable selection of the controlling factors, the AIC Stepwise selection produces better predictive performance. Moreover, when working with replicates the effect of multicollinearity are statistically reduced by the application of the AIC stepwise selection and the results are easily interpretable in geomorphologic terms.

Statistical learning and selective inference.

PubMed

Taylor, Jonathan; Tibshirani, Robert J

2015-06-23

We describe the problem of "selective inference." This addresses the following challenge: Having mined a set of data to find potential associations, how do we properly assess the strength of these associations? The fact that we have "cherry-picked"--searched for the strongest associations--means that we must set a higher bar for declaring significant the associations that we see. This challenge becomes more important in the era of big data and complex statistical modeling. The cherry tree (dataset) can be very large and the tools for cherry picking (statistical learning methods) are now very sophisticated. We describe some recent new developments in selective inference and illustrate their use in forward stepwise regression, the lasso, and principal components analysis.

The impact of intrinsic and extrinsic factors on the job satisfaction of dentists.

PubMed

Goetz, K; Campbell, S M; Broge, B; Dörfer, C E; Brodowski, M; Szecsenyi, J

2012-10-01

The Two-Factor Theory of job satisfaction distinguishes between intrinsic-motivation (i.e. recognition, responsibility) and extrinsic-hygiene (i.e. job security, salary, working conditions) factors. The presence of intrinsic-motivation facilitates higher satisfaction and performance, whereas the absences of extrinsic factors help mitigate against dissatisfaction. The consideration of these factors and their impact on dentists' job satisfaction is essential for the recruitment and retention of dentists. The objective of the study is to assess the level of job satisfaction of German dentists and the factors that are associated with it. This cross-sectional study was based on a job satisfaction survey. Data were collected from 147 dentists working in 106 dental practices. Job satisfaction was measured with the 10-item Warr-Cook-Wall job satisfaction scale. Organizational characteristics were measured with two items. Linear regression analyses were performed in which each of the nine items of the job satisfaction scale (excluding overall satisfaction) were handled as dependent variables. A stepwise linear regression analysis was performed with overall job satisfaction as the dependent outcome variable, the nine items of job satisfaction and the two items of organizational characteristics controlled for age and gender as predictors. The response rate was 95.0%. Dentists were satisfied with 'freedom of working method' and mostly dissatisfied with their 'income'. Both variables are extrinsic factors. The regression analyses identified five items that were significantly associated with each item of the job satisfaction scale: 'age', 'mean weekly working time', 'period in the practice', 'number of dentist's assistant' and 'working atmosphere'. Within the stepwise linear regression analysis the intrinsic factor 'opportunity to use abilities' (β = 0.687) showed the highest score of explained variance (R(2) = 0.468) regarding overall job satisfaction. With respect to the Two-Factor Theory of job satisfaction both components, intrinsic and extrinsic, are essential for dentists but the presence of intrinsic motivating factors like the opportunity to use abilities has most positive impact on job satisfaction. The findings of this study will be helpful for further activities to improve the working conditions of dentists and to ensure quality of care. © 2012 John Wiley & Sons A/S.

Study on relationship of nitric oxide, oxidation, peroxidation, lipoperoxidation with chronic chole-cystitis

PubMed Central

Zhou, Jun-Fu; Cai, Dong; Zhu, You-Gen; Yang, Jin-Lu; Peng, Cheng-Hong; Yu, Yang-Hai

2000-01-01

AIM: To study relationship of injury induced by nitric oxide, oxidation, peroxidation, lipoperoxidation with chronic cholecystitis. METHODS: The values of plasma nitric oxide (P-NO), plasma vitamin C (P-VC), plasma vitamin E (P-VE), plasma β-carotene (P-β-CAR), plasma lipoperoxides (P-LPO), erythrocyte superoxide dismutase (E-SOD), erythrocyte catalase (E-CAT), erythrocyte glutathione peroxidase (E-GSH-Px) activities and erythrocyte lipoperoxides (E-LPO) level in 77 patients with chro nic cholecystitis and 80 healthy control subjects were determined, differences of the above average values between t he patient group and the control group and differences of the average values bet ween preoperative and postoperative patients were analyzed and compared, linear regression and correlation of the disease course with the above determination values as well as the stepwise regression and correlation of the course with th e values were analyzed. RESULTS: Compared with the control group, the average values of P-NO, P-LPO, E-LPO were significantly increased (P < 0.01), and of P-VC, P-VE, P-β-CAR, E-SOD, E-CAT and E-GSH-Px decreased (P < 0.01) in the patient group. The analysis of the lin ear regression and correlation s howed that with prolonging of the course, the values of P-NO, P-LPO and E-LPO in the patients were gradually ascended and the values of P-VC, P-VE, P-β-CAR, E-SOD, E-CAT and E-GSH-Px descended (P < 0.01). The analysis of the stepwise regression and correlation indicated that the correlation of the course with P-NO, P-VE and P-β-CAR values was the closest. Compared with the preoperative patients, the average values of P-NO, P-LPO and E-LPO were significantly decre ased (P < 0.01) and the average values of P-VC, E-SOD, E-CAT and E-GSH-Px in postoperative pa tients increased (P < 0.01) in postoperative patients. But there was no signif icant difference in the average values of P-VE, P-β-CAR preope rative and postoperative patients. CONCLUSION: Chronic cholecystitis could induce the increase of nitric oxide, oxidation, peroxidation and lipoperoxidation. PMID:11819637

Predictors of Long-Term School-Based Behavioral Outcomes in the Multimodal Treatment Study of Children with Attention-Deficit/Hyperactivity Disorder

PubMed Central

Reed, Margot O.; Jakubovski, Ewgeni; Johnson, Jessica A.

2017-01-01

Abstract Objective: To explore predictors of 8-year school-based behavioral outcomes in attention-deficit/hyperactivity disorder (ADHD). Methods: We examined potential baseline predictors of school-based behavioral outcomes in children who completed the 8-year follow-up in the multimodal treatment study of children with ADHD. Stepwise logistic regression and receiver operating characteristic (ROC) analysis identified baseline predictors that were associated with a higher risk of truancy, school discipline, and in-school fights. Results: Stepwise regression analysis explained between 8.1% (in-school fights) and 12.0% (school discipline) of the total variance in school-based behavioral outcomes. Logistic regression identified several baseline characteristics that were associated with school-based behavioral difficulties 8 years later, including being male (associated with truancy and school discipline), African American (school discipline, in-school fights), increased conduct disorder (CD) symptoms (truancy), decreased affection from parents (school discipline), ADHD severity (in-school fights), and study site (truancy and school discipline). ROC analyses identified the most discriminative predictors of truancy, school discipline, and in-school fights, which were Aggression and Conduct Problem Scale Total score, family income, and race, respectively. Conclusions: A modest, but nontrivial portion of school-based behavioral outcomes, was predicted by baseline childhood characteristics. Exploratory analyses identified modifiable (lack of paternal involvement, lower parental knowledge of behavioral principles, and parental use of physical punishment), somewhat modifiable (income and having comorbid CD), and nonmodifiable (African American and male) factors that were associated with school-based behavioral difficulties. Future research should confirm that the associations between earlier specific parenting behaviors and poor subsequent school-based behavioral outcomes are, indeed, causally related and independent cooccurring childhood psychopathology. Future research might target increasing paternal involvement and parental knowledge of behavioral principles and reducing use of physical punishment to improve school-based behavioral outcomes in children with ADHD. PMID:28253029

Socio-economic factors associated with infant mortality in Italy: an ecological study

PubMed Central

2012-01-01

Introduction One issue that continues to attract the attention of public health researchers is the possible relationship in high-income countries between income, income inequality and infant mortality (IM). The aim of this study was to assess the associations between IM and major socio-economic determinants in Italy. Methods Associations between infant mortality rates in the 20 Italian regions (2006–2008) and the Gini index of income inequality, mean household income, percentage of women with at least 8 years of education, and percentage of unemployed aged 15–64 years were assessed using Pearson correlation coefficients. Univariate linear regression and multiple stepwise linear regression analyses were performed to determine the magnitude and direction of the effect of the four socio-economic variables on IM. Results The Gini index and the total unemployment rate showed a positive strong correlation with IM (r = 0.70; p < 0.001 and r = 0.84; p < 0.001 respectively), mean household income showed a strong negative correlation (r = −0.78; p < 0.001), while female educational attainment presented a weak negative correlation (r = −0.45; p < 0.05). Using a multiple stepwise linear regression model, only unemployment rate was independently associated with IM (b = 0.15, p < 0.001). Conclusions In Italy, a high-income country where health care is universally available, variations in IM were strongly associated with relative and absolute income and unemployment rate. These results suggest that in Italy IM is not only related to income distribution, as demonstrated for other developed countries, but also to economic factors such as absolute income and unemployment. In order to reduce IM and the existing inequalities, the challenge for Italian decision makers is to promote economic growth and enhance employment levels. PMID:22898293

What are the important surgical factors affecting the wound healing after primary total knee arthroplasty?

PubMed

Harato, Kengo; Tanikawa, Hidenori; Morishige, Yutaro; Kaneda, Kazuya; Niki, Yasuo

2016-01-13

Wound condition after primary total knee arthroplasty (TKA) is an important issue to avoid any postoperative adverse events. Our purpose was to investigate and to clarify the important surgical factors affecting wound score after TKA. A total of 139 knees in 128 patients (mean 73 years) without severe comorbidity were enrolled in the present study. All primary unilateral or bilateral TKAs were done using the same skin incision line, measured resection technique, and wound closure technique using unidirectional barbed suture. In terms of the wound healing, Hollander Wound Evaluation Score (HWES) was assessed on postoperative day 14. We performed multiple regression analysis using stepwise method to identify the factors affecting HWES. Variables considered in the analysis were age, sex, body mass index (kg/m(2)), HbA1C (%), femorotibial angle (degrees) on plain radiographs, intraoperative patella eversion during the cutting phase of the femur and the tibia in knee flexion, intraoperative anterior translation of the tibia, patella resurfacing, surgical time (min), tourniquet time (min), length of skin incision (cm), postoperative drainage (ml), patellar height on postoperative lateral radiographs, and HWES. HWES was treated as a dependent variable, and others were as independent variables. The average HWES was 5.0 ± 0.8 point. According to stepwise forward regression test, patella eversion during the cutting phase of the femur and the tibia in knee flexion and anterior translation of the tibia were entered in this model, while other factors were not entered. Standardized partial regression coefficient was as follows: 0.57 in anterior translation of the tibia and 0.38 in patella eversion. Fortunately, in the present study using the unidirectional barbed suture, major wound healing problem did not occur. As to the surgical technique, intraoperative patella eversion and anterior translation of the tibia should be avoided for quality cosmesis in primary TKA.

Diels–Alder Reactions of Allene with Benzene and Butadiene: Concerted, Stepwise, and Ambimodal Transition States

PubMed Central

2015-01-01

Multiconfigurational complete active space methods (CASSCF and CASPT2) have been used to investigate the (4 + 2) cycloadditions of allene with butadiene and with benzene. Both concerted and stepwise radical pathways were examined to determine the mechanism of the Diels–Alder reactions with an allene dienophile. Reaction with butadiene occurs via a single ambimodal transition state that can lead to either the concerted or stepwise trajectories along the potential energy surface, while reaction with benzene involves two separate transition states and favors the concerted mechanism relative to the stepwise mechanism via a diradical intermediate. PMID:25216056

The minimal residual QR-factorization algorithm for reliably solving subset regression problems

NASA Technical Reports Server (NTRS)

Verhaegen, M. H.

1987-01-01

A new algorithm to solve test subset regression problems is described, called the minimal residual QR factorization algorithm (MRQR). This scheme performs a QR factorization with a new column pivoting strategy. Basically, this strategy is based on the change in the residual of the least squares problem. Furthermore, it is demonstrated that this basic scheme might be extended in a numerically efficient way to combine the advantages of existing numerical procedures, such as the singular value decomposition, with those of more classical statistical procedures, such as stepwise regression. This extension is presented as an advisory expert system that guides the user in solving the subset regression problem. The advantages of the new procedure are highlighted by a numerical example.

Rumination, Age, and Years of Experience: A Predictive Study of Burnout

ERIC Educational Resources Information Center

McDuffy, Moriel S.

2016-01-01

This study used a non-experimental design to examine whether job satisfaction, rumination, age and years of experience predict burnout among human service workers serving high-risk populations. The study also used a stepwise regression to assess whether job satisfaction, rumination, age, or years of experience predict burnout equally. Burnout was…

Ecological correlates of flying squirrel microhabitat use and density in temperate rainforests of southeastern Alaska

Treesearch

Winston P. Smith; Scott M. Gende; Jeffrey V. Nichols

2004-01-01

We studied habitat relations of the Prince of Wales flying squirrel (Glaucomys sabrinus griseifrons), an endemic of the temperate, coniferous rainforest of southeastern Alaska, because of concerns over population viability from extensive clear-cut logging in the region. We used stepwise logistic regression to examine relationships between...

Relationships of Reading, MCAT, and USMLE Step 1 Test Results for Medical Students

ERIC Educational Resources Information Center

Haught, Patricia; Walls, Richard

2004-01-01

Students (N = 730) took the Nelson-Denny Reading Test (current forms G or H) during orientation to medical school. Stepwise regression analyses showed the Nelson-Denny Reading Vocabulary, Comprehension, and Rate were significant predictors of MCAT (taken prior to admission to medical school) verbal reasoning. Reading Vocabulary was a significant…

Potential for Suicide and Aggression in Delinquents at Juvenile Court in a Southern City.

ERIC Educational Resources Information Center

Battile, Allen O.; And Others

1993-01-01

Questioned 263 Juvenile Court offenders about whether they wished to be dead, kill themselves, or kill others and about their existential experiences, thoughts, and feelings. Used stepwise multiple logistic regression to pinpoint experiences associated with high likelihood of verbalizing wish for destructive behavior. Found sexual abuse as risk…

Student Physical Education Teachers' Well-Being: Contribution of Basic Psychological Needs

ERIC Educational Resources Information Center

Ciyin, Gülten; Erturan-Ilker, Gökçe

2014-01-01

This study adopted Self-Determination Theory tenets and aimed to explore whether student physical education (PE) teachers' satisfaction of the three basic psychological needs independently predicts well-being. 267 Turkish student PE teachers were recruited for the study. Two stepwise multiple regression analysis was performed in which each outcome…

Quality Curriculum for Under-Threes: The Impact of Structural Standards

ERIC Educational Resources Information Center

Wertfein, Monika; Spies-Kofler, Anita; Becker-Stoll, Fabienne

2009-01-01

The purpose of this study conducted in 36 infant-toddler centres ("Kinderkrippen") in the city of Munich in Bavaria/Germany was to explore structural characteristics of early child care and education and their effects on child care quality. Stepwise regressions and variance analysis (Manova) examined the relation between quality of care…

Scholarly Networking among Business Students: Structured Discussion Board Activity and Academic Outcomes

ERIC Educational Resources Information Center

Walker, Kristen; Curren, Mary T.; Kiesler, Tina; Lammers, H. Bruce; Goldenson, Jamie

2013-01-01

The authors' intent was to show the effect of student discussion board activity on academic outcomes, after accounting for past academic performance. Data were collected from 516 students enrolled in a junior-level required business course. Controlling for students' grade point average, stepwise regression showed a significant…

Factors Affecting Code Status in a University Hospital Intensive Care Unit

ERIC Educational Resources Information Center

Van Scoy, Lauren Jodi; Sherman, Michael

2013-01-01

The authors collected data on diagnosis, hospital course, and end-of-life preparedness in patients who died in the intensive care unit (ICU) with "full code" status (defined as receiving cardiopulmonary resuscitation), compared with those who didn't. Differences were analyzed using binary and stepwise logistic regression. They found no…

Variation in Environmentalism among University Students: Majoring in Outdoor Recreation, Parks, and Tourism Predicts Environmental Concerns and Behaviors

ERIC Educational Resources Information Center

Arnocky, Steven; Stroink, Mirella L.

2011-01-01

In a survey of Canadian university students (N = 205), the relationship between majoring in an outdoor recreation university program and environmental concern, cooperation, and behavior were examined. Stepwise linear regression indicated that enrollment in outdoor recreation was predictive of environmental behavior and ecological cooperation; and…

Identifying Pedophiles "Eligible" for Community Notification under Megan's Law: A Multivariate Model for Actuarially Anchored Decisions.

ERIC Educational Resources Information Center

Pallone, Nathaniel J.; Hennessy, James J.; Voelbel, Gerald T.

1998-01-01

A scientifically sound methodology for identifying offenders about whose presence the community should be notified is demonstrated. A stepwise multiple regression was calculated among incarcerated pedophiles (N=52) including both psychological and legal data; a precision-weighted equation produced 90.4% "true positives." This methodology can be…

[Aggression and related factors in elementary school students].

PubMed

Ji, Eun Sun; Jang, Mi Heui

2010-10-01

This study was done to explore the relationship between aggression and internet over-use, depression-anxiety, self-esteem, all of which are known to be behavior and psychological characteristics linked to "at-risk" children for aggression. Korean-Child Behavior Check List (K-CBCL), Korean-Internet Addiction Self-Test Scale, and Self-Esteem Scale by Rosenberg (1965) were used as measurement tools with a sample of 743, 5th-6th grade students from 3 elementary schools in Jecheon city. Chi-square, t-test, ANOVA, Pearson's correlation and stepwise multiple regression with SPSS/Win 13.0 version were used to analyze the collected data. Aggression for the elementary school students was positively correlated with internet over-use and depression-anxiety, whereas self-esteem was negatively correlated with aggression. Stepwise multiple regression analysis showed that 68.4% of the variance for aggression was significantly accounted for by internet over-use, depression-anxiety, and self-esteem. The most significant factor influencing aggression was depression-anxiety. These results suggest that earlier screening and intervention programs for depression-anxiety and internet over-use for elementary student will be helpful in preventing aggression.

Nucleated red blood cells in growth-restricted fetuses: associations with short-term neonatal outcome.

PubMed

Minior, V K; Bernstein, P S; Divon, M Y

2000-01-01

To determine the utility of the neonatal nucleated red blood cell (NRBC) count as an independent predictor of short-term perinatal outcome in growth-restricted fetuses. Hospital charts of neonates with a discharge diagnosis indicating a birth weight <10th percentile were reviewed for perinatal outcome. We studied all eligible neonates who had a complete blood count on the first day of life. After multiple gestations, anomalous fetuses and diabetic pregnancies were excluded; 73 neonates comprised the study group. Statistical analysis included ANOVA, simple and stepwise regression. Elevated NRBC counts were significantly associated with cesarean section for non-reassuring fetal status, neonatal intensive care unit admission and duration of neonatal intensive care unit stay, respiratory distress and intubation, thrombocytopenia, hyperbilirubinemia, intraventricular hemorrhage and neonatal death. Stepwise regression analysis including gestational age at birth, birth weight and NRBC count demonstrated that in growth-restricted fetuses, NRBC count was the strongest predictor of neonatal intraventricular hemorrhage, neonatal respiratory distress and neonatal death. An elevated NRBC count independently predicts adverse perinatal outcome in growth-restricted fetuses. Copyright 2000 S. Karger AG, Basel.

Wheat flour dough Alveograph characteristics predicted by Mixolab regression models.

PubMed

Codină, Georgiana Gabriela; Mironeasa, Silvia; Mironeasa, Costel; Popa, Ciprian N; Tamba-Berehoiu, Radiana

2012-02-01

In Romania, the Alveograph is the most used device to evaluate the rheological properties of wheat flour dough, but lately the Mixolab device has begun to play an important role in the breadmaking industry. These two instruments are based on different principles but there are some correlations that can be found between the parameters determined by the Mixolab and the rheological properties of wheat dough measured with the Alveograph. Statistical analysis on 80 wheat flour samples using the backward stepwise multiple regression method showed that Mixolab values using the ‘Chopin S’ protocol (40 samples) and ‘Chopin + ’ protocol (40 samples) can be used to elaborate predictive models for estimating the value of the rheological properties of wheat dough: baking strength (W), dough tenacity (P) and extensibility (L). The correlation analysis confirmed significant findings (P < 0.05 and P < 0.01) between the parameters of wheat dough studied by the Mixolab and its rheological properties measured with the Alveograph. A number of six predictive linear equations were obtained. Linear regression models gave multiple regression coefficients with R²(adjusted) > 0.70 for P, R²(adjusted) > 0.70 for W and R²(adjusted) > 0.38 for L, at a 95% confidence interval. Copyright © 2011 Society of Chemical Industry.

QSAR Analysis of 2-Amino or 2-Methyl-1-Substituted Benzimidazoles Against Pseudomonas aeruginosa

PubMed Central

Podunavac-Kuzmanović, Sanja O.; Cvetković, Dragoljub D.; Barna, Dijana J.

2009-01-01

A set of benzimidazole derivatives were tested for their inhibitory activities against the Gram-negative bacterium Pseudomonas aeruginosa and minimum inhibitory concentrations were determined for all the compounds. Quantitative structure activity relationship (QSAR) analysis was applied to fourteen of the abovementioned derivatives using a combination of various physicochemical, steric, electronic, and structural molecular descriptors. A multiple linear regression (MLR) procedure was used to model the relationships between molecular descriptors and the antibacterial activity of the benzimidazole derivatives. The stepwise regression method was used to derive the most significant models as a calibration model for predicting the inhibitory activity of this class of molecules. The best QSAR models were further validated by a leave one out technique as well as by the calculation of statistical parameters for the established theoretical models. To confirm the predictive power of the models, an external set of molecules was used. High agreement between experimental and predicted inhibitory values, obtained in the validation procedure, indicated the good quality of the derived QSAR models. PMID:19468332

[Simulation of three-dimensional green biomass of urban forests in Shenyang City and the factors affecting the biomass].

PubMed

Liu, Chang-Fu; He, Xing-Yuan; Chen, Wei; Zhao, Gui-Ling; Xue, Wen-Duo

2008-06-01

Based on the fractal theory of forest growth, stepwise regression was employed to pursue a convenient and efficient method of measuring the three-dimensional green biomass (TGB) of urban forests in small area. A total of thirteen simulation equations of TGB of urban forests in Shenyang City were derived, with the factors affecting the TGB analyzed. The results showed that the coefficients of determination (R2) of the 13 simulation equations ranged from 0.612 to 0.842. No evident pattern was shown in residual analysis, and the precisions were all higher than 87% (alpha = 0.05) and 83% (alpha = 0.01). The most convenient simulation equation was ln Y = 7.468 + 0.926 lnx1, where Y was the simulated TGB and x1 was basal area at breast height per hectare (SDB). The correlations between the standard regression coefficients of the simulation equations and 16 tree characteristics suggested that SDB was the main factor affecting the TGB of urban forests in Shenyang.

To Identify the Important Soil Properties Affecting Dinoseb Adsorption with Statistical Analysis

PubMed Central

Guan, Yiqing; Wei, Jianhui; Zhang, Danrong; Zu, Mingjuan; Zhang, Liru

2013-01-01

Investigating the influences of soil characteristic factors on dinoseb adsorption parameter with different statistical methods would be valuable to explicitly figure out the extent of these influences. The correlation coefficients and the direct, indirect effects of soil characteristic factors on dinoseb adsorption parameter were analyzed through bivariate correlation analysis, and path analysis. With stepwise regression analysis the factors which had little influence on the adsorption parameter were excluded. Results indicate that pH and CEC had moderate relationship and lower direct effect on dinoseb adsorption parameter due to the multicollinearity with other soil factors, and organic carbon and clay contents were found to be the most significant soil factors which affect the dinoseb adsorption process. A regression is thereby set up to explore the relationship between the dinoseb adsorption parameter and the two soil factors: the soil organic carbon and clay contents. A 92% of the variation of dinoseb sorption coefficient could be attributed to the variation of the soil organic carbon and clay contents. PMID:23737715

Tibiofemoral contact forces during walking, running and sidestepping.

PubMed

Saxby, David J; Modenese, Luca; Bryant, Adam L; Gerus, Pauline; Killen, Bryce; Fortin, Karine; Wrigley, Tim V; Bennell, Kim L; Cicuttini, Flavia M; Lloyd, David G

2016-09-01

We explored the tibiofemoral contact forces and the relative contributions of muscles and external loads to those contact forces during various gait tasks. Second, we assessed the relationships between external gait measures and contact forces. A calibrated electromyography-driven neuromusculoskeletal model estimated the tibiofemoral contact forces during walking (1.44±0.22ms(-1)), running (4.38±0.42ms(-1)) and sidestepping (3.58±0.50ms(-1)) in healthy adults (n=60, 27.3±5.4years, 1.75±0.11m, and 69.8±14.0kg). Contact forces increased from walking (∼1-2.8 BW) to running (∼3-8 BW), sidestepping had largest maximum total (8.47±1.57 BW) and lateral contact forces (4.3±1.05 BW), while running had largest maximum medial contact forces (5.1±0.95 BW). Relative muscle contributions increased across gait tasks (up to 80-90% of medial contact forces), and peaked during running for lateral contact forces (∼90%). Knee adduction moment (KAM) had weak relationships with tibiofemoral contact forces (all R(2)<0.36) and the relationships were gait task-specific. Step-wise regression of multiple external gait measures strengthened relationships (0.20

Variable Selection for Regression Models of Percentile Flows

NASA Astrophysics Data System (ADS)

Fouad, G.

2017-12-01

Percentile flows describe the flow magnitude equaled or exceeded for a given percent of time, and are widely used in water resource management. However, these statistics are normally unavailable since most basins are ungauged. Percentile flows of ungauged basins are often predicted using regression models based on readily observable basin characteristics, such as mean elevation. The number of these independent variables is too large to evaluate all possible models. A subset of models is typically evaluated using automatic procedures, like stepwise regression. This ignores a large variety of methods from the field of feature (variable) selection and physical understanding of percentile flows. A study of 918 basins in the United States was conducted to compare an automatic regression procedure to the following variable selection methods: (1) principal component analysis, (2) correlation analysis, (3) random forests, (4) genetic programming, (5) Bayesian networks, and (6) physical understanding. The automatic regression procedure only performed better than principal component analysis. Poor performance of the regression procedure was due to a commonly used filter for multicollinearity, which rejected the strongest models because they had cross-correlated independent variables. Multicollinearity did not decrease model performance in validation because of a representative set of calibration basins. Variable selection methods based strictly on predictive power (numbers 2-5 from above) performed similarly, likely indicating a limit to the predictive power of the variables. Similar performance was also reached using variables selected based on physical understanding, a finding that substantiates recent calls to emphasize physical understanding in modeling for predictions in ungauged basins. The strongest variables highlighted the importance of geology and land cover, whereas widely used topographic variables were the weakest predictors. Variables suffered from a high degree of multicollinearity, possibly illustrating the co-evolution of climatic and physiographic conditions. Given the ineffectiveness of many variables used here, future work should develop new variables that target specific processes associated with percentile flows.

Does Stepwise Voltage Ramping Protect the Kidney from Injury During Extracorporeal Shockwave Lithotripsy? Results of a Prospective Randomized Trial.

PubMed

Skuginna, Veronika; Nguyen, Daniel P; Seiler, Roland; Kiss, Bernhard; Thalmann, George N; Roth, Beat

2016-02-01

Renal damage is more frequent with new-generation lithotripters. However, animal studies suggest that voltage ramping minimizes the risk of complications following extracorporeal shock wave lithotripsy (SWL). In the clinical setting, the optimal voltage strategy remains unclear. To evaluate whether stepwise voltage ramping can protect the kidney from damage during SWL. A total of 418 patients with solitary or multiple unilateral kidney stones were randomized to receive SWL using a Modulith SLX-F2 lithotripter with either stepwise voltage ramping (n=213) or a fixed maximal voltage (n=205). SWL. The primary outcome was sonographic evidence of renal hematomas. Secondary outcomes included levels of urinary markers of renal damage, stone disintegration, stone-free rate, and rates of secondary interventions within 3 mo of SWL. Descriptive statistics were used to compare clinical outcomes between the two groups. A logistic regression model was generated to assess predictors of hematomas. Significantly fewer hematomas occurred in the ramping group(12/213, 5.6%) than in the fixed group (27/205, 13%; p=0.008). There was some evidence that the fixed group had higher urinary β2-microglobulin levels after SWL compared to the ramping group (p=0.06). Urinary microalbumin levels, stone disintegration, stone-free rate, and rates of secondary interventions did not significantly differ between the groups. The logistic regression model showed a significantly higher risk of renal hematomas in older patients (odds ratio [OR] 1.03, 95% confidence interval [CI] 1.00-1.05; p=0.04). Stepwise voltage ramping was associated with a lower risk of hematomas (OR 0.39, 95% CI 0.19-0.80; p=0.01). The study was limited by the use of ultrasound to detect hematomas. In this prospective randomized study, stepwise voltage ramping during SWL was associated with a lower risk of renal damage compared to a fixed maximal voltage without compromising treatment effectiveness. Lithotripsy is a noninvasive technique for urinary stone disintegration using ultrasonic energy. In this study, two voltage strategies are compared. The results show that a progressive increase in voltage during lithotripsy decreases the risk of renal hematomas while maintaining excellent outcomes. ISRCTN95762080. Copyright © 2015 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Incorporating wind availability into land use regression modelling of air quality in mountainous high-density urban environment.

PubMed

Shi, Yuan; Lau, Kevin Ka-Lun; Ng, Edward

2017-08-01

Urban air quality serves as an important function of the quality of urban life. Land use regression (LUR) modelling of air quality is essential for conducting health impacts assessment but more challenging in mountainous high-density urban scenario due to the complexities of the urban environment. In this study, a total of 21 LUR models are developed for seven kinds of air pollutants (gaseous air pollutants CO, NO 2 , NO x , O 3 , SO 2 and particulate air pollutants PM 2.5 , PM 10 ) with reference to three different time periods (summertime, wintertime and annual average of 5-year long-term hourly monitoring data from local air quality monitoring network) in Hong Kong. Under the mountainous high-density urban scenario, we improved the traditional LUR modelling method by incorporating wind availability information into LUR modelling based on surface geomorphometrical analysis. As a result, 269 independent variables were examined to develop the LUR models by using the "ADDRESS" independent variable selection method and stepwise multiple linear regression (MLR). Cross validation has been performed for each resultant model. The results show that wind-related variables are included in most of the resultant models as statistically significant independent variables. Compared with the traditional method, a maximum increase of 20% was achieved in the prediction performance of annual averaged NO 2 concentration level by incorporating wind-related variables into LUR model development. Copyright © 2017 Elsevier Inc. All rights reserved.

Utility of an Abbreviated Dizziness Questionnaire to Differentiate between Causes of Vertigo and Guide Appropriate Referral: A Multicenter Prospective Blinded Study

PubMed Central

Roland, Lauren T.; Kallogjeri, Dorina; Sinks, Belinda C.; Rauch, Steven D.; Shepard, Neil T.; White, Judith A.; Goebel, Joel A.

2015-01-01

Objective Test performance of a focused dizziness questionnaire’s ability to discriminate between peripheral and non-peripheral causes of vertigo. Study Design Prospective multi-center Setting Four academic centers with experienced balance specialists Patients New dizzy patients Interventions A 32-question survey was given to participants. Balance specialists were blinded and a diagnosis was established for all participating patients within 6 months. Main outcomes Multinomial logistic regression was used to evaluate questionnaire performance in predicting final diagnosis and differentiating between peripheral and non-peripheral vertigo. Univariate and multivariable stepwise logistic regression were used to identify questions as significant predictors of the ultimate diagnosis. C-index was used to evaluate performance and discriminative power of the multivariable models. Results 437 patients participated in the study. Eight participants without confirmed diagnoses were excluded and 429 were included in the analysis. Multinomial regression revealed that the model had good overall predictive accuracy of 78.5% for the final diagnosis and 75.5% for differentiating between peripheral and non-peripheral vertigo. Univariate logistic regression identified significant predictors of three main categories of vertigo: peripheral, central and other. Predictors were entered into forward stepwise multivariable logistic regression. The discriminative power of the final models for peripheral, central and other causes were considered good as measured by c-indices of 0.75, 0.7 and 0.78, respectively. Conclusions This multicenter study demonstrates a focused dizziness questionnaire can accurately predict diagnosis for patients with chronic/relapsing dizziness referred to outpatient clinics. Additionally, this survey has significant capability to differentiate peripheral from non-peripheral causes of vertigo and may, in the future, serve as a screening tool for specialty referral. Clinical utility of this questionnaire to guide specialty referral is discussed. PMID:26485598

Utility of an Abbreviated Dizziness Questionnaire to Differentiate Between Causes of Vertigo and Guide Appropriate Referral: A Multicenter Prospective Blinded Study.

PubMed

Roland, Lauren T; Kallogjeri, Dorina; Sinks, Belinda C; Rauch, Steven D; Shepard, Neil T; White, Judith A; Goebel, Joel A

2015-12-01

Test performance of a focused dizziness questionnaire's ability to discriminate between peripheral and nonperipheral causes of vertigo. Prospective multicenter. Four academic centers with experienced balance specialists. New dizzy patients. A 32-question survey was given to participants. Balance specialists were blinded and a diagnosis was established for all participating patients within 6 months. Multinomial logistic regression was used to evaluate questionnaire performance in predicting final diagnosis and differentiating between peripheral and nonperipheral vertigo. Univariate and multivariable stepwise logistic regression were used to identify questions as significant predictors of the ultimate diagnosis. C-index was used to evaluate performance and discriminative power of the multivariable models. In total, 437 patients participated in the study. Eight participants without confirmed diagnoses were excluded and 429 were included in the analysis. Multinomial regression revealed that the model had good overall predictive accuracy of 78.5% for the final diagnosis and 75.5% for differentiating between peripheral and nonperipheral vertigo. Univariate logistic regression identified significant predictors of three main categories of vertigo: peripheral, central, and other. Predictors were entered into forward stepwise multivariable logistic regression. The discriminative power of the final models for peripheral, central, and other causes was considered good as measured by c-indices of 0.75, 0.7, and 0.78, respectively. This multicenter study demonstrates a focused dizziness questionnaire can accurately predict diagnosis for patients with chronic/relapsing dizziness referred to outpatient clinics. Additionally, this survey has significant capability to differentiate peripheral from nonperipheral causes of vertigo and may, in the future, serve as a screening tool for specialty referral. Clinical utility of this questionnaire to guide specialty referral is discussed.

[Studies of marker screening efficiency and corresponding influencing factors in QTL composite interval mapping].

PubMed

Gao, Yong-Ming; Wan, Ping

2002-06-01

Screening markers efficiently is the foundation of mapping QTLs by composite interval mapping. Main and interaction markers distinguished, besides using background control for genetic variation, could also be used to construct intervals of two-way searching for mapping QTLs with epistasis, which can save a lot of calculation time. Therefore, the efficiency of marker screening would affect power and precision of QTL mapping. A doubled haploid population with 200 individuals and 5 chromosomes was constructed, with 50 markers evenly distributed at 10 cM space. Among a total of 6 QTLs, one was placed on chromosome I, two linked on chromosome II, and the other three linked on chromosome IV. QTL setting included additive effects and epistatic effects of additive x additive, the corresponding QTL interaction effects were set if data were collected under multiple environments. The heritability was assumed to be 0.5 if no special declaration. The power of marker screening by stepwise regression, forward regression, and three methods for random effect prediction, e.g. best linear unbiased prediction (BLUP), linear unbiased prediction (LUP) and adjusted unbiased prediction (AUP), was studied and compared through 100 Monte Carlo simulations. The results indicated that the marker screening power by stepwise regression at 0.1, 0.05 and 0.01 significant level changed from 2% to 68%, the power changed from 2% to 72% by forward regression. The larger the QTL effects, the higher the marker screening power. While the power of marker screening by three random effect prediction was very low, the maximum was only 13%. That suggested that regression methods were much better than those by using the approaches of random effect prediction to identify efficient markers flanking QTLs, and forward selection method was more simple and efficient. The results of simulation study on heritability showed that heightening of both general heritability and interaction heritability of genotype x environments could enhance marker screening power, the former had a greater influence on QTLs with larger main and/or epistatic effects, while the later on QTLs with small main and/or epistatic effects. The simulation of 100 times was also conducted to study the influence of different marker number and density on marker screening power. It is indicated that the marker screening power would decrease if there were too many markers, especially with high density in a mapping population, which suggested that a mapping population with definite individuals could only hold limited markers. According to the simulation study, the reasonable number of markers should not be more than individuals. The simulation study of marker screening under multiple environments showed high total power of marker screening. In order to relieve the problem that marker screening power restricted the efficiency of QTL mapping, markers identified in multiple environments could be used to construct two search intervals.

Inverse modelling for real-time estimation of radiological consequences in the early stage of an accidental radioactivity release.

PubMed

Pecha, Petr; Šmídl, Václav

2016-11-01

A stepwise sequential assimilation algorithm is proposed based on an optimisation approach for recursive parameter estimation and tracking of radioactive plume propagation in the early stage of a radiation accident. Predictions of the radiological situation in each time step of the plume propagation are driven by an existing short-term meteorological forecast and the assimilation procedure manipulates the model parameters to match the observations incoming concurrently from the terrain. Mathematically, the task is a typical ill-posed inverse problem of estimating the parameters of the release. The proposed method is designated as a stepwise re-estimation of the source term release dynamics and an improvement of several input model parameters. It results in a more precise determination of the adversely affected areas in the terrain. The nonlinear least-squares regression methodology is applied for estimation of the unknowns. The fast and adequately accurate segmented Gaussian plume model (SGPM) is used in the first stage of direct (forward) modelling. The subsequent inverse procedure infers (re-estimates) the values of important model parameters from the actual observations. Accuracy and sensitivity of the proposed method for real-time forecasting of the accident propagation is studied. First, a twin experiment generating noiseless simulated "artificial" observations is studied to verify the minimisation algorithm. Second, the impact of the measurement noise on the re-estimated source release rate is examined. In addition, the presented method can be used as a proposal for more advanced statistical techniques using, e.g., importance sampling. Copyright © 2016 Elsevier Ltd. All rights reserved.

Generalized linear and generalized additive models in studies of species distributions: Setting the scene

USGS Publications Warehouse

Guisan, Antoine; Edwards, T.C.; Hastie, T.

2002-01-01

An important statistical development of the last 30 years has been the advance in regression analysis provided by generalized linear models (GLMs) and generalized additive models (GAMs). Here we introduce a series of papers prepared within the framework of an international workshop entitled: Advances in GLMs/GAMs modeling: from species distribution to environmental management, held in Riederalp, Switzerland, 6-11 August 2001. We first discuss some general uses of statistical models in ecology, as well as provide a short review of several key examples of the use of GLMs and GAMs in ecological modeling efforts. We next present an overview of GLMs and GAMs, and discuss some of their related statistics used for predictor selection, model diagnostics, and evaluation. Included is a discussion of several new approaches applicable to GLMs and GAMs, such as ridge regression, an alternative to stepwise selection of predictors, and methods for the identification of interactions by a combined use of regression trees and several other approaches. We close with an overview of the papers and how we feel they advance our understanding of their application to ecological modeling. ?? 2002 Elsevier Science B.V. All rights reserved.

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

PubMed

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

2016-09-01

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

Asynchronous Knowledge Sharing and Conversation Interaction Impact on Grade in an Online Business Course

ERIC Educational Resources Information Center

Strang, Kenneth David

2011-01-01

Student knowledge sharing and conversation theory interactions were coded from asynchronous discussion forums to measure the effect of learning-oriented utterances on academic performance. The sample was 3 terms of an online business course (in an accredited MBA program) at a U.S.-based university. Correlation, stepwise regression, and multiple…

Information Scent Determines Attention Allocation and Link Selection among Multiple Information Patches on a Webpage

ERIC Educational Resources Information Center

Blackmon, Marilyn Hughes

2012-01-01

This paper draws from cognitive psychology and cognitive neuroscience to develop a preliminary similarity-choice theory of how people allocate attention among information patches on webpages while completing search tasks in complex informational websites. Study 1 applied stepwise multiple regression to a large dataset and showed that success rate…

Handling Missing Data: Analysis of a Challenging Data Set Using Multiple Imputation

ERIC Educational Resources Information Center

Pampaka, Maria; Hutcheson, Graeme; Williams, Julian

2016-01-01

Missing data is endemic in much educational research. However, practices such as step-wise regression common in the educational research literature have been shown to be dangerous when significant data are missing, and multiple imputation (MI) is generally recommended by statisticians. In this paper, we provide a review of these advances and their…

Heterosexual Risk Behaviors Among Urban Young Adolescents

ERIC Educational Resources Information Center

O'Donnell, Lydia; Stueve, Ann; Wilson-Simmons, Renee; Dash, Kim; Agronick, Gail; JeanBaptiste, Varzi

2006-01-01

Urban 6th graders (n = 294) participate in a survey assessing early heterosexual risk behaviors as part of the Reach for Health Middle Childhood Study. About half the boys (47%) and 20% of girls report having a girlfriend or boyfriend; 42% of boys and 10% of girls report kissing and hugging for a long time. Stepwise regressions model the…

Beyond the Black-White Test Score Gap: Latinos' Early School Experiences and Literacy Outcomes

ERIC Educational Resources Information Center

Delgado, Enilda A.; Stoll, Laurie Cooper

2015-01-01

Data from the Early Childhood Longitudinal Survey-Birth Cohort are used to analyze the factors that lead to the reading readiness of children who participate in nonparental care the year prior to kindergarten (N = 4,550), with a specific focus on Latino children (N = 800). Stepwise multiple linear regression analysis demonstrates that reading…

Consequences of ignoring geologic variation in evaluating grazing impacts

Treesearch

Jonathan W. Long; Alvin L. Medina

2006-01-01

The geologic diversity of landforms in the Southwest complicates efforts to evaluate impacts of land uses such as livestock grazing. We examined a research study that evaluated relationships between trout biomass and stream habitat in the White Mountains of east-central Arizona. That study interpreted results of stepwise regressions and a nonparametric test of “grazed...

The Relationship of Selected Supply- and Demand-Side Factors to Forms of Perceived Discrimination among Adults with Multiple Sclerosis

ERIC Educational Resources Information Center

Roessler, Richard T.; Neath, Jeanne; McMahon, Brian T.; Rumrill, Phillip D.

2007-01-01

Single-predictor and stepwise multinomial logistic regression analyses and an external validation were completed on 3,082 allegations of employment discrimination by adults with multiple sclerosis. Women filed two thirds of the allegations, and individuals between 31 and 50 made the vast majority of discrimination charges (73%). Allegations…

Age, Sex, and Body Composition as Predictors of Children's Performance on Basic Motor Abilities and Health-Related Fitness Items.

ERIC Educational Resources Information Center

Pissanos, Becky W.; And Others

1983-01-01

Step-wise linear regressions were used to relate children's age, sex, and body composition to performance on basic motor abilities including balance, speed, agility, power, coordination, and reaction time, and to health-related fitness items including flexibility, muscle strength and endurance and cardiovascular functions. Eighty subjects were in…

Application of the stepwise focusing method to optimize the cost-effectiveness of genome-wide association studies with limited research budgets for genotyping and phenotyping.

PubMed

Ohashi, J; Clark, A G

2005-05-01

The recent cataloguing of a large number of SNPs enables us to perform genome-wide association studies for detecting common genetic variants associated with disease. Such studies, however, generally have limited research budgets for genotyping and phenotyping. It is therefore necessary to optimize the study design by determining the most cost-effective numbers of SNPs and individuals to analyze. In this report we applied the stepwise focusing method, with two-stage design, developed by Satagopan et al. (2002) and Saito & Kamatani (2002), to optimize the cost-effectiveness of a genome-wide direct association study using a transmission/disequilibrium test (TDT). The stepwise focusing method consists of two steps: a large number of SNPs are examined in the first focusing step, and then all the SNPs showing a significant P-value are tested again using a larger set of individuals in the second focusing step. In the framework of optimization, the numbers of SNPs and families and the significance levels in the first and second steps were regarded as variables to be considered. Our results showed that the stepwise focusing method achieves a distinct gain of power compared to a conventional method with the same research budget.

Regional danger assessment of Debris flow and its engineering mitigation practice in Sichuan-Tibet highway

NASA Astrophysics Data System (ADS)

Su, Pengcheng; Sun, Zhengchao; li, Yong

2017-04-01

Luding-Kangding highway cross the eastern edge of Qinghai-Tibet Plateau where belong to the most deep canyon area of plateau and mountains in western Sichuan with high mountain and steep slope. This area belongs to the intersection among Xianshuihe, Longmenshan and Anninghe fault zones which are best known in Sichuan province. In the region, seismic intensity is with high frequency and strength, new tectonic movement is strong, rock is cracked, there are much loose solid materials. Debris flow disaster is well developed under the multiple effects of the earthquake, strong rainfall and human activity which poses a great threat to the local people's life and property security. So this paper chooses Kangding and LuDing as the study area to do the debris flow hazard assessment through the in-depth analysis of development characteristics and formation mechanism of debris flow. Which can provide important evidence for local disaster assessment and early warning forecast. It also has the important scientific significance and practical value to safeguard the people's life and property safety and the security implementation of the national major project. In this article, occurrence mechanism of debris flow disasters in the study area is explored, factor of evaluation with high impact to debris flow hazards is identified, the database of initial evaluation factors is made by the evaluation unit of basin. The factors with high impact to hazards occurrence are selected by using the stepwise regression method of logistic regression model, at the same time the factors with low impact are eliminated, then the hazard evaluation factor system of debris flow is determined in the study area. Then every factors of evaluation factor system are quantified, and the weights of all evaluation factors are determined by using the analysis of stepwise regression. The debris flows hazard assessment and regionalization of all the whole study area are achieved eventually after establishing the hazard assessment model. In this paper, regional debris flows hazard assessment method with strong universality and reliable evaluation result is presented. The whole study area is divided into 1674 units by automatically extracting and artificial identification, and then 11 factors are selected as the initial assessment factors of debris flow hazard assessment in the study area. The factors of the evaluation index system are quantified using the method of standardized watershed unit amount ratio. The relationship between debris flow occurrence and each evaluation factor is simulated using logistic regression model. The weights of evaluation factors are determined, and the model of debris flows hazard assessment is established in the study area. Danger assessment result of debris flow was applied in line optimization and engineering disaster reduction of Sichuan-Tibet highway (section of Luding-Kangding).

Inclusion of Endogenous Hormone Levels in Risk Prediction Models of Postmenopausal Breast Cancer

PubMed Central

Tworoger, Shelley S.; Zhang, Xuehong; Eliassen, A. Heather; Qian, Jing; Colditz, Graham A.; Willett, Walter C.; Rosner, Bernard A.; Kraft, Peter; Hankinson, Susan E.

2014-01-01

Purpose Endogenous hormones are risk factors for postmenopausal breast cancer, and their measurement may improve our ability to identify high-risk women. Therefore, we evaluated whether inclusion of plasma estradiol, estrone, estrone sulfate, testosterone, dehydroepiandrosterone sulfate, prolactin, and sex hormone–binding globulin (SHBG) improved risk prediction for postmenopausal invasive breast cancer (n = 437 patient cases and n = 775 controls not using postmenopausal hormones) in the Nurses' Health Study. Methods We evaluated improvement in the area under the curve (AUC) for 5-year risk of invasive breast cancer by adding each hormone to the Gail and Rosner-Colditz risk scores. We used stepwise regression to identify the subset of hormones most associated with risk and assessed AUC improvement; we used 10-fold cross validation to assess model overfitting. Results Each hormone was associated with breast cancer risk (odds ratio doubling, 0.82 [SHBG] to 1.37 [estrone sulfate]). Individual hormones improved the AUC by 1.3 to 5.2 units relative to the Gail score and 0.3 to 2.9 for the Rosner-Colditz score. Estrone sulfate, testosterone, and prolactin were selected by stepwise regression and increased the AUC by 5.9 units (P = .003) for the Gail score and 3.4 (P = .04) for the Rosner-Colditz score. In cross validation, the average AUC change across the validation data sets was 6.0 (P = .002) and 3.0 units (P = .03), respectively. Similar results were observed for estrogen receptor–positive disease (selected hormones: estrone sulfate, testosterone, prolactin, and SHBG; change in AUC, 8.8 [P < .001] for Gail score and 5.8 [P = .004] for Rosner-Colditz score). Conclusion Our results support that endogenous hormones improve risk prediction for invasive breast cancer and could help identify women who may benefit from chemoprevention or more screening. PMID:25135988

Baastrup's Disease Is Associated with Recurrent of Sciatica after Posterior Lumbar Spinal Decompressions Utilizing Floating Spinous Process Procedures

PubMed Central

Mannoji, Chikato; Murakami, Masazumi; Kinoshita, Tomoaki; Hirayama, Jiro; Miyashita, Tomohiro; Eguchi, Yawara; Yamazaki, Masashi; Suzuki, Takane; Aramomi, Masaaki; Ota, Mitsutoshi; Maki, Satoshi; Takahashi, Kazuhisa; Furuya, Takeo

2016-01-01

Study Design Retrospective case-control study. Purpose To determine whether kissing spine is a risk factor for recurrence of sciatica after lumbar posterior decompression using a spinous process floating approach. Overview of Literature Kissing spine is defined by apposition and sclerotic change of the facing spinous processes as shown in X-ray images, and is often accompanied by marked disc degeneration and decrement of disc height. If kissing spine significantly contributes to weight bearing and the stability of the lumbar spine, trauma to the spinous process might induce a breakdown of lumbar spine stability after posterior decompression surgery in cases of kissing spine. Methods The present study included 161 patients who had undergone posterior decompression surgery for lumbar canal stenosis using a spinous process floating approaches. We defined recurrence of sciatica as that resolved after initial surgery and then recurred. Kissing spine was defined as sclerotic change and the apposition of the spinous process in a plain radiogram. Preoperative foraminal stenosis was determined by the decrease of perineural fat intensity detected by parasagittal T1-weighted magnetic resonance imaging. Preoperative percentage slip, segmental range of motion, and segmental scoliosis were analyzed in preoperative radiographs. Univariate analysis followed by stepwise logistic regression analysis determined factors independently associated with recurrence of sciatica. Results Stepwise logistic regression revealed kissing spine (p=0.024; odds ratio, 3.80) and foraminal stenosis (p<0.01; odds ratio, 17.89) as independent risk factors for the recurrence of sciatica after posterior lumbar spinal decompression with spinous process floating procedures for lumbar spinal canal stenosis. Conclusions When a patient shows kissing spine and concomitant subclinical foraminal stenosis at the affected level, we should sufficiently discuss the selection of an appropriate surgical procedure. PMID:27994785

PREOPERATIVE MRI IMPROVES PREDICTION OF EXTENSIVE OCCULT AXILLARY LYMPH NODE METASTASES IN BREAST CANCER PATIENTS WITH A POSITIVE SENTINEL LYMPH NODE BIOPSY

PubMed Central

Loiselle, Christopher; Eby, Peter R.; Kim, Janice N.; Calhoun, Kristine E.; Allison, Kimberly H.; Gadi, Vijayakrishna K.; Peacock, Sue; Storer, Barry; Mankoff, David A.; Partridge, Savannah C.; Lehman, Constance D.

2014-01-01

Rationale and Objectives To test the ability of quantitative measures from preoperative Dynamic Contrast Enhanced MRI (DCE-MRI) to predict, independently and/or with the Katz pathologic nomogram, which breast cancer patients with a positive sentinel lymph node biopsy will have ≥ 4 positive axillary lymph nodes upon completion axillary dissection. Methods and Materials A retrospective review was conducted to identify clinically node-negative invasive breast cancer patients who underwent preoperative DCE-MRI, followed by sentinel node biopsy with positive findings and complete axillary dissection (6/2005 – 1/2010). Clinical/pathologic factors, primary lesion size and quantitative DCE-MRI kinetics were collected from clinical records and prospective databases. DCE-MRI parameters with univariate significance (p < 0.05) to predict ≥ 4 positive axillary nodes were modeled with stepwise regression and compared to the Katz nomogram alone and to a combined MRI-Katz nomogram model. Results Ninety-eight patients with 99 positive sentinel biopsies met study criteria. Stepwise regression identified DCE-MRI total persistent enhancement and volume adjusted peak enhancement as significant predictors of ≥4 metastatic nodes. Receiver operating characteristic (ROC) curves demonstrated an area under the curve (AUC) of 0.78 for the Katz nomogram, 0.79 for the DCE-MRI multivariate model, and 0.87 for the combined MRI-Katz model. The combined model was significantly more predictive than the Katz nomogram alone (p = 0.003). Conclusion Integration of DCE-MRI primary lesion kinetics significantly improved the Katz pathologic nomogram accuracy to predict presence of metastases in ≥ 4 nodes. DCE-MRI may help identify sentinel node positive patients requiring further localregional therapy. PMID:24331270

[HPLC specific chromatogram spectrum-effect relationship for Shuanghuanglian on MDCK cell injury induced by influenza A virus (H1N1)].

PubMed

Liu, Ting; Wang, Hai-dan; Di, Liu-qing; Kang, An; Zhao, Xiao-li; Zhu, Xuan-xuan; Li, Jun-song

2015-11-01

To establish HPLC specific chromatogram and its correlation with the protection effect of Shuanghuanglian on MDCK (Madin-Darby canine kidney) cell injury induced by influenza A virus( H1N1). Nine recipes of Shuanghuanglian based on the official prescription were prepared according to orthogonal test for HPLC analysis and MDCK cells protection experiment separately (cytopathic effect (CPE) method was used for observing the virus infectivity and MTT staining results were used as the determining indexes for drug concentration selection and analyzing cell viability). The results suggested that all the other Shuang-Huang-Lian recipes except recipe1 demonstrate protecting effect on MDCK cell injury induced by influenza A virus (P < 0.01, P < 0.001). Stepwise regression analysis was used for analyzing the relationships between HPLC fingerprint and the protecting effect of Shuanghuanglian on influenza A virus induced MDCK cell injury. Peak 2, 3, 6, 8 and 12 were found to be strongly related with anti-influenza A virus efficacy. Stepwise regression analysis of recipes data and efficacy data showed that Lonicerae Japonicae Flos and Forsythiae Fructus were positively associated with the protecting effect of cells injury. From HPLC fingerprints, we found that peak 2, 3, 12 were from Lonicerae Japonicae Flos and peak 6, 8 were from Forsythiae Fructus. Four peaks were identified through comparing the retention time between the standard and Shuanghuanglian recipes, and they were chlorogenicacid, cryptochlorogenic acid, forsythoside B and 3,4-dicaffeoylquinic acid respectively. Caffeic acid derivatives in Lonicerae Japonicae Flos and Forsythiae Fructus were found to be greatly correlated with anti-influenza A virus efficacy and maybe the substance basis of Shuanghuanglian.

Impact of catheter ablation with remote magnetic navigation on procedural outcomes in patients with persistent and long-standing persistent atrial fibrillation.

PubMed

Jin, Qi; Pehrson, Steen; Jacobsen, Peter Karl; Chen, Xu

2015-11-01

The objectives of this study were to assess the procedural outcomes of persistent and long-standing persistent atrial fibrillation (PsAF and L-PsAF) ablation guided by remote magnetic navigation (RMN), and to detect factors predicting acute restoration of sinus rhythm (SR) by ablation with RMN. A total of 313 patients (275 male, age 59 ± 9.5 years) with PsAF (187/313) or L-PsAF (126/313) undergoing ablation using RMN were included. Patients' disease history, pulmonary venous anatomy, left atrial (LA) volume, procedure time, mapping plus ablation time, radiofrequency (RF) ablation time, fluoroscopy time, radiation dose, and complications were assessed. Stepwise regression was used to predict which variable could best predict acute restoration from AF to SR by ablation. Compared to PsAF, procedure time and RF ablation time were significantly increased in patients with L-PsAF (P = 0.01 and P < 0.001, respectively). No major complications occurred during the procedures in either PsAF or L-PsAF patients. Fifty five of 313 patients converted directly to SR by ablation. Compared to L-PsAF, the rate of SR restoration was significantly higher in PsAF (21 vs 12%, P = 0.03). Stepwise regression analysis showed LA volume was the primary parameter affecting SR restoration (P = 0.01). The LA volume of patients without direct SR restoration by ablation was 24% greater than that of patients with SR restoration (P < 0.001). Catheter ablation using RMN is a safe and effective method for PsAF and L-PsAF. LA volume could be a predictor of direct restoration of SR from sustaining AF by ablation using RMN.

Suicidal Ideation and Schizophrenia: Contribution of Appraisal, Stigmatization, and Cognition

PubMed Central

Stip, Emmanuel; Caron, Jean; Tousignant, Michel

2017-01-01

Objective: To predict suicidal ideation in people with schizophrenia, certain studies have measured its relationship with the variables of defeat and entrapment. The relationships are positive, but their interactions remain undefined. To further their understanding, this research sought to measure the relationship between suicidal ideation with the variables of loss, entrapment, and humiliation. Method: The convenience sample included 30 patients with schizophrenia spectrum disorders. The study was prospective (3 measurement times) during a 6-month period. Results were analyzed by stepwise multiple regression. Results: The contribution of the 3 variables to the variance of suicidal ideation was not significant at any of the 3 times (T1: 16.2%, P = 0.056; T2: 19.9%, P = 0.117; T3: 11.2%, P = 0.109). Further analyses measured the relationship between the variables of stigmatization, perceived cognitive dysfunction, symptoms, depression, self-esteem, reason to live, spirituality, social provision, and suicidal ideation. Stepwise multiple regression demonstrated that the contribution of the variables of stigmatization and perceived cognitive dysfunction to the variance of suicidal ideation was significant at all 3 times (T1: 41.7.5%, P = 0.000; T2: 35.2%, P = 0.001; T3: 21.5%, P = 0.012). Yet, over time, the individual contribution of the variables changed: T1, stigmatization (β = 0.518; P = 0.002); T2, stigmatization (β = 0.394; P = 0.025) and perceived cognitive dysfunction (β = 0.349; P = 0.046). Then, at T3, only perceived cognitive dysfunction contributed significantly to suicidal ideation (β = 0.438; P = 0.016). Conclusion: The results highlight the importance of the contribution of the variables of perceived cognitive dysfunction and stigmatization in the onset of suicidal ideation in people with schizophrenia spectrum disorders. PMID:28673099

Quantifying Training Loads in Contemporary Dance.

PubMed

Jeffries, Annie C; Wallace, Lee; Coutts, Aaron J

2017-07-01

To describe the training demands of contemporary dance and determine the validity of using the session rating of perceived exertion (sRPE) to monitor exercise intensity and training load in this activity. In addition, the authors examined the contribution of training (ie, accelerometry and heart rate) and non-training-related factors (ie, sleep and wellness) to perceived exertion during dance training. Training load and ActiGraphy for 16 elite amateur contemporary dancers were collected during a 49-d period, using heart-rate monitors, accelerometry, and sRPE. Within-individual correlation analysis was used to determine relationships between sRPE and several other measures of training intensity and load. Stepwise multiple regressions were used to determine a predictive equation to estimate sRPE during dance training. Average weekly training load was 4283 ± 2442 arbitrary units (AU), monotony 2.13 ± 0.92 AU, strain 10677 ± 9438 AU, and average weekly vector magnitude load 1809,707 ± 1015,402 AU. There were large to very large within-individual correlations between training-load sRPE and various other internal and external measures of intensity and load. The stepwise multiple-regression analysis also revealed that 49.7% of the adjusted variance in training-load sRPE was explained by peak heart rate, metabolic equivalents, soreness, motivation, and sleep quality (y = -4.637 + 13.817%HR peak + 0.316 METS + 0.100 soreness + 0.116 motivation - 0.204 sleep quality). The current findings demonstrate the validity of the sRPE method for quantifying training load in dance, that dancers undertake very high training loads, and a combination of training and nontraining factors contribute to perceived exertion in dance training.

Lifestyle and health-related quality of life: a cross-sectional study among civil servants in China.

PubMed

Xu, Jun; Qiu, Jincai; Chen, Jie; Zou, Liai; Feng, Liyi; Lu, Yan; Wei, Qian; Zhang, Jinhua

2012-05-04

Health-related quality of life (HRQoL) has been increasingly acknowledged as a valid and appropriate indicator of public health and chronic morbidity. However, limited research was conducted among Chinese civil servants owing to the different lifestyle. The aim of the study was to evaluate the HRQoL among Chinese civil servants and to identify factors might be associated with their HRQoL. A cross-sectional study was conducted to investigate HRQoL of 15,000 civil servants in China using stratified random sampling methods. Independent-Samples t-Test, one-way ANOVA, and multiple stepwise regression were used to analyse the influencing factors and the HRQoL of the civil servants. A univariate analysis showed that there were significant differences among physical component summary (PCS), mental component summary (MCS), and TS between lifestyle factors, such as smoking, drinking alcohol, having breakfast, sleep time, physical exercise, work time, operating computers, and sedentariness (P < 0.05). Multiple stepwise regressions showed that there were significant differences among TS between lifestyle factors, such as breakfast, sleep time, physical exercise, operating computers, sedentariness, work time, and drinking (P < 0.05). In this study, using Short Form 36 items (SF-36), we assessed the association of HRQoL with lifestyle factors, including smoking, drinking alcohol, having breakfast, sleep time, physical exercise, work time, operating computers, and sedentariness in China. The performance of the questionnaire in the large-scale survey is satisfactory and provides a large picture of the HRQoL status in Chinese civil servants. Our results indicate that lifestyle factors such as smoking, drinking alcohol, having breakfast, sleep time, physical exercise, work time, operating computers, and sedentariness affect the HRQoL of civil servants in China.

Factors Associated with the Competencies of Public Health Workers in Township Hospitals: A Cross-Sectional Survey in Chongqing Municipality, China

PubMed Central

He, Zhifei; Cheng, Zhaohui; Fu, Hang; Tang, Shangfeng; Fu, Qian; Fang, Haiqing; Xian, Yue; Ming, Hui; Feng, Zhanchun

2015-01-01

Purpose: This study aimed to explore the competencies of public health workers (PHWs) of township hospitals in Chongqing Municipality (China), and determine the related impact factors of the competencies of PHWs; Methods: A cross-sectional research was conducted on 314 PHWs from 27 township hospitals in three districts in Chongqing Municipality (China), from June to August 2014. A self-assessment questionnaire was established on the basis of literature reviews and a competency dictionary. The differences in competencies among the three districts were determined by adopting the chi-square test, t-test, analysis of variance (ANOVA) method, and the impact factors of the competencies of PHWs were determined by adopting stepwise regression analysis. Results: (1) Results of the demographic characteristics of PHWs in three sample districts of Chongqing Municipality showed that a significant difference in age of PHWs (p = 0.021 < 0.05) and the majors of PHWs (p = 0.045 < 0.05); (2) In terms of the self-evaluation competency results of PHWs in township hospitals, seven among the 11 aspects were found to have significant differences in the three districts by the ANOVA test; (3) By adopting the t-test and ANOVA method, results of the relationship between the characteristics of PHWs and their competency scores showed that significant differences were found in the economic level (p = 0.000 < 0.05), age (p = 0.000 < 0.05), years of working (p = 0.000 < 0.05) and title of PHWs (p = 0.000 < 0.05); (4) Stepwise regression analysis was used to determine the impact factors of the competencies of PHWs in township hospitals, including the economic level (p = 0.000 < 0.001), years of working (p = 0.000 < 0.001), title (p = 0.001 < 0.005), and public health major (p = 0.007 < 0.01). Conclusions: The competencies of the township hospital staff in Chongqing Municipality (China), are generally insufficient, therefore, regulating the medical education and training skills of PHWs is crucial to improve the competencies of PHWs in the township hospitals of Chongqing Municipality. The results of this study can be mirrored in other areas of China. PMID:26569273

Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression

USGS Publications Warehouse

Kokaly, R.F.; Clark, R.N.

1999-01-01

We develop a new method for estimating the biochemistry of plant material using spectroscopy. Normalized band depths calculated from the continuum-removed reflectance spectra of dried and ground leaves were used to estimate their concentrations of nitrogen, lignin, and cellulose. Stepwise multiple linear regression was used to select wavelengths in the broad absorption features centered at 1.73 ??m, 2.10 ??m, and 2.30 ??m that were highly correlated with the chemistry of samples from eastern U.S. forests. Band depths of absorption features at these wavelengths were found to also be highly correlated with the chemistry of four other sites. A subset of data from the eastern U.S. forest sites was used to derive linear equations that were applied to the remaining data to successfully estimate their nitrogen, lignin, and cellulose concentrations. Correlations were highest for nitrogen (R2 from 0.75 to 0.94). The consistent results indicate the possibility of establishing a single equation capable of estimating the chemical concentrations in a wide variety of species from the reflectance spectra of dried leaves. The extension of this method to remote sensing was investigated. The effects of leaf water content, sensor signal-to-noise and bandpass, atmospheric effects, and background soil exposure were examined. Leaf water was found to be the greatest challenge to extending this empirical method to the analysis of fresh whole leaves and complete vegetation canopies. The influence of leaf water on reflectance spectra must be removed to within 10%. Other effects were reduced by continuum removal and normalization of band depths. If the effects of leaf water can be compensated for, it might be possible to extend this method to remote sensing data acquired by imaging spectrometers to give estimates of nitrogen, lignin, and cellulose concentrations over large areas for use in ecosystem studies.We develop a new method for estimating the biochemistry of plant material using spectroscopy. Normalized band depths calculated from the continuum-removed reflectance spectra of dried and ground leaves were used to estimate their concentrations of nitrogen, lignin, and cellulose. Stepwise multiple linear regression was used to select wavelengths in the broad absorption features centered at 1.73 ??m, 2.10 ??m, and 2.301 ??m that were highly correlated with the chemistry of samples from eastern U.S. forests. Band depths of absorption features at these wavelengths were found to also be highly correlated with the chemistry of four other sites. A subset of data from the eastern U.S. forest sites was used to derive linear equations that were applied to the remaining data to successfully estimate their nitrogen, lignin, and cellulose concentrations. Correlations were highest for nitrogen (R2 from 0.75 to 0.94). The consistent results indicate the possibility of establishing a single equation capable of estimating the chemical concentrations in a wide variety of species from the reflectance spectra of dried leaves. The extension of this method to remote sensing was investigated. The effects of leaf water content, sensor signal-to-noise and bandpass, atmospheric effects, and background soil exposure were examined. Leaf water was found to be the greatest challenge to extending this empirical method to the analysis of fresh whole leaves and complete vegetation canopies. The influence of leaf water on reflectance spectra must be removed to within 10%. Other effects were reduced by continuum removal and normalization of band depths. If the effects of leaf water can be compensated for, it might be possible to extend this method to remote sensing data acquired by imaging spectrometers to give estimates of nitrogen, lignin, and cellulose concentrations over large areas for use in ecosystem studies.

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

PubMed

Grigor'eva, M A

2018-01-01

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

The correlation between calcaneal valgus angle and asymmetrical thoracic-lumbar rotation angles in patients with adolescent scoliosis.

PubMed

Park, Jaeyong; Lee, Sang Gil; Bae, Jongjin; Lee, Jung Chul

2015-12-01

[Purpose] This study aimed to provide a predictable evaluation method for the progression of scoliosis in adolescents based on quick and reliable measurements using the naked eye, such as the calcaneal valgus angle of the foot, which can be performed at public facilities such as schools. [Subjects and Methods] Idiopathic scoliosis patients with a Cobb's angle of 10° or more (96 females, 22 males) were included in this study. To identify relationships between factors, Pearson's product-moment correlation coefficient was computed. The degree of scoliosis was set as a dependent variable to predict thoracic and lumbar scoliosis using ankle angle and physique factors. Height, weight, and left and right calcaneal valgus angles were set as independent variables; thereafter, multiple regression analysis was performed. This study extracted variables at a significance level (α) of 0.05 by applying a stepwise method, and calculated a regression equation. [Results] Negative correlation (R=-0.266) was shown between lumbar lordosis and asymmetrical lumbar rotation angles. A correlation (R=0.281) was also demonstrated between left calcaneal valgus angles and asymmetrical thoracic rotation angles. [Conclusion] Prediction of scoliosis progress was revealed to be possible through ocular inspection of the calcaneus and Adams forward bending test and the use of a scoliometer.

Disorganized Symptoms Predicted Worse Functioning Outcome in Schizophrenia Patients with Established Illness.

PubMed

Ortiz, Bruno Bertolucci; Gadelha, Ary; Higuchi, Cinthia Hiroko; Noto, Cristiano; Medeiros, Daiane; Pitta, José Cássio do Nascimento; de Araújo Filho, Gerardo Maria; Hallak, Jaime Eduardo Cecílio; Bressan, Rodrigo Affonseca

Most patients with schizophrenia will have subsequent relapses of the disorder, with continuous impairments in functioning. However, evidence is lacking on how symptoms influence functioning at different phases of the disease. This study aims to investigate the relationship between symptom dimensions and functioning at different phases: acute exacerbation, nonremission and remission. Patients with schizophrenia were grouped into acutely ill (n=89), not remitted (n=89), and remitted (n=69). Three exploratory stepwise linear regression analyses were performed for each phase of schizophrenia, in which the five PANSS factors and demographic variables were entered as the independent variables and the total Global Assessment of Functioning Scale (GAF) score was entered as the dependent variable. An additional exploratory stepwise logistic regression analysis was performed to predict subsequent remission at discharge in the inpatient population. The Disorganized factor was the most significant predictor for acutely ill patients (p<0.001), while the Hostility factor was the most significant for not-remitted patients and the Negative factor was the most significant for remitted patients (p=0.001 and p<0.001, respectively). In the logistic regression, the Disorganized factor score presented a significant negative association with remission (p=0.007). Higher disorganization symptoms showed the greatest impact in functioning at acute phase, and prevented patients from achieving remission, suggesting it may be a marker of symptom severity and worse outcome in schizophrenia.

Delineating chalk sand distribution of Ekofisk formation using probabilistic neural network (PNN) and stepwise regression (SWR): Case study Danish North Sea field

NASA Astrophysics Data System (ADS)

Haris, A.; Nafian, M.; Riyanto, A.

2017-07-01

Danish North Sea Fields consist of several formations (Ekofisk, Tor, and Cromer Knoll) that was started from the age of Paleocene to Miocene. In this study, the integration of seismic and well log data set is carried out to determine the chalk sand distribution in the Danish North Sea field. The integration of seismic and well log data set is performed by using the seismic inversion analysis and seismic multi-attribute. The seismic inversion algorithm, which is used to derive acoustic impedance (AI), is model-based technique. The derived AI is then used as external attributes for the input of multi-attribute analysis. Moreover, the multi-attribute analysis is used to generate the linear and non-linear transformation of among well log properties. In the case of the linear model, selected transformation is conducted by weighting step-wise linear regression (SWR), while for the non-linear model is performed by using probabilistic neural networks (PNN). The estimated porosity, which is resulted by PNN shows better suited to the well log data compared with the results of SWR. This result can be understood since PNN perform non-linear regression so that the relationship between the attribute data and predicted log data can be optimized. The distribution of chalk sand has been successfully identified and characterized by porosity value ranging from 23% up to 30%.

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.

[Optimization of lime milk precipitation process of Lonicera Japonica aqueous extract based on quality by design concept].

PubMed

Shen, Jin-Jing; Gong, Xing-Chu; Pan, Jian-Yang; Qu, Hai-Bin

2017-03-01

Design space approach was applied in this study to optimize the lime milk precipitation process of Lonicera Japonica (Jinyinhua) aqueous extract. The evaluation indices for this process were total organic acid purity and amounts of 6 organic acids obtained from per unit mass of medicinal materials. Four critical process parameters (CPPs) including drop speed of lime milk, pH value after adding lime milk, settling time and settling temperature were identified by using the weighted standardized partial regression coefficient method. Quantitative models between process evaluation indices and CPPs were established by a stepwise regression analysis. A design space was calculated by a Monte-Carlo simulation method, and then verified. The verification test results showed that the operation within the design space can guarantee the stability of the lime milk precipitation process. The recommended normal operation space is as follows： drop speed of lime milk of 1.00-1.25 mL•min⁻¹, pH value of 11.5-11.7, settling time of 1.0-1.2 h, and settling temperature of 10-20 ℃.. Copyright© by the Chinese Pharmaceutical Association.

Beef customer satisfaction: factors affecting consumer evaluations of clod steaks.

PubMed

Goodson, K J; Morgan, W W; Reagan, J O; Gwartney, B L; Courington, S M; Wise, J W; Savell, J W

2002-02-01

An in-home beef study evaluated consumer ratings of clod steaks (n = 1,264) as influenced by USDA quality grade (Top Choice, Low Choice, High Select, and Low Select), city (Chicago and Philadelphia), consumer segment (Beef Loyals, who are heavy consumers of beef; Budget Rotators, who are cost-driven and split meat consumption between beef and chicken; and Variety Rotators, who have higher incomes and education and split their meat consumption among beef, poultry, and other foods), degree of doneness, and cooking method. Consumers evaluated each steak for Overall Like, Tenderness, Juiciness, Flavor Like, and Flavor Amount using 10-point scales. Grilling was the predominant cooking method used, and steaks were cooked to medium-well and greater degrees of doneness. Interactions existed involving the consumer-controlled factors of degree of doneness and(or) cooking method for all consumer-evaluated traits for the clod steak (P < 0.05). USDA grade did not affect any consumer evaluation traits or Warner-Bratzler shear force values (P > 0.05). One significant main effect, segment (P = 0.006), and one significant interaction, cooking method x city (P = 0.0407), existed for Overall Like ratings. Consumers in the Beef Loyals segment rated clod steaks higher in Overall Like than the other segments. Consumers in Chicago tended to give more uniform Overall Like ratings to clod steaks cooked by various methods; however, consumers in Philadelphia gave among the highest ratings to clod steaks that were fried and among the lowest to those that were grilled. Additionally, although clod steaks that were fried were given generally high ratings by consumers in Philadelphia, consumers in Chicago rated clod steaks cooked in this manner significantly lower than those in Philadelphia. Conversely, consumers in Chicago rated clod steaks that were grilled significantly higher than consumers in Philadelphia. Correlation and stepwise regression analyses indicated that Flavor Like was driving customer satisfaction of the clod steak. Flavor Like was the sensory trait most highly correlated to Overall Like, followed by Tenderness, Flavor Amount, and Juiciness. Flavor Like was the first variable to enter into the stepwise regression equation for predicting Overall Like, followed by Tenderness and Flavor Amount. For the clod steak, it is likely that preparation techniques that improve flavor without reducing tenderness positively affect customer satisfaction.

Deciphering factors controlling groundwater arsenic spatial variability in Bangladesh

NASA Astrophysics Data System (ADS)

Tan, Z.; Yang, Q.; Zheng, C.; Zheng, Y.

2017-12-01

Elevated concentrations of geogenic arsenic in groundwater have been found in many countries to exceed 10 μg/L, the WHO's guideline value for drinking water. A common yet unexplained characteristic of groundwater arsenic spatial distribution is the extensive variability at various spatial scales. This study investigates factors influencing the spatial variability of groundwater arsenic in Bangladesh to improve the accuracy of models predicting arsenic exceedance rate spatially. A novel boosted regression tree method is used to establish a weak-learning ensemble model, which is compared to a linear model using a conventional stepwise logistic regression method. The boosted regression tree models offer the advantage of parametric interaction when big datasets are analyzed in comparison to the logistic regression. The point data set (n=3,538) of groundwater hydrochemistry with 19 parameters was obtained by the British Geological Survey in 2001. The spatial data sets of geological parameters (n=13) were from the Consortium for Spatial Information, Technical University of Denmark, University of East Anglia and the FAO, while the soil parameters (n=42) were from the Harmonized World Soil Database. The aforementioned parameters were regressed to categorical groundwater arsenic concentrations below or above three thresholds: 5 μg/L, 10 μg/L and 50 μg/L to identify respective controlling factors. Boosted regression tree method outperformed logistic regression methods in all three threshold levels in terms of accuracy, specificity and sensitivity, resulting in an improvement of spatial distribution map of probability of groundwater arsenic exceeding all three thresholds when compared to disjunctive-kriging interpolated spatial arsenic map using the same groundwater arsenic dataset. Boosted regression tree models also show that the most important controlling factors of groundwater arsenic distribution include groundwater iron content and well depth for all three thresholds. The probability of a well with iron content higher than 5mg/L to contain greater than 5 μg/L, 10 μg/L and 50 μg/L As is estimated to be more than 91%, 85% and 51%, respectively, while the probability of a well from depth more than 160m to contain more than 5 μg/L, 10 μg/L and 50 μg/L As is estimated to be less than 38%, 25% and 14%, respectively.

Advancing a Model-Validated Statistical Method for Decomposing the Key Oceanic Drivers of Regional Climate: Focus on Northern and Tropical African Climate Variability in the Community Earth System Model (CESM)

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

Wang, Fuyao; Yu, Yan; Notaro, Michael

This study advances the practicality and stability of the traditional multivariate statistical method, generalized equilibrium feedback assessment (GEFA), for decomposing the key oceanic drivers of regional atmospheric variability, especially when available data records are short. An advanced stepwise GEFA methodology is introduced, in which unimportant forcings within the forcing matrix are eliminated through stepwise selection. Method validation of stepwise GEFA is performed using the CESM, with a focused application to northern and tropical Africa (NTA). First, a statistical assessment of the atmospheric response to each primary oceanic forcing is carried out by applying stepwise GEFA to a fully coupled controlmore » run. Then, a dynamical assessment of the atmospheric response to individual oceanic forcings is performed through ensemble experiments by imposing sea surface temperature anomalies over focal ocean basins. Finally, to quantify the reliability of stepwise GEFA, the statistical assessment is evaluated against the dynamical assessment in terms of four metrics: the percentage of grid cells with consistent response sign, the spatial correlation of atmospheric response patterns, the area-averaged seasonal cycle of response magnitude, and consistency in associated mechanisms between assessments. In CESM, tropical modes, namely El Niño–Southern Oscillation and the tropical Indian Ocean Basin, tropical Indian Ocean dipole, and tropical Atlantic Niño modes, are the dominant oceanic controls of NTA climate. In complementary studies, stepwise GEFA is validated in terms of isolating terrestrial forcings on the atmosphere, and observed oceanic and terrestrial drivers of NTA climate are extracted to establish an observational benchmark for subsequent coupled model evaluation and development of process-based weights for regional climate projections.« less

Advancing a Model-Validated Statistical Method for Decomposing the Key Oceanic Drivers of Regional Climate: Focus on Northern and Tropical African Climate Variability in the Community Earth System Model (CESM)

DOE PAGES

Wang, Fuyao; Yu, Yan; Notaro, Michael; ...

2017-09-27

This study advances the practicality and stability of the traditional multivariate statistical method, generalized equilibrium feedback assessment (GEFA), for decomposing the key oceanic drivers of regional atmospheric variability, especially when available data records are short. An advanced stepwise GEFA methodology is introduced, in which unimportant forcings within the forcing matrix are eliminated through stepwise selection. Method validation of stepwise GEFA is performed using the CESM, with a focused application to northern and tropical Africa (NTA). First, a statistical assessment of the atmospheric response to each primary oceanic forcing is carried out by applying stepwise GEFA to a fully coupled controlmore » run. Then, a dynamical assessment of the atmospheric response to individual oceanic forcings is performed through ensemble experiments by imposing sea surface temperature anomalies over focal ocean basins. Finally, to quantify the reliability of stepwise GEFA, the statistical assessment is evaluated against the dynamical assessment in terms of four metrics: the percentage of grid cells with consistent response sign, the spatial correlation of atmospheric response patterns, the area-averaged seasonal cycle of response magnitude, and consistency in associated mechanisms between assessments. In CESM, tropical modes, namely El Niño–Southern Oscillation and the tropical Indian Ocean Basin, tropical Indian Ocean dipole, and tropical Atlantic Niño modes, are the dominant oceanic controls of NTA climate. In complementary studies, stepwise GEFA is validated in terms of isolating terrestrial forcings on the atmosphere, and observed oceanic and terrestrial drivers of NTA climate are extracted to establish an observational benchmark for subsequent coupled model evaluation and development of process-based weights for regional climate projections.« less

Selecting predictors for discriminant analysis of species performance: an example from an amphibious softwater plant.

PubMed

Vanderhaeghe, F; Smolders, A J P; Roelofs, J G M; Hoffmann, M

2012-03-01

Selecting an appropriate variable subset in linear multivariate methods is an important methodological issue for ecologists. Interest often exists in obtaining general predictive capacity or in finding causal inferences from predictor variables. Because of a lack of solid knowledge on a studied phenomenon, scientists explore predictor variables in order to find the most meaningful (i.e. discriminating) ones. As an example, we modelled the response of the amphibious softwater plant Eleocharis multicaulis using canonical discriminant function analysis. We asked how variables can be selected through comparison of several methods: univariate Pearson chi-square screening, principal components analysis (PCA) and step-wise analysis, as well as combinations of some methods. We expected PCA to perform best. The selected methods were evaluated through fit and stability of the resulting discriminant functions and through correlations between these functions and the predictor variables. The chi-square subset, at P < 0.05, followed by a step-wise sub-selection, gave the best results. In contrast to expectations, PCA performed poorly, as so did step-wise analysis. The different chi-square subset methods all yielded ecologically meaningful variables, while probable noise variables were also selected by PCA and step-wise analysis. We advise against the simple use of PCA or step-wise discriminant analysis to obtain an ecologically meaningful variable subset; the former because it does not take into account the response variable, the latter because noise variables are likely to be selected. We suggest that univariate screening techniques are a worthwhile alternative for variable selection in ecology. © 2011 German Botanical Society and The Royal Botanical Society of the Netherlands.

Predictors of Global Self-Worth and Academic Performance among Regular Education, Learning Disabled, and Continuation High School Students.

ERIC Educational Resources Information Center

Wiest, Dudley J.; Wong, Eugene H.; Kreil, Dennis A.

1998-01-01

The ability of measures of perceived competence, control, and autonomy support to predict self-worth and academic performance was studied across groups of high school students. Stepwise regression analyses indicate these variables in model predict self-worth and grade point average. In addition, levels of school status and depression predict…

Tailoring Multimedia Instruction to Soldier Needs

DTIC Science & Technology

2014-12-01

Pretest Score (Mean % Items Correct) 39% 34% 48% 51% 51% 45% Posttest (Mean % Items Correct) 47% 44% 66% 60% 63% 56...Stepwise regression was used to examine the relationship between Soldiers’ posttest scores (criterion) and their pretest scores, training time, type of...differences among IMI types had no effect.) Pretest scores predicted posttest scores for both Adjust Indirect Fire (βstandardized = .66, t = 6.36

The Effect of College Selection Factors on Persistence: An Examination of Black and Latino Males in the Community College

ERIC Educational Resources Information Center

Wood, J. Luke; Harris, Frank, III

2015-01-01

The purpose of this study was to understand the relationship (if any) between college selection factors and persistence for Black and Latino males in the community college. Using data derived from the Educational Longitudinal Study, backwards stepwise logistic regression models were developed for both groups. Findings are contextualized in light…

Factors influencing storm-generated suspended-sediment concentrations and loads in four basins of contrasting land use, humid-tropical Puerto Rico

Treesearch

A. C. Gellis; NO-VALUE

2013-01-01

The significant characteristics controlling the variability in storm-generated suspended-sediment loads and concentrations were analyzed for four basins of differing land use (forest, pasture, cropland, and urbanizing) in humid-tropical Puerto Rico. Statistical analysis involved stepwise regression on factor scores. The explanatory variables were attributes of flow,...

Analysis of oscillatory motion of a light airplane at high values of lift coefficient

NASA Technical Reports Server (NTRS)

Batterson, J. G.

1983-01-01

A modified stepwise regression is applied to flight data from a light research air-plane operating at high angles at attack. The well-known phenomenon referred to as buckling or porpoising is analyzed and modeled using both power series and spline expansions of the aerodynamic force and moment coefficients associated with the longitudinal equations of motion.

"Hits" (Not "Discussion Posts") Predict Student Success in Online Courses: A Double Cross-Validation Study

ERIC Educational Resources Information Center

Ramos, Cheryl; Yudko, Errol

2008-01-01

The efficacy of individual components of an online course on positive course outcome was examined via stepwise multiple regression analysis. Outcome was measured as the student's total score on all exams given during the course. The predictors were page hits, discussion posts, and discussion reads. The vast majority of the variance of outcome was…

Factors Associated with Level of Living in Washington County, Mississippi. Technical Bulletin No. 1501.

ERIC Educational Resources Information Center

McCoy, John L.

Step-wise multiple regression and typological analysis were used to analyze the extent to which selected factors influence vertical mobility and achieved level of living. A sample of 418 male household heads who were 18 to 45 years old in Washington County, Mississippi were interviewed during 1971. A prescreening using census and local housing…

The Role of Perceived Learning and Communities of Inquiry in Predicting International Students' Course Grades in Computer-Mediated Graduate Courses

ERIC Educational Resources Information Center

Wendt, Jillian L.; Nisbet, Deanna L.

2017-01-01

This study examined the predictive relationship among international students' sense of community, perceived learning, and end-of-course grades in computer-mediated, U.S. graduate-level courses. The community of inquiry (CoI) framework served as the theoretical foundation for the study. Step-wise hierarchical multiple regression showed no…

Assessment of weather-associated causes of red spruce winter injury and consequences to aboveground carbon sequestration

Treesearch

Paul G. Schaberg; Brynne E. Lazarus; Gary J. Hawley; Joshua M. Halman; Catherine H. Borer; Christopher F. Hansen

2011-01-01

Despite considerable study, it remains uncertain what environmental factors contribute to red spruce (Picea rubens Sarg.) foliar winter injury and how much this injury influences tree C stores. We used a long-term record of winter injury in a plantation in New Hampshire and conducted stepwise linear regression analyses with local weather and regional...

Environmental influences on alcohol consumption practices of alcoholic beverage servers.

PubMed

Nusbaumer, Michael R; Reiling, Denise M

2002-11-01

Public drinking establishments have long been associated with heavy drinking among both their patrons and servers. Whether these environments represent locations where heavy drinking is learned (learning hypothesis) or simply places where already-heavy drinkers gather in a supportive environment (selection hypothesis) remains an important question. A sample of licensed alcoholic beverage servers in the state of Indiana, USA, was surveyed to better understand the drinking behaviors of servers within the alcohol service industry. Responses (N = 938) to a mailed questionnaire were analyzed to assess the relative influence of environmental and demographic factors on the drinking behavior of servers. Stepwise regression revealed "drinking on the job" as the most influential environmental factor on heavy drinking behaviors, followed by age and gender as influential demographic factors. Support was found for the selection hypothesis, but not for the learning hypothesis. Policy implications are discussed. factors on the drinking behavior of servers. Stepwise regression revealed "drinking on the job" as the most influential environmental factor on heavy drinking behaviors, followed by age and gender as influential demographic factors. Support was found for the selection hypothesis, but not for the learning hypothesis. Policy implications are discussed.

Handheld Tissue Hardness Meters for Assessing the Mechanical Properties of Skeletal Muscle: A Feasibility Study.

PubMed

Chino, Kentaro; Takahashi, Hideyuki

2016-09-01

The purpose of this study was to examine the feasibility of using handheld tissue hardness meters to assess the mechanical properties of skeletal muscle. This observational study included 33 healthy men (age, 22.4 ± 4.4 years) and 33 healthy women (age, 23.7 ± 4.2 years). Participants were placed in a supine position, and tissue hardness overlying the rectus femoris and the shear modulus of the muscle were measured on the right side of the body at 50% thigh length. In the same position, subcutaneous adipose tissue thickness and muscle thickness were measured using B-mode ultrasonography. To examine the associations of subcutaneous adipose tissue thickness, muscle thickness, and muscle shear modulus with tissue hardness, linear regression using a stepwise bidirectional elimination approach was performed. Stepwise linear regression revealed that subcutaneous adipose tissue thickness (r = -0.38, P = .002) and muscle shear modulus (r = 0.27, P = .03) were significantly associated with tissue hardness. Significant associations among adipose tissue thickness, muscle shear modulus, and tissue hardness show the limitations and feasibility of handheld tissue hardness meters for assessing the mechanical properties of skeletal muscles. Copyright © 2016. Published by Elsevier Inc.

Satisfaction among early and mid-career dentists in a metropolitan dental hospital in China

PubMed Central

Cui, Xiaoxi; Dunning, David G; An, Na

2017-01-01

A growing body of research has examined career satisfaction among dentists using a standardized instrument, dentist satisfaction survey (DSS). This project examined career satisfaction of early to mid-career dentists in China, a population whose career satisfaction, heretofore, has not been studied. This is an especially critical time to examine career satisfaction because of health care reform measures being implemented in China. A culturally sensitive Chinese-language version of the DSS (CDSS) was developed and electronically administered to 367 early and mid-career dentists in a tertiary dental hospital in Beijing, China. One hundred and seventy respondents completed the survey. The average total career score was 123, with a range of 82–157. Data analysis showed some significant differences in total career score and several subscales based on gender, working hours per week, and years in practice. A stepwise regression model revealed that two variables predicted total career score: working hours per week and gender. Stepwise regression also demonstrated that four subscales significantly predicted the overall professional satisfaction subscale score: respect, delivery of care, income and patient relations. Implications of these results are discussed in light of the health care delivery system and dentist career paths in China. PMID:29355243

Satisfaction among early and mid-career dentists in a metropolitan dental hospital in China.

PubMed

Cui, Xiaoxi; Dunning, David G; An, Na

2017-01-01

A growing body of research has examined career satisfaction among dentists using a standardized instrument, dentist satisfaction survey (DSS). This project examined career satisfaction of early to mid-career dentists in China, a population whose career satisfaction, heretofore, has not been studied. This is an especially critical time to examine career satisfaction because of health care reform measures being implemented in China. A culturally sensitive Chinese-language version of the DSS (CDSS) was developed and electronically administered to 367 early and mid-career dentists in a tertiary dental hospital in Beijing, China. One hundred and seventy respondents completed the survey. The average total career score was 123, with a range of 82-157. Data analysis showed some significant differences in total career score and several subscales based on gender, working hours per week, and years in practice. A stepwise regression model revealed that two variables predicted total career score: working hours per week and gender. Stepwise regression also demonstrated that four subscales significantly predicted the overall professional satisfaction subscale score: respect, delivery of care, income and patient relations. Implications of these results are discussed in light of the health care delivery system and dentist career paths in China.

Impact of livestock Scale on Rice Production in Battambang of Cambodia

NASA Astrophysics Data System (ADS)

Siek, D.; Xu, S. W.; Wyu; Ahmed, A.-G.

2017-10-01

Increasing the awareness of environmental protection especially in the rural regions is important as most the farmers reside in that region. Crop-livestock proudciton has proven in many ways to encourage environmental protection. This study analyzes among other factors the impacto of livestock scale on rice production. Two regressions: Ordinary Least Square (OLS) and stepwise regression was applied to investigate these interrelationship. The result stress of three factors encouraging livestock production namely size of farmland, scale of livestock and income acquired from other jobs. The study further provides recommends to the government based on the findings of the study.

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

DTIC Science & Technology

1990-05-01

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

Relationship between COMLEX-USA scores and performance on the American Osteopathic Board of Emergency Medicine Part I certifying examination.

PubMed

Li, Feiming; Gimpel, John R; Arenson, Ethan; Song, Hao; Bates, Bruce P; Ludwin, Fredric

2014-04-01

Few studies have investigated how well scores from the Comprehensive Osteopathic Medical Licensing Examination-USA (COMLEX-USA) series predict resident outcomes, such as performance on board certification examinations. To determine how well COMLEX-USA predicts performance on the American Osteopathic Board of Emergency Medicine (AOBEM) Part I certification examination. The target study population was first-time examinees who took AOBEM Part I in 2011 and 2012 with matched performances on COMLEX-USA Level 1, Level 2-Cognitive Evaluation (CE), and Level 3. Pearson correlations were computed between AOBEM Part I first-attempt scores and COMLEX-USA performances to measure the association between these examinations. Stepwise linear regression analysis was conducted to predict AOBEM Part I scores by the 3 COMLEX-USA scores. An independent t test was conducted to compare mean COMLEX-USA performances between candidates who passed and who failed AOBEM Part I, and a stepwise logistic regression analysis was used to predict the log-odds of passing AOBEM Part I on the basis of COMLEX-USA scores. Scores from AOBEM Part I had the highest correlation with COMLEX-USA Level 3 scores (.57) and slightly lower correlation with COMLEX-USA Level 2-CE scores (.53). The lowest correlation was between AOBEM Part I and COMLEX-USA Level 1 scores (.47). According to the stepwise regression model, COMLEX-USA Level 1 and Level 2-CE scores, which residency programs often use as selection criteria, together explained 30% of variance in AOBEM Part I scores. Adding Level 3 scores explained 37% of variance. The independent t test indicated that the 397 examinees passing AOBEM Part I performed significantly better than the 54 examinees failing AOBEM Part I in all 3 COMLEX-USA levels (P<.001 for all 3 levels). The logistic regression model showed that COMLEX-USA Level 1 and Level 3 scores predicted the log-odds of passing AOBEM Part I (P=.03 and P<.001, respectively). The present study empirically supported the predictive and discriminant validities of the COMLEX-USA series in relation to the AOBEM Part I certification examination. Although residency programs may use COMLEX-USA Level 1 and Level 2-CE scores as partial criteria in selecting residents, Level 3 scores, though typically not available at the time of application, are actually the most statistically related to performances on AOBEM Part I.

Regression and multivariate models for predicting particulate matter concentration level.

PubMed

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

2018-01-01

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

Dental age estimation in the living after completion of third molar mineralization: new data for Gustafson's criteria.

PubMed

Timme, M; Timme, W H; Olze, A; Ottow, C; Ribbecke, S; Pfeiffer, H; Dettmeyer, R; Schmeling, A

2017-03-01

There is a need for dental age estimation methods after completion of the third molar mineralization. Degenerative dental characteristics appear to be suitable for forensic age diagnostics beyond the 18th year of life. In 2012, Olze et al. investigated the criteria studied by Gustafson using orthopantomograms. The objective of this study was to prove the applicability and reliability of this method with a large cohort and a wide age range, including older individuals. For this purpose, 2346 orthopantomograms of 1167 female and 1179 male Germans aged 15 to 70 years were reviewed. The characteristics of secondary dentin formation, cementum apposition, periodontal recession and attrition were evaluated in all the mandibular premolars. The correlation of the individual characteristics with the chronological age was examined by means of a stepwise multiple regression analysis, in which the chronological age formed the dependent variable. Following those results, R 2 values amounted to 0.73 to 0.8; the standard error of estimate was 6.8 to 8.2 years. Fundamentally, the recommendation for conducting age estimations in the living by these methods can be shared. The values for the quality of the regression are, however, not precise enough for a reliable age estimation around regular retirement date ages. More precise regression formulae for the age group of 15 to 40 years of life are separately presented in this study. Further research should investigate the influence of ethnicity, dietary habits and modern health care on the degenerative characteristics in question.

Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients.

PubMed

Kim, Yun Hak; Jeong, Dae Cheon; Pak, Kyoungjune; Goh, Tae Sik; Lee, Chi-Seung; Han, Myoung-Eun; Kim, Ji-Young; Liangwen, Liu; Kim, Chi Dae; Jang, Jeon Yeob; Cha, Wonjae; Oh, Sae-Ock

2017-09-29

Accurate prediction of prognosis is critical for therapeutic decisions regarding cancer patients. Many previously developed prognostic scoring systems have limitations in reflecting recent progress in the field of cancer biology such as microarray, next-generation sequencing, and signaling pathways. To develop a new prognostic scoring system for cancer patients, we used mRNA expression and clinical data in various independent breast cancer cohorts (n=1214) from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO). A new prognostic score that reflects gene network inherent in genomic big data was calculated using Network-Regularized high-dimensional Cox-regression (Net-score). We compared its discriminatory power with those of two previously used statistical methods: stepwise variable selection via univariate Cox regression (Uni-score) and Cox regression via Elastic net (Enet-score). The Net scoring system showed better discriminatory power in prediction of disease-specific survival (DSS) than other statistical methods (p=0 in METABRIC training cohort, p=0.000331, 4.58e-06 in two METABRIC validation cohorts) when accuracy was examined by log-rank test. Notably, comparison of C-index and AUC values in receiver operating characteristic analysis at 5 years showed fewer differences between training and validation cohorts with the Net scoring system than other statistical methods, suggesting minimal overfitting. The Net-based scoring system also successfully predicted prognosis in various independent GEO cohorts with high discriminatory power. In conclusion, the Net-based scoring system showed better discriminative power than previous statistical methods in prognostic prediction for breast cancer patients. This new system will mark a new era in prognosis prediction for cancer patients.

Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients

PubMed Central

Kim, Yun Hak; Jeong, Dae Cheon; Pak, Kyoungjune; Goh, Tae Sik; Lee, Chi-Seung; Han, Myoung-Eun; Kim, Ji-Young; Liangwen, Liu; Kim, Chi Dae; Jang, Jeon Yeob; Cha, Wonjae; Oh, Sae-Ock

2017-01-01

Accurate prediction of prognosis is critical for therapeutic decisions regarding cancer patients. Many previously developed prognostic scoring systems have limitations in reflecting recent progress in the field of cancer biology such as microarray, next-generation sequencing, and signaling pathways. To develop a new prognostic scoring system for cancer patients, we used mRNA expression and clinical data in various independent breast cancer cohorts (n=1214) from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO). A new prognostic score that reflects gene network inherent in genomic big data was calculated using Network-Regularized high-dimensional Cox-regression (Net-score). We compared its discriminatory power with those of two previously used statistical methods: stepwise variable selection via univariate Cox regression (Uni-score) and Cox regression via Elastic net (Enet-score). The Net scoring system showed better discriminatory power in prediction of disease-specific survival (DSS) than other statistical methods (p=0 in METABRIC training cohort, p=0.000331, 4.58e-06 in two METABRIC validation cohorts) when accuracy was examined by log-rank test. Notably, comparison of C-index and AUC values in receiver operating characteristic analysis at 5 years showed fewer differences between training and validation cohorts with the Net scoring system than other statistical methods, suggesting minimal overfitting. The Net-based scoring system also successfully predicted prognosis in various independent GEO cohorts with high discriminatory power. In conclusion, the Net-based scoring system showed better discriminative power than previous statistical methods in prognostic prediction for breast cancer patients. This new system will mark a new era in prognosis prediction for cancer patients. PMID:29100405

[Contents of vitreous humor of dead body with different postmortem intervals].

PubMed

Tao, Tao; Xu, Jing; Luo, Tong-Xing; Liao, Zhi-Gang; Pan, Hong-Fu

2006-11-01

To establish regression correlations between postmortem interval (PMI) and contents of human vitreous humor of dead bodies for forensic purposes. The human vitreous humor were taken from 126 dead bodies between 0.5 to 216 hours after death, and 11 chemical elements were detected by the OLYMPUS AU400 auto-biochemistry instrument. (1) The glucose, natrium and chlorine in human vitreous humor decreased, while the urea, creatinine, uric acid, potassium, calcium, magnesium, phosphorus, and micro-protein increased after death. The change of glucose, potassium and phosphorus were well correlated with the PMI (r = 0.824, 0.967, 0.880). But the uric acid and micro-protein did not have a good correlation with the PMI(r = 0.350, 0.153). (2) The stepwise regression analysis established the following equations for the PMI (Y): Y = -35. 15+6.05X, R2 = 0.957 (X = potassium); Y = -27.83+ 5.49X(1) - 1.35X(2), R2 = 0.960 (X(1) = potassium, X(2) = glucose); Y = -6.37+3.93X(1) -2.29X(2) + 5.36X(3), R2 = 0.966 (X(1) = potassium, X(2) = glucose, X(3) = phosphorus). (1) Eleven chemical components in human vitreous humor change after death, among which postassium has the best linear correlation with the PMI within 72 hours after death. (2) The accuracy of the estimation of PMI could be improved by establishing a multi-variable equation through stepwise regression.

Predicting the demand of physician workforce: an international model based on "crowd behaviors".

PubMed

Tsai, Tsuen-Chiuan; Eliasziw, Misha; Chen, Der-Fang

2012-03-26

Appropriateness of physician workforce greatly influences the quality of healthcare. When facing the crisis of physician shortages, the correction of manpower always takes an extended time period, and both the public and health personnel suffer. To calculate an appropriate number of Physician Density (PD) for a specific country, this study was designed to create a PD prediction model, based on health-related data from many countries. Twelve factors that could possibly impact physicians' demand were chosen, and data of these factors from 130 countries (by reviewing 195) were extracted. Multiple stepwise-linear regression was used to derive the PD prediction model, and a split-sample cross-validation procedure was performed to evaluate the generalizability of the results. Using data from 130 countries, with the consideration of the correlation between variables, and preventing multi-collinearity, seven out of the 12 predictor variables were selected for entry into the stepwise regression procedure. The final model was: PD = (5.014 - 0.128 × proportion under age 15 years + 0.034 × life expectancy)2, with R2 of 80.4%. Using the prediction equation, 70 countries had PDs with "negative discrepancy", while 58 had PDs with "positive discrepancy". This study provided a regression-based PD model to calculate a "norm" number of PD for a specific country. A large PD discrepancy in a country indicates the needs to examine physician's workloads and their well-being, the effectiveness/efficiency of medical care, the promotion of population health and the team resource management.

Estimation of standard liver volume in Chinese adult living donors.

PubMed

Fu-Gui, L; Lu-Nan, Y; Bo, L; Yong, Z; Tian-Fu, W; Ming-Qing, X; Wen-Tao, W; Zhe-Yu, C

2009-12-01

To determine a formula predicting the standard liver volume based on body surface area (BSA) or body weight in Chinese adults. A total of 115 consecutive right-lobe living donors not including the middle hepatic vein underwent right hemi-hepatectomy. No organs were used from prisoners, and no subjects were prisoners. Donor anthropometric data including age, gender, body weight, and body height were recorded prospectively. The weights and volumes of the right lobe liver grafts were measured at the back table. Liver weights and volumes were calculated from the right lobe graft weight and volume obtained at the back table, divided by the proportion of the right lobe on computed tomography. By simple linear regression analysis and stepwise multiple linear regression analysis, we correlated calculated liver volume and body height, body weight, or body surface area. The subjects had a mean age of 35.97 +/- 9.6 years, and a female-to-male ratio of 60:55. The mean volume of the right lobe was 727.47 +/- 136.17 mL, occupying 55.59% +/- 6.70% of the whole liver by computed tomography. The volume of the right lobe was 581.73 +/- 96.137 mL, and the estimated liver volume was 1053.08 +/- 167.56 mL. Females of the same body weight showed a slightly lower liver weight. By simple linear regression analysis and stepwise multiple linear regression analysis, a formula was derived based on body weight. All formulae except the Hong Kong formula overestimated liver volume compared to this formula. The formula of standard liver volume, SLV (mL) = 11.508 x body weight (kg) + 334.024, may be applied to estimate liver volumes in Chinese adults.

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

PubMed

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

2017-05-01

The purpose of this study was to investigate which spatial and temporal parameters of the Timed Up and Go (TUG) test are associated with motor function in elderly individuals. This study included 99 community-dwelling women aged 72.9 ± 6.3 years. Step length, step width, single support time, variability of the aforementioned parameters, gait velocity, cadence, reaction time from starting signal to first step, and minimum distance between the foot and a marker placed to 3 in front of the chair were measured using our analysis system. The 10-m walk test, five times sit-to-stand (FTSTS) test, and one-leg standing (OLS) test were used to assess motor function. Stepwise multivariate linear regression analysis was used to determine which TUG test parameters were associated with each motor function test. Finally, we calculated a predictive model for each motor function test using each regression coefficient. In stepwise linear regression analysis, step length and cadence were significantly associated with the 10-m walk test, FTSTS and OLS test. Reaction time was associated with the FTSTS test, and step width was associated with the OLS test. Each predictive model showed a strong correlation with the 10-m walk test and OLS test (P < 0.01), which was not significant higher correlation than TUG test time. We showed which TUG test parameters were associated with each motor function test. Moreover, the TUG test time regarded as the lower extremity function and mobility has strong predictive ability in each motor function test. Copyright © 2017 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.

Evaluating Disease Severity in Chronic Pain Patients with and without Fibromyalgia: A Comparison of the Symptom Impact Questionnaire and the Polysymptomatic Distress Scale.

PubMed

Friend, Ronald; Bennett, Robert M

2015-12-01

To compare the relative effectiveness of the Polysymptomatic Distress Scale (PSD) with the Symptom Impact Questionnaire (SIQR), the disease-neutral revision of the updated Fibromyalgia Impact Questionnaire (FIQR), in their ability to assess disease activity in patients with rheumatic disorders both with and without fibromyalgia (FM). The study included 321 patients from 8 clinical practices with some 16 different chronic pain disorders. Disease severity was assessed by the Medical Outcomes Study Short Form-36 (SF-36). Univariate analyses were used to assess the magnitude of PSD and SIQR correlations with SF-36 subscales. Hierarchical stepwise regression was used to evaluate the unique contribution of the PSD and SIQR to the SF-36. Random forest regression probed the relative importance of the SIQR and PSD components as predictors of SF-36. The correlations with the SF-36 subscales were significantly higher for the SIQR (0.48 to 0.78) than the PSD (0.29 to 0.56; p < 0.001). Stepwise regression revealed that the SIQR was contributing additional unique variance on SF-36 subscales, which was not the case for the PSD. Random forest regression showed SIQR Function, Symptoms, and Global Impact subscales were more important predictors of SF-36 than the PSD. The single SIQR pain item contributed 55% of SF-36 pain variance compared to 23% with the 19-point WPI (the Widespread Pain Index component of PSD). The SIQR, the disease-neutral revision of the updated FIQ, has several important advantages over the PSD in the evaluation of disease severity in chronic pain disorders.

Identification of Extremely Premature Infants at High Risk of Rehospitalization

PubMed Central

Carlo, Waldemar A.; McDonald, Scott A.; Yao, Qing; Das, Abhik; Higgins, Rosemary D.

2011-01-01

OBJECTIVE: Extremely low birth weight infants often require rehospitalization during infancy. Our objective was to identify at the time of discharge which extremely low birth weight infants are at higher risk for rehospitalization. METHODS: Data from extremely low birth weight infants in Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network centers from 2002–2005 were analyzed. The primary outcome was rehospitalization by the 18- to 22-month follow-up, and secondary outcome was rehospitalization for respiratory causes in the first year. Using variables and odds ratios identified by stepwise logistic regression, scoring systems were developed with scores proportional to odds ratios. Classification and regression-tree analysis was performed by recursive partitioning and automatic selection of optimal cutoff points of variables. RESULTS: A total of 3787 infants were evaluated (mean ± SD birth weight: 787 ± 136 g; gestational age: 26 ± 2 weeks; 48% male, 42% black). Forty-five percent of the infants were rehospitalized by 18 to 22 months; 14.7% were rehospitalized for respiratory causes in the first year. Both regression models (area under the curve: 0.63) and classification and regression-tree models (mean misclassification rate: 40%–42%) were moderately accurate. Predictors for the primary outcome by regression were shunt surgery for hydrocephalus, hospital stay of >120 days for pulmonary reasons, necrotizing enterocolitis stage II or higher or spontaneous gastrointestinal perforation, higher fraction of inspired oxygen at 36 weeks, and male gender. By classification and regression-tree analysis, infants with hospital stays of >120 days for pulmonary reasons had a 66% rehospitalization rate compared with 42% without such a stay. CONCLUSIONS: The scoring systems and classification and regression-tree analysis models identified infants at higher risk of rehospitalization and might assist planning for care after discharge. PMID:22007016

Concurrent Validity of Hill's Educational Cognitive Style Model as a Prototype for Successful Academic Programs Among Lower-Class Students.

ERIC Educational Resources Information Center

London, David T.

Data from the stepwise multiple regression of four educational cognitive style predictor sets on each of six academic competence criteria were used to define the concurrent validity of Hill's educational cognitive style model. The purpose was to determine how appropriate it may be to use this model as a prototype for successful academic programs…

Do Basic Skills Predict Youth Unemployment (16- to 24-Year-Olds) Also when Controlled for Accomplished Upper-Secondary School? A Cross-Country Comparison

ERIC Educational Resources Information Center

Lundetrae, Kjersti; Gabrielsen, Egil; Mykletun, Reidar

2010-01-01

Basic skills and educational level are closely related, and both might affect employment. Data from the Adult Literacy and Life Skills Survey were used to examine whether basic skills in terms of literacy and numeracy predicted youth unemployment (16-24 years) while controlling for educational level. Stepwise logistic regression showed that in…

Objective Quantification of Pre-and Postphonosurgery Vocal Fold Vibratory Characteristics Using High-Speed Videoendoscopy and a Harmonic Waveform Model

ERIC Educational Resources Information Center

Ikuma, Takeshi; Kunduk, Melda; McWhorter, Andrew J.

2014-01-01

Purpose: The model-based quantitative analysis of high-speed videoendoscopy (HSV) data at a low frame rate of 2,000 frames per second was assessed for its clinical adequacy. Stepwise regression was employed to evaluate the HSV parameters using harmonic models and their relationships to the Voice Handicap Index (VHI). Also, the model-based HSV…

A statistical model of expansion in a colony of black-tailed prairie dogs

Treesearch

R. P. Cincotta; Daniel W. Uresk; R. M. Hansen

1988-01-01

To predict prairie dog establishment in areas adjacent to a colony we sample: (1) VISIBILITY through the vegetation using a target, (2) POPULATION DENSITY at the cology edge, (3) DISTANCE from the edge to the potential site of settlement, and (4) % FORB COVER. Step-wise regression analysis indicated that establishment of prairie dogs in adjacent prairie was most likely...

Factors that influence the efficacy of acarbose and metformin as initial therapy in Chinese patients with newly diagnosed type 2 diabetes: a subanalysis of the MARCH trial.

PubMed

Zhang, Jinping; Wang, Na; Xing, Xiaoyan; Yang, Zhaojun; Wang, Xin; Yang, Wenying

2016-01-01

To conduct a subanalysis of the randomized MARCH (Metformin and AcaRbose in Chinese as the initial Hypoglycemic treatment) trial to investigate whether specific characteristics are associated with the efficacy of either acarbose or metformin as initial therapy. A total of 657 type 2 diabetes patients who were randomly assigned to 48 weeks of therapy with either acarbose or metformin in the MARCH trial were divided into two groups based upon their hemoglobin A1c (HbA1c) levels at the end of follow-up: HbA1c <7% (<53 mmol/mol) and ≥7% (≥53 mmol/mol). Univariate, multivariate, and stepwise linear regression analyses were applied to identify the factors associated with treatment efficacy. Because this was a subanalysis, no measurement was performed. Univariate analysis showed that the efficacy of acarbose and metformin was influenced by HbA1c, fasting blood glucose (FBG), and 2 hour postprandial venous blood glucose (2hPPG) levels, as well as by changes in body mass index (BMI) (p ≤ 0.006). Multivariate analysis and stepwise linear regression analyses indicated that lower baseline 2hPPG values and greater changes in BMI were factors that positively influenced efficacy in both treatment groups (p ≤ 0.05). Stepwise regression model analysis also revealed that a lower baseline homeostasis model assessment-estimated insulin resistance (HOMA-IR) and higher serum insulin area under the curve (AUC) were factors positively influencing HbA1c normalization in all patients (p ≤ 0.032). Newly diagnosed type 2 diabetes patients with lower baseline 2hPPG and HOMA-IR values are more likely to achieve glucose control with acarbose or metformin treatment. Furthermore, the change in BMI after acarbose or metformin treatment is also a factor influencing HbA1c normalization. A prospective study with a larger sample size is necessary to confirm our results as well as measure β cell function and examine the influence of the patients' dietary habits.

Hyperspectral analysis of soil organic matter in coal mining regions using wavelets, correlations, and partial least squares regression.

PubMed

Lin, Lixin; Wang, Yunjia; Teng, Jiyao; Wang, Xuchen

2016-02-01

Hyperspectral estimation of soil organic matter (SOM) in coal mining regions is an important tool for enhancing fertilization in soil restoration programs. The correlation--partial least squares regression (PLSR) method effectively solves the information loss problem of correlation--multiple linear stepwise regression, but results of the correlation analysis must be optimized to improve precision. This study considers the relationship between spectral reflectance and SOM based on spectral reflectance curves of soil samples collected from coal mining regions. Based on the major absorption troughs in the 400-1006 nm spectral range, PLSR analysis was performed using 289 independent bands of the second derivative (SDR) with three levels and measured SOM values. A wavelet-correlation-PLSR (W-C-PLSR) model was then constructed. By amplifying useful information that was previously obscured by noise, the W-C-PLSR model was optimal for estimating SOM content, with smaller prediction errors in both calibration (R(2) = 0.970, root mean square error (RMSEC) = 3.10, and mean relative error (MREC) = 8.75) and validation (RMSEV = 5.85 and MREV = 14.32) analyses, as compared with other models. Results indicate that W-C-PLSR has great potential to estimate SOM in coal mining regions.

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

PubMed

Bilgili, Mehmet; Sahin, Besir; Sangun, Levent

2013-01-01

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

Predictive equations for the estimation of body size in seals and sea lions (Carnivora: Pinnipedia)

PubMed Central

Churchill, Morgan; Clementz, Mark T; Kohno, Naoki

2014-01-01

Body size plays an important role in pinniped ecology and life history. However, body size data is often absent for historical, archaeological, and fossil specimens. To estimate the body size of pinnipeds (seals, sea lions, and walruses) for today and the past, we used 14 commonly preserved cranial measurements to develop sets of single variable and multivariate predictive equations for pinniped body mass and total length. Principal components analysis (PCA) was used to test whether separate family specific regressions were more appropriate than single predictive equations for Pinnipedia. The influence of phylogeny was tested with phylogenetic independent contrasts (PIC). The accuracy of these regressions was then assessed using a combination of coefficient of determination, percent prediction error, and standard error of estimation. Three different methods of multivariate analysis were examined: bidirectional stepwise model selection using Akaike information criteria; all-subsets model selection using Bayesian information criteria (BIC); and partial least squares regression. The PCA showed clear discrimination between Otariidae (fur seals and sea lions) and Phocidae (earless seals) for the 14 measurements, indicating the need for family-specific regression equations. The PIC analysis found that phylogeny had a minor influence on relationship between morphological variables and body size. The regressions for total length were more accurate than those for body mass, and equations specific to Otariidae were more accurate than those for Phocidae. Of the three multivariate methods, the all-subsets approach required the fewest number of variables to estimate body size accurately. We then used the single variable predictive equations and the all-subsets approach to estimate the body size of two recently extinct pinniped taxa, the Caribbean monk seal (Monachus tropicalis) and the Japanese sea lion (Zalophus japonicus). Body size estimates using single variable regressions generally under or over-estimated body size; however, the all-subset regression produced body size estimates that were close to historically recorded body length for these two species. This indicates that the all-subset regression equations developed in this study can estimate body size accurately. PMID:24916814

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

PubMed

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

2010-08-01

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

The role of hyperinsulinemia as a cardiometabolic risk factor independent of obesity in polycystic ovary syndrome.

PubMed

Csenteri, Orsolya Karola; Sándor, János; Kalina, Edit; Bhattoa, Harjit Pal; Gődény, Sándor

2017-01-01

The aim of this study was to utilize various insulin resistance measuring methods to determine whether insulin resistance and other parameters impact the serum lipid levels of polycystic ovary syndrome (PCOS) patients and how the serum lipid levels in these patients are affected by the body mass index (BMI). Our dataset included patients between the ages of 16 and 42 (N = 228) from the outpatient endocrinology clinic of the Department of Obstetrics and Gynecology, who demonstrated increased hair growth and bleeding disorders and came for a routine oral glucose tolerance test (OGTT). Differences in the serum lipid levels were evaluated by t-test and linear regression analysis after adjusting for BMI. A stepwise regression model was constructed to evaluate the influence of each variable on the lipid levels. In PCOS patients, we found that dyslipidemia is more prevalent among hyperinsulinemic women compared with normoinsulinemic women, even after normalizing for BMI. PCOS patients with insulin resistance, determined by the insulin sensitivity index (ISI) method, showed more significant lipid abnormalities such as low high-density lipoprotein (HDL) and apo-A levels and high total cholesterol, low-density lipoprotein (LDL) and apo-B levels than if insulin resistance (IR) determination was based on insulin level or homeostatic model assessment (HOMA).

Estimation of abbreviated mycophenolic acid area under the concentration-time curve during early posttransplant period by limited sampling strategy.

PubMed

Mohammadpour, A-H; Nazemian, F; Abtahi, B; Naghibi, M; Gholami, K; Rezaee, S; Nazari, M-R A; Rajabi, O

2008-12-01

Area under the concentration curve (AUC) of mycophenolic acid (MPA) could help to optimize therapeutic drug monitoring during the early post-renal transplant period. The aim of this study was to develop a limited sampling strategy to estimate an abbreviated MPA AUC within the first month after renal transplantation. In this study we selected 19 patients in the early posttransplant period with normal renal graft function (glomerular filtration rate > 70 mL/min). Plasma MPA concentrations were measured using reverse-phase high-performance liquid chromatography. MPA AUC(0-12h) was calculated using the linear trapezoidal rule. Multiple stepwise regression analysis was used to determine the minimal and convenient time points of MPA levels that could be used to derive model equations best fitted to MPA AUC(0-12h). The regression equation for AUC estimation that gave the best performance was AUC = 14.46 C(10) + 15.547 (r(2) = .882). The validation of the method was performed using the jackknife method. Mean prediction error of this model was not different from zero (P > .05) and had a high root mean square prediction error (8.06). In conclusion, this limited sampling strategy provided an effective approach for therapeutic drug monitoring during the early posttransplant period.

Preliminary bioelectrical impedance analysis (BIA) equation for body composition assessment in young females from Colombia

NASA Astrophysics Data System (ADS)

Caicedo-Eraso, J. C.; González-Correa, C. H.; González-Correa, C. A.

2013-04-01

A previous study showed that reported BIA equations for body composition are not suitable for Colombian population. The purpose of this study was to develop and validate a preliminary BIA equation for body composition assessment in young females from Colombia, using hydrodensitometry as reference method. A sample of 30 young females was evaluated. Inclusion and exclusion criteria were defined to minimize the variability of BIA. Height, weight, BIA, residual lung volume (RV) and underwater weight (UWW) were measured. A preliminary BIA equation was developed (r2 = 0.72, SEE = 2.48 kg) by stepwise multiple regression with fat-free mass (FFM) as dependent variable and weight, height and impedance measurements as independent variables. The quality of regression was evaluated and a cross-validation against 50% of sample confirmed that results obtained with the preliminary BIA equation is interchangeable with results obtained with hydrodensitometry (r2 = 0.84, SEE = 2.62 kg). The preliminary BIA equation can be used for body composition assessment in young females from Colombia until a definitive equation is developed. The next step will be increasing the sample, including a second reference method, as deuterium oxide dilution (D2O), and using multi-frequency BIA (MF-BIA). It would also be desirable to develop equations for males and other ethnic groups in Colombia.

Personal Well-being and Stress Symptoms in Wives of Iranian Martyrs, Prisoners of wars and Disabled Veterans

PubMed Central

Sharif, Nasim

2010-01-01

Objective This study was conducted to compare the personal well-being among the wives of Iranian veterans living in the city of Qom. Method A sample of 300 was randomly selected from a database containing the addresses of veteran's families at Iran's Veterans Foundation in Qom (Bonyad-e-Shahid va Omoore Isargaran). The veterans' wives were divided into three groups: wives of martyrs (killed veterans), wives of prisoners of war, and wives of disabled veterans. The Persian translation of Personal Well-being Index and Stress Symptoms Checklist (SSC) were administered for data collection. Four women chose not to respond to Personal Well-being Index. Data were then analyzed using linear multivariate regression (stepwise method), analysis of variance, and by computing the correlation between variables. Results Results showed a negative correlation between well-being and stress symptoms. However, each group demonstrated different levels of stress symptoms. Furthermore, multivariate linear regression in the 3 groups showed that overall satisfaction of life and personal well-being (total score and its domains) could be predicted by different symptoms. Conclusion Each group experienced different challenges and thus different stress symptoms. Therefore, although they all need help, each group needs to be helped in a different way. PMID:22952487

Investigation of gene expressions in differentiated cell derived bone marrow stem cells during bone morphogenetic protein-4 treatments with Fourier transform infrared spectroscopy

NASA Astrophysics Data System (ADS)

Zafari, Jaber; Jouni, Fatemeh Javani; Ahmadvand, Ali; Abdolmaleki, Parviz; Soodi, Malihe; Zendehdel, Rezvan

2017-02-01

A model was set up to predict the differentiation patterns based on the data extracted from FTIR spectroscopy. For this reason, bone marrow stem cells (BMSCs) were differentiated to primordial germ cells (PGCs). Changes in cellular macromolecules in the time of 0, 24, 48, 72, and 96 h of differentiation, as different steps of the differentiation procedure were investigated by using FTIR spectroscopy. Also, the expression of pluripotency (Oct-4, Nanog and c-Myc) and specific genes (Mvh, Stella and Fragilis) were investigated by real-time PCR. However, the expression of genes in five steps of differentiation was predicted by FTIR spectroscopy. FTIR spectra showed changes in the template of band intensities at different differentiation steps. There are increasing changes in the stepwise differentiation procedure for the ratio area of CH2, which is symmetric to CH2 asymmetric stretching. An ensemble of expert methods, including regression tree (RT), boosting algorithm (BA), and generalized regression neural network (GRNN), was the best method to predict the gene expression by FTIR spectroscopy. In conclusion, the model was able to distinguish the pattern of different steps from cell differentiation by using some useful features extracted from FTIR spectra.

Improved Variable Selection Algorithm Using a LASSO-Type Penalty, with an Application to Assessing Hepatitis B Infection Relevant Factors in Community Residents

PubMed Central

Guo, Pi; Zeng, Fangfang; Hu, Xiaomin; Zhang, Dingmei; Zhu, Shuming; Deng, Yu; Hao, Yuantao

2015-01-01

Objectives In epidemiological studies, it is important to identify independent associations between collective exposures and a health outcome. The current stepwise selection technique ignores stochastic errors and suffers from a lack of stability. The alternative LASSO-penalized regression model can be applied to detect significant predictors from a pool of candidate variables. However, this technique is prone to false positives and tends to create excessive biases. It remains challenging to develop robust variable selection methods and enhance predictability. Material and methods Two improved algorithms denoted the two-stage hybrid and bootstrap ranking procedures, both using a LASSO-type penalty, were developed for epidemiological association analysis. The performance of the proposed procedures and other methods including conventional LASSO, Bolasso, stepwise and stability selection models were evaluated using intensive simulation. In addition, methods were compared by using an empirical analysis based on large-scale survey data of hepatitis B infection-relevant factors among Guangdong residents. Results The proposed procedures produced comparable or less biased selection results when compared to conventional variable selection models. In total, the two newly proposed procedures were stable with respect to various scenarios of simulation, demonstrating a higher power and a lower false positive rate during variable selection than the compared methods. In empirical analysis, the proposed procedures yielding a sparse set of hepatitis B infection-relevant factors gave the best predictive performance and showed that the procedures were able to select a more stringent set of factors. The individual history of hepatitis B vaccination, family and individual history of hepatitis B infection were associated with hepatitis B infection in the studied residents according to the proposed procedures. Conclusions The newly proposed procedures improve the identification of significant variables and enable us to derive a new insight into epidemiological association analysis. PMID:26214802

Factors Associated with the Competencies of Public Health Workers in Township Hospitals: A Cross-Sectional Survey in Chongqing Municipality, China.

PubMed

He, Zhifei; Cheng, Zhaohui; Fu, Hang; Tang, Shangfeng; Fu, Qian; Fang, Haiqing; Xian, Yue; Ming, Hui; Feng, Zhanchun

2015-11-09

This study aimed to explore the competencies of public health workers (PHWs) of township hospitals in Chongqing Municipality (China), and determine the related impact factors of the competencies of PHWs; A cross-sectional research was conducted on 314 PHWs from 27 township hospitals in three districts in Chongqing Municipality (China), from June to August 2014. A self-assessment questionnaire was established on the basis of literature reviews and a competency dictionary. The differences in competencies among the three districts were determined by adopting the chi-square test, t-test, analysis of variance (ANOVA) method, and the impact factors of the competencies of PHWs were determined by adopting stepwise regression analysis. (1) RESULTS of the demographic characteristics of PHWs in three sample districts of Chongqing Municipality showed that a significant difference in age of PHWs (p = 0.021 < 0.05) and the majors of PHWs (p = 0.045 < 0.05); (2) In terms of the self-evaluation competency results of PHWs in township hospitals, seven among the 11 aspects were found to have significant differences in the three districts by the ANOVA test; (3) By adopting the t-test and ANOVA method, results of the relationship between the characteristics of PHWs and their competency scores showed that significant differences were found in the economic level (p = 0.000 < 0.05), age (p = 0.000 < 0.05), years of working (p = 0.000 < 0.05) and title of PHWs (p = 0.000 < 0.05); (4) Stepwise regression analysis was used to determine the impact factors of the competencies of PHWs in township hospitals, including the economic level (p = 0.000 < 0.001), years of working (p = 0.000 < 0.001), title (p = 0.001 < 0.005), and public health major (p = 0.007 < 0.01). The competencies of the township hospital staff in Chongqing Municipality (China), are generally insufficient, therefore, regulating the medical education and training skills of PHWs is crucial to improve the competencies of PHWs in the township hospitals of Chongqing Municipality. The results of this study can be mirrored in other areas of China.

Quantifying prognosis with risk predictions.

PubMed

Pace, Nathan L; Eberhart, Leopold H J; Kranke, Peter R

2012-01-01

Prognosis is a forecast, based on present observations in a patient, of their probable outcome from disease, surgery and so on. Research methods for the development of risk probabilities may not be familiar to some anaesthesiologists. We briefly describe methods for identifying risk factors and risk scores. A probability prediction rule assigns a risk probability to a patient for the occurrence of a specific event. Probability reflects the continuum between absolute certainty (Pi = 1) and certified impossibility (Pi = 0). Biomarkers and clinical covariates that modify risk are known as risk factors. The Pi as modified by risk factors can be estimated by identifying the risk factors and their weighting; these are usually obtained by stepwise logistic regression. The accuracy of probabilistic predictors can be separated into the concepts of 'overall performance', 'discrimination' and 'calibration'. Overall performance is the mathematical distance between predictions and outcomes. Discrimination is the ability of the predictor to rank order observations with different outcomes. Calibration is the correctness of prediction probabilities on an absolute scale. Statistical methods include the Brier score, coefficient of determination (Nagelkerke R2), C-statistic and regression calibration. External validation is the comparison of the actual outcomes to the predicted outcomes in a new and independent patient sample. External validation uses the statistical methods of overall performance, discrimination and calibration and is uniformly recommended before acceptance of the prediction model. Evidence from randomised controlled clinical trials should be obtained to show the effectiveness of risk scores for altering patient management and patient outcomes.

RRegrs: an R package for computer-aided model selection with multiple regression models.

PubMed

Tsiliki, Georgia; Munteanu, Cristian R; Seoane, Jose A; Fernandez-Lozano, Carlos; Sarimveis, Haralambos; Willighagen, Egon L

2015-01-01

Predictive regression models can be created with many different modelling approaches. Choices need to be made for data set splitting, cross-validation methods, specific regression parameters and best model criteria, as they all affect the accuracy and efficiency of the produced predictive models, and therefore, raising model reproducibility and comparison issues. Cheminformatics and bioinformatics are extensively using predictive modelling and exhibit a need for standardization of these methodologies in order to assist model selection and speed up the process of predictive model development. A tool accessible to all users, irrespectively of their statistical knowledge, would be valuable if it tests several simple and complex regression models and validation schemes, produce unified reports, and offer the option to be integrated into more extensive studies. Additionally, such methodology should be implemented as a free programming package, in order to be continuously adapted and redistributed by others. We propose an integrated framework for creating multiple regression models, called RRegrs. The tool offers the option of ten simple and complex regression methods combined with repeated 10-fold and leave-one-out cross-validation. Methods include Multiple Linear regression, Generalized Linear Model with Stepwise Feature Selection, Partial Least Squares regression, Lasso regression, and Support Vector Machines Recursive Feature Elimination. The new framework is an automated fully validated procedure which produces standardized reports to quickly oversee the impact of choices in modelling algorithms and assess the model and cross-validation results. The methodology was implemented as an open source R package, available at https://www.github.com/enanomapper/RRegrs, by reusing and extending on the caret package. The universality of the new methodology is demonstrated using five standard data sets from different scientific fields. Its efficiency in cheminformatics and QSAR modelling is shown with three use cases: proteomics data for surface-modified gold nanoparticles, nano-metal oxides descriptor data, and molecular descriptors for acute aquatic toxicity data. The results show that for all data sets RRegrs reports models with equal or better performance for both training and test sets than those reported in the original publications. Its good performance as well as its adaptability in terms of parameter optimization could make RRegrs a popular framework to assist the initial exploration of predictive models, and with that, the design of more comprehensive in silico screening applications.Graphical abstractRRegrs is a computer-aided model selection framework for R multiple regression models; this is a fully validated procedure with application to QSAR modelling.

Stepwise Inquiry into Hard Water in a High School Chemistry Laboratory

ERIC Educational Resources Information Center

Kakisako, Mami; Nishikawa, Kazuyuki; Nakano, Masayoshi; Harada, Kana S.; Tatsuoka, Tomoyuki; Koga, Nobuyoshi

2016-01-01

This study focuses on the design of a learning program to introduce complexometric titration as a method for determining water hardness in a high school chemistry laboratory. Students are introduced to the different properties and reactions of hard water in a stepwise manner so that they gain the necessary chemical knowledge and conceptual…

Influence of overstory on snow depth and density in hemlock-spruce stands: implications for management of deer habitat in Southeastern Alaska.

Treesearch

Thomas A. Hanley; Cathy L. Rose

1987-01-01

Snow depth and density were measured in 33 stands of western hemlock-Sitka spruce (Tsuga heterophylla [Rat] Sarg.-Picea sitchensis [Bong.] Carr.) over a 3-year period. The stands, near Juneau, Alaska, provided broad ranges of species composition, age, over-story canopy coverage, tree density, and wood volume. Stepwise multiple regression analyses indicated that both...

Patient satisfaction in Dental Healthcare Centers.

PubMed

Ali, Dena A

2016-01-01

This study aimed to (1) measure the degree of patient satisfaction among the clinical and nonclinical dental services offered at specialty dental centers and (2) investigate the factors associated with the degree of overall satisfaction. Four hundred and ninety-seven participants from five dental centers were recruited for this study. Each participant completed a self-administered questionnaire to measure patient satisfaction with clinical and nonclinical dental services. Analysis of variance, t-tests, a general linear model, and stepwise regression analysis was applied. The respondents were generally satisfied, but internal differences were observed. The exhibited highest satisfaction with the dentists' performance, followed by the dental assistants' services, and the lowest satisfaction with the center's physical appearance and accessibility. Females, participants with less than a bachelor's degree, and younger individuals were more satisfied with the clinical and nonclinical dental services. The stepwise regression analysis revealed that the coefficient of determination (R (2)) was 40.4%. The patient satisfaction with the performance of the dentists explained 42.6% of the overall satisfaction, whereas their satisfaction with the clinical setting explained 31.5% of the overall satisfaction. Additional improvements with regard to the accessibility and physical appearance of the dental centers are needed. In addition, interventions regarding accessibility, particularly when booking an appointment, are required.

Psychophysiological responses to competition and the big five personality traits.

PubMed

Binboga, Erdal; Guven, Senol; Catıkkaş, Fatih; Bayazıt, Onur; Tok, Serdar

2012-06-01

This study examines the relationship between psychophysiological arousal, cognitive anxiety, and personality traits in young taekwondo athletes. A total of 20 male and 10 female taekwondo athletes (mean age = 18.6 years; ± 1.8) volunteered for the study. The Five Factor Personality Inventory and the state scale of the Spielberger State-Trait Anxiety Inventory (STAI) were used to measure personality and cognitive state anxiety. Electrodermal activity (EDA) was measured twice, one day and approximately one hour prior to the competition, to determine psychophysiological arousal. Descriptive statistics, Pearson product-moment correlations, and stepwise regression were used to analyze the data. Several "Big Five" facets were related to the EDA delta scores that were measured both one day and one hour before the competition. Two stepwise regressions were conducted to examine whether personality traits could significantly predict both EDA delta scores. The final model, containing only neuroticism from the Big Five factors, can significantly explain the variations in the EDA delta scores measured one day before the competition. Agreeableness can significantly explain variations in the EDA delta scores measured one hour before the competition. No relationship was found between cognitive anxiety and the EDA delta scores measured one hour before the competition. In conclusion, personality traits, especially agreeableness and neuroticism, might be useful in understanding arousal responses to competition.

Psychophysiological Responses to Competition and the Big Five Personality Traits

PubMed Central

Binboga, Erdal; Guven, Senol; Çatıkkaş, Fatih; Bayazıt, Onur; Tok, Serdar

2012-01-01

This study examines the relationship between psychophysiological arousal, cognitive anxiety, and personality traits in young taekwondo athletes. A total of 20 male and 10 female taekwondo athletes (mean age = 18.6 years; ± 1.8) volunteered for the study. The Five Factor Personality Inventory and the state scale of the Spielberger State-Trait Anxiety Inventory (STAI) were used to measure personality and cognitive state anxiety. Electrodermal activity (EDA) was measured twice, one day and approximately one hour prior to the competition, to determine psychophysiological arousal. Descriptive statistics, Pearson product-moment correlations, and stepwise regression were used to analyze the data. Several “Big Five” facets were related to the EDA delta scores that were measured both one day and one hour before the competition. Two stepwise regressions were conducted to examine whether personality traits could significantly predict both EDA delta scores. The final model, containing only neuroticism from the Big Five factors, can significantly explain the variations in the EDA delta scores measured one day before the competition. Agreeableness can significantly explain variations in the EDA delta scores measured one hour before the competition. No relationship was found between cognitive anxiety and the EDA delta scores measured one hour before the competition. In conclusion, personality traits, especially agreeableness and neuroticism, might be useful in understanding arousal responses to competition. PMID:23486906

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.

STEP and STEPSPL: Computer programs for aerodynamic model structure determination and parameter estimation

NASA Technical Reports Server (NTRS)

Batterson, J. G.

1986-01-01

The successful parametric modeling of the aerodynamics for an airplane operating at high angles of attack or sideslip is performed in two phases. First the aerodynamic model structure must be determined and second the associated aerodynamic parameters (stability and control derivatives) must be estimated for that model. The purpose of this paper is to document two versions of a stepwise regression computer program which were developed for the determination of airplane aerodynamic model structure and to provide two examples of their use on computer generated data. References are provided for the application of the programs to real flight data. The two computer programs that are the subject of this report, STEP and STEPSPL, are written in FORTRAN IV (ANSI l966) compatible with a CDC FTN4 compiler. Both programs are adaptations of a standard forward stepwise regression algorithm. The purpose of the adaptation is to facilitate the selection of a adequate mathematical model of the aerodynamic force and moment coefficients of an airplane from flight test data. The major difference between STEP and STEPSPL is in the basis for the model. The basis for the model in STEP is the standard polynomial Taylor's series expansion of the aerodynamic function about some steady-state trim condition. Program STEPSPL utilizes a set of spline basis functions.

Relation between trinucleotide GAA repeat length and sensory neuropathy in Friedreich's ataxia.

PubMed

Santoro, L; De Michele, G; Perretti, A; Crisci, C; Cocozza, S; Cavalcanti, F; Ragno, M; Monticelli, A; Filla, A; Caruso, G

1999-01-01

To verify if GAA expansion size in Friedreich's ataxia could account for the severity of sensory neuropathy. Retrospective study of 56 patients with Friedreich's ataxia selected according to homozygosity for GAA expansion and availability of electrophysiological findings. Orthodromic sensory conduction velocity in the median nerve was available in all patients and that of the tibial nerve in 46 of them. Data of sural nerve biopsy and of a morphometric analysis were available in 12 of the selected patients. The sensory action potential amplitude at the wrist (wSAP) and at the medial malleolus (m mal SAP) and the percentage of myelinated fibres with diameter larger than 7, 9, and 11 microm in the sural nerve were correlated with disease duration and GAA expansion size on the shorter (GAA1) and larger (GAA2) expanded allele in each pair. Pearson's correlation test and stepwise multiple regression were used for statistical analysis. A significant inverse correlation between GAA1 size and wSAP, m mal SAP, and percentage of myelinated fibres was found. Stepwise multiple regression showed that GAA1 size significantly affects electrophysiological and morphometric data, whereas duration of disease has no effect. The data suggest that the severity of the sensory neuropathy is probably genetically determined and that it is not progressive.

Predicting Retention Times of Naturally Occurring Phenolic Compounds in Reversed-Phase Liquid Chromatography: A Quantitative Structure-Retention Relationship (QSRR) Approach

PubMed Central

Akbar, Jamshed; Iqbal, Shahid; Batool, Fozia; Karim, Abdul; Chan, Kim Wei

2012-01-01

Quantitative structure-retention relationships (QSRRs) have successfully been developed for naturally occurring phenolic compounds in a reversed-phase liquid chromatographic (RPLC) system. A total of 1519 descriptors were calculated from the optimized structures of the molecules using MOPAC2009 and DRAGON softwares. The data set of 39 molecules was divided into training and external validation sets. For feature selection and mapping we used step-wise multiple linear regression (SMLR), unsupervised forward selection followed by step-wise multiple linear regression (UFS-SMLR) and artificial neural networks (ANN). Stable and robust models with significant predictive abilities in terms of validation statistics were obtained with negation of any chance correlation. ANN models were found better than remaining two approaches. HNar, IDM, Mp, GATS2v, DISP and 3D-MoRSE (signals 22, 28 and 32) descriptors based on van der Waals volume, electronegativity, mass and polarizability, at atomic level, were found to have significant effects on the retention times. The possible implications of these descriptors in RPLC have been discussed. All the models are proven to be quite able to predict the retention times of phenolic compounds and have shown remarkable validation, robustness, stability and predictive performance. PMID:23203132

Predictors of competitive achievement among pubescent synchronized swimmers: an analysis of the solo-figure competition.

PubMed

Peric, M; Cavar, M; Zenic, N; Sekulic, D; Sajber, D

2014-02-01

This study examined the applicability of sport-specific fitness tests (SSTs), anthropometrics, and respiratory parameters in predicting competitive results among pubescent synchronized swimmers. A total of 25 synchronized swimmers (16-17 years; 166.2 ± 5.4 cm; and 58.4 ± 4.3 kg) volunteered for this study. The independent variables were body mass, body height, Body Mass Index (BMI), body fat percentage (BF%), lean body mass percentage, respiratory variables, and four SSTs (two specific power tests plus one aerobic- and one anaerobic-endurance test). The dependent variable was competitive achievement in the solo figure competition. The reliability analyses, Pearson's correlation coefficient and forward stepwise regression were calculated. The SSTs were reliable for testing fitness status among pubescent synchronized swimmers. The forward stepwise regression retained two SSTs, BF% and forced vital capacity (FVC, relative for age and stature) in a set of predictors of competitive achievement. Significant Beta coefficients are found for aerobic-endurance, SST and FVC. The sport-specific measure of aerobic endurance and FVC appropriately predicted competitive achievement with regard to the figures used in the competition when competitive results (the dependent variable) were obtained. Athletes and coaches should be aware of the probable negative influence of very low body fat levels on competitive achievement.

Discussion of the Investigation Method on the Reaction Kinetics of Metallurgical Reaction Engineering

NASA Astrophysics Data System (ADS)

Du, Ruiling; Wu, Keng; Zhang, Jiazhi; Zhao, Yong

Reaction kinetics of metallurgical physical chemistry which was successfully applied in metallurgy (as ferrous metallurgy, non-ferrous metallurgy) became an important theoretical foundation for subject system of traditional metallurgy. Not only the research methods were very perfect, but also the independent structures and systems of it had been formed. One of the important tasks of metallurgical reaction engineering was the simulation of metallurgical process. And then, the mechanism of reaction process and the conversion time points of different control links should be obtained accurately. Therefore, the research methods and results of reaction kinetics in metallurgical physical chemistry were not very suitable for metallurgical reaction engineering. In order to provide the definite conditions of transmission, reaction kinetics parameters and the conversion time points of different control links for solving the transmission and reaction equations in metallurgical reaction engineering, a new method for researching kinetics mechanisms in metallurgical reaction engineering was proposed, which was named stepwise attempt method. Then the comparison of results between the two methods and the further development of stepwise attempt method were discussed in this paper. As a new research method for reaction kinetics in metallurgical reaction engineering, stepwise attempt method could not only satisfy the development of metallurgical reaction engineering, but also provide necessary guarantees for establishing its independent subject system.

The microbiological profile and presence of bloodstream infection influence mortality rates in necrotizing fasciitis

PubMed Central

2011-01-01

Introduction Necrotizing fasciitis (NF) is a life threatening infectious disease with a high mortality rate. We carried out a microbiological characterization of the causative pathogens. We investigated the correlation of mortality in NF with bloodstream infection and with the presence of co-morbidities. Methods In this retrospective study, we analyzed 323 patients who presented with necrotizing fasciitis at two different institutions. Bloodstream infection (BSI) was defined as a positive blood culture result. The patients were categorized as survivors and non-survivors. Eleven clinically important variables which were statistically significant by univariate analysis were selected for multivariate regression analysis and a stepwise logistic regression model was developed to determine the association between BSI and mortality. Results Univariate logistic regression analysis showed that patients with hypotension, heart disease, liver disease, presence of Vibrio spp. in wound cultures, presence of fungus in wound cultures, and presence of Streptococcus group A, Aeromonas spp. or Vibrio spp. in blood cultures, had a significantly higher risk of in-hospital mortality. Our multivariate logistic regression analysis showed a higher risk of mortality in patients with pre-existing conditions like hypotension, heart disease, and liver disease. Multivariate logistic regression analysis also showed that presence of Vibrio spp in wound cultures, and presence of Streptococcus Group A in blood cultures were associated with a high risk of mortality while debridement > = 3 was associated with improved survival. Conclusions Mortality in patients with necrotizing fasciitis was significantly associated with the presence of Vibrio in wound cultures and Streptococcus group A in blood cultures. PMID:21693053

Stepwise detection of recombination breakpoints in sequence alignments.

PubMed

Graham, Jinko; McNeney, Brad; Seillier-Moiseiwitsch, Françoise

2005-03-01

We propose a stepwise approach to identify recombination breakpoints in a sequence alignment. The approach can be applied to any recombination detection method that uses a permutation test and provides estimates of breakpoints. We illustrate the approach by analyses of a simulated dataset and alignments of real data from HIV-1 and human chromosome 7. The presented simulation results compare the statistical properties of one-step and two-step procedures. More breakpoints are found with a two-step procedure than with a single application of a given method, particularly for higher recombination rates. At higher recombination rates, the additional breakpoints were located at the cost of only a slight increase in the number of falsely declared breakpoints. However, a large proportion of breakpoints still go undetected. A makefile and C source code for phylogenetic profiling and the maximum chi2 method, tested with the gcc compiler on Linux and WindowsXP, are available at http://stat-db.stat.sfu.ca/stepwise/ jgraham@stat.sfu.ca.

Predicting meat yields and commercial meat cuts from carcasses of young bulls of Spanish breeds by the SEUROP method and an image analysis system.

PubMed

Oliver, A; Mendizabal, J A; Ripoll, G; Albertí, P; Purroy, A

2010-04-01

The SEUROP system is currently in use for carcass classification in Europe. Image analysis and other new technologies are being developed to enhance and supplement this classification system. After slaughtering, 91 carcasses of local Spanish beef breeds were weighed and classified according to the SEUROP system. Two digital photographs (a side and a dorsal view) were taken of the left carcass sides, and a total of 33 morphometric measurements (lengths, perimeters, areas) were made. Commercial butchering of these carcasses took place 24 h postmortem, and the different cuts were grouped according to four commercial meat cut quality categories: extra, first, second, and third. Multiple regression analysis of carcass weight and the SEUROP conformation score (x variables) on meat yield and the four commercial cut quality category yields (y variables) was performed as a measure of the accuracy of the SEUROP system. Stepwise regression analysis of carcass weight and the 33 morphometric image analysis measurements (x variables) and meat yield and yields of the four commercial cut quality categories (y variables) was carried out. Higher accuracy was achieved using image analysis than using only the current SEUROP conformation score. The regression coefficient values were between R(2)=0.66 and R(2)=0.93 (P<0.001) for the SEUROP system and between R(2)=0.81 and R(2)=0.94 (P<0.001) for the image analysis method. These results suggest that the image analysis method should be helpful as a means of supplementing and enhancing the SEUROP system for grading beef carcasses. 2009 Elsevier Ltd. All rights reserved.

Reliability of a Bayesian network to predict an elevated aldosterone-to-renin ratio.

PubMed

Ducher, Michel; Mounier-Véhier, Claire; Lantelme, Pierre; Vaisse, Bernard; Baguet, Jean-Philippe; Fauvel, Jean-Pierre

2015-05-01

Resistant hypertension is common, mainly idiopathic, but sometimes related to primary aldosteronism. Thus, most hypertension specialists recommend screening for primary aldosteronism. To optimize the selection of patients whose aldosterone-to-renin ratio (ARR) is elevated from simple clinical and biological characteristics. Data from consecutive patients referred between 1 June 2008 and 30 May 2009 were collected retrospectively from five French 'European excellence hypertension centres' institutional registers. Patients were included if they had at least one of: onset of hypertension before age 40 years, resistant hypertension, history of hypokalaemia, efficient treatment by spironolactone, and potassium supplementation. An ARR>32 ng/L and aldosterone>160 ng/L in patients treated without agents altering the renin-angiotensin system was considered as elevated. Bayesian network and stepwise logistic regression were used to predict an elevated ARR. Of 334 patients, 89 were excluded (31 for incomplete data, 32 for taking agents that alter the renin-angiotensin system and 26 for other reasons). Among 245 included patients, 110 had an elevated ARR. Sensitivity reached 100% or 63.3% using Bayesian network or logistic regression, respectively, and specificity reached 89.6% or 67.2%, respectively. The area under the receiver-operating-characteristic curve obtained with the Bayesian network was significantly higher than that obtained by stepwise regression (0.93±0.02 vs. 0.70±0.03; P<0.001). In hypertension centres, Bayesian network efficiently detected patients with an elevated ARR. An external validation study is required before use in primary clinical settings. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Differentiating major depressive disorder in youths with attention deficit hyperactivity disorder.

PubMed

Diler, Rasim Somer; Daviss, W Burleson; Lopez, Adriana; Axelson, David; Iyengar, Satish; Birmaher, Boris

2007-09-01

Youths with attention deficit hyperactivity disorders (ADHD) frequently have comorbid major depressive disorders (MDD) sharing overlapping symptoms. Our objective was to examine which depressive symptoms best discriminate MDD among youths with ADHD. One-hundred-eleven youths with ADHD (5.2-17.8 years old) and their parents completed interviews with the K-SADS-PL and respective versions of the child or the parent Mood and Feelings Questionnaire (MFQ-C, MFQ-P). Controlling for group differences, logistic regression was used to calculate odds ratios reflecting the accuracy with which various depressive symptoms on the MFQ-C or MFQ-P discriminated MDD. Stepwise logistic regression then identified depressive symptoms that best discriminated the groups with and without MDD, using cross-validated misclassification rate as the criterion. Symptoms that discriminated youths with MDD (n=18) from those without MDD (n=93) were 4 of 6 mood/anhedonia symptoms, all 14 depressed cognition symptoms, and only 3 of 11 physical/vegetative symptoms. Mild irritability, miserable/unhappy moods, and symptoms related to sleep, appetite, energy levels and concentration did not discriminate MDD. A stepwise logistic regression correctly classified 89% of the comorbid MDD subjects, with only age, anhedonia at school, thoughts about killing self, thoughts that bad things would happen, and talking more slowly remaining in the final model. Results of this study may not generalize to community samples because subjects were drawn largely from a university-based outpatient psychiatric clinic. These findings stress the importance of social withdrawal, anhedonia, depressive cognitions, suicidal thoughts, and psychomotor retardation when trying to identify MDD among ADHD youths.

Identification of Key Beliefs Explaining Male Circumcision Motivation Among Adolescent Boys in Zimbabwe: Targets for Behavior Change Communication.

PubMed

Kasprzyk, Danuta; Tshimanga, Mufuta; Hamilton, Deven T; Gorn, Gerald J; Montaño, Daniel E

2018-02-01

Male circumcision (MC) significantly reduces HIV acquisition among men, leading WHO/UNAIDS to recommend high HIV and low MC prevalence countries circumcise 80% of adolescents and men age 15-49. Despite significant investment to increase MC capacity only 27% of the goal has been achieved in Zimbabwe. To increase adoption, research to create evidence-based messages is greatly needed. The Integrated Behavioral Model (IBM) was used to investigate factors affecting MC motivation among adolescents. Based on qualitative elicitation study results a survey was designed and administered to a representative sample of 802 adolescent boys aged 13-17 in two urban and two rural areas in Zimbabwe. Multiple regression analysis found all six IBM constructs (2 attitude, 2 social influence, 2 personal agency) significantly explained MC intention (R 2  = 0.55). Stepwise regression analysis of beliefs underlying each IBM belief-based construct found 9 behavioral, 6 injunctive norm, 2 descriptive norm, 5 efficacy, and 8 control beliefs significantly explained MC intention. A final stepwise regression of all the significant IBM construct beliefs identified 12 key beliefs best explaining intention. Similar analyses were carried out with subgroups of adolescents by urban-rural and age. Different sets of behavioral, normative, efficacy, and control beliefs were significant for each sub-group. This study demonstrates the application of theory-driven research to identify evidence-based targets for the design of effective MC messages for interventions to increase adolescents' motivation. Incorporating these findings into communication campaigns is likely to improve demand for MC.

Evaluation of 41 Candidate Gene Variants for Obesity in the EPIC-Potsdam Cohort by Multi-Locus Stepwise Regression

PubMed Central

Knüppel, Sven; Rohde, Klaus; Meidtner, Karina; Drogan, Dagmar; Holzhütter, Hermann-Georg; Boeing, Heiner; Fisher, Eva

2013-01-01

Objective Obesity has become a leading preventable cause of morbidity and mortality in many parts of the world. It is thought to originate from multiple genetic and environmental determinants. The aim of the current study was to introduce haplotype-based multi-locus stepwise regression (MSR) as a method to investigate combinations of unlinked single nucleotide polymorphisms (SNPs) for obesity phenotypes. Methods In 2,122 healthy randomly selected men and women of the EPIC-Potsdam cohort, the association between 41 SNPs from 18 obesity-candidate genes and either body mass index (BMI, mean = 25.9 kg/m2, SD = 4.1) or waist circumference (WC, mean = 85.2 cm, SD = 12.6) was assessed. Single SNP analyses were done by using linear regression adjusted for age, sex, and other covariates. Subsequently, MSR was applied to search for the ‘best’ SNP combinations. Combinations were selected according to specific AICc and p-value criteria. Model uncertainty was accounted for by a permutation test. Results The strongest single SNP effects on BMI were found for TBC1D1 rs637797 (β = −0.33, SE = 0.13), FTO rs9939609 (β = 0.28, SE = 0.13), MC4R rs17700144 (β = 0.41, SE = 0.15), and MC4R rs10871777 (β = 0.34, SE = 0.14). All these SNPs showed similar effects on waist circumference. The two ‘best’ six-SNP combinations for BMI (global p-value = 3.45⋅10–6 and 6.82⋅10–6) showed effects ranging from −1.70 (SE = 0.34) to 0.74 kg/m2 (SE = 0.21) per allele combination. We selected two six-SNP combinations on waist circumference (global p-value = 7.80⋅10–6 and 9.76⋅10–6) with an allele combination effect of −2.96 cm (SE = 0.76) at maximum. Additional adjustment for BMI revealed 15 three-SNP combinations (global p-values ranged from 3.09⋅10–4 to 1.02⋅10–2). However, after carrying out the permutation test all SNP combinations lost significance indicating that the statistical associations might have occurred by chance. Conclusion MSR provides a tool to search for risk-related SNP combinations of common traits or diseases. However, the search process does not always find meaningful SNP combinations in a dataset. PMID:23874820

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

PubMed

Balabin, Roman M; Smirnov, Sergey V

2011-04-29

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

Developing Screening Services for Colorectal Cancer on Android Smartphones

PubMed Central

Wu, Hui-Ching; Chang, Chiao-Jung; Lin, Chun-Che; Tsai, Ming-Chang; Chang, Che-Chia

2014-01-01

Abstract Introduction: Colorectal cancer (CRC) is an important health problem in Western countries and also in Asia. It is the third leading cause of cancer deaths in both men and women in Taiwan. According to the well-known adenoma-to-carcinoma sequence, the majority of CRC develops from colorectal adenomatous polyps. This concept provides the rationale for screening and prevention of CRC. Removal of colorectal adenoma could reduce the mortality and incidence of CRC. Mobile phones are now playing an ever more crucial role in people's daily lives. The latest generation of smartphones is increasingly viewed as hand-held computers rather than as phones, because of their powerful on-board computing capability, capacious memories, large screens, and open operating systems that encourage development of applications (apps). Subjects and Methods: If we can detect the potential CRC patients early and offer them appropriate treatments and services, this would not only promote the quality of life, but also reduce the possible serious complications and medical costs. In this study, an intelligent CRC screening app on Android™ (Google™, Mountain View, CA) smartphones has been developed based on a data mining approach using decision tree algorithms. For comparison, the stepwise backward multivariate logistic regression model and the fecal occult blood test were also used. Results: Compared with the stepwise backward multivariate logistic regression model and the fecal occult blood test, the proposed app system not only provides an easy and efficient way to quickly detect high-risk groups of potential CRC patients, but also brings more information about CRC to customer-oriented services. Conclusions: We developed and implemented an app system on Android platforms for ubiquitous healthcare services for CRC screening. It can assist people in achieving early screening, diagnosis, and treatment purposes, prevent the occurrence of complications, and thus reach the goal of preventive medicine. PMID:24848873

Clinical and Dosimetric Predictors of Acute Severe Lymphopenia During Radiation Therapy and Concurrent Temozolomide for High-Grade Glioma

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

Huang, Jiayi, E-mail: jhuang@radonc.wustl.edu; DeWees, Todd A.; Badiyan, Shahed N.

Purpose: Acute severe lymphopenia (ASL) frequently develops during radiation therapy (RT) and concurrent temozolomide (TMZ) for high-grade glioma (HGG) and is associated with decreased survival. The current study was designed to identify potential predictors of ASL, with a focus on actionable RT-specific dosimetric parameters. Methods and Materials: From January 2007 to December 2012, 183 patients with HGG were treated with RT+TMZ and had available data including total lymphocyte count (TLC) and radiation dose-volume histogram parameters. ASL was defined as TLC of <500/μL within the first 3 months from the start of RT. Stepwise logistic regression analysis was used to determine themore » most important predictors of ASL. Results: Fifty-three patients (29%) developed ASL. Patients with ASL had significantly worse overall survival than those without (median: 12.5 vs 20.2 months, respectively, P<.001). Stepwise logistic regression analysis identified female sex (odds ratio [OR]: 5.30; 95% confidence interval [CI]: 2.46-11.41), older age (OR: 1.05; 95% CI: 1.02-1.09), lower baseline TLC (OR: 0.92; 95% CI: 0.87-0.98), and higher brain volume receiving 25 Gy (V{sub 25Gy}) (OR: 1.03; 95% CI: 1.003-1.05) as the most significant predictors for ASL. Brain V{sub 25Gy} <56% appeared to be the optimal threshold (OR: 2.36; 95% CI: 1.11-5.01), with an ASL rate of 38% versus 20% above and below this threshold, respectively (P=.006). Conclusions: Female sex, older age, lower baseline TLC, and higher brain V{sub 25Gy} are significant predictors of ASL during RT+TMZ therapy for HGG. Maintaining the V{sub 25Gy} of brain below 56% may reduce the risk of ASL.« less

Postoperative Delirium in Severely Burned Patients Undergoing Early Escharotomy: Incidence, Risk Factors, and Outcomes.

PubMed

Guo, Zhenggang; Liu, Jiabin; Li, Jia; Wang, Xiaoyan; Guo, Hui; Ma, Panpan; Su, Xiaojun; Li, Ping

The aim of this study is to investigate the incidence, related risk factors, and outcomes of postoperative delirium (POD) in severely burned patients undergoing early escharotomy. This study included 385 severely burned patients (injured <1 week; TBSA, 31-50% or 11-20%; American Society of Anesthesiologists physical status, II-IV) aged 18 to 65 years, who underwent early escharotomy between October 2014 and December 2015, and were selected by cluster sampling. The authors excluded patients with preoperative delirium or diagnosed dementia, depression, or cognitive dysfunction. Preoperative, perioperative, intraoperative, and postoperative information, such as demographic characteristics, vital signs, and health history were collected. The Confusion Assessment Method was used once daily for 5 days after surgery to identify POD. Stepwise binary logistic regression analysis was used to identify the risk factors for POD, t-tests, and χ tests were performed to compare the outcomes of patients with and without the condition. Fifty-six (14.55%) of the patients in the sample were diagnosed with POD. Stepwise binary logistic regression showed that the significant risk factors for POD in severely burned patients undergoing early escharotomy were advanced age (>50 years old), a history of alcohol consumption (>3/week), high American Society of Anesthesiologists classification (III or IV), time between injury and surgery (>2 days), number of previous escharotomies (>2), combined intravenous and inhalation anesthesia, no bispectral index applied, long duration surgery (>180 min), and intraoperative hypotension (mean arterial pressure < 55 mm Hg). On the basis of the different odds ratios, the authors established a weighted model. When the score of a patient's weighted odds ratios is more than 6, the incidence of POD increased significantly (P < .05). When the score of a patient's weighted odds ratios is more than 6, the incidence of POD increased significantly (P < .05). Further, POD was associated with more postoperative complications, including hepatic and renal function impairment and hypernatremia, as well as prolonged hospitalization, increased medical costs, and higher mortality.

Personality features, dissociation, self-stigma, hope, and the complex treatment of depressive disorder

PubMed Central

Prasko, Jan; Ociskova, Marie; Grambal, Ales; Sigmundova, Zuzana; Kasalova, Petra; Marackova, Marketa; Holubova, Michaela; Vrbova, Kristyna; Latalova, Klara; Slepecky, Milos

2016-01-01

Objective Identifying the predictors of response to psychiatric and psychotherapeutic treatments may be useful for increasing treatment efficacy in pharmacoresistant depressive patients. The goal of this study was to examine the influence of dissociation, hope, personality trait, and selected demographic factors in treatment response of this group of patients. Methods Pharmacoresistant depressive inpatients were enrolled in the study. All patients completed Clinical Global Impression – both objective and subjective form (CGI), Beck Depression Inventory (BDI), and Beck Anxiety Inventory (BAI) at baseline and after 6 weeks of combined pharmacotherapy and psychotherapy (group cognitive-behavioral or group psychodynamic) treatment as an outcome measures. The Internalized Stigma of Mental Illness Scale (ISMI), Dissociative Experience Scale (DES), Adult Dispositional Hope Scale (ADHS), and Temperament and Character Inventory (TCI-R) were completed at the start of the treatment with the intention to find the predictors of treatment efficacy. Results The study included 72 patients who were hospitalized for the pharmacoresistant major depression; 63 of them completed the study. The mean scores of BDI-II, BAI, subjCGI, and objCGI significantly decreased during the treatment. BDI-II relative change statistically significantly correlated with the total ISMI score, Discrimination Experience (ISMI subscale), and Harm Avoidance (TCI-R personality trait). According to stepwise regression, the strongest factors connected to BDI-II relative change were the duration of the disorder and Discrimination Experience (domain of ISMI). ObjCGI relative change significantly correlated with the level of dissociation (DES), the total ISMI score, hope in ADHS total score, and Self-Directedness (TCI-R). According to stepwise regression, the strongest factor connected to objCGI relative change was Discrimination Experience (domain of ISMI). The existence of comorbid personality disorder did not influence the treatment response. Conclusion According to the results of the present study, patients with pharmacoresistant depressive disorders, who have had more experience with discrimination because of their mental struggles, showed a poorer response to treatment. PMID:27785031

Work disability in Argentinian patients with systemic lupus erythematosus is prevalent and it is due to ethnic, socioeconomic and disease-related factors.

PubMed

Pisoni, Cecilia N; Muñoz, Sebastián A; Tamborenea, María N; García, Mercedes; Curti, Ana; Cappuccio, Ana; Rillo, Oscar; Imamura, Patricia M; Schneeberger, Emilce; Ballent, Marcela; Cousseau, Mario L; Velasco Zamora, Jorge; Saurit, Verónica; Toloza, Sergio; Danielsen, María C; Bellomio, Verónica I; Graf, Cesar; Paira, Sergio; Cavallasca, Javier; Pons Estel, Bernardo; Moreno, José L C; Díaz, Mónica; Alba, Paula; Verando, Marcela; Tate, Guillermo; Mysler, Eduardo; Sarano, Judith; Civit, Emma E; Risueño, Fabián; Álvarez Sepúlveda, Pablo; Larroude, María S; Méndez, Marcos F; Conforti, Andrea; Sohn, Débora

2018-04-02

To study the prevalence and the associated factors of work disability (WD) in systemic lupus erythematosus (SLE) patients. A sample of 419 SLE patients from an observational cross-sectional multicenter study was included. Sociodemographic features, disease characteristics, comorbidities, quality of life, unhealthy behaviors, and work-related factors were measured in a standardized interview. Work disability was defined by patient self-report of not being able to work because of SLE. To identify variables associated with work disability, two different multivariate regression models using a stepwise backward method were performed. Prevalence of WD due to SLE was 24.3%. Eighty-nine percent were female and 51% were Caucasians. Mean disease duration was 8.9 ± 7.2 years, and median System Lupus International Collaborating Clinics/American College of Rheumatology damage index SLICC-SDI was 1.5 (range 0-17). In stepwise multivariate logistic regression, living below the poverty line (odds ratio [OR] = 4.65), less than 12 years of education (OR = 2.84), Mestizo ethnicity (OR = 1.94) and SLICC-SDI (OR = 1.25) were predictors of WD. A second model was performed including patient-derived measures; in this model sedentary lifestyle (OR = 2.69) and lower emotional health domain score of the Lupus Quality of Life (LupusQoL) questionnaire (OR = 1.03) were found to be associated to WD and a higher score in LupusQoL physical health domain (OR = 0.93) was protective. The prevalence of WD in Argentinian SLE patients was 24.3%. WD was associated with ethnic (Mestizo), socioeconomic (poverty) and disease-related factors. Patient-related outcomes such us sedentary lifestyle and poor emotional quality of life were also associated with WD. © 2018 Asia Pacific League of Associations for Rheumatology and John Wiley & Sons Australia, Ltd.

Chronic obstructive pulmonary disease and coronary disease: COPDCoRi, a simple and effective algorithm for predicting the risk of coronary artery disease in COPD patients.

PubMed

Cazzola, Mario; Calzetta, Luigino; Matera, Maria Gabriella; Muscoli, Saverio; Rogliani, Paola; Romeo, Francesco

2015-08-01

Chronic obstructive pulmonary disease (COPD) is often associated with cardiovascular artery disease (CAD), representing a potential and independent risk factor for cardiovascular morbidity. Therefore, the aim of this study was to identify an algorithm for predicting the risk of CAD in COPD patients. We analyzed data of patients afferent to the Cardiology ward and the Respiratory Diseases outpatient clinic of Tor Vergata University (2010-2012, 1596 records). The study population was clustered as training population (COPD patients undergoing coronary arteriography), control population (non-COPD patients undergoing coronary arteriography), test population (COPD patients whose records reported information on the coronary status). The predicting model was built via causal relationship between variables, stepwise binary logistic regression and Hosmer-Lemeshow analysis. The algorithm was validated via split-sample validation method and receiver operating characteristics (ROC) curve analysis. The diagnostic accuracy was assessed. In training population the variables gender (men/women OR: 1.7, 95%CI: 1.237-2.5, P < 0.05), dyslipidemia (OR: 1.8, 95%CI: 1.2-2.5, P < 0.01) and smoking habit (OR: 1.5, 95%CI: 1.2-1.9, P < 0.001) were significantly associated with CAD in COPD patients, whereas in control population also age and diabetes were correlated. The stepwise binary logistic regressions permitted to build a well fitting predictive model for training population but not for control population. The predictive algorithm shown a diagnostic accuracy of 81.5% (95%CI: 77.78-84.71) and an AUC of 0.81 (95%CI: 0.78-0.85) for the validation set. The proposed algorithm is effective for predicting the risk of CAD in COPD patients via a rapid, inexpensive and non-invasive approach. Copyright © 2015 Elsevier Ltd. All rights reserved.

A whole blood gene expression-based signature for smoking status

PubMed Central

2012-01-01

Background Smoking is the leading cause of preventable death worldwide and has been shown to increase the risk of multiple diseases including coronary artery disease (CAD). We sought to identify genes whose levels of expression in whole blood correlate with self-reported smoking status. Methods Microarrays were used to identify gene expression changes in whole blood which correlated with self-reported smoking status; a set of significant genes from the microarray analysis were validated by qRT-PCR in an independent set of subjects. Stepwise forward logistic regression was performed using the qRT-PCR data to create a predictive model whose performance was validated in an independent set of subjects and compared to cotinine, a nicotine metabolite. Results Microarray analysis of whole blood RNA from 209 PREDICT subjects (41 current smokers, 4 quit ≤ 2 months, 64 quit > 2 months, 100 never smoked; NCT00500617) identified 4214 genes significantly correlated with self-reported smoking status. qRT-PCR was performed on 1,071 PREDICT subjects across 256 microarray genes significantly correlated with smoking or CAD. A five gene (CLDND1, LRRN3, MUC1, GOPC, LEF1) predictive model, derived from the qRT-PCR data using stepwise forward logistic regression, had a cross-validated mean AUC of 0.93 (sensitivity=0.78; specificity=0.95), and was validated using 180 independent PREDICT subjects (AUC=0.82, CI 0.69-0.94; sensitivity=0.63; specificity=0.94). Plasma from the 180 validation subjects was used to assess levels of cotinine; a model using a threshold of 10 ng/ml cotinine resulted in an AUC of 0.89 (CI 0.81-0.97; sensitivity=0.81; specificity=0.97; kappa with expression model = 0.53). Conclusion We have constructed and validated a whole blood gene expression score for the evaluation of smoking status, demonstrating that clinical and environmental factors contributing to cardiovascular disease risk can be assessed by gene expression. PMID:23210427

Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building.

PubMed

Sherer, Eric A; Sale, Mark E; Pollock, Bruce G; Belani, Chandra P; Egorin, Merrill J; Ivy, Percy S; Lieberman, Jeffrey A; Manuck, Stephen B; Marder, Stephen R; Muldoon, Matthew F; Scher, Howard I; Solit, David B; Bies, Robert R

2012-08-01

A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q₁ = 4.9 % and q₃ = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data.

Evaluation of alternative model selection criteria in the analysis of unimodal response curves using CART

USGS Publications Warehouse

Ribic, C.A.; Miller, T.W.

1998-01-01

We investigated CART performance with a unimodal response curve for one continuous response and four continuous explanatory variables, where two variables were important (ie directly related to the response) and the other two were not. We explored performance under three relationship strengths and two explanatory variable conditions: equal importance and one variable four times as important as the other. We compared CART variable selection performance using three tree-selection rules ('minimum risk', 'minimum risk complexity', 'one standard error') to stepwise polynomial ordinary least squares (OLS) under four sample size conditions. The one-standard-error and minimum-risk-complexity methods performed about as well as stepwise OLS with large sample sizes when the relationship was strong. With weaker relationships, equally important explanatory variables and larger sample sizes, the one-standard-error and minimum-risk-complexity rules performed better than stepwise OLS. With weaker relationships and explanatory variables of unequal importance, tree-structured methods did not perform as well as stepwise OLS. Comparing performance within tree-structured methods, with a strong relationship and equally important explanatory variables, the one-standard-error-rule was more likely to choose the correct model than were the other tree-selection rules 1) with weaker relationships and equally important explanatory variables; and 2) under all relationship strengths when explanatory variables were of unequal importance and sample sizes were lower.

Bootstrap investigation of the stability of a Cox regression model.

PubMed

Altman, D G; Andersen, P K

1989-07-01

We describe a bootstrap investigation of the stability of a Cox proportional hazards regression model resulting from the analysis of a clinical trial of azathioprine versus placebo in patients with primary biliary cirrhosis. We have considered stability to refer both to the choice of variables included in the model and, more importantly, to the predictive ability of the model. In stepwise Cox regression analyses of 100 bootstrap samples using 17 candidate variables, the most frequently selected variables were those selected in the original analysis, and no other important variable was identified. Thus there was no reason to doubt the model obtained in the original analysis. For each patient in the trial, bootstrap confidence intervals were constructed for the estimated probability of surviving two years. It is shown graphically that these intervals are markedly wider than those obtained from the original model.

RNA Viruses that Cause Hemorrhagic, Encephalitic, and Febrile Disease

DTIC Science & Technology

1990-01-01

doses to levels that are subopti- effective dose (ED,0) values for Rift Valley mal for cures in other bunyavirus mouse Fever ( RVF ) virus (ED,, = 80 g...serum protein and AST Etiologic Agent (SGOT) identified in the placebo group by logistic regression], utilizing a stepwise lo- RVF , an old-world...treatment of H FRS in this study. Treatment reduced mortality RVF , distributed throughout sub-Saharan and improved several important aspects of Africa

Population dynamics of pond zooplankton II Daphnia ambigua Scourfield

USGS Publications Warehouse

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

1973-01-01

Calcium was the most important of 27 environmental components affecting density for a 50 week period. Simultaneous stepwise regression accounted for more variability in total number/1 and in the number of ovigerous females/1 than did any of the lag analyses; 1-week lag accounted for the greatest amount of variability in clutch size. Total number and clutch size were little affected by measures of food. ?? 1973 Dr. W. Junk b.v. Publishers.

A new computational strategy for predicting essential genes.

PubMed

Cheng, Jian; Wu, Wenwu; Zhang, Yinwen; Li, Xiangchen; Jiang, Xiaoqian; Wei, Gehong; Tao, Shiheng

2013-12-21

Determination of the minimum gene set for cellular life is one of the central goals in biology. Genome-wide essential gene identification has progressed rapidly in certain bacterial species; however, it remains difficult to achieve in most eukaryotic species. Several computational models have recently been developed to integrate gene features and used as alternatives to transfer gene essentiality annotations between organisms. We first collected features that were widely used by previous predictive models and assessed the relationships between gene features and gene essentiality using a stepwise regression model. We found two issues that could significantly reduce model accuracy: (i) the effect of multicollinearity among gene features and (ii) the diverse and even contrasting correlations between gene features and gene essentiality existing within and among different species. To address these issues, we developed a novel model called feature-based weighted Naïve Bayes model (FWM), which is based on Naïve Bayes classifiers, logistic regression, and genetic algorithm. The proposed model assesses features and filters out the effects of multicollinearity and diversity. The performance of FWM was compared with other popular models, such as support vector machine, Naïve Bayes model, and logistic regression model, by applying FWM to reciprocally predict essential genes among and within 21 species. Our results showed that FWM significantly improves the accuracy and robustness of essential gene prediction. FWM can remarkably improve the accuracy of essential gene prediction and may be used as an alternative method for other classification work. This method can contribute substantially to the knowledge of the minimum gene sets required for living organisms and the discovery of new drug targets.

Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France

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

Caillouet, Laurie; Vidal, Jean -Philippe; Sauquet, Eric

This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the 1871–2012 period built on the NOAA Twentieth Century global extended atmospheric reanalysis (20CR). The objective is to fill in the spatial and temporal data gaps in surface observations in order to improve our knowledge on the local-scale climate variability from the late nineteenth century onwards. The SANDHY (Stepwise ANalogue Downscaling method for HYdrology) statistical downscaling method, initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between large-scale 20CR predictors and local-scale predictands from the Safran high-resolution near-surface reanalysis,more » available from 1958 onwards only. SANDHY provides a daily ensemble of 125 analogue dates over the 1871–2012 period for 608 climatically homogeneous zones paving France. Large precipitation biases in intermediary seasons are shown to occur in regions with high seasonal asymmetry like the Mediterranean. Moreover, winter and summer temperatures are respectively over- and under-estimated over the whole of France. Two analogue subselection methods are therefore developed with the aim of keeping the structure of the SANDHY method unchanged while reducing those seasonal biases. The calendar selection keeps the analogues closest to the target calendar day. The stepwise selection applies two new analogy steps based on similarity of the sea surface temperature (SST) and the large-scale 2 m temperature ( T). Comparisons to the Safran reanalysis over 1959–2007 and to homogenized series over the whole twentieth century show that biases in the interannual cycle of precipitation and temperature are reduced with both methods. The stepwise subselection moreover leads to a large improvement of interannual correlation and reduction of errors in seasonal temperature time series. When the calendar subselection is an easily applicable method suitable in a quantitative precipitation forecast context, the stepwise subselection method allows for potential season shifts and SST trends and is therefore better suited for climate reconstructions and climate change studies. Furthermore, the probabilistic downscaling of 20CR over the period 1871–2012 with the SANDHY probabilistic downscaling method combined with the stepwise subselection thus constitutes a perfect framework for assessing the recent observed meteorological events but also future events projected by climate change impact studies and putting them in a historical perspective.« less

Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France

DOE PAGES

Caillouet, Laurie; Vidal, Jean -Philippe; Sauquet, Eric; ...

2016-03-16

This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the 1871–2012 period built on the NOAA Twentieth Century global extended atmospheric reanalysis (20CR). The objective is to fill in the spatial and temporal data gaps in surface observations in order to improve our knowledge on the local-scale climate variability from the late nineteenth century onwards. The SANDHY (Stepwise ANalogue Downscaling method for HYdrology) statistical downscaling method, initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between large-scale 20CR predictors and local-scale predictands from the Safran high-resolution near-surface reanalysis,more » available from 1958 onwards only. SANDHY provides a daily ensemble of 125 analogue dates over the 1871–2012 period for 608 climatically homogeneous zones paving France. Large precipitation biases in intermediary seasons are shown to occur in regions with high seasonal asymmetry like the Mediterranean. Moreover, winter and summer temperatures are respectively over- and under-estimated over the whole of France. Two analogue subselection methods are therefore developed with the aim of keeping the structure of the SANDHY method unchanged while reducing those seasonal biases. The calendar selection keeps the analogues closest to the target calendar day. The stepwise selection applies two new analogy steps based on similarity of the sea surface temperature (SST) and the large-scale 2 m temperature ( T). Comparisons to the Safran reanalysis over 1959–2007 and to homogenized series over the whole twentieth century show that biases in the interannual cycle of precipitation and temperature are reduced with both methods. The stepwise subselection moreover leads to a large improvement of interannual correlation and reduction of errors in seasonal temperature time series. When the calendar subselection is an easily applicable method suitable in a quantitative precipitation forecast context, the stepwise subselection method allows for potential season shifts and SST trends and is therefore better suited for climate reconstructions and climate change studies. Furthermore, the probabilistic downscaling of 20CR over the period 1871–2012 with the SANDHY probabilistic downscaling method combined with the stepwise subselection thus constitutes a perfect framework for assessing the recent observed meteorological events but also future events projected by climate change impact studies and putting them in a historical perspective.« less

Low thrust spacecraft transfers optimization method with the stepwise control structure in the Earth-Moon system in terms of the L1-L2 transfer

NASA Astrophysics Data System (ADS)

Fain, M. K.; Starinova, O. L.

2016-04-01

The paper outlines the method for determination of the locally optimal stepwise control structure in the problem of the low thrust spacecraft transfer optimization in the Earth-Moon system, including the L1-L2 transfer. The total flight time as an optimization criterion is considered. The optimal control programs were obtained by using the Pontryagin's maximum principle. As a result of optimization, optimal control programs, corresponding trajectories, and minimal total flight times were determined.

New formulas for calculating the lung-to-head ratio in healthy fetuses between 20 and 40 weeks' gestation.

PubMed

Kehl, Sven; Eckert, Sven; Berlit, Sebastian; Tuschy, Benjamin; Sütterlin, Marc; Siemer, Jörn

2013-11-01

The purpose of this study was to develop new formulas for the expected fetal lung area-to-head circumference ratio in normal singleton pregnancies between 20 and 40 weeks' gestation. The lung-to-head ratio and complete fetal biometric parameters of 126 fetuses between 20 and 40 weeks' gestation were prospectively measured. The lung-to-head ratio was measured by 3 different methods (longest diameter, anteroposterior diameter, and tracing). Formulas for predicting right and left lung-to-head ratios with regard to gestational age and biometric parameters were derived by stepwise regression analysis. New formulas for calculating right and left lung-to-head ratios by each measurement method were derived. The formulas included gestational age only and no biometric parameters. The new formulas for estimating the expected lung-to-head ratio by the 3 different methods in normal singleton pregnancies up to 40 weeks' gestation may help improve the prognostic power of observed-to-expected lung-to-head ratio assessment in fetuses with congenital diaphragmatic hernias.

An accurate and rapid radiographic method of determining total lung capacity

PubMed Central

Reger, R. B.; Young, A.; Morgan, W. K. C.

1972-01-01

The accuracy and reliability of Barnhard's radiographic method of determining total lung capacity have been confirmed by several groups of investigators. Despite its simplicity and general reliability, it has several shortcomings, especially when used in large-scale epidemiological surveys. Of these, the most serious is related to film technique; thus, when the cardiac and diaphragmatic shadows are poorly defined, the appropriate measurements cannot be made accurately. A further drawback involves the time needed to measure the segments and to perform the necessary calculations. We therefore set out to develop an abbreviated and simpler radiographic method for determining total lung capacity. This uses a step-wise multiple regression model which allows total lung capacity to be derived as follows: posteroanterior and lateral films are divided into the standard sections as described in the text, the width, depth, and height of sections 1 and 4 are measured in centimetres, finally the necessary derivations and substitutions are made and applied to the formula Ŷ = −1·41148 + (0·00479 X1) + (0·00097 X4), where Ŷ is the total lung capacity. In our hands this method has provided a simple, rapid, and acceptable method of determining total lung capacity. PMID:5034594

Stepwise and Pulse Transient Methods of Thermophysical Parameters Measurement

NASA Astrophysics Data System (ADS)

Malinarič, Svetozár; Dieška, Peter

2016-12-01

Stepwise transient and pulse transient methods are experimental techniques for measuring the thermal diffusivity and conductivity of solid materials. Theoretical models and experimental apparatus are presented, and the influence of the heat source capacity and the heat transfer coefficient is investigated using the experiment simulation. The specimens from low-density polyethylene (LDPE) and polymethylmethacrylate (PMMA) were measured by both methods. Coefficients of variation were better than 0.9 % for LDPE and 2.8 % for PMMA measurements. The time dependence of the temperature response to the input heat flux showed a small drop, which was caused by thermoelastic wave generated by thermal expansions of the heat source.

A nomogram to predict the survival of stage IIIA-N2 non-small cell lung cancer after surgery.

PubMed

Mao, Qixing; Xia, Wenjie; Dong, Gaochao; Chen, Shuqi; Wang, Anpeng; Jin, Guangfu; Jiang, Feng; Xu, Lin

2018-04-01

Postoperative survival of patients with stage IIIA-N2 non-small cell lung cancer (NSCLC) is highly heterogeneous. Here, we aimed to identify variables associated with postoperative survival and develop a tool for survival prediction. A retrospective review was performed in the Surveillance, Epidemiology, and End Results database from January 2004 to December 2009. Significant variables were selected by use of the backward stepwise method. The nomogram was constructed with multivariable Cox regression. The model's performance was evaluated by concordance index and calibration curve. The model was validated via an independent cohort from the Jiangsu Cancer Hospital Lung Cancer Center. A total of 1809 patients with stage IIIA-N2 NSCLC who underwent surgery were included in the training cohort. Age, sex, grade, histology, tumor size, visceral pleural invasion, positive lymph nodes, lymph nodes examined, and surgery type (lobectomy vs pneumonectomy) were identified as significant prognostic variables using backward stepwise method. A nomogram was developed from the training cohort and validated using an independent Chinese cohort. The concordance index of the model was 0.673 (95% confidence interval, 0.654-0.692) in training cohort and 0.664 in validation cohort (95% confidence interval, 0.614-0.714). The calibration plot showed optimal consistency between nomogram predicted survival and observed survival. Survival analyses demonstrated significant differences between different subgroups stratified by prognostic scores. This nomogram provided the individual survival prediction for patients with stage IIIA-N2 NSCLC after surgery, which might benefit survival counseling for patients and clinicians, clinical trial design and follow-up, as well as postoperative strategy-making. Copyright © 2017 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

An efficient approach for surveillance of childhood diabetes by type derived from electronic health record data: the SEARCH for Diabetes in Youth Study

PubMed Central

Zhong, Victor W; Obeid, Jihad S; Craig, Jean B; Pfaff, Emily R; Thomas, Joan; Jaacks, Lindsay M; Beavers, Daniel P; Carey, Timothy S; Lawrence, Jean M; Dabelea, Dana; Hamman, Richard F; Bowlby, Deborah A; Pihoker, Catherine; Saydah, Sharon H

2016-01-01

Objective To develop an efficient surveillance approach for childhood diabetes by type across 2 large US health care systems, using phenotyping algorithms derived from electronic health record (EHR) data. Materials and Methods Presumptive diabetes cases <20 years of age from 2 large independent health care systems were identified as those having ≥1 of the 5 indicators in the past 3.5 years, including elevated HbA1c, elevated blood glucose, diabetes-related billing codes, patient problem list, and outpatient anti-diabetic medications. EHRs of all the presumptive cases were manually reviewed, and true diabetes status and diabetes type were determined. Algorithms for identifying diabetes cases overall and classifying diabetes type were either prespecified or derived from classification and regression tree analysis. Surveillance approach was developed based on the best algorithms identified. Results We developed a stepwise surveillance approach using billing code–based prespecified algorithms and targeted manual EHR review, which efficiently and accurately ascertained and classified diabetes cases by type, in both health care systems. The sensitivity and positive predictive values in both systems were approximately ≥90% for ascertaining diabetes cases overall and classifying cases with type 1 or type 2 diabetes. About 80% of the cases with “other” type were also correctly classified. This stepwise surveillance approach resulted in a >70% reduction in the number of cases requiring manual validation compared to traditional surveillance methods. Conclusion EHR data may be used to establish an efficient approach for large-scale surveillance for childhood diabetes by type, although some manual effort is still needed. PMID:27107449

Application of near-infrared spectroscopy for the rapid quality assessment of Radix Paeoniae Rubra

NASA Astrophysics Data System (ADS)

Zhan, Hao; Fang, Jing; Tang, Liying; Yang, Hongjun; Li, Hua; Wang, Zhuju; Yang, Bin; Wu, Hongwei; Fu, Meihong

2017-08-01

Near-infrared (NIR) spectroscopy with multivariate analysis was used to quantify gallic acid, catechin, albiflorin, and paeoniflorin in Radix Paeoniae Rubra, and the feasibility to classify the samples originating from different areas was investigated. A new high-performance liquid chromatography method was developed and validated to analyze gallic acid, catechin, albiflorin, and paeoniflorin in Radix Paeoniae Rubra as the reference. Partial least squares (PLS), principal component regression (PCR), and stepwise multivariate linear regression (SMLR) were performed to calibrate the regression model. Different data pretreatments such as derivatives (1st and 2nd), multiplicative scatter correction, standard normal variate, Savitzky-Golay filter, and Norris derivative filter were applied to remove the systematic errors. The performance of the model was evaluated according to the root mean square of calibration (RMSEC), root mean square error of prediction (RMSEP), root mean square error of cross-validation (RMSECV), and correlation coefficient (r). The results show that compared to PCR and SMLR, PLS had a lower RMSEC, RMSECV, and RMSEP and higher r for all the four analytes. PLS coupled with proper pretreatments showed good performance in both the fitting and predicting results. Furthermore, the original areas of Radix Paeoniae Rubra samples were partly distinguished by principal component analysis. This study shows that NIR with PLS is a reliable, inexpensive, and rapid tool for the quality assessment of Radix Paeoniae Rubra.

A combined evaluation of the characteristics and acute toxicity of antibiotic wastewater.

PubMed

Yu, Xin; Zuo, Jiane; Li, Ruixia; Gan, Lili; Li, Zaixing; Zhang, Fei

2014-08-01

The conventional parameters and acute toxicities of antibiotic wastewater collected from each treatment unit of an antibiotic wastewater treatment plant have been investigated. The investigation of the conventional parameters indicated that the antibiotic wastewater treatment plant performed well under the significant fluctuation in influent water quality. The results of acute toxicity indicated that the toxicity of antibiotic wastewater could be reduced by 94.3 percent on average after treatment. However, treated antibiotic effluents were still toxic to Vibrio fischeri. The toxicity of antibiotic production wastewater could be attributed to the joint effects of toxic compound mixtures in wastewater. Moreover, aerobic biological treatment processes, including sequencing batch reactor (SBR) and aerobic biofilm reactor, played the most important role in reducing toxicity by 92.4 percent. Pearson׳s correlation coefficients revealed that toxicity had a strong and positive linear correlation with organic substances, nitrogenous compounds, S(2-), volatile phenol, cyanide, As, Zn, Cd, Ni and Fe. Ammonia nitrogen (NH4(+)) was the greatest contributor to toxicity according to the stepwise regression method. The multiple regression model was a good fit for [TU50-15 min] as a function of [NH₄(+)] with the determination coefficient of 0.981. Copyright © 2014 Elsevier Inc. All rights reserved.

Sex-related perceptions associated with sexual activity status among Japanese adolescents who heavily use text messaging.

PubMed

Kawamura, Yoko

2012-01-01

This study examines the relationship between sex-related perceptions and engagement in sexual intercourse among adolescents in Japan who were heavy users of text massaging. Using the data from the 6th National Survey on Youth Sexual Behavior of 548 high school students who heavily use text messaging, multinomial logistic regression analyses on variables constructing sexual norms and gender-role attitudes were conducted to assess the relationship with sexual activity status as the first step. A backward stepwise elimination method of multinomial logistic regression was used as the second step at which variables for each set of two factors were tested, and as the third step at which variables of two factors were simultaneously tested. The study results showed that perceptions were related to engagement in sexual intercourse among adolescents who heavily used text messaging. In particular, those who perceived that sex is an act to be engaged in at an earlier stage of a relationship and that men have a stronger sex drive tended to be sexually active or have experienced sexual intercourse. These findings could be utilized to design more effective sexual health education messages for Japanese adolescents who are at an elevated risk.

Predictors of Dropout by Female Obese Patients Treated with a Group Cognitive Behavioral Therapy to Promote Weight Loss

PubMed Central

Sawamoto, Ryoko; Nozaki, Takehiro; Furukawa, Tomokazu; Tanahashi, Tokusei; Morita, Chihiro; Hata, Tomokazu; Komaki, Gen; Sudo, Nobuyuki

2016-01-01

Objective To investigate predictors of dropout from a group cognitive behavioral therapy (CBT) intervention for overweight or obese women. Methods 119 overweight and obese Japanese women aged 25-65 years who attended an outpatient weight loss intervention were followed throughout the 7-month weight loss phase. Somatic characteristics, socioeconomic status, obesity-related diseases, diet and exercise habits, and psychological variables (depression, anxiety, self-esteem, alexithymia, parenting style, perfectionism, and eating attitude) were assessed at baseline. Significant variables, extracted by univariate statistical analysis, were then used as independent variables in a stepwise multiple logistic regression analysis with dropout as the dependent variable. Results 90 participants completed the weight loss phase, giving a dropout rate of 24.4%. The multiple logistic regression analysis demonstrated that compared to completers the dropouts had significantly stronger body shape concern, tended to not have jobs, perceived their mothers to be less caring, and were more disorganized in temperament. Of all these factors, the best predictor of dropout was shape concern. Conclusion Shape concern, job condition, parenting care, and organization predicted dropout from the group CBT weight loss intervention for overweight or obese Japanese women. PMID:26745715

Aerodynamic parameters of High-Angle-of attack Research Vehicle (HARV) estimated from flight data

NASA Technical Reports Server (NTRS)

Klein, Vladislav; Ratvasky, Thomas R.; Cobleigh, Brent R.

1990-01-01

Aerodynamic parameters of the High-Angle-of-Attack Research Aircraft (HARV) were estimated from flight data at different values of the angle of attack between 10 degrees and 50 degrees. The main part of the data was obtained from small amplitude longitudinal and lateral maneuvers. A small number of large amplitude maneuvers was also used in the estimation. The measured data were first checked for their compatibility. It was found that the accuracy of air data was degraded by unexplained bias errors. Then, the data were analyzed by a stepwise regression method for obtaining a structure of aerodynamic model equations and least squares parameter estimates. Because of high data collinearity in several maneuvers, some of the longitudinal and all lateral maneuvers were reanalyzed by using two biased estimation techniques, the principal components regression and mixed estimation. The estimated parameters in the form of stability and control derivatives, and aerodynamic coefficients were plotted against the angle of attack and compared with the wind tunnel measurements. The influential parameters are, in general, estimated with acceptable accuracy and most of them are in agreement with wind tunnel results. The simulated responses of the aircraft showed good prediction capabilities of the resulting model.

Associations between state economic and health systems capacities and service use by children with special health care needs.

PubMed

Margolis, Lewis H; Mayer, Michelle; Clark, Kathryn A; Farel, Anita M

2011-08-01

To examine the relationship between measures of state economic, political, health services, and Title V capacity and individual level measures of the well-being of CSHCN. We selected five measures of Title V capacity from the Title V Information System and 13 state capacity measures from a variety of data sources, and eight indicators of intermediate health outcomes from the National Survey of Children with Special Health Care Needs. To assess the associations between Title V capacity and health services outcomes, we used stepwise regression to identify significant capacity measures while accounting for the survey design and clustering of observations by state. To assess the associations between economic, political and health systems capacity and health outcomes we fit weighted logistic regression models for each outcome, using a stepwise procedure to reduce the models. Using statistically significant capacity measures from the stepwise models, we fit reduced random effects logistic regression models to account for clustering of observations by state. Few measures of Title V and state capacity were associated with health services outcomes. For health systems measures, a higher percentage of uninsured children was associated with decreased odds of receipt of early intervention services, decreased odds of receipt of professional care coordination, and increased odds of delayed or missed care. Parents in states with higher per capita Medicaid expenditures on children were more likely to report receipt of special education services. Only two state capacity measures were associated explicitly with Title V: states with higher generalist physician to population ratios were associated with a greater likelihood of parent report of having heard of Title V and states with higher per capita gross state product were less likely to be associated with a report of using Title V services, conditional on having heard of Title V. The state level measure of family participation in Title V governance was negatively associated with receipt of care coordination and having used Title V services. The measures of state economic, political, health systems, and Title V capacity that we have analyzed are only weakly associated with the well-being of children with special health care needs. If Congress and other policymakers increase the expectations of the states in assuring that the needs of CSHCN and their families are addressed, it is essential to be cognizant of the capacities of the states to undertake that role.

Screening for increased cardiometabolic risk in primary care: a systematic review

PubMed Central

den Engelsen, Corine; Koekkoek, Paula S; Godefrooij, Merijn B; Spigt, Mark G; Rutten, Guy E

2014-01-01

Background Many programmes to detect and prevent cardiovascular disease (CVD) have been performed, but the optimal strategy is not yet clear. Aim To present a systematic review of cardiometabolic screening programmes performed among apparently healthy people (not yet known to have CVD, diabetes, or cardiometabolic risk factors) and mixed populations (apparently healthy people and people diagnosed with risk factor or disease) to define the optimal screening strategy. Design and setting Systematic review of studies performed in primary care in Western countries. Method MEDLINE, Embase, and CINAHL databases were searched for studies screening for increased cardiometabolic risk. Exclusion criteria were studies designed to assess prevalence of risk factors without follow-up or treatment; without involving a GP; when fewer than two risk factors were considered as the primary outcome; and studies constrained to ethnic minorities. Results The search strategy yielded 11 445 hits; 26 met the inclusion criteria. Five studies (1995–2012) were conducted in apparently healthy populations: three used a stepwise method. Response rates varied from 24% to 79%. Twenty-one studies (1967–2012) were performed in mixed populations; one used a stepwise method. Response rates varied from 50% to 75%. Prevalence rates could not be compared because of heterogeneity of used thresholds and eligible populations. Observed time trends were a shift from mixed to apparently healthy populations, increasing use of risk scores, and increasing use of stepwise screening methods. Conclusion The optimal screening strategy in primary care is likely stepwise, in apparently healthy people, with the use of risk scores. Increasing public awareness and actively involving GPs might facilitate screening efficiency and uptake. PMID:25267047

Application of the modified chi-square ratio statistic in a stepwise procedure for cascade impactor equivalence testing.

PubMed

Weber, Benjamin; Lee, Sau L; Delvadia, Renishkumar; Lionberger, Robert; Li, Bing V; Tsong, Yi; Hochhaus, Guenther

2015-03-01

Equivalence testing of aerodynamic particle size distribution (APSD) through multi-stage cascade impactors (CIs) is important for establishing bioequivalence of orally inhaled drug products. Recent work demonstrated that the median of the modified chi-square ratio statistic (MmCSRS) is a promising metric for APSD equivalence testing of test (T) and reference (R) products as it can be applied to a reduced number of CI sites that are more relevant for lung deposition. This metric is also less sensitive to the increased variability often observed for low-deposition sites. A method to establish critical values for the MmCSRS is described here. This method considers the variability of the R product by employing a reference variance scaling approach that allows definition of critical values as a function of the observed variability of the R product. A stepwise CI equivalence test is proposed that integrates the MmCSRS as a method for comparing the relative shapes of CI profiles and incorporates statistical tests for assessing equivalence of single actuation content and impactor sized mass. This stepwise CI equivalence test was applied to 55 published CI profile scenarios, which were classified as equivalent or inequivalent by members of the Product Quality Research Institute working group (PQRI WG). The results of the stepwise CI equivalence test using a 25% difference in MmCSRS as an acceptance criterion provided the best matching with those of the PQRI WG as decisions of both methods agreed in 75% of the 55 CI profile scenarios.

[Introduction and advantage analysis of the stepwise method for the construction of vascular trees].

PubMed

Zhang, Yan; Xie, Haiwei; Zhu, Kai

2010-08-01

A new method for constructing the model of vascular trees was proposed in this paper. By use of this method, the arterial trees in good agreement with the actual structure could be grown. In this process, all vessels in the vascular tree were divided into two groups: the conveying vessels, and the delivering branches. And different branches could be built by different ways. Firstly, the distributing rules of conveying vessels were ascertained by use of measurement data, and then the conveying vessels were constructed in accordance to the statistical rule and optimization criterion. Lastly, delivering branches were modeled by constrained constructive optimization (CCO) on the conveying vessel-trees which had already been generated. In order to compare the CCO method and stepwise method proposed here, two 3D arterial trees of human tongue were grown with their vascular tree having a special structure. Based on the corrosion casts of real arterial tree of human tongue, the data about the two trees constructed by different methods were compared and analyzed, including the averaged segment diameters at respective levels, the distribution and the diameters of the branches of first level at respective directions. The results show that the vascular tree built by stepwise method is more similar to the true arterial of human tongue when compared against the tree built by CCO method.

[Effect of occupational stress on mental health].

PubMed

Yu, Shan-fa; Zhang, Rui; Ma, Liang-qing; Gu, Gui-zhen; Yang, Yan; Li, Kui-rong

2003-02-01

To study the effect of job psychological demands and job control on mental health and their interaction. 93 male freight train dispatchers were evaluated by using revised Job Demand-Control Scale and 7 strain scales. Stepwise regression analysis, Univariate ANOVA, Kruskal-Wallis H and Modian methods were used in statistic analysis. Kruskal-Wallis H and Modian methods analysis revealed the difference in mental health scores among groups of decision latitude (mean rank 55.57, 47.95, 48.42, 33.50, P < 0.05), the differences in scores of mental health (37.45, 40.01, 58.35), job satisfaction (53.18, 46.91, 32.43), daily life strains (33.00, 44.96, 56.12) and depression (36.45, 42.25, 53.61) among groups of job time demands (P < 0.05) were all statistically significant. ANOVA showed that job time demands and decision latitude had interaction effects on physical complains (R(2) = 0.24), state-anxiety (R(2) = 0.26), and daytime fatigue (R(2) = 0.28) (P < 0.05). Regression analysis revealed a significant job time demands and job decision latitude interaction effect as well as significant main effects of the some independent variables on different job strains (R(2) > 0.05). Job time demands and job decision latitude have direct and interactive effects on psychosomatic health, the more time demands, the more psychological strains, the effect of job time demands is greater than that of job decision latitude.

Optical system for tablet variety discrimination using visible/near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Shao, Yongni; He, Yong; Hu, Xingyue

2007-12-01

An optical system based on visible/near-infrared spectroscopy (Vis/NIRS) for variety discrimination of ginkgo (Ginkgo biloba L.) tablets was developed. This system consisted of a light source, beam splitter system, sample chamber, optical detector (diffuse reflection detector), and data collection. The tablet varieties used in the research include Da na kang, Xin bang, Tian bao ning, Yi kang, Hua na xing, Dou le, Lv yuan, Hai wang, and Ji yao. All samples (n=270) were scanned in the Vis/NIR region between 325 and 1075 nm using a spectrograph. The chemometrics method of principal component artificial neural network (PC-ANN) was used to establish discrimination models of them. In PC-ANN models, the scores of the principal components were chosen as the input nodes for the input layer of ANN, and the best discrimination rate of 91.1% was reached. Principal component analysis was also executed to select several optimal wavelengths based on loading values. Wavelengths at 481, 458, 466, 570, 1000, 662, and 400 nm were then used as the input data of stepwise multiple linear regression, the regression equation of ginkgo tablets was obtained, and the discrimination rate was researched 84.4%. The results indicated that this optical system could be applied to discriminating ginkgo (Ginkgo biloba L.) tablets, and it supplied a new method for fast ginkgo tablet variety discrimination.

Effects of stepwise dry/wet-aging and freezing on meat quality of beef loins.

PubMed

Kim, Yuan H Brad; Meyers, Brandon; Kim, Hyun-Wook; Liceaga, Andrea M; Lemenager, Ronald P

2017-01-01

The objective of this study was to evaluate the effects of stepwise dry/wet-aging and freezing method on quality attributes of beef loins. Paired loins (M. Longissimus lumborum) from eight carcasses were assigned to either stepwise dry/wet-aging (carcass dry-aging for 10days then further wet-aging for 7days in vacuum bags) or carcass dry-aging only for 17days. Then, each loin was divided into three sections for freezing (never-frozen, blast or cryogenic freezing). Stepwise dry/wet-aged loin had lower purge/drip loss and shear force than conventionally dry-aged loin (P<0.05), but similar color and sensory characteristics (P>0.05). The cryogenic freezing resulted in a significant decrease in shear force values and a significant improvement in water-holding capacity (WHC). These findings indicate that the stepwise dry/wet-aging coupled with cryogenic freezing could provide beneficial impacts to the local meat industry by providing equivalent quality attributes as conventional dry-aging and improving WHC of frozen/thawed meat, while reducing the time needed for dry-aging. Copyright © 2016 Elsevier Ltd. All rights reserved.

The study on the near infrared spectrum technology of sauce component analysis

NASA Astrophysics Data System (ADS)

Li, Shangyu; Zhang, Jun; Chen, Xingdan; Liang, Jingqiu; Wang, Ce

2006-01-01

The author, Shangyu Li, engages in supervising and inspecting the quality of products. In soy sauce manufacturing, quality control of intermediate and final products by many components such as total nitrogen, saltless soluble solids, nitrogen of amino acids and total acid is demanded. Wet chemistry analytical methods need much labor and time for these analyses. In order to compensate for this problem, we used near infrared spectroscopy technology to measure the chemical-composition of soy sauce. In the course of the work, a certain amount of soy sauce was collected and was analyzed by wet chemistry analytical methods. The soy sauce was scanned by two kinds of the spectrometer, the Fourier Transform near infrared spectrometer (FT-NIR spectrometer) and the filter near infrared spectroscopy analyzer. The near infrared spectroscopy of soy sauce was calibrated with the components of wet chemistry methods by partial least squares regression and stepwise multiple linear regression. The contents of saltless soluble solids, total nitrogen, total acid and nitrogen of amino acids were predicted by cross validation. The results are compared with the wet chemistry analytical methods. The correlation coefficient and root-mean-square error of prediction (RMSEP) in the better prediction run were found to be 0.961 and 0.206 for total nitrogen, 0.913 and 1.215 for saltless soluble solids, 0.855 and 0.199 nitrogen of amino acids, 0.966 and 0.231 for total acid, respectively. The results presented here demonstrate that the NIR spectroscopy technology is promising for fast and reliable determination of major components of soy sauce.

Hyperspectral Image Classification via Multitask Joint Sparse Representation and Stepwise MRF Optimization.

PubMed

Yuan, Yuan; Lin, Jianzhe; Wang, Qi

2016-12-01

Hyperspectral image (HSI) classification is a crucial issue in remote sensing. Accurate classification benefits a large number of applications such as land use analysis and marine resource utilization. But high data correlation brings difficulty to reliable classification, especially for HSI with abundant spectral information. Furthermore, the traditional methods often fail to well consider the spatial coherency of HSI that also limits the classification performance. To address these inherent obstacles, a novel spectral-spatial classification scheme is proposed in this paper. The proposed method mainly focuses on multitask joint sparse representation (MJSR) and a stepwise Markov random filed framework, which are claimed to be two main contributions in this procedure. First, the MJSR not only reduces the spectral redundancy, but also retains necessary correlation in spectral field during classification. Second, the stepwise optimization further explores the spatial correlation that significantly enhances the classification accuracy and robustness. As far as several universal quality evaluation indexes are concerned, the experimental results on Indian Pines and Pavia University demonstrate the superiority of our method compared with the state-of-the-art competitors.

Relationship between serum bilirubin concentrations and diabetic nephropathy in Shanghai Han's patients with type 1 diabetes mellitus.

PubMed

Li, Xu; Zhang, Lei; Chen, Haibing; Guo, Kaifeng; Yu, Haoyong; Zhou, Jian; Li, Ming; Li, Qing; Li, Lianxi; Yin, Jun; Liu, Fang; Bao, Yuqian; Han, Junfeng; Jia, Weiping

2017-03-31

Recent studies highlight a negative association between total bilirubin concentrations and albuminuria in patients with type 2 diabetes mellitus. Our study evaluated the relationship between bilirubin concentrations and the prevalence of diabetic nephropathy (DN) in Chinese patients with type 1 diabetes mellitus (T1DM). A total of 258 patients with T1DM were recruited and bilirubin concentrations were compared between patients with or without diabetic nephropathy. Multiple stepwise regression analysis was used to examine the relationship between bilirubin concentrations and 24 h urinary microalbumin. Binary logistic regression analysis was performed to assess independent risk factors for diabetic nephropathy. Participants were divided into four groups according to the quartile of total bilirubin concentrations (Q1, 0.20-0.60; Q2, 0.60-0.80; Q3, 0.80-1.00; Q4, 1.00-1.90 mg/dL) and the chi-square test was used to compare the prevalence of DN in patients with T1DM. The median bilirubin level was 0.56 (interquartile: 0.43-0.68 mg/dL) in the DN group, significantly lower than in the non-DN group (0.70 [interquartile: 0.58-0.89 mg/dL], P < 0.001). Spearman's correlational analysis showed bilirubin concentrations were inversely correlated with 24 h urinary microalbumin (r = -0.13, P < 0.05) and multiple stepwise regression analysis showed bilirubin concentrations were independently associated with 24 h urinary microalbumin. In logistic regression analysis, bilirubin concentrations were significantly inversely associated with nephropathy. In addition, in stratified analysis, from the first to the fourth quartile group, increased bilirubin concentrations were associated with decreased prevalence of DN from 21.90% to 2.00%. High bilirubin concentrations are independently and negatively associated with albuminuria and the prevalence of DN in patients with T1DM.

The relationship between hemoglobin level and the type 1 diabetic nephropathy in Anhui Han's patients.

PubMed

Jiang, Jun; Lei, Lan; Zhou, Xiaowan; Li, Peng; Wei, Ren

2018-02-20

Recent studies have shown that low hemoglobin (Hb) level promote the progression of chronic kidney disease. This study assessed the relationship between Hb level and type 1 diabetic nephropathy (DN) in Anhui Han's patients. There were a total of 236 patients diagnosed with type 1 diabetes mellitus and (T1DM) seen between January 2014 and December 2016 in our centre. Hemoglobin levels in patients with DN were compared with those without DN. The relationship between Hb level and the urinary albumin-creatinine ratio (ACR) was examined by Spearman's correlational analysis and multiple stepwise regression analysis. The binary logistic multivariate regression analysis was performed to analyze the correlated factors for type 1 DN, calculate the Odds Ratio (OR) and 95%confidence interval (CI). The predicting value of Hb level for DN was evaluated by area under receiver operation characteristic curve (AUROC) for discrimination and Hosmer-Lemeshow goodness-of-fit test for calibration. The average Hb levels in the DN group (116.1 ± 20.8 g/L) were significantly lower than the non-DN group (131.9 ± 14.4 g/L) , P < 0.001. Hb levels were independently correlated with the urinary ACR in multiple stepwise regression analysis. The logistic multivariate regression analysis showed that the Hb level (OR: 0.936, 95% CI: 0.910 to 0.963, P < 0.001) was inversely correlated with DN in patients with T1DM. In sub-analysis, low Hb level (Hb < 120g/L in female, Hb < 130g/L in male) was still negatively associated with DN in patients with T1DM. The AUROC was 0.721 (95% CI: 0.655 to 0.787) in assessing the discrimination of the Hb level for DN. The value of P was 0.593 in Hosmer-Lemeshow goodness-of-fit test. In Anhui Han's patients with T1DM, the Hb level is inversely correlated with urinary ACR and DN. This article is protected by copyright. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2016-11-01

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

Visual Analysis of North Atlantic Hurricane Trends Using Parallel Coordinates and Statistical Techniques

DTIC Science & Technology

2008-07-07

analyzing multivariate data sets. The system was developed using the Java Development Kit (JDK) version 1.5; and it yields interactive performance on a... script and captures output from the MATLAB’s “regress” and “stepwisefit” utilities that perform simple and stepwise regression, respectively. The MATLAB...Statistical Association, vol. 85, no. 411, pp. 664–675, 1990. [9] H. Hauser, F. Ledermann, and H. Doleisch, “ Angular brushing of extended parallel coordinates

Predicting alienation in a sample of Nigerian Igbo subjects.

PubMed

Morah, E I

1990-08-01

Seeman in 1959 suggested that alienation is a multidimensional concept. Using two aspects of Seeman's concept of alienation, powerlessness and social alienation, and two concepts derived from Lachar's 1978 Minnesota Multiphasic Personality Inventory Cookbook, emotional and self-alienation, the present work was undertaken to ascertain which concept will more likely predict feelings of alienation. A stepwise multiple regression showed that among 160 Nigerian (Igbo) subjects the feeling of powerlessness predicted alienation more than did the other concept.

Prayer at Midlife is Associated with Reduced Risk of Cognitive Decline in Arabic Women

PubMed Central

Inzelberg, Rivka; Afgin, Anne E; Massarwa, Magda; Schechtman, Edna; Israeli-Korn, Simon D.; Strugatsky, Rosa; Abuful, Amin; Kravitz, Efrat; Farrer, Lindsay A.; Friedland, Robert P.

2013-01-01

Midlife habits may be important for the later development of Alzheimer's disease (AD). We estimated the contribution of midlife prayer to the development of cognitive decline. In a door-to-door survey, residents aged ≥65 years were systematically evaluated in Arabic including medical history, neurological, cognitive examination, and a midlife leisure-activities questionnaire. Praying was assessed by the number of monthly praying hours at midlife. Stepwise logistic regression models were used to evaluate the effect of prayer on the odds of mild cognitive impairment (MCI) and AD versus cognitively normal individuals. Of 935 individuals that were approached, 778 [normal controls (n=448), AD (n=92) and MCI (n=238)] were evaluated. A higher proportion of cognitively normal individuals engaged in prayer at midlife [(87%) versus MCI (71%) or AD (69%) (p<0.0001)]. Since 94% of males engaged in prayer, the effect on cognitive decline could not be assessed in men. Among women, stepwise logistic regression adjusted for age and education, showed that prayer was significantly associated with reduced risk of MCI (p=0.027, OR=0.55, 95% CI 0.33-0.94), but not AD. Among individuals endorsing prayer activity, the amount of prayer was not associated with MCI or AD in either gender. Praying at midlife is associated with lower risk of mild cognitive impairment in women. PMID:23116476

Quantitative evaluation of light scattering intensities of the crystalline lens for radiation related minimal change in interventional radiologists: a cross-sectional pilot study.

PubMed

Abe, Toshi; Furui, Shigeru; Sasaki, Hiroshi; Sakamoto, Yasuo; Suzuki, Shigeru; Ishitake, Tatsuya; Terasaki, Kinuyo; Kohtake, Hiroshi; Norbash, Alexander M; Behrman, Richard H; Hayabuchi, Naofumi

2013-03-01

To evaluate low-dose X-ray radiation effects on the eye by measuring the amount of light scattering in specific regions of the lens, we compared exposed subjects (interventional radiologists) with unexposed subjects (employees of medical service companies), as a pilot study. According to numerous exclusionary rules, subjects with confounding variables contributing to cataract formation were excluded. Left eye examinations were performed on 68 exposed subjects and 171 unexposed subjects. The eye examinations consisted of an initial screening examination, followed by Scheimpflug imaging of the lens using an anterior eye segment analysis system. The subjects were assessed for the quantity of light scattering intensities found in each of the six layers of the lens. Multiple stepwise regression analyses were performed with the stepwise regression for six variables: age, radiation exposure, smoking, drinking, wearing glasses and workplace. In addition, an age-matched comparison between exposed and unexposed subjects was performed. Minimal increased light scattering intensity in the posterior subcapsular region showed statistical significance. Our results indicate that occupational radiation exposure in interventional radiologists may affect the posterior subcapsular region of the lens. Since by its very nature this retrospective study had many limitations, further well-designed studies concerning minimal radiation-related lens changes should be carried out in a low-dose exposure group.

Biomechanical correlates of symptomatic and asymptomatic neurophysiological impairment in high school football.

PubMed

Breedlove, Evan L; Robinson, Meghan; Talavage, Thomas M; Morigaki, Katherine E; Yoruk, Umit; O'Keefe, Kyle; King, Jeff; Leverenz, Larry J; Gilger, Jeffrey W; Nauman, Eric A

2012-04-30

Concussion is a growing public health issue in the United States, and chronic traumatic encephalopathy (CTE) is the chief long-term concern linked to repeated concussions. Recently, attention has shifted toward subconcussive blows and the role they may play in the development of CTE. We recruited a cohort of high school football players for two seasons of observation. Acceleration sensors were placed in the helmets, and all contact activity was monitored. Pre-season computer-based neuropsychological tests and functional magnetic resonance imaging (fMRI) tests were also obtained in order to assess cognitive and neurophysiological health. In-season follow-up scans were then obtained both from individuals who had sustained a clinically-diagnosed concussion and those who had not. These changes were then related through stepwise regression to history of blows recorded throughout the football season up to the date of the scan. In addition to those subjects who had sustained a concussion, a substantial portion of our cohort who did not sustain concussions showed significant neurophysiological changes. Stepwise regression indicated significant relationships between the number of blows sustained by a subject and the ensuing neurophysiological change. Our findings reinforce the hypothesis that the effects of repetitive blows to the head are cumulative and that repeated exposure to subconcussive blows is connected to pathologically altered neurophysiology. Copyright © 2012 Elsevier Ltd. All rights reserved.

The influence of attention deficits on functional recovery post stroke during the first 12 months after discharge from hospital.

PubMed

Hyndman, D; Pickering, R M; Ashburn, A

2008-06-01

Attention deficits have been linked to poor recovery after stroke and may predict outcome. We explored the influence of attention on functional recovery post stroke in the first 12 months after discharge from hospital. People with stroke completed measures of attention, balance, mobility and activities of daily living (ADL) ability at the point of discharge from hospital, and 6 and 12 months later. We used correlational analysis and stepwise linear regression to explore potential predictors of outcome. We recruited 122 men and women, mean age 70 years. At discharge, 56 (51%) had deficits of divided attention, 45 (37%) of sustained attention, 43 (36%) of auditory selective attention and 41 (37%) had visual selective attention deficits. Attention at discharge correlated with mobility, balance and ADL outcomes 12 months later. After controlling for the level of the outcome at discharge, correlations remained significant in only five of the 12 relationships. Stepwise linear regression revealed that the outcome measured at discharge, days until discharge and number of medications were better predictors of outcome: in no case was an attention variable at discharge selected as a predictor of outcome at 12 months. Although attention and function correlated significantly, this correlation was reduced after controlling for functional ability at discharge. Furthermore, side of lesion and the attention variables were not demonstrated as important predictors of outcome 12 months later.

Factors associated with fall-related fractures in Parkinson's disease.

PubMed

Cheng, Kuei-Yueh; Lin, Wei-Che; Chang, Wen-Neng; Lin, Tzu-Kong; Tsai, Nai-Wen; Huang, Chih-Cheng; Wang, Hung-Chen; Huang, Yung-Cheng; Chang, Hsueh-Wen; Lin, Yu-Jun; Lee, Lian-Hui; Cheng, Ben-Chung; Kung, Chia-Te; Chang, Ya-Ting; Su, Chih-Min; Chiang, Yi-Fang; Su, Yu-Jih; Lu, Cheng-Hsien

2014-01-01

Fall-related fracture is one of the most disabling features of idiopathic Parkinson's disease (PD). A better understanding of the associated factors is needed to predict PD patients who will require treatment. This prospective study enrolled 100 adult idiopathic PD patients. Stepwise logistic regressions were used to evaluate the relationships between clinical factors and fall-related fracture. Falls occurred in 56 PD patients, including 32 with fall-related fractures. The rate of falls in the study period was 2.2 ± 1.4 per 18 months. The percentage of osteoporosis was 34% (19/56) and 11% in PD patients with and without falls, respectively. Risk factors associated with fall-related fracture were sex, underlying knee osteoarthritis, mean Unified Parkinson's Disease Rating Scale score, mean Morse fall scale, mean Hoehn and Yahr stage, and exercise habit. By stepwise logistic regression, sex and mean Morse fall scale were independently associated with fall-related fracture. Females had an odds ratio of 3.8 compared to males and the cut-off value of the Morse fall scale for predicting fall-related fracture was 72.5 (sensitivity 72% and specificity 70%). Higher mean Morse fall scales (>72.5) and female sex are associated with higher risk of fall-related fractures. Preventing falls in the high-risk PD group is an important safety issue and highly relevant for their quality of life. Copyright © 2013 Elsevier Ltd. All rights reserved.

Identification of factors that affect the adoption of an ergonomic intervention among Emergency Medical Service workers.

PubMed

Weiler, Monica R; Lavender, Steven A; Crawford, J Mac; Reichelt, Paul A; Conrad, Karen M; Browne, Michael W

2012-01-01

This study explored factors contributing to intervention adoption decisions among Emergency Medical Service (EMS) workers. Emergency Medical Service workers (n = 190), from six different organisations, participated in a two-month longitudinal study following the introduction of a patient transfer-board (also known as slide-board) designed to ease lateral transfers of patients to and from ambulance cots. Surveys administered at baseline, after one month and after two months sampled factors potentially influencing the EMS providers' decision process. 'Ergonomics Advantage' and 'Patient Advantage' entered into a stepwise regression model predicting 'intention to use' at the end of month one (R (2 )= 0.78). After the second month, the stepwise regression indicated only two factors were predictive of intention to use: 'Ergonomics Advantage,' and 'Endorsed by Champions' (R (2 )= 0.58). Actual use was predicted by: 'Ergonomics Advantage' and 'Previous Tool Experience.' These results relate to key concepts identified in the diffusion of innovation literature and have the potential to further ergonomics intervention adoption efforts. Practitioner Summary. This study explored factors that potentially facilitate the adoption of voluntarily used ergonomics interventions. EMS workers were provided with foldable transfer-boards (slideboards) designed to reduce the physical demands when laterally transferring patients. Factors predictive of adoption measures included perceived ergonomics advantage, the endorsement by champions, and prior tool experience.

Prayer at midlife is associated with reduced risk of cognitive decline in Arabic women.

PubMed

Inzelberg, Rivka; Afgin, Anne E; Massarwa, Magda; Schechtman, Edna; Israeli-Korn, Simon D; Strugatsky, Rosa; Abuful, Amin; Kravitz, Efrat; Farrer, Lindsay A; Friedland, Robert P

2013-03-01

Midlife habits may be important for the later development of Alzheimer's disease (AD). We estimated the contribution of midlife prayer to the development of cognitive decline. In a door-to-door survey, residents aged ≥65 years were systematically evaluated in Arabic including medical history, neurological, cognitive examination, and a midlife leisure-activities questionnaire. Praying was assessed by the number of monthly praying hours at midlife. Stepwise logistic regression models were used to evaluate the effect of prayer on the odds of mild cognitive impairment (MCI) and AD versus cognitively normal individuals. Of 935 individuals that were approached, 778 [normal controls (n=448), AD (n=92) and MCI (n=238)] were evaluated. A higher proportion of cognitively normal individuals engaged in prayer at midlife [(87%) versus MCI (71%) or AD (69%) (p<0.0001)]. Since 94% of males engaged in prayer, the effect on cognitive decline could not be assessed in men. Among women, stepwise logistic regression adjusted for age and education, showed that prayer was significantly associated with reduced risk of MCI (p=0.027, OR=0.55, 95% CI 0.33-0.94), but not AD. Among individuals endorsing prayer activity, the amount of prayer was not associated with MCI or AD in either gender. Praying at midlife is associated with lower risk of mild cognitive impairment in women.

Estimation of stature from the foot and its segments in a sub-adult female population of North India

PubMed Central

2011-01-01

Background Establishing personal identity is one of the main concerns in forensic investigations. Estimation of stature forms a basic domain of the investigation process in unknown and co-mingled human remains in forensic anthropology case work. The objective of the present study was to set up standards for estimation of stature from the foot and its segments in a sub-adult female population. Methods The sample for the study constituted 149 young females from the Northern part of India. The participants were aged between 13 and 18 years. Besides stature, seven anthropometric measurements that included length of the foot from each toe (T1, T2, T3, T4, and T5 respectively), foot breadth at ball (BBAL) and foot breadth at heel (BHEL) were measured on both feet in each participant using standard methods and techniques. Results The results indicated that statistically significant differences (p < 0.05) between left and right feet occur in both the foot breadth measurements (BBAL and BHEL). Foot length measurements (T1 to T5 lengths) did not show any statistically significant bilateral asymmetry. The correlation between stature and all the foot measurements was found to be positive and statistically significant (p-value < 0.001). Linear regression models and multiple regression models were derived for estimation of stature from the measurements of the foot. The present study indicates that anthropometric measurements of foot and its segments are valuable in the estimation of stature. Foot length measurements estimate stature with greater accuracy when compared to foot breadth measurements. Conclusions The present study concluded that foot measurements have a strong relationship with stature in the sub-adult female population of North India. Hence, the stature of an individual can be successfully estimated from the foot and its segments using different regression models derived in the study. The regression models derived in the study may be applied successfully for the estimation of stature in sub-adult females, whenever foot remains are brought for forensic examination. Stepwise multiple regression models tend to estimate stature more accurately than linear regression models in female sub-adults. PMID:22104433

[Predictors of employment intention for mentally disabled persons].

PubMed

Han, Sang-Sook; Han, Jeong Hye; Yun, Eun Kyoung

2008-08-01

This study was conducted to determine the predictors of employment intention for mentally disabled persons. Mentally disabled persons who had participated in rehabilitation programs in one of 16 mental health centers and 9 community rehabilitation centers located in Seoul and Kyunggi province were recruited for this study. A random sampling method was used and 414 respondents were used for final analysis. Data was analyzed by Pearson's correlation, and stepwise multiple regression using the SPSS Win 14.0. The predictors influencing employment intention of the mentally disabled person were observed as employment desire (beta=.48), guardian's expectation (beta=.26), professional's support (beta=.23), financial management (beta=.10), eating habits (beta=.07), and quality of life (beta=-.01). Six factors explained 61.1% of employment intention of mentally disabled persons. The employment intention of a mentally disabled person was influenced by employment desire, diet self-efficacy, guardian's expectation, professional's support, quality of life, financial management and eating habits.

High Titers ofChlamydia trachomatis Antibodies in Brazilian Women with Tubal Occlusion or Previous Ectopic Pregnancy

PubMed Central

Machado, A. C. S.; Guimarães, E. M. B.; Sakurai, E.; Fioravante, F. C. R.; Amaral, W. N.; Alves, M. F. C.

2007-01-01

Objective. To evaluate serum chlamydia antibody titers (CATs) in tubal occlusion or previous ectopic pregnancy and the associated risk factors.Methods. The study population consisted of 55 women wih tubal damage and 55 parous women. CAT was measured using the whole-cell inclusion immunofluorescence test and cervical chlamydial DNA detected by PCR. Odds ratios were calculated to assess variables associated withC. trachomatis infection.Results. The prevalence of chlamydial antibodies and antibody titers in women with tubal occlusion or previous ectopic pregnancy was significantly higher (P < .01) than in parous women. Stepwise logistic regression analysis showed that chlamydia IgG antibodies were associated with tubal damage and with a larger number of lifetime sexual partners.Conclusions. Chlamydia antibody titers were associated with tubal occlusion, prior ectopic pregnancy, and with sexual behavior, suggesting that a chlamydia infection was the major contributor to the tubal damage in these women. PMID:17541464

Which factor contribute most to empower farmers through e-Agriculture in Bangladesh?

PubMed

Rashid, Sheikh Mohammed Mamur; Islam, Md Rezwan; Quamruzzaman, Md

2016-01-01

The purpose of this research was designed to investigate the impact of e-Agriculture on farmers of Bangladesh. Empowerment is stratified as economic, family and social, political, knowledge and psychological empowerment. Data were collected in Bhatbour Block of Dhighi union under Sadar Upazila of Minikganj District. Data were collected in two phases from the same group of respondents (in August, 2013 and September, 2015). Two sample t test and step-wise multiple regression method were used for analysis. The results showed that e-Agriculture had significant impact on the empowerment of farmers of Bangladesh. Additionally, the study concluded that the most significant factor behind the empowerment of farmer was the use of e-Agriculture which could explain almost 84 % of the total variation of the empowerment. Based on the findings, it is recommended that government should implement e-Agriculture based projects on a massive scale for the empowerment of the farmers.

Comprehensive evaluation system of intelligent urban growth

NASA Astrophysics Data System (ADS)

Li, Lian-Yan; Ren, Xiao-Bin

2017-06-01

With the rapid urbanization of the world, urban planning has become increasingly important and necessary to ensure people have access to equitable and sustainable homes, resources and jobs.This article is to talk about building an intelligent city evaluation system.First,using System Analysis Model(SAM) which concludes literature data analysis and stepwise regression analysis to describe intelligent growth scientifically and obtain the evaluation index. Then,using the improved entropy method to obtain the weight of the evaluation index.Afterwards, establishing a complete Smart Growth Comprehensive Evaluation Model(SGCEM).Finally,testing the correctness of the model.Choosing Otago(New Zealand )and Yumen(China) as research object by data mining and SGCEM model,then we get Yumen and Otago’s rational degree’s values are 0.3485 and 0.5376 respectively. It’s believed that the Otago’s smart level is higher,and it is found that the estimated value of rationality is consistent with the reality.

Model selection bias and Freedman's paradox

USGS Publications Warehouse

Lukacs, P.M.; Burnham, K.P.; Anderson, D.R.

2010-01-01

In situations where limited knowledge of a system exists and the ratio of data points to variables is small, variable selection methods can often be misleading. Freedman (Am Stat 37:152-155, 1983) demonstrated how common it is to select completely unrelated variables as highly "significant" when the number of data points is similar in magnitude to the number of variables. A new type of model averaging estimator based on model selection with Akaike's AIC is used with linear regression to investigate the problems of likely inclusion of spurious effects and model selection bias, the bias introduced while using the data to select a single seemingly "best" model from a (often large) set of models employing many predictor variables. The new model averaging estimator helps reduce these problems and provides confidence interval coverage at the nominal level while traditional stepwise selection has poor inferential properties. ?? The Institute of Statistical Mathematics, Tokyo 2009.

Comparable ultrasound measurements of ten anatomical specimens of infant hip joints by the methods of Graf and Terjesen.

PubMed

Falliner, A; Hahne, H J; Hedderich, J; Brossmann, J; Hassenpflug, J

2004-04-01

To define which sonographic section planes relative to the acetabular inlet plane will produce analyzable images with the methods of Graf and Terjesen. Anatomical specimens of infant hip joints were investigated in a water bath using the methods of Graf and Terjesen. Acetabular position was varied in defined increments with respect to the ultrasound beam. The alpha angles and the femoral head coverage (FHC) were measured. To obtain images analyzable by the two methods, the ultrasound beam had to intersect with the acetabular inlet plane at defined angles. The acetabular notch had to be anteriorly rotated from the ultrasound beam plane by at least 20 degrees. Beam entry within a 50 degrees sector posterior to the perpendicular on the inlet plane resulted in analyzable images. The stepwise multiple linear regression analysis showed that alpha angles and FHC were much affected by the coronal-plane transducer tilt. The fact that caudal tilts of the transducer are associated with reduced alpha angles and FHC values should be kept in mind in clinical ultrasound investigations. It is recommended that the transducer should be put on the greater trochanter perpendicular to the transverse axis of the body.

Urinary Incontinence of Women in a Nationwide Study in Sri Lanka: Prevalence and Risk Factors.

PubMed

Pathiraja, Ramya; Prathapan, Shamini; Goonawardena, Sampatha

2017-05-23

Urinary incontinence, be stress incontinence or urge incontinence or a mixed type incontinence affects women of all ages. The aim of this study was to describe the prevalence and risk factors of urinary incontinence in Sri Lanka. A community based cross-sectional study was performed in Sri Lanka. The age group of the women in Sri Lanka was categorized into 3 age groups: Less than or equal to 35 years, 36 to 50 years of age and more than or equal to 51 years of age. A sample size of 675 women was obtained from each age category obtaining a total sample of 2025 from Sri Lanka. An interviewer-administered questionnaire consisting of two parts; Socio demographic factors, Medical and Obstetric History, and the King's Health Questionnaire (KHQ), was used for data collection. Stepwise logistic regression analysis was performed. The Prevalence of women with only stress incontinence was 10%, with urge incontinence was 15.6% and with stress and urge incontinence was 29.9%. Stepwise logistic regression analysis showed that the age groups of 36 - 50 years (OR = 2.03; 95% CI = 1.56 - 2.63) and 51 years and above (OR = 2.61; 95% CI= 1.95 - 3.48), Living in one of the districts in Sri Lanka (OR = 4.58; 95% CI = 3.35 - 6.27) and having given birth to multiple children (OR = 1.1; 95% CI = 1.02 - 1.21), diabetes mellitus (OR = 1.97; 95% CI = 1.19 - 3.23), and respiratory diseases (OR = 2.17; 95% CI = 1.48 - 3.19 ) showed a significant risk in the regression analysis. The risk factor, mostly modifiable, if prevented early, could help to reduce the symptoms of urinary incontinence.

Socio-economic factors associated with infant mortality in Italy: an ecological study.

PubMed

Dallolio, Laura; Di Gregori, Valentina; Lenzi, Jacopo; Franchino, Giuseppe; Calugi, Simona; Domenighetti, Gianfranco; Fantini, Maria Pia

2012-08-16

One issue that continues to attract the attention of public health researchers is the possible relationship in high-income countries between income, income inequality and infant mortality (IM). The aim of this study was to assess the associations between IM and major socio-economic determinants in Italy. Associations between infant mortality rates in the 20 Italian regions (2006-2008) and the Gini index of income inequality, mean household income, percentage of women with at least 8 years of education, and percentage of unemployed aged 15-64 years were assessed using Pearson correlation coefficients. Univariate linear regression and multiple stepwise linear regression analyses were performed to determine the magnitude and direction of the effect of the four socio-economic variables on IM. The Gini index and the total unemployment rate showed a positive strong correlation with IM (r = 0.70; p < 0.001 and r = 0.84; p < 0.001 respectively), mean household income showed a strong negative correlation (r = -0.78; p < 0.001), while female educational attainment presented a weak negative correlation (r = -0.45; p < 0.05). Using a multiple stepwise linear regression model, only unemployment rate was independently associated with IM (b = 0.15, p < 0.001). In Italy, a high-income country where health care is universally available, variations in IM were strongly associated with relative and absolute income and unemployment rate. These results suggest that in Italy IM is not only related to income distribution, as demonstrated for other developed countries, but also to economic factors such as absolute income and unemployment. In order to reduce IM and the existing inequalities, the challenge for Italian decision makers is to promote economic growth and enhance employment levels.

Evidence-based identification of key beliefs explaining adult male circumcision motivation in Zimbabwe: targets for behavior change messaging.

PubMed

Montaño, Daniel E; Kasprzyk, Danuta; Hamilton, Deven T; Tshimanga, Mufuta; Gorn, Gerald

2014-05-01

Male circumcision (MC) reduces HIV acquisition among men, leading WHO/UNAIDS to recommend a goal to circumcise 80 % of men in high HIV prevalence countries. Significant investment to increase MC capacity in priority countries was made, yet only 5 % of the goal has been achieved in Zimbabwe. The integrated behavioral model (IBM) was used as a framework to investigate the factors affecting MC motivation among men in Zimbabwe. A survey instrument was designed based on elicitation study results, and administered to a representative household-based sample of 1,201 men aged 18-30 from two urban and two rural areas in Zimbabwe. Multiple regression analysis found all five IBM constructs significantly explained MC Intention. Nearly all beliefs underlying the IBM constructs were significantly correlated with MC Intention. Stepwise regression analysis of beliefs underlying each construct respectively found that 13 behavioral beliefs, 5 normative beliefs, 4 descriptive norm beliefs, 6 efficacy beliefs, and 10 control beliefs were significant in explaining MC Intention. A final stepwise regression of the five sets of significant IBM construct beliefs identified 14 key beliefs that best explain Intention. Similar analyses were carried out with subgroups of men by urban-rural and age. Different sets of behavioral, normative, efficacy, and control beliefs were significant for each sub-group, suggesting communication messages need to be targeted to be most effective for sub-groups. Implications for the design of effective MC demand creation messages are discussed. This study demonstrates the application of theory-driven research to identify evidence-based targets for intervention messages to increase men's motivation to get circumcised and thereby improve demand for male circumcision.

Factors Associated With Work Ability in Patients Undergoing Surgery for Cervical Radiculopathy.

PubMed

Ng, Eunice; Johnston, Venerina; Wibault, Johanna; Löfgren, Håkan; Dedering, Åsa; Öberg, Birgitta; Zsigmond, Peter; Peolsson, Anneli

2015-08-15

Cross-sectional study. To investigate the factors associated with work ability in patients undergoing surgery for cervical radiculopathy. Surgery is a common treatment of cervical radiculopathy in people of working age. However, few studies have investigated the impact on the work ability of these patients. Patients undergoing surgery for cervical radiculopathy (n = 201) were recruited from spine centers in Sweden to complete a battery of questionnaires and physical measures the day before surgery. The associations between various individual, psychological, and work-related factors and self-reported work ability were investigated by Spearman rank correlation coefficient, multivariate linear regression, and forward stepwise regression analyses. Factors that were significant (P < 0.05) in each statistical analysis were entered into the successive analysis to reveal the factors most related to work ability. Work ability was assessed using the Work Ability Index. The mean Work Ability Index score was 28 (SD, 9.0). The forward stepwise regression analysis revealed 6 factors significantly associated with work ability, which explained 62% of the variance in the Work Ability Index. Factors highly correlated with greater work ability included greater self-efficacy in performing self-cares, lower physical load on the neck at work, greater self-reported chance of being able to work in 6 months' time, greater use of active coping strategies, lower frequency of hand weakness, and higher health-related quality of life. Psychological, work-related and individual factors were significantly associated with work ability in patients undergoing surgery for cervical radiculopathy. High self-efficacy was most associated with greater work ability. Consideration of these factors by surgeons preoperatively may provide optimal return to work outcomes after surgery. 3.

Evaluation of job satisfaction and working atmosphere of dental nurses in Germany.

PubMed

Goetz, Katja; Hasse, Philipp; Campbell, Stephen M; Berger, Sarah; Dörfer, Christof E; Hahn, Karolin; Szecsenyi, Joachim

2016-02-01

The purpose of the study was to assess the level of job satisfaction of dental nurses in ambulatory care and to explore the impact of aspects of working atmosphere on and their association with job satisfaction. This cross-sectional study was based on a job satisfaction survey. Data were collected from 612 dental nurses working in 106 dental care practices. Job satisfaction was measured with the 10-item Warr-Cook-Wall job satisfaction scale. Working atmosphere was measured with five items. Linear regression analyses were performed in which each item of the job satisfaction scale was handled as dependent variables. A stepwise linear regression analysis was performed with overall job satisfaction and the five items of working atmosphere, job satisfaction, and individual characteristics. The response rate was 88.3%. Dental nurses were satisfied with 'colleagues' and least satisfied with 'income.' Different aspects of job satisfaction were mostly associated with the following working atmosphere issues: 'responsibilities within the practice team are clear,' 'suggestions for improvement are taken seriously,' 'working atmosphere in the practice team is good,' and 'made easier to admit own mistakes.' Within the stepwise linear regression analysis, the aspect 'physical working condition' (β = 0.304) showed the highest association with overall job satisfaction. The total explained variance of the 14 associated variables was 0.722 with overall job satisfaction. Working atmosphere within this discrete sample of dental care practice seemed to be an important influence on reported working condition and job satisfaction for dental nurses. Because of the high association of job satisfaction with physical working condition, the importance of paying more attention to an ergonomic working position for dental nurses to ensure optimal quality of care is highlighted. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Factors affecting match performance in professional Australian football.

PubMed

Sullivan, Courtney; Bilsborough, Johann C; Cianciosi, Michael; Hocking, Joel; Cordy, Justin T; Coutts, Aaron J

2014-05-01

To determine the physical activity measures and skill-performance characteristics that contribute to coaches' perception of performance and player performance rank in professional Australian Football (AF). Prospective, longitudinal. Physical activity profiles were assessed via microtechnology (GPS and accelerometer) from 40 professional AF players from the same team during 15 Australian Football League games. Skill-performance measure and player-rank scores (Champion Data Rank) were provided by a commercial statistical provider. The physical-performance variables, skill involvements, and individual player performance scores were expressed relative to playing time for each quarter. A stepwise multiple regression was used to examine the contribution of physical activity and skill involvements to coaches' perception of performance and player rank in AF. Stepwise multiple-regression analysis revealed that 42.2% of the variance in coaches' perception of a player's performance could be explained by the skill-performance characteristics (player rank/min, effective kicks/min, pressure points/min, handballs/min, and running bounces/ min), with a small contribution from physical activity measures (accelerations/min) (adjusted R2 = .422, F6,282 = 36.054, P < .001). Multiple regression also revealed that 66.4% of the adjusted variance in player rank could be explained by total disposals/min, effective kicks/min, pressure points/min, kick clangers/min, marks/min, speed (m/min), and peak speed (adjusted R2 = .664, F7,281 = 82.289, P < .001). Increased physical activity throughout a match (speed [m/min] β - 0.097 and peak speed β - 0.116) negatively affects player rank in AF. Skill performance rather than increased physical activity is more important to coaches' perception of performance and player rank in professional AF.

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

Application of LANDSAT to the surveillance and control of lake eutrophication in the Great Lakes basin. [Saginaw Bay, Michigan and Wisconsin

NASA Technical Reports Server (NTRS)

Rogers, R. H. (Principal Investigator)

1976-01-01

The author has identified the following significant results. Computer techniques were developed for mapping water quality parameters from LANDSAT data, using surface samples collected in an ongoing survey of water quality in Saginaw Bay. Chemical and biological parameters were measured on 31 July 1975 at 16 bay stations in concert with the LANDSAT overflight. Application of stepwise linear regression bands to nine of these parameters and corresponding LANDSAT measurements for bands 4 and 5 only resulted in regression correlation coefficients that varied from 0.94 for temperature to 0.73 for Secchi depth. Regression equations expressed with the pair of bands 4 and 5, rather than the ratio band 4/band 5, provided higher correlation coefficients for all the water quality parameters studied (temperature, Secchi depth, chloride, conductivity, total kjeldahl nitrogen, total phosphorus, chlorophyll a, total solids, and suspended solids).

Inertial Response of Wind Power Plants: A Comparison of Frequency-Based Inertial Control and Stepwise Inertial Control

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

Wang, Xiao; Gao, Wenzhong; Wang, Jianhui

The frequency regulation capability of a wind power plant plays an important role in enhancing frequency reliability especially in an isolated power system with high wind power penetration levels. A comparison of two types of inertial control methods, namely frequency-based inertial control (FBIC) and stepwise inertial control (SIC), is presented in this paper. Comprehensive case studies are carried out to reveal features of the different inertial control methods, simulated in a modified Western System Coordination Council (WSCC) nine-bus power grid using real-time digital simulator (RTDS) platform. The simulation results provide an insight into the inertial control methods under various scenarios.

Models to predict emissions of health-damaging pollutants and global warming contributions of residential fuel/stove combinations in China.

PubMed

Edwards, Rufus D; Smith, Kirk R; Zhang, Junfeng; Ma, Yuqing

2003-01-01

Residential energy use in developing countries has traditionally been associated with combustion devices of poor energy efficiency, which have been shown to produce substantial health-damaging pollution, contributing significantly to the global burden of disease, and greenhouse gas (GHG) emissions. Precision of these estimates in China has been hampered by limited data on stove use and fuel consumption in residences. In addition limited information is available on variability of emissions of pollutants from different stove/fuel combinations in typical use, as measurement of emission factors requires measurement of multiple chemical species in complex burn cycle tests. Such measurements are too costly and time consuming for application in conjunction with national surveys. Emissions of most of the major health-damaging pollutants (HDP) and many of the gases that contribute to GHG emissions from cooking stoves are the result of the significant portion of fuel carbon that is diverted to products of incomplete combustion (PIC) as a result of poor combustion efficiencies. The approximately linear increase in emissions of PIC with decreasing combustion efficiencies allows development of linear models to predict emissions of GHG and HDP intrinsically linked to CO2 and PIC production, and ultimately allows the prediction of global warming contributions from residential stove emissions. A comprehensive emissions database of three burn cycles of 23 typical fuel/stove combinations tested in a simulated village house in China has been used to develop models to predict emissions of HDP and global warming commitment (GWC) from cooking stoves in China, that rely on simple survey information on stove and fuel use that may be incorporated into national surveys. Stepwise regression models predicted 66% of the variance in global warming commitment (CO2, CO, CH4, NOx, TNMHC) per 1 MJ delivered energy due to emissions from these stoves if survey information on fuel type was available. Subsequently if stove type is known, stepwise regression models predicted 73% of the variance. Integrated assessment of policies to change stove or fuel type requires that implications for environmental impacts, energy efficiency, global warming and human exposures to HDP emissions can be evaluated. Frequently, this involves measurement of TSP or CO as the major HDPs. Incorporation of this information into models to predict GWC predicted 79% and 78% of the variance respectively. Clearly, however, the complexity of making multiple measurements in conjunction with a national survey would be both expensive and time consuming. Thus, models to predict HDP using simple survey information, and with measurement of either CO/CO2 or TSP/CO2 to predict emission factors for the other HDP have been derived. Stepwise regression models predicted 65% of the variance in emissions of total suspended particulate as grams of carbon (TSPC) per 1 MJ delivered if survey information on fuel and stove type was available and 74% if the CO/CO2 ratio was measured. Similarly stepwise regression models predicted 76% of the variance in COC emissions per MJ delivered with survey information on stove and fuel type and 85% if the TSPC/CO2 ratio was measured. Ultimately, with international agreements on emissions trading frameworks, similar models based on extensive databases of the fate of fuel carbon during combustion from representative household stoves would provide a mechanism for computing greenhouse credits in the residential sector as part of clean development mechanism frameworks and monitoring compliance to control regimes.

Stepwise self-assembly of C60 mediated by atomic scale moiré magnifiers

NASA Astrophysics Data System (ADS)

Gruznev, D. V.; Matetskiy, A. V.; Bondarenko, L. V.; Utas, O. A.; Zotov, A. V.; Saranin, A. A.; Chou, J. P.; Wei, C. M.; Lai, M. Y.; Wang, Y. L.

2013-04-01

Self-assembly of atoms or molecules on a crystal surface is considered one of the most promising methods to create molecular devices. Here we report a stepwise self-assembly of C60 molecules into islands with unusual shapes and preferred sizes on a gold-indium-covered Si(111) surface. Specifically, 19-mer islands prefer a non-compact boomerang shape, whereas hexagonal 37-mer islands exhibit extraordinarily enhanced stability and abundance. The stepwise self-assembly is mediated by the moiré interference between an island with its underlying lattice, which essentially maps out the adsorption-energy landscape of a C60 on different positions of the surface with a lateral magnification factor and dictates the probability for the subsequent attachment of C60 to an island’s periphery. Our discovery suggests a new method for exploiting the moiré interference to dynamically assist the self-assembly of particles and provides an unexplored tactic of engineering atomic scale moiré magnifiers to facilitate the growth of monodispersed mesoscopic structures.

Method of selective reduction of polyhalosilanes with alkyltin hydrides

DOEpatents

Sharp, Kenneth G.; D'Errico, John J.

1989-01-01

The invention relates to the selective and stepwise reduction of polyhalosilanes by reacting at room temperature or below with alkyltin hydrides without the use of free radical intermediates. Alkyltin hydrides selectively and stepwise reduce the Si--Br, Si--Cl, or Si--I bonds while leaving intact any Si--F bonds. When two or more different halogens are present on the polyhalosilane, the halogen with the highest atomic weight is preferentially reduced.

Computer Mapping of Water Quality in Saginaw Bay with LANDSAT Digital Data

NASA Technical Reports Server (NTRS)

Rogers, R. H. (Principal Investigator); Shah, N. J.; Smith, V. E.; Mckeon, J. B.

1976-01-01

The author has identified the following significant results. LANDSAT digital data and ground truth measurements for Saginaw Bay (Lake Huron), Michigan, for 31 July 1975 were correlated by stepwise linear regression and the resulting equations used to estimate invisible water quality parameters in nonsampled areas. Chloride, conductivity, total Kjeldahl nitrogen, total phosphorus, and chlorophyll a were best correlated with the ratio of LANDSAT Band 4 to Band 5. Temperature and Secchi depth correlate best with Band 5.

Cross-sectional and longitudinal study of association between circulating thiobarbituric acid-reacting substance levels and clinicobiochemical parameters in 1,178 middle-aged Japanese men - the Amagasaki Visceral Fat Study

PubMed Central

2011-01-01

Background Circulating thiobarbituric acid-reacting substance (TBARS) levels, a marker of systemic oxidative stress, are predictive of cardiovascular events. However, they has not been evaluated in Japanese, especially with regard to the factors that contribute to the changes in circulating TBARS levels. We investigated the cross-sectional and longitudinal relationships between circulating TBARS levels and various clinicobiochemical parameters in middle-aged men. Methods In this population-based study (The Amagasaki Visceral Fat Study), 1,178 Japanese male urban workers who had undergone health check-ups in 2006, 2007 and 2008 and were not on medications for metabolic disorders during the follow-up period, were enrolled. Serum TBARS levels were measured by the method of Yagi. The estimated visceral fat area (eVFA) by bioelectrical impedance was measured annually. After health check-ups, subjects received health education with lifestyle modification by medical personnel. Results The number of cardiovascular risk factors (hypertension, hyperglycemia, low HDL-C, hypertriglyceridemia, hyperuricemia, hyper-LDL-C and impaired renal function) augmented with the increases in log-eVFA (p < 0.0001) and log-TBARS (p < 0.0001). The combination of TBARS and eVFA had a multiplicative effect on risk factor accumulation (F value = 79.1, p = 0.0065). Stepwise multiple regression analysis identified log-eVFA, as well as age, log-body mass index (BMI), LDL-C, log-adiponectin, γ-glutamyl transpeptidase (γ-GTP) and uric acid as significant determinants of log-TBARS. Stepwise multiple regression analysis identified one-year changes in eVFA as well as BMI, γ-GTP and estimated glomerular filtration rate (eGFR) as significant determinants of one-year change in TBARS, and biennial changes in eVFA as well as BMI and γ-GTP, eGFR as significant determinants of biennial change in TBARS. Conclusions The present study showed a significant cross-sectional and longitudinal correlation between TBARS and eVFA, as well as BMI and γ- GTP, eGFR. Visceral fat reduction may independently associate with the improvement in systemic ROS in middle-aged Japanese men. Trial Registration The Amagasaki Visceral Fat Study UMIN000002391. PMID:22108213

A public hedonic analysis of environmental attributes in an open space preservation program

NASA Astrophysics Data System (ADS)

Nordman, Erik E.

The Town of Brookhaven, on Long Island, NY, has implemented an open space preservation program to protect natural areas, and the ecosystem services they provide, from suburban growth. I used a public hedonic model of Brookhaven's open space purchases to estimate implicit prices for various environmental attributes, locational variables and spatial metrics. I also measured the correlation between cost per acre and non-monetary environmental benefit scores and tested whether including cost data, as opposed to non-monetary environmental benefit score alone, would change the prioritization ranks of acquired properties. The mean acquisition cost per acre was 82,501. I identified the key on-site environmental and locational variables using stepwise regression for four functional forms. The log-log specification performed best ( R2adj= 0.727). I performed a second stepwise regression (log-log form) which included spatial metrics, calculated from a high-resolution land cover classification, in addition to the environmental and locational variables. This markedly improved the model's performance ( R2adj=0.866). Statistically significant variables included the property size, location in the Pine Barrens Compatible Growth Area, location in a FEMA flood zone, adjacency to public land, and several other environmental dummy variables. The single significant spatial metric, the fractal dimension of the tree cover class, had the largest elasticity of any variable. Of the dummy variables, location within the Compatible Growth Area had the largest implicit price (298,792 per acre). The priority rank for the two methods, non-monetary environmental benefit score alone and the ratio of non-monetary environmental benefit score to acquisition cost were significantly positively correlated. This suggests that, despite the lack of cost data in their ranking method, Brookhaven does not suffer from efficiency losses. The economics literature encourages using both environmental benefits and acquisition costs to ensure cost-effective conservation programs. I recommend that Brookhaven consider acquisition costs in addition to environmental benefits to avert potential efficiency losses in future open space purchases. This dissertation shows that the addition of spatial metrics can enhance the performance of hedonic models. It also provides a baseline valuation for the environmental attributes of Brookhaven' open spaces and shows that location is critical when dealing with open space preservation programs.

Above-bottom biomass retrieval of aquatic plants with regression models and SfM data acquired by a UAV platform - A case study in Wild Duck Lake Wetland, Beijing, China

NASA Astrophysics Data System (ADS)

Jing, Ran; Gong, Zhaoning; Zhao, Wenji; Pu, Ruiliang; Deng, Lei

2017-12-01

Above-bottom biomass (ABB) is considered as an important parameter for measuring the growth status of aquatic plants, and is of great significance for assessing health status of wetland ecosystems. In this study, Structure from Motion (SfM) technique was used to rebuild the study area with high overlapped images acquired by an unmanned aerial vehicle (UAV). We generated orthoimages and SfM dense point cloud data, from which vegetation indices (VIs) and SfM point cloud variables including average height (HAVG), standard deviation of height (HSD) and coefficient of variation of height (HCV) were extracted. These VIs and SfM point cloud variables could effectively characterize the growth status of aquatic plants, and thus they could be used to develop a simple linear regression model (SLR) and a stepwise linear regression model (SWL) with field measured ABB samples of aquatic plants. We also utilized a decision tree method to discriminate different types of aquatic plants. The experimental results indicated that (1) the SfM technique could effectively process high overlapped UAV images and thus be suitable for the reconstruction of fine texture feature of aquatic plant canopy structure; and (2) an SWL model based on point cloud variables: HAVG, HSD, HCV and two VIs: NGRDI, ExGR as independent variables has produced the best predictive result of ABB of aquatic plants in the study area, with a coefficient of determination of 0.84 and a relative root mean square error of 7.13%. In this analysis, a novel method for the quantitative inversion of a growth parameter (i.e., ABB) of aquatic plants in wetlands was demonstrated.

Validation of equations and proposed reference values to estimate fat mass in Chilean university students.

PubMed

Gómez Campos, Rossana; Pacheco Carrillo, Jaime; Almonacid Fierro, Alejandro; Urra Albornoz, Camilo; Cossío-Bolaños, Marco

2018-03-01

(i) To propose regression equations based on anthropometric measures to estimate fat mass (FM) using dual energy X-ray absorptiometry (DXA) as reference method, and (ii)to establish population reference standards for equation-derived FM. A cross-sectional study on 6,713 university students (3,354 males and 3,359 females) from Chile aged 17.0 to 27.0years. Anthropometric measures (weight, height, waist circumference) were taken in all participants. Whole body DXA was performed in 683 subjects. A total of 478 subjects were selected to develop regression equations, and 205 for their cross-validation. Data from 6,030 participants were used to develop reference standards for FM. Equations were generated using stepwise multiple regression analysis. Percentiles were developed using the LMS method. Equations for men were: (i) FM=-35,997.486 +232.285 *Weight +432.216 *CC (R 2 =0.73, SEE=4.1); (ii)FM=-37,671.303 +309.539 *Weight +66,028.109 *ICE (R2=0.76, SEE=3.8), while equations for women were: (iii)FM=-13,216.917 +461,302 *Weight+91.898 *CC (R 2 =0.70, SEE=4.6), and (iv) FM=-14,144.220 +464.061 *Weight +16,189.297 *ICE (R 2 =0.70, SEE=4.6). Percentiles proposed included p10, p50, p85, and p95. The developed equations provide valid and accurate estimation of FM in both sexes. The values obtained using the equations may be analyzed from percentiles that allow for categorizing body fat levels by age and sex. Copyright © 2017 SEEN y SED. Publicado por Elsevier España, S.L.U. All rights reserved.

New diagnostic index for sarcopenia in patients with cardiovascular diseases

PubMed Central

Kai, Hisashi; Shibata, Rei; Niiyama, Hiroshi; Nishiyama, Yasuhiro; Murohara, Toyoaki; Yoshida, Noriko; Katoh, Atsushi; Ikeda, Hisao

2017-01-01

Background Sarcopenia is an aging and disease-related syndrome characterized by progressive and generalized loss of skeletal muscle mass and strength, with the risk of frailty and poor quality of life. Sarcopenia is diagnosed by a decrease in skeletal muscle index (SMI) and reduction of either handgrip strength or gait speed. However, measurement of SMI is difficult for general physicians because it requires special equipment for bioelectrical impedance assay or dual-energy X-ray absorptiometry. The purpose of this study was, therefore, to explore a novel, simple diagnostic method of sarcopenia evaluation in patients with cardiovascular diseases (CVD). Methods We retrospectively investigated 132 inpatients with CVD (age: 72±12 years, age range: 27–93 years, males: 61%) Binomial logistic regression and correlation analyses were used to assess the associations of sarcopenia with simple physical data and biomarkers, including muscle-related inflammation makers and nutritional markers. Results Sarcopenia was present in 29.5% of the study population. Serum concentrations of adiponectin and sialic acid were significantly higher in sarcopenic than non-sarcopenic CVD patients. Stepwise multivariate binomial logistic regression analysis revealed that adiponectin, sialic acid, sex, age, and body mass index were independent factors for sarcopenia detection. Sarcopenia index, obtained from the diagnostic regression formula for sarcopenia detection including the five independent factors, indicated a high accuracy in ROC curve analysis (sensitivity 94.9%, specificity 69.9%) and the cutoff value for sarcopenia detection was -1.6134. Sarcopenia index had a significant correlation with the conventional diagnostic parameters of sarcopenia. Conclusions Our new sarcopenia index using simple parameters would be useful for diagnosing sarcopenia in CVD patients. PMID:28542531

Mobile Phone-Based Unobtrusive Ecological Momentary Assessment of Day-to-Day Mood: An Explorative Study.

PubMed

Asselbergs, Joost; Ruwaard, Jeroen; Ejdys, Michal; Schrader, Niels; Sijbrandij, Marit; Riper, Heleen

2016-03-29

Ecological momentary assessment (EMA) is a useful method to tap the dynamics of psychological and behavioral phenomena in real-world contexts. However, the response burden of (self-report) EMA limits its clinical utility. The aim was to explore mobile phone-based unobtrusive EMA, in which mobile phone usage logs are considered as proxy measures of clinically relevant user states and contexts. This was an uncontrolled explorative pilot study. Our study consisted of 6 weeks of EMA/unobtrusive EMA data collection in a Dutch student population (N=33), followed by a regression modeling analysis. Participants self-monitored their mood on their mobile phone (EMA) with a one-dimensional mood measure (1 to 10) and a two-dimensional circumplex measure (arousal/valence, -2 to 2). Meanwhile, with participants' consent, a mobile phone app unobtrusively collected (meta) data from six smartphone sensor logs (unobtrusive EMA: calls/short message service (SMS) text messages, screen time, application usage, accelerometer, and phone camera events). Through forward stepwise regression (FSR), we built personalized regression models from the unobtrusive EMA variables to predict day-to-day variation in EMA mood ratings. The predictive performance of these models (ie, cross-validated mean squared error and percentage of correct predictions) was compared to naive benchmark regression models (the mean model and a lag-2 history model). A total of 27 participants (81%) provided a mean 35.5 days (SD 3.8) of valid EMA/unobtrusive EMA data. The FSR models accurately predicted 55% to 76% of EMA mood scores. However, the predictive performance of these models was significantly inferior to that of naive benchmark models. Mobile phone-based unobtrusive EMA is a technically feasible and potentially powerful EMA variant. The method is young and positive findings may not replicate. At present, we do not recommend the application of FSR-based mood prediction in real-world clinical settings. Further psychometric studies and more advanced data mining techniques are needed to unlock unobtrusive EMA's true potential.

Impacts of land use and population density on seasonal surface water quality using a modified geographically weighted regression.

PubMed

Chen, Qiang; Mei, Kun; Dahlgren, Randy A; Wang, Ting; Gong, Jian; Zhang, Minghua

2016-12-01

As an important regulator of pollutants in overland flow and interflow, land use has become an essential research component for determining the relationships between surface water quality and pollution sources. This study investigated the use of ordinary least squares (OLS) and geographically weighted regression (GWR) models to identify the impact of land use and population density on surface water quality in the Wen-Rui Tang River watershed of eastern China. A manual variable excluding-selecting method was explored to resolve multicollinearity issues. Standard regression coefficient analysis coupled with cluster analysis was introduced to determine which variable had the greatest influence on water quality. Results showed that: (1) Impact of land use on water quality varied with spatial and seasonal scales. Both positive and negative effects for certain land-use indicators were found in different subcatchments. (2) Urban land was the dominant factor influencing N, P and chemical oxygen demand (COD) in highly urbanized regions, but the relationship was weak as the pollutants were mainly from point sources. Agricultural land was the primary factor influencing N and P in suburban and rural areas; the relationship was strong as the pollutants were mainly from agricultural surface runoff. Subcatchments located in suburban areas were identified with urban land as the primary influencing factor during the wet season while agricultural land was identified as a more prevalent influencing factor during the dry season. (3) Adjusted R 2 values in OLS models using the manual variable excluding-selecting method averaged 14.3% higher than using stepwise multiple linear regressions. However, the corresponding GWR models had adjusted R 2 ~59.2% higher than the optimal OLS models, confirming that GWR models demonstrated better prediction accuracy. Based on our findings, water resource protection policies should consider site-specific land-use conditions within each watershed to optimize mitigation strategies for contrasting land-use characteristics and seasonal variations. Copyright © 2016 Elsevier B.V. All rights reserved.

Impact of the Japanese 5S management method on patients' and caretakers' satisfaction: a quasi-experimental study in Senegal.

PubMed

Kanamori, Shogo; Castro, Marcia C; Sow, Seydou; Matsuno, Rui; Cissokho, Alioune; Jimba, Masamine

2016-01-01

The 5S method is a lean management tool for workplace organization, with 5S being an abbreviation for five Japanese words that translate to English as Sort, Set in Order, Shine, Standardize, and Sustain. In Senegal, the 5S intervention program was implemented in 10 health centers in two regions between 2011 and 2014. To identify the impact of the 5S intervention program on the satisfaction of clients (patients and caretakers) who visited the health centers. A standardized 5S intervention protocol was implemented in the health centers using a quasi-experimental separate pre-post samples design (four intervention and three control health facilities). A questionnaire with 10 five-point Likert items was used to measure client satisfaction. Linear regression analysis was conducted to identify the intervention's effect on the client satisfaction scores, represented by an equally weighted average of the 10 Likert items (Cronbach's alpha=0.83). Additional regression analyses were conducted to identify the intervention's effect on the scores of each Likert item. Backward stepwise linear regression ( n= 1,928) indicated a statistically significant effect of the 5S intervention, represented by an increase of 0.19 points in the client satisfaction scores in the intervention group, 6 to 8 months after the intervention ( p= 0.014). Additional regression analyses showed significant score increases of 0.44 ( p= 0.002), 0.14 ( p= 0.002), 0.06 ( p= 0.019), and 0.17 ( p= 0.044) points on four items, which, respectively were healthcare staff members' communication, explanations about illnesses or cases, and consultation duration, and clients' overall satisfaction. The 5S has the potential to improve client satisfaction at resource-poor health facilities and could therefore be recommended as a strategic option for improving the quality of healthcare service in low- and middle-income countries. To explore more effective intervention modalities, further studies need to address the mechanisms by which 5S leads to attitude changes in healthcare staff.

Normal values of reactive oxygen metabolites on the cord-blood of full-term infants with a colorimetric method.

PubMed

Parmigiani, S; Payer, C; Massari, A; Bussolati, G; Bevilacqua, G

2000-01-01

The main end-point of the study was to evaluate the normal values of reactive oxygen metabolites (ROMs) in healthy full-term babies. Secondary end-points were differences between groups related to modality of delivery, Apgar score, birth weight, gestational age and sex. All apparently healthy babies born at our institution between 8 a.m. and 8 p.m. Monday to Friday with gestational age 37-42 weeks, delivered both vaginally or by caesarean section and without foetal distress and perinatal asphyxia. ROMs were evaluated by a colorimetric method (d-ROM test) on cord-blood immediately after birth. The values are reported as arbitrary unit U. Carr. Statistical analysis was performed by t-test and by multiple and stepwise regression analysis. We have analyzed 80 babies with mean birth weight 3301 +/- 446 g. and mean gestational age 39.5 +/- 1.0 weeks. The male:female ratio was 1.56 and the median (range) Apgar score was 9 (7-10) at 1' and 10 (9-10) at 5'. The babies born by vaginal delivery were 37 out of 80 while the remaining 43 were delivered by cesarean section. Because the two groups did not differ for the clinical characteristics they were considered together for the determination of the mean value of ROMs and indicated as "total". The mean value +/- SD of ROMs of the "total" was 115.5 +/- 32.6 U. Carr. Significant differences in the mean value of ROMs were not found related to type of delivery, birth weight, gestational age, and Apgar score at 1' and 5'. Instead the female infants had a significantly lower mean value of ROMs than the male babies (respectively 104.4 +/- 32.2 vs 120.2 +/- 30.6 U. Carr.; p = 0.031). Multiple and stepwise regression analyses both demonstrated that the sex of the neonate is able to independently influence the value of ROMs (respectively p = 0.025 and p = 0.035). The main end-point of the study was to determine the standard reference values for this method in the healthy full-term infant at birth: the values of ROMs we found in the "total" population are lower than those of healthy adults (between 250-300 U. Carr.) and similar to those of adults treated with steroids or antioxidant drugs. The finding that the female sex is able to independently determine lower values of ROMs at birth compared to the male sex, lets speculate that the female infants are less prone to oxidative stress in the first moments of life.

Vapor permeation-stepwise injection simultaneous determination of methanol and ethanol in biodiesel with voltammetric detection.

PubMed

Shishov, Andrey; Penkova, Anastasia; Zabrodin, Andrey; Nikolaev, Konstantin; Dmitrenko, Maria; Ermakov, Sergey; Bulatov, Andrey

2016-02-01

A novel vapor permeation-stepwise injection (VP-SWI) method for the determination of methanol and ethanol in biodiesel samples is discussed. In the current study, stepwise injection analysis was successfully combined with voltammetric detection and vapor permeation. This method is based on the separation of methanol and ethanol from a sample using a vapor permeation module (VPM) with a selective polymer membrane based on poly(phenylene isophtalamide) (PA) containing high amounts of a residual solvent. After the evaporation into the headspace of the VPM, methanol and ethanol were transported, by gas bubbling, through a PA membrane to a mixing chamber equipped with a voltammetric detector. Ethanol was selectively detected at +0.19 V, and both compounds were detected at +1.20 V. Current subtractions (using a correction factor) were used for the selective determination of methanol. A linear range between 0.05 and 0.5% (m/m) was established for each analyte. The limits of detection were estimated at 0.02% (m/m) for ethanol and methanol. The sample throughput was 5 samples h(-1). The method was successfully applied to the analysis of biodiesel samples. Copyright © 2015 Elsevier B.V. All rights reserved.

Stress and coping among elderly African-Americans.

PubMed

Holt-Hill, Shirley Ann

2009-12-01

The purpose of this descriptive correlation was to examine the relationship between the amount of psychological stress experienced and the methods of coping with stress among elderly African-Americans. Demographic variables (age, gender, marital status, education, and occupation), personal resources (health, religion, and social support), and the effects of perception of racial discrimination were included to determine the relationship among the variables and to predict the perceived amount ofpsychological stress and the methods of coping. Subjects were males and females, who were community dwellers, between 65 to 88 years of age. Each subject completed four questionnaires: a Demographic Personal Data Questionnaire, the Short Portable Mental Status Questionnaire, the Stokes/Gordon Stress Scale, and the Ways of Coping Questionnaire. The data were analyzed by computing measures of central tendency, frequency, percentile, and measures of variability. Correlation and stepwise regression were used for predictions and to test null hypotheses. The findings indicated that elderly African-Americans experienced psychologically stressful events in their lives such as concerns for the world, slowing down, physical limitation, financial concerns, and not enough time with their children and grandchildren.

Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection

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

Zhao, Kaiguang; Valle, Denis; Popescu, Sorin

2013-05-15

Model specification remains challenging in spectroscopy of plant biochemistry, as exemplified by the availability of various spectral indices or band combinations for estimating the same biochemical. This lack of consensus in model choice across applications argues for a paradigm shift in hyperspectral methods to address model uncertainty and misspecification. We demonstrated one such method using Bayesian model averaging (BMA), which performs variable/band selection and quantifies the relative merits of many candidate models to synthesize a weighted average model with improved predictive performances. The utility of BMA was examined using a portfolio of 27 foliage spectral–chemical datasets representing over 80 speciesmore » across the globe to estimate multiple biochemical properties, including nitrogen, hydrogen, carbon, cellulose, lignin, chlorophyll (a or b), carotenoid, polar and nonpolar extractives, leaf mass per area, and equivalent water thickness. We also compared BMA with partial least squares (PLS) and stepwise multiple regression (SMR). Results showed that all the biochemicals except carotenoid were accurately estimated from hyerspectral data with R2 values > 0.80.« less

Explanation of obsessive-compulsive disorder and major depressive disorder on the basis of thought-action fusion.

PubMed

Ghamari Kivi, Hossein; Mohammadipour Rik, Ne'mat; Sadeghi Movahhed, Fariba

2013-01-01

Thought-action fusion (TAF) refers to the tendency to assume incorrect causal relationship between one's own thoughts and external reality, in which, thoughts and actions are treated as equivalents. This construct is present to development and maintenance of many psychological disorders. The aim of the present study was to predict obsessive-compulsive disorder (OCD) and its types, and major depressive disorder (MDD) with TAF and its levels. Two groups, included 50 persons with OCD and MDD, respectively, were selected by convenience sampling method in private and governmental psychiatric centers in Ardabil, Iran. Then, they responded to Beck Depression Inventory, Padua Inventory and TAF scale. Data were analysed using multiple regressions analysis by stepwise method. TAF or its subtypes (moral TAF, likelihood-self TAF and likelihood-others TAF) can explain 14% of MDD variance (p < 0.01), 15% of OCD variance (p < 0.01), and 8-21% of OCD types variance (p < 0.05). Moral TAF had high levels in OCD and MDD. The construct of TAF is not specific factor for OCD, and it is present in MDD, too. None.

Skin microrelief profiles as a cutaneous aging index.

PubMed

Kim, Dai Hyun; Rhyu, Yeon Seung; Ahn, Hyo Hyun; Hwang, Eenjun; Uhm, Chang Sub

2016-10-01

An objective measurement of cutaneous topographical information is important for quantifying the degree of skin aging. Our aim was to improve methods for measuring microrelief patterns using a three-dimensional analysis based on silicone replicas and scanning electron microscope (SEM). Another objective was to compare the results with those obtained using a two-dimensional analysis method based on dermoscopy. Silicone replicas were obtained from forearms, dorsum of the hands and fingers of 51 volunteers. Cutaneous profiles obtained by SEM with silicone replicas showed more consistent correlations with age than data obtained by dermoscopy. This indicates the advantage of three-dimensional topography analysis using silicone replicas and SEM over the widely used dermoscopic assessment. The cutaneous age was calculated using stepwise linear regression, and the result was 57.40-9.47 × (number of furrows on dorsum of the hand) × (width of furrows on dorsum of the hand). © The Author 2016. Published by Oxford University Press on behalf of The Japanese Society of Microscopy. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Benign-malignant mass classification in mammogram using edge weighted local texture features

NASA Astrophysics Data System (ADS)

Rabidas, Rinku; Midya, Abhishek; Sadhu, Anup; Chakraborty, Jayasree

2016-03-01

This paper introduces novel Discriminative Robust Local Binary Pattern (DRLBP) and Discriminative Robust Local Ternary Pattern (DRLTP) for the classification of mammographic masses as benign or malignant. Mass is one of the common, however, challenging evidence of breast cancer in mammography and diagnosis of masses is a difficult task. Since DRLBP and DRLTP overcome the drawbacks of Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) by discriminating a brighter object against the dark background and vice-versa, in addition to the preservation of the edge information along with the texture information, several edge-preserving texture features are extracted, in this study, from DRLBP and DRLTP. Finally, a Fisher Linear Discriminant Analysis method is incorporated with discriminating features, selected by stepwise logistic regression method, for the classification of benign and malignant masses. The performance characteristics of DRLBP and DRLTP features are evaluated using a ten-fold cross-validation technique with 58 masses from the mini-MIAS database, and the best result is observed with DRLBP having an area under the receiver operating characteristic curve of 0.982.

An effective approach to quantitative analysis of ternary amino acids in foxtail millet substrate based on terahertz spectroscopy.

PubMed

Lu, Shao Hua; Li, Bao Qiong; Zhai, Hong Lin; Zhang, Xin; Zhang, Zhuo Yong

2018-04-25

Terahertz time-domain spectroscopy has been applied to many fields, however, it still encounters drawbacks in multicomponent mixtures analysis due to serious spectral overlapping. Here, an effective approach to quantitative analysis was proposed, and applied on the determination of the ternary amino acids in foxtail millet substrate. Utilizing three parameters derived from the THz-TDS, the images were constructed and the Tchebichef image moments were used to extract the information of target components. Then the quantitative models were obtained by stepwise regression. The correlation coefficients of leave-one-out cross-validation (R loo-cv 2 ) were more than 0.9595. As for external test set, the predictive correlation coefficients (R p 2 ) were more than 0.8026 and the root mean square error of prediction (RMSE p ) were less than 1.2601. Compared with the traditional methods (PLS and N-PLS methods), our approach is more accurate, robust and reliable, and can be a potential excellent approach to quantify multicomponent with THz-TDS spectroscopy. Copyright © 2017 Elsevier Ltd. All rights reserved.

Identification of eggs from different production systems based on hyperspectra and CS-SVM.

PubMed

Sun, J; Cong, S L; Mao, H P; Zhou, X; Wu, X H; Zhang, X D

2017-06-01

1. To identify the origin of table eggs more accurately, a method based on hyperspectral imaging technology was studied. 2. The hyperspectral data of 200 samples of intensive and extensive eggs were collected. Standard normalised variables combined with a Savitzky-Golay were used to eliminate noise, then stepwise regression (SWR) was used for feature selection. Grid search algorithm (GS), genetic search algorithm (GA), particle swarm optimisation algorithm (PSO) and cuckoo search algorithm (CS) were applied by support vector machine (SVM) methods to establish an SVM identification model with the optimal parameters. The full spectrum data and the data after feature selection were the input of the model, while egg category was the output. 3. The SWR-CS-SVM model performed better than the other models, including SWR-GS-SVM, SWR-GA-SVM, SWR-PSO-SVM and others based on full spectral data. The training and test classification accuracy of the SWR-CS-SVM model were respectively 99.3% and 96%. 4. SWR-CS-SVM proved effective for identifying egg varieties and could also be useful for the non-destructive identification of other types of egg.

Variable selection for zero-inflated and overdispersed data with application to health care demand in Germany

PubMed Central

Wang, Zhu; Shuangge, Ma; Wang, Ching-Yun

2017-01-01

In health services and outcome research, count outcomes are frequently encountered and often have a large proportion of zeros. The zero-inflated negative binomial (ZINB) regression model has important applications for this type of data. With many possible candidate risk factors, this paper proposes new variable selection methods for the ZINB model. We consider maximum likelihood function plus a penalty including the least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP). An EM (expectation-maximization) algorithm is proposed for estimating the model parameters and conducting variable selection simultaneously. This algorithm consists of estimating penalized weighted negative binomial models and penalized logistic models via the coordinated descent algorithm. Furthermore, statistical properties including the standard error formulae are provided. A simulation study shows that the new algorithm not only has more accurate or at least comparable estimation, also is more robust than the traditional stepwise variable selection. The proposed methods are applied to analyze the health care demand in Germany using an open-source R package mpath. PMID:26059498

Discrimination of Active and Weakly Active Human BACE1 Inhibitors Using Self-Organizing Map and Support Vector Machine.

PubMed

Li, Hang; Wang, Maolin; Gong, Ya-Nan; Yan, Aixia

2016-01-01

β-secretase (BACE1) is an aspartyl protease, which is considered as a novel vital target in Alzheimer`s disease therapy. We collected a data set of 294 BACE1 inhibitors, and built six classification models to discriminate active and weakly active inhibitors using Kohonen's Self-Organizing Map (SOM) method and Support Vector Machine (SVM) method. Each molecular descriptor was calculated using the program ADRIANA.Code. We adopted two different methods: random method and Self-Organizing Map method, for training/test set split. The descriptors were selected by F-score and stepwise linear regression analysis. The best SVM model Model2C has a good prediction performance on test set with prediction accuracy, sensitivity (SE), specificity (SP) and Matthews correlation coefficient (MCC) of 89.02%, 90%, 88%, 0.78, respectively. Model 1A is the best SOM model, whose accuracy and MCC of the test set were 94.57% and 0.98, respectively. The lone pair electronegativity and polarizability related descriptors importantly contributed to bioactivity of BACE1 inhibitor. The Extended-Connectivity Finger-Prints_4 (ECFP_4) analysis found some vitally key substructural features, which could be helpful for further drug design research. The SOM and SVM models built in this study can be obtained from the authors by email or other contacts.

Estimation of maximal oxygen uptake by bioelectrical impedance analysis.

PubMed

Stahn, Alexander; Terblanche, Elmarie; Grunert, Sven; Strobel, Günther

2006-02-01

Previous non-exercise models for the prediction of maximal oxygen uptake VO(2max) have failed to accurately discriminate cardiorespiratory fitness within large cohorts. The aim of the present study was to evaluate the feasibility of a completely indirect method for predicting VO(2max) that was based on bioelectrical impedance analysis (BIA) in 66 young, healthy fit men and women. Multiple, stepwise regression analysis was used to determine the usefulness of BIA and additional covariates to estimate VO(2max) (ml min(-1)). BIA was highly correlated to VO(2max) (r = 0.914; P < 0.001) and entered the regression equation first. The inclusion of gender and a physical activity rating further improved the model which accounted for 88% of the variance in VO(2max) and resulted in a relative standard error of the estimate (SEE) of 7.2%. Substantial agreement between the methods was confirmed by the fact that nearly all the differences were within +/-2 SD. Furthermore, in contrast to previously published non-exercise models, no trend of a reduction in prediction accuracy with increasing VO(2max) values was apparent. It was concluded that a non-exercise model based on BIA might be a rapid and useful technique to estimate VO(2max), when a direct test does not seem feasible. However, though the present results are useful to determine the viability of the method, further refinement of the BIA approach and its validation in a large, diverse population is needed before it can be applied to the clinical and epidemiological settings.

Resilient Brain Aging: Characterization of Discordance between Alzheimer’s Disease Pathology and Cognition

PubMed Central

Negash, Selam; Wilson, Robert S.; Leurgans, Sue E.; Wolk, David A.; Schneider, Julie A.; Buchman, Aron S.; Bennett, David A.; Arnold, Steven. E.

2014-01-01

Background Although it is now evident that normal cognition can occur despite significant AD pathology, few studies have attempted to characterize this discordance, or examine factors that may contribute to resilient brain aging in the setting of AD pathology. Methods More than 2,000 older persons underwent annual evaluation as part of participation in the Religious Orders Study or Rush Memory Aging Project. A total of 966 subjects who had brain autopsy and comprehensive cognitive testing proximate to death were analyzed. Resilience was quantified as a continuous measure using linear regression modeling, where global cognition was entered as a dependent variable and global pathology was an independent variable. Studentized residuals generated from the model represented the discordance between cognition and pathology, and served as measure of resilience. The relation of resilience index to known risk factors for AD and related variables was examined. Results Multivariate regression models that adjusted for demographic variables revealed significant associations for early life socioeconomic status, reading ability, APOE-ε4 status, and past cognitive activity. A stepwise regression model retained reading level (estimate = 0.10, SE = 0.02; p < 0.0001) and past cognitive activity (estimate = 0.27, SE = 0.09; p = 0.002), suggesting the potential mediating role of these variables for resilience. Conclusions The construct of resilient brain aging can provide a framework for quantifying the discordance between cognition and pathology, and help identify factors that may mediate this relationship. PMID:23919768

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

PubMed

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

2011-12-15

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

Prediction of sickness absence: development of a screening instrument

PubMed Central

Duijts, S F A; Kant, IJ; Landeweerd, J A; Swaen, G M H

2006-01-01

Objectives To develop a concise screening instrument for early identification of employees at risk for sickness absence due to psychosocial health complaints. Methods Data from the Maastricht Cohort Study on “Fatigue at Work” were used to identify items to be associated with an increased risk of sickness absence. The analytical procedures univariate logistic regression, backward stepwise linear regression, and multiple logistic regression were successively applied. For both men and women, sum scores were calculated, and sensitivity and specificity rates of different cut‐off points on the screening instrument were defined. Results In women, results suggested that feeling depressed, having a burnout, being tired, being less interested in work, experiencing obligatory change in working days, and living alone, were strong predictors of sickness absence due to psychosocial health complaints. In men, statistically significant predictors were having a history of sickness absence, compulsive thinking, being mentally fatigued, finding it hard to relax, lack of supervisor support, and having no hobbies. A potential cut‐off point of 10 on the screening instrument resulted in a sensitivity score of 41.7% for women and 38.9% for men, and a specificity score of 91.3% for women and 90.6% for men. Conclusions This study shows that it is possible to identify predictive factors for sickness absence and to develop an instrument for early identification of employees at risk for sickness absence. The results of this study increase the possibility for both employers and policymakers to implement interventions directed at the prevention of sickness absence. PMID:16698807

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

PubMed

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

2002-01-01

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

Risk stratification personalised model for prediction of life-threatening ventricular tachyarrhythmias in patients with chronic heart failure.

PubMed

Frolov, Alexander Vladimirovich; Vaikhanskaya, Tatjana Gennadjevna; Melnikova, Olga Petrovna; Vorobiev, Anatoly Pavlovich; Guel, Ludmila Michajlovna

2017-01-01

The development of prognostic factors of life-threatening ventricular tachyarrhythmias (VTA) and sudden cardiac death (SCD) continues to maintain its priority and relevance in cardiology. The development of a method of personalised prognosis based on multifactorial analysis of the risk factors associated with life-threatening heart rhythm disturbances is considered a key research and clinical task. To design a prognostic and mathematical model to define personalised risk for life-threatening VTA in patients with chronic heart failure (CHF). The study included 240 patients with CHF (mean-age of 50.5 ± 12.1 years; left ventricular ejection fraction 32.8 ± 10.9%; follow-up period 36.8 ± 5.7 months). The participants received basic therapy for heart failure. The elec-trocardiogram (ECG) markers of myocardial electrical instability were assessed including microvolt T-wave alternans, heart rate turbulence, heart rate deceleration, and QT dispersion. Additionally, echocardiography and Holter monitoring (HM) were performed. The cardiovascular events were considered as primary endpoints, including SCD, paroxysmal ventricular tachycardia/ventricular fibrillation (VT/VF) based on HM-ECG data, and data obtained from implantable device interrogation (CRT-D, ICD) as well as appropriated shocks. During the follow-up period, 66 (27.5%) subjects with CHF showed adverse arrhythmic events, including nine SCD events and 57 VTAs. Data from a stepwise discriminant analysis of cumulative ECG-markers of myocardial electrical instability were used to make a mathematical model of preliminary VTA risk stratification. Uni- and multivariate Cox logistic regression analysis were performed to define an individualised risk stratification model of SCD/VTA. A binary logistic regression model demonstrated a high prognostic significance of discriminant function with a classification sensitivity of 80.8% and specificity of 99.1% (F = 31.2; c2 = 143.2; p < 0.0001). The method of personalised risk stratification using Cox logistic regression allows correct classification of more than 93.9% of CHF cases. A robust body of evidence concerning logistic regression prognostic significance to define VTA risk allows inclusion of this method into the algorithm of subsequent control and selection of the optimal treatment modality to treat patients with CHF.

Explanation of Obsessive-Compulsive Disorder and Major Depressive Disorder on the Basis of Thought-Action Fusion

PubMed Central

Ghamari Kivi, Hossein; Mohammadipour Rik, Ne’mat; Sadeghi Movahhed, Fariba

2013-01-01

Objective: Thought-action fusion (TAF) refers to the tendency to assume incorrect causal relationship between one’s own thoughts and external reality, in which, thoughts and actions are treated as equivalents. This construct is present to development and maintenance of many psychological disorders. The aim of the present study was to predict obsessive-compulsive disorder (OCD) and its types, and major depressive disorder (MDD) with TAF and its levels. Methods: Two groups, included 50 persons with OCD and MDD, respectively, were selected by convenience sampling method in private and governmental psychiatric centers in Ardabil, Iran. Then, they responded to Beck Depression Inventory, Padua Inventory and TAF scale. Data were analysed using multiple regressions analysis by stepwise method. Results: TAF or its subtypes (moral TAF, likelihood-self TAF and likelihood-others TAF) can explain 14% of MDD variance (p < 0.01), 15% of OCD variance (p < 0.01), and 8-21% of OCD types variance (p < 0.05). Moral TAF had high levels in OCD and MDD. Conclusion: The construct of TAF is not specific factor for OCD, and it is present in MDD, too. Declaration of interest: None. PMID:24644509

Neighborhood Structural Similarity Mapping for the Classification of Masses in Mammograms.

PubMed

Rabidas, Rinku; Midya, Abhishek; Chakraborty, Jayasree

2018-05-01

In this paper, two novel feature extraction methods, using neighborhood structural similarity (NSS), are proposed for the characterization of mammographic masses as benign or malignant. Since gray-level distribution of pixels is different in benign and malignant masses, more regular and homogeneous patterns are visible in benign masses compared to malignant masses; the proposed method exploits the similarity between neighboring regions of masses by designing two new features, namely, NSS-I and NSS-II, which capture global similarity at different scales. Complementary to these global features, uniform local binary patterns are computed to enhance the classification efficiency by combining with the proposed features. The performance of the features are evaluated using the images from the mini-mammographic image analysis society (mini-MIAS) and digital database for screening mammography (DDSM) databases, where a tenfold cross-validation technique is incorporated with Fisher linear discriminant analysis, after selecting the optimal set of features using stepwise logistic regression method. The best area under the receiver operating characteristic curve of 0.98 with an accuracy of is achieved with the mini-MIAS database, while the same for the DDSM database is 0.93 with accuracy .

Stereomicroscopic study on unsectioned extracted teeth

PubMed Central

Narayan, V. Keerthi; Varsha, V. K.; Girish, H. C.; Murgod, Sanjay

2017-01-01

Introduction: Age has been considered as a reliable marker for establishing the identity of a person in the field of forensic medicine. Teeth are useful skeletal indicators of age at death since it can survive for decades. Nondestructive methods ensure the evident preservation of dental hard tissues that reflect age changes from the cradle to the grave. Therefore, an attempt was made for estimating the age using the nondestructive method. Aims and Objectives: The aim of this study to assess whether physiological changes of the teeth allow possible correlation for accurate age estimation and to establish a graduation standard by microscopic observation for a better age correlation. Materials and Methods: The study was carried on 209 teeth samples extracted for orthodontic treatment or periodontal diseases comprised both maxillary and mandibular teeth across different age groups. The assessment of these changes was carried out by well-established standard methods with some proposed modifications. Results: Pearson correlation analyses revealed root dentin translucency with the highest correlation (r = 0.97) followed by periodontal ligament attachment (r = 0.95), root dentin color (r = 0.95), and attrition being the least correlated (r = 0.90). All the parameters taken for the study contributed to stepwise linear regression analysis (R = 0.98; P < 0.01) indicating a strongly positive relationship between age and the changes observed. A regression formula was obtained with mean error age difference ±1.0 years. Conclusion: The present study showed that extracted tooth is highly significant in identifying the age without being sectioned or further processed and also signifies the use of microscope for observation of these changes, thus reducing the errors of calibrating the age. PMID:29657494

An Original Stepwise Multilevel Logistic Regression Analysis of Discriminatory Accuracy: The Case of Neighbourhoods and Health

PubMed Central

Wagner, Philippe; Ghith, Nermin; Leckie, George

2016-01-01

Background and Aim Many multilevel logistic regression analyses of “neighbourhood and health” focus on interpreting measures of associations (e.g., odds ratio, OR). In contrast, multilevel analysis of variance is rarely considered. We propose an original stepwise analytical approach that distinguishes between “specific” (measures of association) and “general” (measures of variance) contextual effects. Performing two empirical examples we illustrate the methodology, interpret the results and discuss the implications of this kind of analysis in public health. Methods We analyse 43,291 individuals residing in 218 neighbourhoods in the city of Malmö, Sweden in 2006. We study two individual outcomes (psychotropic drug use and choice of private vs. public general practitioner, GP) for which the relative importance of neighbourhood as a source of individual variation differs substantially. In Step 1 of the analysis, we evaluate the OR and the area under the receiver operating characteristic (AUC) curve for individual-level covariates (i.e., age, sex and individual low income). In Step 2, we assess general contextual effects using the AUC. Finally, in Step 3 the OR for a specific neighbourhood characteristic (i.e., neighbourhood income) is interpreted jointly with the proportional change in variance (i.e., PCV) and the proportion of ORs in the opposite direction (POOR) statistics. Results For both outcomes, information on individual characteristics (Step 1) provide a low discriminatory accuracy (AUC = 0.616 for psychotropic drugs; = 0.600 for choosing a private GP). Accounting for neighbourhood of residence (Step 2) only improved the AUC for choosing a private GP (+0.295 units). High neighbourhood income (Step 3) was strongly associated to choosing a private GP (OR = 3.50) but the PCV was only 11% and the POOR 33%. Conclusion Applying an innovative stepwise multilevel analysis, we observed that, in Malmö, the neighbourhood context per se had a negligible influence on individual use of psychotropic drugs, but appears to strongly condition individual choice of a private GP. However, the latter was only modestly explained by the socioeconomic circumstances of the neighbourhoods. Our analyses are based on real data and provide useful information for understanding neighbourhood level influences in general and on individual use of psychotropic drugs and choice of GP in particular. However, our primary aim is to illustrate how to perform and interpret a multilevel analysis of individual heterogeneity in social epidemiology and public health. Our study shows that neighbourhood “effects” are not properly quantified by reporting differences between neighbourhood averages but rather by measuring the share of the individual heterogeneity that exists at the neighbourhood level. PMID:27120054

Perceived Social Support, Self-Esteem, and Internet Addiction Among Students of Al-Zahra University, Tehran, Iran.

PubMed

Naseri, Laila; Mohamadi, Jalal; Sayehmiri, Koroush; Azizpoor, Yosra

2015-09-01

Internet addiction is a global phenomenon that causes serious problems in mental health and social communication. Students form a vulnerable group, since they have free, easy, and daily access to the internet. The current study aimed to investigate perceived social support, self-esteem, and internet addiction among Al-Zahra University students. In the current descriptive research, the statistical sample consisted of 101 female students residing at AL-Zahra University dormitory, Tehran, Iran. Participants were randomly selected and their identities were classified. Then, they completed the Multidimensional Scale of Perceived Social Support, Rosenberg's Self-esteem Scale, and Yang Internet Addiction Test. After completion of the questionnaires, the data were analyzed using the correlation test and stepwise regression. The Pearson correlation coefficient indicated significant relationships between self-esteem and internet addiction (P < 0.05, r = -0.345), perceived social support (r = 0.224, P < 0.05), and the subscale of family (r = 0.311, P < 0.05). The findings also demonstrated a significant relationship between internet addiction and perceived social support (r = -0.332, P < 0.05), the subscale of family (P < 0.05, r = -0.402), and the other subscales (P < 0.05, r = -0.287). Results of the stepwise regression showed that the scale of internet addiction and the family subscale were predicative variables for self-esteem (r = 0.137, P < 0.01, F2, 96 = 77.7). Findings of the current study showed that persons with low self-esteem were more vulnerable to internet addiction.

Health related quality of life and influencing factors among welders.

PubMed

Qin, Jingxiang; Liu, Wuzhong; Zhu, Jun; Weng, Wei; Xu, Jiaming; Ai, Zisheng

2014-01-01

Occupational exposure to welding fumes is a serious occupational health problem all over the world. Welders are exposed to many occupational hazards; these hazards might cause some occupational diseases. The aim of the study was to assess the health related quality of life (HRQL) of electric welders in Shanghai China and explore influencing factors to HRQL of welders. 301 male welders (without pneumoconiosis) and 305 non-dust male workers in Shanghai were enrolled in this study. Short Form-36 (SF-36) health survey questionnaires were applied in this cross-sectional study. Socio-demographic, working and health factors were also collected. Multiple stepwise regress analysis was used to identify significant factors related to the eight dimension scores. Six dimensions including role-physical (RP), bodily pain (BP), general health (GH), validity (VT), social function (SF), and mental health (MH) were significantly worse in welders compared to non-dust workers. Multiple stepwise regress analysis results show that native place, monthly income, quantity of children, drinking, sleep time, welding type, use of personal protective equipment (PPE), great events in life, and some symptoms including dizziness, discomfort of cervical vertebra, low back pain, cough and insomnia may be influencing factors for HRQL of welders. Among these factors, only sleep time and the use of PPE were salutary. Some dimensions of HRQL of these welders have been affected. Enterprises which employ welders should take measures to protect the health of these people and improve their HRQL.

Correlation between increased urinary sodium excretion and decreased left ventricular diastolic function in patients with type 2 diabetes mellitus.

PubMed

Kagiyama, Shuntaro; Koga, Tokushi; Kaseda, Shigeru; Ishihara, Shiro; Kawazoe, Nobuyuki; Sadoshima, Seizo; Matsumura, Kiyoshi; Takata, Yutaka; Tsuchihashi, Takuya; Iida, Mitsuo

2009-10-01

Increased salt intake may induce hypertension, lead to cardiac hypertrophy, and exacerbate heart failure. When elderly patients develop heart failure, diastolic dysfunction is often observed, although the ejection fraction has decreased. Diabetes mellitus (DM) is an established risk factor for heart failure. However, little is known about the relationship between cardiac function and urinary sodium excretion (U-Na) in patients with DM. We measured 24-hour U-Na; cardiac function was evaluated directly during coronary catheterization in type 2 DM (n = 46) or non-DM (n = 55) patients with preserved cardiac systolic function (ejection fraction > or = 60%). Cardiac diastolic and systolic function was evaluated as - dp/dt and + dp/dt, respectively. The average of U-Na was 166.6 +/- 61.2 mEq/24 hour (mean +/- SD). In all patients, stepwise multivariate regression analysis revealed that - dp/dt had a negative correlation with serum B-type natriuretic peptide (BNP; beta = - 0.23, P = .021) and U-Na (beta = - 0.24, P = .013). On the other hand, + dp/dt negatively correlated with BNP (beta = - 0.30, P < .001), but did not relate to U-Na. In the DM-patients, stepwise multivariate regression analysis showed that - dp/dt still had a negative correlation with U-Na (beta = - 0.33, P = .025). The results indicated that increased urinary sodium excretion is associated with an impairment of cardiac diastolic function, especially in patients with DM, suggesting that a reduction of salt intake may improve cardiac diastolic function.

The rate of adherence to urate-lowering therapy and associated factors in Chinese gout patients: a cross-sectional study.

PubMed

Yin, Rulan; Cao, Haixia; Fu, Ting; Zhang, Qiuxiang; Zhang, Lijuan; Li, Liren; Gu, Zhifeng

2017-07-01

The aim of this study was to assess adherence rate and predictors of non-adherence with urate-lowering therapy (ULT) in Chinese gout patients. A cross-sectional study was administered to 125 gout patients using the Compliance Questionnaire on Rheumatology (CQR) for adherence to ULT. Patients were asked to complete the Treatment Satisfaction Questionnaire for Medication version II, Health Assessment Questionnaire, Confidence in Gout Treatment Questionnaire, Gout Knowledge Questionnaire, Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, and 36-Item Short Form Health Survey. Data were analyzed by independent sample t test, rank sum test, Chi-square analysis as well as binary stepwise logistic regression modeling. The data showed that the rate of adherence (CQR ≥80%) to ULT was 9.6% in our investigated gout patients. Adherence was associated with functional capacity, gout-related knowledge, satisfaction with medication, confidence in gout treatment and mental components summary. Multivariable analysis of binary stepwise logistic regression identified gout-related knowledge and satisfaction of effectiveness with medication was the independent risk factors of medication non-adherence. Patients unaware of gout-related knowledge, or with low satisfaction of effectiveness with medication, were more likely not to adhere to ULT. Non-adherence to ULT among gout patients is exceedingly common, particularly in patients unaware of gout-related knowledge, or with low satisfaction of effectiveness with medication. These findings could help medical personnel develop useful interventions to improve gout patients' medication adherence.

Relationships between temperaments, occupational stress, and insomnia among Japanese workers.

PubMed

Deguchi, Yasuhiko; Iwasaki, Shinichi; Ishimoto, Hideyuki; Ogawa, Koichiro; Fukuda, Yuichi; Nitta, Tomoko; Mitake, Tomoe; Nogi, Yukako; Inoue, Koki

2017-01-01

Insomnia among workers reduces the quality of life, contributes toward the economic burden of healthcare costs and losses in work performance. The relationship between occupational stress and insomnia has been reported in previous studies, but there has been little attention to temperament in occupational safety and health research. The aim of this study was to clarify the relationships between temperament, occupational stress, and insomnia. The subjects were 133 Japanese daytime local government employees. Temperament was assessed using the Temperament Evaluation of Memphis, Pisa, Paris, and San Diego-Auto questionnaire (TEMPS-A). Occupational stress was assessed using the Generic Job Stress Questionnaire (GJSQ). Insomnia was assessed using the Athens Insomnia Scale (AIS). Stepwise multiple logistic regression analyses were conducted. In a stepwise multivariate logistic regression analysis, it was found that the higher subdivided stress group by "role conflict" (OR = 5.29, 95% CI, 1.61-17.32) and anxious temperament score (OR = 1.33; 95% CI, 1.19-1.49) was associated with the presence of insomnia using an adjusted model, whereas other factors were excluded from the model. The study limitations were the sample size and the fact that only Japanese local government employees were surveyed. This study demonstrated the relationships between workers' anxious temperament, role conflict, and insomnia. Recognizing one's own anxious temperament would lead to self-insight, and the recognition of anxious temperament and reduction of role conflict by their supervisors or coworkers would reduce the prevalence of insomnia among workers in the workplace.

Job stress, achievement motivation and occupational burnout among male nurses.

PubMed

Hsu, Hsiu-Yueh; Chen, Sheng-Hwang; Yu, Hsing-Yi; Lou, Jiunn-Horng

2010-07-01

This paper is a report of an exploration of job stress, achievement motivation and occupational burnout in male nurses and to identify predictors of occupational burnout. Since the Nightingale era, the nursing profession has been recognized as 'women's work'. The data indicate that there are more female nurses than male nurses in Taiwan. However, the turnover rate for male nurses is twice that of female nurses. Understanding the factors that affect occupational burnout of male nurses may help researchers find ways to reduce the likelihood that they will quit. A survey was conducted in Taiwan in 2008 using a cross-sectional design. A total of 121 male nurses participated in the study. Mailed questionnaires were used to collect data, which were analysed using descriptive statistics and stepwise multiple regression. The job stress of male nurses was strongly correlated with occupational burnout (r = 0.64, P < 0.001). Stepwise multiple regression analyses indicated that job stress was the only factor to have a statistically significant direct influence on occupational burnout, accounting for 45.8% of the variance in this. Job stress was comprised of three dimensions, of which role conflict accounted for 40.8% of the variance in occupational burnout. The contribution of job stress to occupational burnout of male nurses was confirmed. As occupational burnout may influence the quality of care by these nurses, nurse managers should strive to decrease male nurses' job stress as this should lead to a reduction of negative outcomes of occupational burnout.

Factors Predicting Recovery of Oral Intake in Stroke Survivors with Dysphagia in a Convalescent Rehabilitation Ward.

PubMed

Ikenaga, Yasunori; Nakayama, Sayaka; Taniguchi, Hiroki; Ohori, Isao; Komatsu, Nahoko; Nishimura, Hitoshi; Katsuki, Yasuo

2017-05-01

Percutaneous endoscopic gastrostomy may be performed in dysphagic stroke patients. However, some patients regain complete oral intake without gastrostomy. This study aimed to investigate the predictive factors of intake, thereby determining gastrostomy indications. Stroke survivors admitted to our convalescent rehabilitation ward who underwent gastrostomy or nasogastric tube placement from 2009 to 2015 were divided into 2 groups based on intake status at discharge. Demographic data and Functional Independence Measure (FIM), Dysphagia Severity Scale (DSS), National Institutes of Health Stroke Scale, and Glasgow Coma Scale (GCS) scores on admission were compared between groups. We evaluated the factors predicting intake using a stepwise logistic regression analysis. Thirty-four patients recovered intake, whereas 38 achieved incomplete intake. Mean age was lower, mean body mass index (BMI) was higher, and mean time from stroke onset to admission was shorter in the complete intake group. The complete intake group had less impairment in terms of GCS, FIM, and DSS scores. In the stepwise logistic regression analysis, BMI, FIM-cognitive score, and DSS score were significant independent factors predicting intake. The formula of BMI × .26 + FIM cognitive score × .19 + DSS score × 1.60 predicted recovery of complete intake with a sensitivity of 88.2% and a specificity of 84.2%. Stroke survivors with dysphagia with a high BMI and FIM-cognitive and DSS scores tended to recover oral intake. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Stigma Resistance in Stable Schizophrenia: The Relative Contributions of Stereotype Endorsement, Self-Reflection, Self-Esteem, and Coping Styles.

PubMed

Kao, Yu-Chen; Lien, Yin-Ju; Chang, Hsin-An; Tzeng, Nian-Sheng; Yeh, Chin-Bin; Loh, Ching-Hui

2017-10-01

Stigma resistance (SR) has recently emerged as a prominent aspect of research on recovery from schizophrenia, partly because studies have suggested that the development of stigma-resisting beliefs may help individuals lead a fulfilling life and recover from their mental illness. The present study assessed the relationship between personal SR ability and prediction variables such as self-stigma, self-esteem, self-reflection, coping styles, and psychotic symptomatology. We performed an exploratory cross-sectional study of 170 community-dwelling patients with schizophrenia. Self-stigma, self-esteem, self-reflection, coping skills, and SR were assessed through self-report. Psychotic symptom severity was rated by the interviewers. Factors showing significant association in univariate analyses were included in a stepwise backward regression model. Stepwise regressions revealed that acceptance of stereotypes of mental illness, self-esteem, self-reflection, and only 2 adaptive coping strategies (positive reinterpretation and religious coping) were significant predictors of SR. The prediction model accounted for 27.1% of the variance in the SR subscale score in our sample. Greater reflective capacity, greater self-esteem, greater preferences for positive reinterpretation and religious coping, and fewer endorsements of the stereotypes of mental illness may be key factors that relate to higher levels of SR. These factors are potentially modifiable in tailored interventions, and such modification may produce considerable improvements in the SR of the investigated population. This study has implications for psychosocial rehabilitation and emerging views of recovery from mental illness.

Prediction of reported consumption of selected fat-containing foods.

PubMed

Tuorila, H; Pangborn, R M

1988-10-01

A total of 100 American females (mean age = 20.8 years) completed a questionnaire, in which their beliefs, evaluations, liking and consumption (frequency, consumption compared to others, intention to consume) of milk, cheese, ice cream, chocolate and "high-fat foods" were measured. For the design and analysis, the basic frame of reference was the Fishbein-Ajzen model of reasoned action, but the final analyses were carried out with stepwise multiple regression analysis. In addition to the components of the Fishbein-Ajzen model, beliefs and evaluations were used as independent variables. On the average, subjects reported liking all the products but not "high-fat foods", and thought that milk and cheese were "good for you" whereas the remaining items were "bad for you". Principal component analysis for beliefs revealed factors related to pleasantness/benefit aspects, to health and weight concern and to the "functionality" of the foods. In stepwise multiple regression analyses, liking was the predominant predictor of reported consumption for all the foods, but various belief factors, particularly those related to concern with weight, also significantly predicted consumption. Social factors played only a minor role. The multiple R's of the predictive functions varied from 0.49 to 0.74. The fact that all four foods studied elicited individual sets of beliefs and belief structures, and that none of them was rated similar to the generic "high-fat foods", emphasizes that consumers attach meaning to integrated food entities rather than to ingredients.

Behavioral activation: Is it the expectation or achievement, of mastery or pleasure that contributes to improvement in depression?

PubMed

Furukawa, Toshi A; Imai, Hissei; Horikoshi, Masaru; Shimodera, Shinji; Hiroe, Takahiro; Funayama, Tadashi; Akechi, Tatsuo

2018-06-06

Behavioral activation (BA) is receiving renewed interest as a stand-alone or as a component of cognitive-behavior therapy (CBT) for depression. However, few studies have examined which aspects of BA are most contributory to its efficacy. This is a secondary analysis of a 9-week randomized controlled trial of smartphone CBT for patients with major depression. Depression severity was measured at baseline and at end of treatment by the Patient Health Questionnaire-9. All aspects of behavioral activation tasks that the participants had engaged in, including their expected mastery and pleasure and obtained mastery and pleasure, were recorded in the web server. We examined their contribution to improvement in depression as simple correlations and in stepwise multivariable linear regression. Among the 78 patients who completed at least one behavioral experiment, all aspects of expected or achieved mastery or pleasure correlated with change in depression severity. Discrepancy between the expectation and achievement, representing unexpected gain in mastery or pleasure, was not correlated. In stepwise regression, expected mastery and pleasure, especially the maximum level of the latter, emerged as the strongest contributing factors. The study is observational and cannot deduce cause-effect relationships. It may be the expected and continued sense of pleasure in planning activities that are most meaningful and rewarding to individuals, and not the simple level or amount of obtained pleasure, that contributes to the efficacy of BA. Copyright © 2018. Published by Elsevier B.V.

Association between red blood cell distribution width (RDW) and carotid artery atherosclerosis (CAS) in patients with primary ischemic stroke.

PubMed

Jia, He; Li, Huimian; Zhang, Yan; Li, Che; Hu, Yingyun; Xia, Chunfang

2015-01-01

The present study aimed to explore the association between RDW and CAS in patients with ischemic stroke, expecting to find a new and significant diagnosis index for clinical practice. This cross-sectional study involves 432 consecutive patients with primary ischemic stroke (within 72 h). All subjects were confirmed by magnetic resonance imaging, and underwent physical examination, laboratory tests and carotid ultrasonography check. Finally, 392 patients were included according to the exclusion criteria. The odds ratios of independent variables were calculated using stepwise multiple logistic regression. Carotid intimal-medial thickness (IMT) and RDW are both significantly different between CAS group and control group. Univariate analyses show that high-sensitive C-reactive protein (Hs-CRP) and RDW (r=0.436) are both in significantly positive association with IMT. Stepwise multiple logistic regression shows that RDW is an independent protective factor of CAS in patients with ischemic stroke. Compared with the lowest quartile, the second to fourth quartiles are 1.13 (95% CI: 1.13-3.05), 2.02 (95% CI: 1.66-4.67), and 3.10 (95% CI: 2.46-7.65), respectively. The present study suggested that RDW level were higher than non-CAS in patients with primary ischemic stroke. Our results facilitated a bridge to connect RDW with ischemic stroke and further confirmed the role of RDW in the progression of the ischemic stroke. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Relationships between temperaments, occupational stress, and insomnia among Japanese workers

PubMed Central

Ishimoto, Hideyuki; Ogawa, Koichiro; Fukuda, Yuichi; Nitta, Tomoko; Mitake, Tomoe; Nogi, Yukako; Inoue, Koki

2017-01-01

Insomnia among workers reduces the quality of life, contributes toward the economic burden of healthcare costs and losses in work performance. The relationship between occupational stress and insomnia has been reported in previous studies, but there has been little attention to temperament in occupational safety and health research. The aim of this study was to clarify the relationships between temperament, occupational stress, and insomnia. The subjects were 133 Japanese daytime local government employees. Temperament was assessed using the Temperament Evaluation of Memphis, Pisa, Paris, and San Diego-Auto questionnaire (TEMPS-A). Occupational stress was assessed using the Generic Job Stress Questionnaire (GJSQ). Insomnia was assessed using the Athens Insomnia Scale (AIS). Stepwise multiple logistic regression analyses were conducted. In a stepwise multivariate logistic regression analysis, it was found that the higher subdivided stress group by “role conflict” (OR = 5.29, 95% CI, 1.61–17.32) and anxious temperament score (OR = 1.33; 95% CI, 1.19–1.49) was associated with the presence of insomnia using an adjusted model, whereas other factors were excluded from the model. The study limitations were the sample size and the fact that only Japanese local government employees were surveyed. This study demonstrated the relationships between workers’ anxious temperament, role conflict, and insomnia. Recognizing one’s own anxious temperament would lead to self-insight, and the recognition of anxious temperament and reduction of role conflict by their supervisors or coworkers would reduce the prevalence of insomnia among workers in the workplace. PMID:28407025

Is patriarchy the source of men's higher mortality?

PubMed Central

Stanistreet, D; Bambra, C; Scott-Samuel, A

2005-01-01

Objective: To examine the relation between levels of patriarchy and male health by comparing female homicide rates with male mortality within countries. Hypothesis: High levels of patriarchy in a society are associated with increased mortality among men. Design: Cross sectional ecological study design. Setting: 51 countries from four continents were represented in the data—America, Europe, Australasia, and Asia. No data were available for Africa. Results: A multivariate stepwise linear regression model was used. Main outcome measure was age standardised male mortality rates for 51 countries for the year 1995. Age standardised female homicide rates and GDP per capita ranking were the explanatory variables in the model. Results were also adjusted for the effects of general rates of homicide. Age standardised female homicide rates and ranking of GDP were strongly correlated with age standardised male mortality rates (Pearson's r = 0.699 and Spearman's 0.744 respectively) and both correlations achieved significance (p<0.005). Both factors were subsequently included in the stepwise regression model. Female homicide rates explained 48.8% of the variance in male mortality, and GDP a further 13.6% showing that the higher the rate of female homicide, and hence the greater the indicator of patriarchy, the higher is the rate of mortality among men. Conclusion: These data suggest that oppression and exploitation harm the oppressors as well as those they oppress, and that men's higher mortality is a preventable social condition, which could be tackled through global social policy measures. PMID:16166362

Relationship between Spiritual Health and Quality of Life in Patients with Cancer.

PubMed

Mohebbifar, Rafat; Pakpour, Amir H; Nahvijou, Azin; Sadeghi, Atefeh

2015-01-01

As the essence of health in humans, spiritual health is a fundamental concept for discussing chronic diseases such as cancer and a major approach for improving quality of life in patients is through creating meaningfulness and purpose. The present descriptive analytical study was conducted to assess the relationship between spiritual health and quality of life in 210 patients with cancer admitted to the Cancer Institute of Iran, selected through convenience sampling in 2014. Data were collected using Spiritual Health Questionnaire and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC-QLQ). Patients' performance was assessed through the Karnofsky Performance Status Indicator and their cognitive status through the Mini-Mental State Examination (MMSE). Data were analyzed in SPSS-16 using descriptive statistics and stepwise linear regression. The results obtained reported the mean and standard deviation of the patients' spiritual health scoreas 78.4±16.1and the mean and standard deviation of their quality of life score as 58.1±18.7. The stepwise linear regression analysis confirmed a positive and significant relationship between spiritual health and quality of life in patients with cancer (β=0.688 and r=0.00). The results of the study show that spiritual health should be more emphasized and reinforced as a factor involved in improving quality of life in patients with cancer. Designing care therapies and spiritual interventions is a priority in the treatment of these patients.

QSPR models for half-wave reduction potential of steroids: a comparative study between feature selection and feature extraction from subsets of or entire set of descriptors.

PubMed

Hemmateenejad, Bahram; Yazdani, Mahdieh

2009-02-16

Steroids are widely distributed in nature and are found in plants, animals, and fungi in abundance. A data set consists of a diverse set of steroids have been used to develop quantitative structure-electrochemistry relationship (QSER) models for their half-wave reduction potential. Modeling was established by means of multiple linear regression (MLR) and principle component regression (PCR) analyses. In MLR analysis, the QSPR models were constructed by first grouping descriptors and then stepwise selection of variables from each group (MLR1) and stepwise selection of predictor variables from the pool of all calculated descriptors (MLR2). Similar procedure was used in PCR analysis so that the principal components (or features) were extracted from different group of descriptors (PCR1) and from entire set of descriptors (PCR2). The resulted models were evaluated using cross-validation, chance correlation, application to prediction reduction potential of some test samples and accessing applicability domain. Both MLR approaches represented accurate results however the QSPR model found by MLR1 was statistically more significant. PCR1 approach produced a model as accurate as MLR approaches whereas less accurate results were obtained by PCR2 approach. In overall, the correlation coefficients of cross-validation and prediction of the QSPR models resulted from MLR1, MLR2 and PCR1 approaches were higher than 90%, which show the high ability of the models to predict reduction potential of the studied steroids.

Applicability of linear regression equation for prediction of chlorophyll content in rice leaves

NASA Astrophysics Data System (ADS)

Li, Yunmei

2005-09-01

A modeling approach is used to assess the applicability of the derived equations which are capable to predict chlorophyll content of rice leaves at a given view direction. Two radiative transfer models, including PROSPECT model operated at leaf level and FCR model operated at canopy level, are used in the study. The study is consisted of three steps: (1) Simulation of bidirectional reflectance from canopy with different leaf chlorophyll contents, leaf-area-index (LAI) and under storey configurations; (2) Establishment of prediction relations of chlorophyll content by stepwise regression; and (3) Assessment of the applicability of these relations. The result shows that the accuracy of prediction is affected by different under storey configurations and, however, the accuracy tends to be greatly improved with increase of LAI.

Epidemiological characteristics and deaths of premature infants in a referral hospital for high-risk pregnancies

PubMed Central

de Freitas, Brunnella Alcantara Chagas; Sant'Ana, Luciana Ferreira da Rocha; Longo, Giana Zarbato; Siqueira-Batista, Rodrigo; Priore, Silvia Eloiza; Franceschin, Sylvia do Carmo Castro

2012-01-01

Objective To analyze the process of care provided to premature infants in a neonatal intensive care unit and the factors associated with their mortality. Methods Cross-sectional retrospective study of premature infants in an intensive care unit between 2008 and 2010. The characteristics of the mothers and premature infants were described, and a bivariate analysis was performed on the following characteristics: the study period and the "death" outcome (hospital, neonatal and early) using Pearson's chi-square test, Fisher's exact test or a chi-square test for linear trends. Bivariate and multivariable logistic regression analyses were performed using a stepwise backward logistic regression method between the variables with p<0.20 and the "death" outcome. A p value <0.05 was considered to be significant. Results In total, 293 preterm infants were studied. Increased access to complementary tests (transfontanellar ultrasound and Doppler echocardiogram) and breastfeeding rates were indicators of improving care. Mortality was concentrated in the neonatal period, especially in the early neonatal period, and was associated with extreme prematurity, small size for gestational age and an Apgar score <7 at 5 minutes after birth. The late-onset sepsis was also associated with a greater chance of neonatal death, and antenatal corticosteroids were protective against neonatal and early deaths. Conclusions Although these results are comparable to previous findings regarding mortality among premature infants in Brazil, the study emphasizes the need to implement strategies that promote breastfeeding and reduce neonatal mortality and its early component. PMID:23917938

LIFESTYLE INDICATORS AND CARDIORESPIRATORY FITNESS IN ADOLESCENTS

PubMed Central

de Victo, Eduardo Rossato; Ferrari, Gerson Luis de Moraes; da Silva, João Pedro; Araújo, Timóteo Leandro; Matsudo, Victor Keihan Rodrigues

2017-01-01

ABSTRACT Objective: To evaluate the lifestyle indicators associated with cardiorespiratory fitness in adolescents from Ilhabela, São Paulo, Brazil. Methods: The sample consisted of 181 adolescents (53% male) from the Mixed Longitudinal Project on Growth, Development, and Physical Fitness of Ilhabela. Body composition (weight, height, and body mass index, or BMI), school transportation, time spent sitting, physical activity, sports, television time (TV), having a TV in the bedroom, sleep, health perception, diet, and economic status (ES) were analyzed. Cardiorespiratory fitness was estimated by the submaximal progressive protocol performed on a cycle ergometer. Linear regression models were used with the stepwise method. Results: The sample average age was 14.8 years, and the average cardiorespiratory fitness was 42.2 mL.kg-1.min-1 (42.9 for boys and 41.4 for girls; p=0.341). In the total sample, BMI (unstandardized regression coefficient [B]=-0.03), height (B=-0.01), ES (B=0.10), gender (B=0.12), and age (B=0.03) were significantly associated with cardiorespiratory fitness. In boys, BMI, height, not playing any sports, and age were significantly associated with cardiorespiratory fitness. In girls, BMI, ES, and having a TV in the bedroom were significantly associated with cardiorespiratory fitness. Conclusions: Lifestyle indicators influenced the cardiorespiratory fitness; BMI, ES, and age influenced both sexes. Not playing any sports, for boys, and having a TV in the bedroom, for girls, also influenced cardiorespiratory fitness. Public health measures to improve lifestyle indicators can help to increase cardiorespiratory fitness levels. PMID:28977318

Topographic Controls on Soil Carbon Distribution in Iowa Croplands, USA

NASA Astrophysics Data System (ADS)

McCarty, Greg; Li, Xia

2017-04-01

Topography is a key factor affecting soil organic carbon (SOC) redistribution (erosion or deposition) because it influences several hydrological indices including soil moisture dynamics, runoff velocity and acceleration, and flow divergence and convergence. In this study, we examined the relationship between 15 topographic metrics derived from Light Detection and Ranging (Lidar) data and SOC redistribution in agricultural fields. We adopted the fallout 137Cesium (137Cs) technique to estimate SOC redistribution rates across 560 sampling plots in Iowa. Then, using stepwise ordinarily least square regression (SOLSR) and stepwise principle component analysis (SPCA), topography-based SOC models were developed to simulate spatial patterns of SOC content and redistribution. Results suggested that erosion and deposition of topsoil SOC were regulated by topography with SOC gain in lowland areas and SOC loss in sloping areas. Topographic wetness index (TWI) and slope were the most influential variables controlling SOC content and redistribution. The topography-based models exhibited good performances in simulating SOC content and redistribution across two crop sites with intensive samplings. SPCA models had slightly lower coefficients of determination and Nash-Sutcliffe efficiency values compared to SOLSR models at the field scale. However, significantly SPCA outperformed SOLAR in predicting SOC redistribution patterns at the watershed scale. Results of this study suggest that the topography-based SPCA model was more robust for scaling up models to the watershed scale because SPCA models may better represent the landscapes and are less subject to over fitting. This work suggests an improved method to sample and characterize landscapes for better prediction of soil property distribution.

Stepwise and stagewise approaches for spatial cluster detection

PubMed Central

Xu, Jiale

2016-01-01

Spatial cluster detection is an important tool in many areas such as sociology, botany and public health. Previous work has mostly taken either hypothesis testing framework or Bayesian framework. In this paper, we propose a few approaches under a frequentist variable selection framework for spatial cluster detection. The forward stepwise methods search for multiple clusters by iteratively adding currently most likely cluster while adjusting for the effects of previously identified clusters. The stagewise methods also consist of a series of steps, but with tiny step size in each iteration. We study the features and performances of our proposed methods using simulations on idealized grids or real geographic area. From the simulations, we compare the performance of the proposed methods in terms of estimation accuracy and power of detections. These methods are applied to the the well-known New York leukemia data as well as Indiana poverty data. PMID:27246273

Stepwise and stagewise approaches for spatial cluster detection.

PubMed

Xu, Jiale; Gangnon, Ronald E

2016-05-01

Spatial cluster detection is an important tool in many areas such as sociology, botany and public health. Previous work has mostly taken either a hypothesis testing framework or a Bayesian framework. In this paper, we propose a few approaches under a frequentist variable selection framework for spatial cluster detection. The forward stepwise methods search for multiple clusters by iteratively adding currently most likely cluster while adjusting for the effects of previously identified clusters. The stagewise methods also consist of a series of steps, but with a tiny step size in each iteration. We study the features and performances of our proposed methods using simulations on idealized grids or real geographic areas. From the simulations, we compare the performance of the proposed methods in terms of estimation accuracy and power. These methods are applied to the the well-known New York leukemia data as well as Indiana poverty data. Copyright © 2016 Elsevier Ltd. All rights reserved.

Factors affecting cadmium absorbed by pistachio kernel in calcareous soils, southeast of Iran.

PubMed

Shirani, H; Hosseinifard, S J; Hashemipour, H

2018-03-01

Cadmium (Cd) which does not have a biological role is one of the most toxic heavy metals for organisms. This metal enters environment through industrial processes and fertilizers. The main objective of this study was to determine the relationships between absorbed Cd by pistachio kernel and some of soil physical and chemical characteristics using modeling by stepwise regression and Artificial Neural Network (ANN), in calcareous soils in Rafsanjan region, southeast of Iran. For these purposes, 220 pistachio orchards were selected, and soil samples were taken from two depths of 0-40 and 40-80cm. Besides, fruit and leaf samples from branches with and without fruit were taken in each sampling point. The results showed that affecting factors on absorbed Cd by pistachio kernel which were obtained by regression method (pH and clay percent) were not interpretable, and considering unsuitable vales of determinant coefficient (R 2 ) and Root Mean Squares Error (RMSE), the model did not have sufficient validity. However, ANN modeling was highly accurate and reliable. Based on its results, soil available P and Zn and soil salinity were the most important factors affecting the concentration of Cd in pistachio kernel in pistachio growing areas of Rafsanjan. Copyright © 2017 Elsevier B.V. All rights reserved.

A linear spectral matching technique for retrieving equivalent water thickness and biochemical constituents of green vegetation

NASA Technical Reports Server (NTRS)

Gao, Bo-Cai; Goetz, Alexander F. H.

1992-01-01

Over the last decade, technological advances in airborne imaging spectrometers, having spectral resolution comparable with laboratory spectrometers, have made it possible to estimate biochemical constituents of vegetation canopies. Wessman estimated lignin concentration from data acquired with NASA's Airborne Imaging Spectrometer (AIS) over Blackhawk Island in Wisconsin. A stepwise linear regression technique was used to determine the single spectral channel or channels in the AIS data that best correlated with measured lignin contents using chemical methods. The regression technique does not take advantage of the spectral shape of the lignin reflectance feature as a diagnostic tool nor the increased discrimination among other leaf components with overlapping spectral features. A nonlinear least squares spectral matching technique was recently reported for deriving both the equivalent water thicknesses of surface vegetation and the amounts of water vapor in the atmosphere from contiguous spectra measured with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). The same technique was applied to a laboratory reflectance spectrum of fresh, green leaves. The result demonstrates that the fresh leaf spectrum in the 1.0-2.5 microns region consists of spectral components of dry leaves and the spectral component of liquid water. A linear least squares spectral matching technique for retrieving equivalent water thickness and biochemical components of green vegetation is described.

Factors Associated with Hospital Length of Stay among Cancer Patients with Febrile Neutropenia

PubMed Central

Rosa, Regis G.; Goldani, Luciano Z.

2014-01-01

Purpose This study sought to evaluate factors associated with hospital length of stay in cancer patients with febrile neutropenia. Methods A prospective cohort study was performed at a single tertiary referral hospital in southern Brazil from October 2009 to August 2011. All adult cancer patients with febrile neutropenia admitted to the hematology ward were evaluated. Stepwise random-effects negative binomial regression was performed to identify risk factors for prolonged length of hospital stay. Results In total, 307 cases of febrile neutropenia were evaluated. The overall median length of hospital stay was 16 days (interquartile range 18 days). According to multiple negative binomial regression analysis, hematologic neoplasms (P = 0.003), high-dose chemotherapy regimens (P<0.001), duration of neutropenia (P<0.001), and bloodstream infection involving Gram-negative multi-drug-resistant bacteria (P = 0.003) were positively associated with prolonged hospital length of stay in patients with febrile neutropenia. The condition index showed no evidence of multi-collinearity effect among the independent variables. Conclusions Hematologic neoplasms, high-dose chemotherapy regimens, prolonged periods of neutropenia, and bloodstream infection with Gram-negative multi-drug-resistant bacteria are predictors of prolonged length hospital of stay among adult cancer patients with febrile neutropenia. PMID:25285790

Modeling and Prediction of Solvent Effect on Human Skin Permeability using Support Vector Regression and Random Forest.

PubMed

Baba, Hiromi; Takahara, Jun-ichi; Yamashita, Fumiyoshi; Hashida, Mitsuru

2015-11-01

The solvent effect on skin permeability is important for assessing the effectiveness and toxicological risk of new dermatological formulations in pharmaceuticals and cosmetics development. The solvent effect occurs by diverse mechanisms, which could be elucidated by efficient and reliable prediction models. However, such prediction models have been hampered by the small variety of permeants and mixture components archived in databases and by low predictive performance. Here, we propose a solution to both problems. We first compiled a novel large database of 412 samples from 261 structurally diverse permeants and 31 solvents reported in the literature. The data were carefully screened to ensure their collection under consistent experimental conditions. To construct a high-performance predictive model, we then applied support vector regression (SVR) and random forest (RF) with greedy stepwise descriptor selection to our database. The models were internally and externally validated. The SVR achieved higher performance statistics than RF. The (externally validated) determination coefficient, root mean square error, and mean absolute error of SVR were 0.899, 0.351, and 0.268, respectively. Moreover, because all descriptors are fully computational, our method can predict as-yet unsynthesized compounds. Our high-performance prediction model offers an attractive alternative to permeability experiments for pharmaceutical and cosmetic candidate screening and optimizing skin-permeable topical formulations.

Comparing auditory filter bandwidths, spectral ripple modulation detection, spectral ripple discrimination, and speech recognition: Normal and impaired hearinga)

PubMed Central

Davies-Venn, Evelyn; Nelson, Peggy; Souza, Pamela

2015-01-01

Some listeners with hearing loss show poor speech recognition scores in spite of using amplification that optimizes audibility. Beyond audibility, studies have suggested that suprathreshold abilities such as spectral and temporal processing may explain differences in amplified speech recognition scores. A variety of different methods has been used to measure spectral processing. However, the relationship between spectral processing and speech recognition is still inconclusive. This study evaluated the relationship between spectral processing and speech recognition in listeners with normal hearing and with hearing loss. Narrowband spectral resolution was assessed using auditory filter bandwidths estimated from simultaneous notched-noise masking. Broadband spectral processing was measured using the spectral ripple discrimination (SRD) task and the spectral ripple depth detection (SMD) task. Three different measures were used to assess unamplified and amplified speech recognition in quiet and noise. Stepwise multiple linear regression revealed that SMD at 2.0 cycles per octave (cpo) significantly predicted speech scores for amplified and unamplified speech in quiet and noise. Commonality analyses revealed that SMD at 2.0 cpo combined with SRD and equivalent rectangular bandwidth measures to explain most of the variance captured by the regression model. Results suggest that SMD and SRD may be promising clinical tools for diagnostic evaluation and predicting amplification outcomes. PMID:26233047

Comparing auditory filter bandwidths, spectral ripple modulation detection, spectral ripple discrimination, and speech recognition: Normal and impaired hearing.

PubMed

Davies-Venn, Evelyn; Nelson, Peggy; Souza, Pamela

2015-07-01

Some listeners with hearing loss show poor speech recognition scores in spite of using amplification that optimizes audibility. Beyond audibility, studies have suggested that suprathreshold abilities such as spectral and temporal processing may explain differences in amplified speech recognition scores. A variety of different methods has been used to measure spectral processing. However, the relationship between spectral processing and speech recognition is still inconclusive. This study evaluated the relationship between spectral processing and speech recognition in listeners with normal hearing and with hearing loss. Narrowband spectral resolution was assessed using auditory filter bandwidths estimated from simultaneous notched-noise masking. Broadband spectral processing was measured using the spectral ripple discrimination (SRD) task and the spectral ripple depth detection (SMD) task. Three different measures were used to assess unamplified and amplified speech recognition in quiet and noise. Stepwise multiple linear regression revealed that SMD at 2.0 cycles per octave (cpo) significantly predicted speech scores for amplified and unamplified speech in quiet and noise. Commonality analyses revealed that SMD at 2.0 cpo combined with SRD and equivalent rectangular bandwidth measures to explain most of the variance captured by the regression model. Results suggest that SMD and SRD may be promising clinical tools for diagnostic evaluation and predicting amplification outcomes.

The Impact of Obesity on Back and Core Muscular Endurance in Firefighters

PubMed Central

Mayer, John M.; Nuzzo, James L.; Chen, Ren; Quillen, William S.; Verna, Joe L.; Miro, Rebecca; Dagenais, Simon

2012-01-01

The purpose of this study was to assess the relationships between obesity and measures of back and core muscular endurance in firefighters. Methods. A cross-sectional study was conducted in career firefighters without low back pain. Obesity measures included body mass index (BMI) and body fat percentage assessed with air displacement plethysmography. Muscular endurance was assessed with the Modified Biering Sorensen (back) and Plank (core) tests. Relationships were explored using t-tests and regression analyses. Results. Of the 83 participants enrolled, 24 (29%) were obese (BMI ≥ 30). Back and core muscular endurance was 27% lower for obese participants. Significant negative correlations were observed for BMI and body fat percentage with back and core endurance (r = −0.42 to −0.52). Stepwise regression models including one obesity measure (BMI, body fat percentage, and fat mass/fat-free mass), along with age and self-reported physical exercise, accounted for 17–19% of the variance in back muscular endurance and 29–37% of the variance in core muscular endurance. Conclusions. Obesity is associated with reduced back and core muscular endurance in firefighters, which may increase the risk of musculoskeletal injuries. Obesity should be considered along with back and core muscular endurance when designing exercise programs for back pain prevention in firefighters. PMID:23213491

Preoperative Galactose Elimination Capacity Predicts Complications and Survival After Hepatic Resection

PubMed Central

Redaelli, Claudio A.; Dufour, Jean-François; Wagner, Markus; Schilling, Martin; Hüsler, Jürg; Krähenbühl, Lukas; Büchler, Markus W.; Reichen, Jürg

2002-01-01

Objective To analyze a single center’s 6-year experience with 258 consecutive patients undergoing major hepatic resection for primary or secondary malignancy of the liver, and to examine the predictive value of preoperative liver function assessment. Summary Background Data Despite the substantial improvements in diagnostic and surgical techniques that have made liver surgery a safer procedure, careful patient selection remains mandatory to achieve good results in patients with hepatic tumors. Methods In this prospective study, 258 patients undergoing hepatic resection were enrolled: 111 for metastases, 78 for hepatocellular carcinoma (HCC), 21 for cholangiocellular carcinoma, and 48 for other primary hepatic tumors. One hundred fifty-eight patients underwent segment-oriented liver resection, including hemihepatectomies, and 100 had subsegmental resections. Thirty-two clinical and biochemical parameters were analyzed, including liver function assessment by the galactose elimination capacity (GEC) test, a measure of hepatic functional reserve, to predict postoperative (60-day) rates of death and complications and long-term survival. All variables were determined within 5 days before surgery. Data were subjected to univariate and multivariate analysis for two patient subgroups (HCC and non-HCC). The cutoffs for GEC in both groups were predefined. Long-term survival (>60 days) was subjected to Kaplan-Meier analysis and the Cox proportional hazard model. Results In the entire group of 258 patients, a GEC less than 6 mg/min/kg was the only preoperative biochemical parameter that predicted postoperative complications and death by univariate and stepwise regression analysis. A GEC of more than 6 mg/min/kg was also significantly associated with longer survival. This predictive value could also be shown in the subgroup of 180 patients with tumors other than HCC. In the subgroup of 78 patients with HCC, a GEC less than 4 mg/min/kg predicted postoperative complications and death by univariate and stepwise regression analysis. Further, a GEC of more than 4 mg/min/kg was also associated with longer survival. Conclusions This prospective study establishes the preoperative determination of the hepatic reserve by GEC as a strong independent and valuable predictor for short- and long-term outcome in patients with primary and secondary hepatic tumors undergoing resection. PMID:11753045

Arterial stiffness is an independent predictor for albuminuria progression among Asians with type 2 diabetes-A prospective cohort study.

PubMed

Zhang, Xiao; Low, Serena; Sum, Chee Fang; Tavintharan, Subramaniam; Yeoh, Lee Ying; Liu, Jianjun; Li, Na; Ang, Keven; Lee, Simon Bm; Tang, Wern Ee; Lim, Su Chi

2017-06-01

Albuminuria progression has been associated with renal deterioration in type 2 diabetes (T2DM). Central arterial stiffness can aggravate systemic vasculopathy by propagating elevated systolic and pulse pressures forward, thereby accentuating global vascular injury. We aim to investigate whether central arterial stiffness is an independent predictor for albuminuria progression in a multi-ethnic T2DM Asian cohort in Singapore. In a prospective cohort, 1012 T2DM patients were assessed at baseline and after a median follow-up of 3.1years. 880 patients with baseline normo- (urinary albumin-to-creatinine ratio (ACR)<30mg/g, n=579) and microalbuminuria (ACR=30-299mg/g, n=301) were divided into progression and non-progression groups according to ACR changes. Progression was defined as transition from normo- to microalbuminuria, micro- to macroalbuminuria, or normo- to macroalbuminuria. Central arterial stiffness was estimated by carotid-femoral pulse wave velocity (PWV) using applanation tonometry method. Stepwise multiple regression analysis was used to determine the predictor(s) for albuminuria progression. Albuminuria progression occurred in 178 patients (20.2%). Baseline PWV was higher in progression (10.1±2.9m/s) than non-progression group (9.2±2.4m/s, p<0.001). 1-SD increase in baseline PWV was associated with albuminuria progression (OR=1.457, 95% CI, 1.236-1.718, p<0.001). Stepwise regression analysis identified that baseline PWV (OR=1.241, 95% CI, 1.033-1.490, p=0.021), BMI (OR=1.046, 95% CI, 1.012-1.080, p=0.008), nature log-transformed estimated glomerular filtration rate (LneGFR) (OR=0.320, 95% CI, 0.192-0.530, p=0.010) and LnACR (OR=1.344, 95% CI, 1.187-1.522, p=0.008) are predictors for albuminuria progression. Increased central arterial stiffness at baseline predicted future progression of albuminuria. Our results suggest the potential benefit of ameliorating central arterial stiffness to retard albuminuria progression in T2DM. Copyright © 2017 Elsevier Inc. All rights reserved.

[Has the pregnancy outcome of women with pregestational diabetes mellitus improved in ten years?].

PubMed

Čechurová, Daniela; Krčma, Michal; Jankovec, Zdeněk; Dort, Jiří; Turek, Jan; Lacigová, Silvie; Rušavý, Zdeněk

2015-02-01

In spite of progress in medicine, studies from a number of countries indicate steadily increased risk of perinatal morbidity and mortality in the offspring of diabetic mothers. No data regarding the pregnancy outcome in women with diabetes mellitus type 1 and 2 (pregestational DM) have been published in the Czech Republic. The aim of the study was to evaluate the pregnancy course of women with pregestational DM and outcome of their offspring and to assess whether it has improved in ten years. A retrospective evaluation of pregnancy outcome of pregestational DM women followed up in the University Hospital Pilsen in years 2000-2009 (Group A, n = 107) and comparison with the period 1990-1997 (Group B, n = 39) were performed. Wilcoxon non-paired test, contingency tables, step-wise logistic regression and step-wise linear multiple regression methods were used for statistical analyses. Data is presented as median (interquartile range). Women from the Group A were older 28 (25, 31) vs 25 (22, 27) years, p = 0.01. Otherwise, the groups did not statistically significantly differ in diabetes duration, BMI, and representation of women with type 2 diabetes. A better glycemic control (HbA1c, mmol/mol) was achieved in the Group A in all trimesters - 1st trimester: 59 (47, 67) vs 66 (56, 76), 2nd trimester: 46 (40, 52) vs 54 (48, 59) and 3rd trimester: 46 (40, 51) vs 53 (47, 60), p = 0.01. The caesarean section rate decreased (65.2 % vs 87.5 %, p < 0.05). The incidence of the respiratory distress syndrome after adjustment for age and diabetes duration also decreased (8.9 % vs 18.2 %, p < 0.05). A decreasing trend in the rate of premature delivery before 34th week of gestation (1.1 % vs 6.3 %) and neonatal mortality (1.1 % vs 2.9 %) was observed, however, the differences were not statistically significant. The achieved improved glycemic control led to only a partial improvement in the course of pregnancy and outcome of the offspring of diabetic mothers.

Effects of Cerebral Blood Flow and Vessel Conditions on Speech Recognition in Patients With Postlingual Adult Cochlear Implant: Predictable Factors for the Efficacy of Cochlear Implant.

PubMed

Ishino, Takashi; Ragaee, Mahmoud Ali; Maruhashi, Tatsuya; Kajikawa, Masato; Higashi, Yukihito; Sonoyama, Toru; Takeno, Sachio; Hirakawa, Katsuhiro

Cochlear implantation (CI) has been the most successful procedure for restoring hearing in a patient with severe and profound hearing loss. However, possibly owing to the variable brain functions of each patient, its performance and the associated patient satisfaction are widely variable. The authors hypothesize that peripheral and cerebral circulation can be assessed by noninvasive and globally available methods, yielding superior presurgical predictive factors of the performance of CI in adult patients with postlingual hearing loss who are scheduled to undergo CI. Twenty-two adult patients with cochlear implants for postlingual hearing loss were evaluated using Doppler sonography measurement of the cervical arteries (reflecting cerebral blood flow), flow-mediated dilation (FMD; reflecting the condition of cerebral arteries), and their pre-/post-CI best score on a monosyllabic discrimination test (pre-/post-CI best monosyllabic discrimination [BMD] score). Correlations between post-CI BMD score and the other factors were examined using univariate analysis and stepwise multiple linear regression analysis. The prediction factors were calculated by examining the receiver-operating characteristic curve between post-CI BMD score and the significantly positively correlated factors. Age and duration of deafness had a moderately negative correlation. The mean velocity of the internal carotid arteries and FMD had a moderate-to-strong positive correlation with the post-CI BMD score in univariate analysis. Stepwise multiple linear regression analysis revealed that only FMD was significantly positively correlated with post-CI BMD score. Analysis of the receiver-operating characteristic curve showed that a FMD cutoff score of 1.8 significantly predicted post-CI BMD score. These data suggest that FMD is a convenient, noninvasive, and widely available tool for predicting the efficacy of cochlear implants. An FMD cutoff score of 1.8 could be a good index for determining whether patients will hear well with cochlear implants. It could also be used to predict whether cochlear implants will provide good speech recognition benefits to candidates, even if their speech discrimination is poor. This FMD index could become a useful predictive tool for candidates with poor speech discrimination to determine the efficacy of CI before surgery.

Simplified criteria for diagnosing superficial esophageal squamous neoplasms using Narrow Band Imaging magnifying endoscopy

PubMed Central

Dobashi, Akira; Goda, Kenichi; Yoshimura, Noboru; Ohya, Tomohiko R; Kato, Masayuki; Sumiyama, Kazuki; Matsushima, Masato; Hirooka, Shinichi; Ikegami, Masahiro; Tajiri, Hisao

2016-01-01

AIM To simplify the diagnostic criteria for superficial esophageal squamous cell carcinoma (SESCC) on Narrow Band Imaging combined with magnifying endoscopy (NBI-ME). METHODS This study was based on the post-hoc analysis of a randomized controlled trial. We performed NBI-ME for 147 patients with present or a history of squamous cell carcinoma in the head and neck, or esophagus between January 2009 and June 2011. Two expert endoscopists detected 89 lesions that were suspicious for SESCC lesions, which had been prospectively evaluated for the following 6 NBI-ME findings in real time: “intervascular background coloration”; “proliferation of intrapapillary capillary loops (IPCL)”; and “dilation”, “tortuosity”, “change in caliber”, and “various shapes (VS)” of IPCLs (i.e., Inoue’s tetrad criteria). The histologic examination of specimens was defined as the gold standard for diagnosis. A stepwise logistic regression analysis was used to identify candidates for the simplified criteria from among the 6 NBI-ME findings for diagnosing SESCCs. We evaluated diagnostic performance of the simplified criteria compared with that of Inoue’s criteria. RESULTS Fifty-four lesions (65%) were histologically diagnosed as SESCCs and the others as low-grade intraepithelial neoplasia or inflammation. In the univariate analysis, proliferation, tortuosity, change in caliber, and VS were significantly associated with SESCC (P < 0.01). The combination of VS and proliferation was statistically extracted from the 6 NBI-ME findings by using the stepwise logistic regression model. We defined the combination of VS and proliferation as simplified dyad criteria for SESCC. The areas under the curve of the simplified dyad criteria and Inoue’s tetrad criteria were 0.70 and 0.73, respectively. No significant difference was shown between them. The sensitivity, specificity, and accuracy of diagnosis for SESCC were 77.8%, 57.1%, 69.7% and 51.9%, 80.0%, 62.9% for the simplified dyad criteria and Inoue’s tetrad criteria, respectively. CONCLUSION The combination of proliferation and VS may serve as simplified criteria for the diagnosis of SESCC using NBI-ME. PMID:27895406

Prevalence and Predisposing Factors for Depressive Status in Chinese Patients with Obstructive Sleep Apnoea: A Large-Sample Survey

PubMed Central

Dai, Yaozhang; Li, Xuewu; Zhang, Xin; Wang, Sihua; Sang, Jianzhong; Tian, Xiufen; Cao, Hua

2016-01-01

Background and Objective Recently, there are few studies reporting on depressive status and obstructive sleep apnoea (OSA) in China. A large-sample survey was to be performed to explore the prevalence of depressive status and related factors in Chinese patients with OSA. Methods From among a randomly-selected group of OSA patients, 1,327 met inclusion criteria. After screening with the Symptom Checklist 90 (SCL-90) and Self-Rating Depression Scale (SDS), patients were assigned to OSA without depressive status (control group, n = 698) and OSA with depressive status (n = 629) groups. Using chi-squared testing, the correlation analyses between the depressive status and OSA patient demographic and clinical variables were tested. Then depression-related risk factors in OSA patients were analysed using stepwise linear regression analysis. The effects of family and social factors on depressive status in OSA patients were investigated using Mann-Whitney U (one of nonparametric test). Results The prevalence of depressive status was 47.4% in OSA patients. Depressive status was significantly associated with female gender, single status, Family Burden Scale of Disease (FBS), Family APGAR Index (APGAR), apnoea-hypopnea index (AHI), and Perceived Social Support Scale (PSSS). Stepwise linear regression analysis further indicated that single status, hypoxemia, APGAR, AHI, PSSS, AHI, and FBS were all risk factors for depressive status in OSA patients. The total of the FBS score and three of its sub-factors scores (family daily activities, family relationships and mental health of family members) were higher, and the total of the APGAR score and two of its sub-factors scores (adaptability and affection) were lower in OSA with depressive status compared with the control group. Besides, the total score for the PSSS and scores for its two sub-factors (family support and social support) were all lower in OSA patients with depressive status than those of the control group. Conclusions Depressive status has high comorbid rate in Chinese OSA patients and is significantly associated with single status, apnoea-hypopnea index, hypoxemia, family and social supports. PMID:26934192

What Factors Influence Community Oral and Maxillofacial Surgeons' Choice to Use Capnography in the Office-Based Ambulatory Anesthesia Setting?

PubMed

Matin, Mehdi B; Gonzalez, Martin L; Dodson, Thomas B

2015-08-01

The American Association of Oral and Maxillofacial Surgeons Board of Trustees mandated monitoring using capnography during moderate sedation (MS) and deep sedation or general anesthesia (DS/GA) delivered in the office setting effective January 1, 2014. The purpose of this study was to estimate the frequency of capnography use and to identify variables associated with a clinician's choice to use capnography before the mandate. To address the research purpose, the authors designed a prospective cohort study and enrolled 2 samples: 1) American private practicing oral and maxillofacial surgeons (OMSs) and 2) all eligible patients for whom these OMSs delivered MS or DS/GA. The predictor variables were categorized as surgeon or patient demographics, anesthesia risk factors, procedure-related variables, and anesthetic medications. The outcome variable was capnography use during MS or DS/GA. Descriptive, bivariate, and forward stepwise multiple logistic regression statistics were computed to evaluate the association between the predictor variables and capnography use, with statistical significance set at a P value less than or equal to .05. The surgeon sample was composed of 95 OMSs and 13.7% reported using capnography. The patient sample included 3,495 patients with a mean age of 30.6 years (standard deviation, 17.8 yr), 43.5% were men, and 5.6% were monitored using capnography. Based on bivariate analyses, 17 variables were associated with capnography use. Forward stepwise regression modeling identified 9 variables statistically associated with capnography use. These variables were patient's age, Mallampati airway score, alcohol consumption, board certification, sevoflurane use, number of monitoring methods, electrocardiogram use, precordial stethoscope use, and number of personnel in operating suite. Although this study might be of historical interest at this time, the results offer insight into OMSs' practice patterns before the mandatory requirement to use capnography. As more OMSs comply with the capnography mandate, their practice patterns involving variables found to statistically correlate with capnography use might become more similar to those of early adopters of this technology. Copyright © 2015 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.

Health-Related Lifestyle Behaviors among Male and Female Rural-to-Urban Migrant Workers in Shanghai, China

PubMed Central

Yang, Hua; He, Fang; Wang, Tianhao; Liu, Yao; Shen, Yao; Gong, Jian; Dai, Wei; Zhou, Jing; Gu, Jie; Tu, Yimin; Wang, Tianying; Shen, Lei; Wu, Yumiao; Xia, Xiuping; Xu, Donghao; Pan, Zhigang; Zhu, Shanzhu

2015-01-01

Background Lifestyle behaviors significantly impact health, yet remain poorly defined in Chinese rural-to-urban migrants. Methods In a cross-sectional study of health-related behaviors of 5484 rural-to-urban migrants who had worked in Shanghai for at least six months, we assessed the contribution of demographics and physical and mental health to lifestyle behaviors in male and female participants by multiple stepwise cumulative odds logistic regression. Results Respondents were 51.3% male. 9.9% exhibited abnormal blood pressure; 27.0% were overweight or obese; 11.2% reported abnormal mental health; 36.9% reported healthy lifestyle. Multiple stepwise cumulative odds logistic regression indicated that men working in manufacturing reported less unhealthy lifestyle than those in hospitality (cumulative odds ratio (COR) = 1.806, 95%CI 1.275–2.559) or recreation/leisure (COR = 3.248, 95%CI 2.379–4.435); and women working in manufacturing and construction reported less unhealthy lifestyle than those in all other sectors. Unhealthy lifestyle was associated with small workplaces for men (COR = 1.422, 95%CI 1.154–1.752), working more than 8 or 11 hours per day for women and men, respectively, and earning over 3500 RMB in women (COR = 1.618, 95%CI 1.137–2.303). Single women and women who had previously resided in three or more cities were more likely to report unhealthy lifestyle (COR = 2.023, 95%CI 1.664–2.461, and COR = 1.311, 95%CI 1.072–1.602, respectively). Abnormal mental status was also correlated with unhealthy lifestyle in men (COR = 3.105, 95%CI 2.454–3.930) and women (COR = 2.566, 95%CI 2.024–3.252). Conclusions There were different risk factors of unhealthy lifestyle score in male and female rural-to-urban migrants, especially in number of cities experienced, salary, marital status, work place scale. Several demographic groups: employment sectors (e.g. hospitality and recreation/leisure), working conditions (e.g. long hours) and abnormal mental status were associated with unhealthy lifestyle behaviors in Chinese rural-to-urban migrants, and health interventions should be targeted to these groups. PMID:25710464

Needlestick Injuries in Interventional Radiology Are Common and Underreported.

PubMed

Deipolyi, Amy R; Prabhakar, Anand M; Naidu, Sailendra; Oklu, Rahmi

2017-12-01

Purpose To determine the prevalence of and risk factors for needlesticks in interventional radiology physicians, as well as the attitudes, behaviors, and conditions that promote or interfere with reporting of these injuries. Materials and Methods A total of 3889 interventional radiologists from academic and private practice in the United States were surveyed by emailing all interventional radiologist members of the Society of Interventional Radiology, including attending-level physicians and trainees (April-August 2016). The institutional review board waived the need for consent. Questions inquired about the nature, frequency, and type of needlestick and sharps injuries and whether and to whom these incidents were reported. Stepwise regression was used to determine variables predicting whether injuries were reported. Results In total, 908 (23%) interventional radiologists completed at least a portion of the survey. Eight hundred fourteen (91%) of 895 respondents reported a prior needlestick injury, 583 (35%) of 895 reported at least one injury while treating an HIV-positive patient, and 626 (71%) of 884 reported prior training regarding needlestick injury. There was, on average, one needlestick for every 5 years of practice. Most needlestick or sharps injuries were self inflicted (711 [87%] of 817) and involved a hollow-bore device (464 [56%] of 824). Only 566 (66%) of 850 injuries were reported. The most common reasons for not reporting included perceived lack of utility of reporting (79 [28%] of 282), perceived low risk for injury (56 [20%] of 282), noncontaminated needle (53 [19%] of 282), too-lengthy reporting process (37 [13%] of 282), and associated stigma (23 [8%] of 282). Only 156 (25%) of 624 respondents informed their significant other. Stepwise regression assessing variables affecting the likelihood of reporting showed that male sex (P = .009), low-risk patient (P < .0001), self injury (P = .010), trainee status (P < .0001), and the total number of prior injuries (P = .019) were independent predictors of not reporting. Conclusion Needlestick injuries are ubiquitous among interventional radiologists and are often not reported. © RSNA, 2017 Online supplemental material is available for this article.

Step-by-step training in basic laparoscopic skills using two-way web conferencing software for remote coaching: A multicenter randomized controlled study.

PubMed

Mizota, Tomoko; Kurashima, Yo; Poudel, Saseem; Watanabe, Yusuke; Shichinohe, Toshiaki; Hirano, Satoshi

2018-07-01

Despite its advantages, few trainees outside of North America have access to simulation training. We hypothesized that a stepwise training method using tele-mentoring system would be an efficient technique for training in basic laparoscopic skills. Residents were randomized into two groups and trained to proficiency in intracorporeal suturing. The stepwise group (SG) practiced the task step-by-step, while the other group practiced comprehensively (CG). Each participant received weekly coaching via two-way web conferencing software. The duration of the coaching sessions and self-practice time were compared between the two groups. Twenty residents from 15 institutions participated, and all achieved proficiency. Coaching sessions using tele-mentoring system were completed without difficulties. The SG required significantly shorter coaching time per session than the CG (p = .002). There was no significant difference in self-practice time. The stepwise training method with the tele-mentoring system appears to make efficient use of surgical trainees' and trainers' time. Copyright © 2017 Elsevier Inc. All rights reserved.

Non-Invasive Methodology to Estimate Polyphenol Content in Extra Virgin Olive Oil Based on Stepwise Multilinear Regression.

PubMed

Martínez Gila, Diego Manuel; Cano Marchal, Pablo; Gómez Ortega, Juan; Gámez García, Javier

2018-03-25

Normally the olive oil quality is assessed by chemical analysis according to international standards. These norms define chemical and organoleptic markers, and depending on the markers, the olive oil can be labelled as lampante, virgin, or extra virgin olive oil (EVOO), the last being an indicator of top quality. The polyphenol content is related to EVOO organoleptic features, and different scientific works have studied the positive influence that these compounds have on human health. The works carried out in this paper are focused on studying relations between the polyphenol content in olive oil samples and its spectral response in the near infrared spectra. In this context, several acquisition parameters have been assessed to optimize the measurement process within the virgin olive oil production process. The best regression model reached a mean error value of 156.14 mg/kg in leave one out cross validation, and the higher regression coefficient was 0.81 through holdout validation.

Non-Invasive Methodology to Estimate Polyphenol Content in Extra Virgin Olive Oil Based on Stepwise Multilinear Regression

PubMed Central

Cano Marchal, Pablo; Gómez Ortega, Juan; Gámez García, Javier

2018-01-01

Normally the olive oil quality is assessed by chemical analysis according to international standards. These norms define chemical and organoleptic markers, and depending on the markers, the olive oil can be labelled as lampante, virgin, or extra virgin olive oil (EVOO), the last being an indicator of top quality. The polyphenol content is related to EVOO organoleptic features, and different scientific works have studied the positive influence that these compounds have on human health. The works carried out in this paper are focused on studying relations between the polyphenol content in olive oil samples and its spectral response in the near infrared spectra. In this context, several acquisition parameters have been assessed to optimize the measurement process within the virgin olive oil production process. The best regression model reached a mean error value of 156.14 mg/kg in leave one out cross validation, and the higher regression coefficient was 0.81 through holdout validation. PMID:29587403

Relationship between affective determinants and achievement in science for seventeen-year-olds

NASA Astrophysics Data System (ADS)

Napier, John D.; Riley, Joseph P.

Data collected in the 1976-1977 NAEP survey of seventeen-year-olds was used to reanalyze the hypothesis that there are affective determinates of science achievement. Factor and item analysis procedures were used to examine affective and cognitive items from Booklet 4. Eight affective scales and one cognitive achievement scale were identified. Using stepwise multiple regression procedures, the four affective scales of Motivation, Anxiety, Student Choice, and Teacher Support were found to account for the majority of the correlation between the affective determinants and achievement.

Military Return to Duty and Civilian Return to Work Factors Following Burns With Focus on the Hand and Literature Review

DTIC Science & Technology

2008-10-01

the three parameters of skin graft - ing, treatment time and presence of a face burn where all reports agree as to the influence of each one of these...cluding the presence of hand burns. Of the studies that agree on factor influence, five document that skin grafting had a significant effect on...stepwise multiple regression analysis, Helm et al determined TBSA to be the stron- gest predictor of RTW followed equally by skin graft - ing and hand burn

Effect of perceived organizational support on suicidal ideation of young employees: The mediator role of self-esteem.

PubMed

Sang, Jinyan; Ji, Yongbao; Li, Ping; Zhao, Hao

2017-09-01

This study aimed to explore the relationships among perceived organizational support, self-esteem, and suicidal ideation of young employees. A total of 447 unmarried employees completed the survey of perceived organizational support, Rosenberg self-esteem scale, and suicide ideation scale. The results revealed that perceived organizational support, self-esteem, and suicidal ideation were significantly correlated with each other. Stepwise regression analysis and path analysis both indicated that self-esteem partially mediated the effect of perceived organizational support on suicidal ideation.

Exploration of charity toward busking (street performance) as a function of religion.

PubMed

Lemay, John O; Bates, Larry W

2013-04-01

To examine conceptions of religion and charity in a new venue--busking (street performance)--103 undergraduate students at a regional university in the southeastern U.S. completed a battery of surveys regarding religion, and attitudes and behaviors toward busking. For those 85 participants who had previously encountered a busker, stepwise regression was used to predict increased frequency of giving to buskers. The best predictive model of giving to buskers consisted of three variables including less experienced irritation toward buskers, prior experience with giving to the homeless, and lower religious fundamentalism.

Design and implementation of a novel mechanical testing system for cellular solids.

PubMed

Nazarian, Ara; Stauber, Martin; Müller, Ralph

2005-05-01

Cellular solids constitute an important class of engineering materials encompassing both man-made and natural constructs. Materials such as wood, cork, coral, and cancellous bone are examples of cellular solids. The structural analysis of cellular solid failure has been limited to 2D sections to illustrate global fracture patterns. Due to the inherent destructiveness of 2D methods, dynamic assessment of fracture progression has not been possible. Image-guided failure assessment (IGFA), a noninvasive technique to analyze 3D progressive bone failure, has been developed utilizing stepwise microcompression in combination with time-lapsed microcomputed tomographic imaging (microCT). This method allows for the assessment of fracture progression in the plastic region, where much of the structural deformation/energy absorption is encountered in a cellular solid. Therefore, the goal of this project was to design and fabricate a novel micromechanical testing system to validate the effectiveness of the stepwise IGFA technique compared to classical continuous mechanical testing, using a variety of engineered and natural cellular solids. In our analysis, we found stepwise compression to be a valid approach for IGFA with high precision and accuracy comparable to classical continuous testing. Therefore, this approach complements the conventional mechanical testing methods by providing visual insight into the failure propagation mechanisms of cellular solids. (c) 2005 Wiley Periodicals, Inc.

Evaluation of the stepwise collimation method for the reduction of the patient dose in full spine radiography

NASA Astrophysics Data System (ADS)

Lee, Boram; Lee, Sunyoung; Yang, Injeong; Yoon, Myeonggeun

2014-05-01

The purpose of this study is to evaluate the dose reduction when using the stepwise collimation method for scoliosis patients undergoing full spine radiography. A Monte Carlo simulation was carried out to acquire dose vs. volume data for organs at risk (OAR) in the human body. While the effective doses in full spine radiography were reduced by 8, 15, 27 and 44% by using four different sizes of the collimation, the doses to the skin were reduced by 31, 44, 55 and 66%, indicating that the reduction of the dose to the skin is higher than that to organs inside the body. Although the reduction rates were low for the gonad, being 9, 14, 18 and 23%, there was more than a 30% reduction in the dose to the heart, suggesting that the dose reduction depends significantly on the location of the OARs in the human body. The reduction rate of the secondary cancer risk based on the excess absolute risk (EAR) varied from 0.6 to 3.4 per 10,000 persons, depending on the size of the collimation. Our results suggest that the stepwise collimation method in full spine radiography can effectively reduce the patient dose and the radiation-induced secondary cancer risk.

Distinguishing fibromyalgia from rheumatoid arthritis and systemic lupus in clinical questionnaires: an analysis of the revised Fibromyalgia Impact Questionnaire (FIQR) and its variant, the Symptom Impact Questionnaire (SIQR), along with pain locations

PubMed Central

2011-01-01

Introduction The purpose of this study was to explore a data set of patients with fibromyalgia (FM), rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) who completed the Revised Fibromyalgia Impact Questionnaire (FIQR) and its variant, the Symptom Impact Questionnaire (SIQR), for discriminating features that could be used to differentiate FM from RA and SLE in clinical surveys. Methods The frequency and means of comparing FM, RA and SLE patients on all pain sites and SIQR variables were calculated. Multiple regression analysis was then conducted to identify the significant pain sites and SIQR predictors of group membership. Thereafter stepwise multiple regression analysis was performed to identify the order of variables in predicting their maximal statistical contribution to group membership. Partial correlations assessed their unique contribution, and, last, two-group discriminant analysis provided a classification table. Results The data set contained information on the SIQR and also pain locations in 202 FM, 31 RA and 20 SLE patients. As the SIQR and pain locations did not differ much between the RA and SLE patients, they were grouped together (RA/SLE) to provide a more robust analysis. The combination of eight SIQR items and seven pain sites correctly classified 99% of FM and 90% of RA/SLE patients in a two-group discriminant analysis. The largest reported SIQR differences (FM minus RA/SLE) were seen for the parameters "tenderness to touch," "difficulty cleaning floors" and "discomfort on sitting for 45 minutes." Combining the SIQR and pain locations in a stepwise multiple regression analysis revealed that the seven most important predictors of group membership were mid-lower back pain (29%; 79% vs. 16%), tenderness to touch (11.5%; 6.86 vs. 3.02), neck pain (6.8%; 91% vs. 39%), hand pain (5%; 64% vs. 77%), arm pain (3%; 69% vs. 18%), outer lower back pain (1.7%; 80% vs. 22%) and sitting for 45 minutes (1.4%; 5.56 vs. 1.49). Conclusions A combination of two SIQR questions ("tenderness to touch" and "difficulty sitting for 45 minutes") plus pain in the lower back, neck, hands and arms may be useful in the construction of clinical questionnaires designed for patients with musculoskeletal pain. This combination provided the correct diagnosis in 97% of patients, with only 7 of 253 patients misclassified. PMID:21477308

Stepwise Distributed Open Innovation Contests for Software Development: Acceleration of Genome-Wide Association Analysis

PubMed Central

Hill, Andrew; Loh, Po-Ru; Bharadwaj, Ragu B.; Pons, Pascal; Shang, Jingbo; Guinan, Eva; Lakhani, Karim; Kilty, Iain

2017-01-01

Abstract Background: The association of differing genotypes with disease-related phenotypic traits offers great potential to both help identify new therapeutic targets and support stratification of patients who would gain the greatest benefit from specific drug classes. Development of low-cost genotyping and sequencing has made collecting large-scale genotyping data routine in population and therapeutic intervention studies. In addition, a range of new technologies is being used to capture numerous new and complex phenotypic descriptors. As a result, genotype and phenotype datasets have grown exponentially. Genome-wide association studies associate genotypes and phenotypes using methods such as logistic regression. As existing tools for association analysis limit the efficiency by which value can be extracted from increasing volumes of data, there is a pressing need for new software tools that can accelerate association analyses on large genotype-phenotype datasets. Results: Using open innovation (OI) and contest-based crowdsourcing, the logistic regression analysis in a leading, community-standard genetics software package (PLINK 1.07) was substantially accelerated. OI allowed us to do this in <6 months by providing rapid access to highly skilled programmers with specialized, difficult-to-find skill sets. Through a crowd-based contest a combination of computational, numeric, and algorithmic approaches was identified that accelerated the logistic regression in PLINK 1.07 by 18- to 45-fold. Combining contest-derived logistic regression code with coarse-grained parallelization, multithreading, and associated changes to data initialization code further developed through distributed innovation, we achieved an end-to-end speedup of 591-fold for a data set size of 6678 subjects by 645 863 variants, compared to PLINK 1.07's logistic regression. This represents a reduction in run time from 4.8 hours to 29 seconds. Accelerated logistic regression code developed in this project has been incorporated into the PLINK2 project. Conclusions: Using iterative competition-based OI, we have developed a new, faster implementation of logistic regression for genome-wide association studies analysis. We present lessons learned and recommendations on running a successful OI process for bioinformatics. PMID:28327993

Stepwise Distributed Open Innovation Contests for Software Development: Acceleration of Genome-Wide Association Analysis.

PubMed

Hill, Andrew; Loh, Po-Ru; Bharadwaj, Ragu B; Pons, Pascal; Shang, Jingbo; Guinan, Eva; Lakhani, Karim; Kilty, Iain; Jelinsky, Scott A

2017-05-01

The association of differing genotypes with disease-related phenotypic traits offers great potential to both help identify new therapeutic targets and support stratification of patients who would gain the greatest benefit from specific drug classes. Development of low-cost genotyping and sequencing has made collecting large-scale genotyping data routine in population and therapeutic intervention studies. In addition, a range of new technologies is being used to capture numerous new and complex phenotypic descriptors. As a result, genotype and phenotype datasets have grown exponentially. Genome-wide association studies associate genotypes and phenotypes using methods such as logistic regression. As existing tools for association analysis limit the efficiency by which value can be extracted from increasing volumes of data, there is a pressing need for new software tools that can accelerate association analyses on large genotype-phenotype datasets. Using open innovation (OI) and contest-based crowdsourcing, the logistic regression analysis in a leading, community-standard genetics software package (PLINK 1.07) was substantially accelerated. OI allowed us to do this in <6 months by providing rapid access to highly skilled programmers with specialized, difficult-to-find skill sets. Through a crowd-based contest a combination of computational, numeric, and algorithmic approaches was identified that accelerated the logistic regression in PLINK 1.07 by 18- to 45-fold. Combining contest-derived logistic regression code with coarse-grained parallelization, multithreading, and associated changes to data initialization code further developed through distributed innovation, we achieved an end-to-end speedup of 591-fold for a data set size of 6678 subjects by 645 863 variants, compared to PLINK 1.07's logistic regression. This represents a reduction in run time from 4.8 hours to 29 seconds. Accelerated logistic regression code developed in this project has been incorporated into the PLINK2 project. Using iterative competition-based OI, we have developed a new, faster implementation of logistic regression for genome-wide association studies analysis. We present lessons learned and recommendations on running a successful OI process for bioinformatics. © The Author 2017. Published by Oxford University Press.

Caregiver preferences regarding Personal Health Records in the management of ADHD

PubMed Central

Ronis, Sarah D; Baldwin, Constance D; McIntosh, Scott; McConnochie, Kenneth; Szilagyi, Peter G; Dolan, James

2015-01-01

Objective PHRs may address the needs of children with ADHD. Among parents, we assessed acceptance, barriers, and intentions regarding use of PHR for their children with ADHD. Methods Survey of parents from three practices in Rochester NY. Stepwise logistic regression analysis explored factors predicting respondents’ intentions for future use of PHR, accounting for care coordination needs, caregiver education, SES and satisfaction with providers. Results Of 184 respondents, 23% had used the PHR for their child, 82% intended future use. No difference was observed between users and non-users regarding gender, age, race, or education. Users were more likely than non-users to reside in the suburbs (p=0.03). Caregivers were more likely to plan future use of the PHR if they felt engaged as partners in their child’s care (AOR 2.3, 95%CI 1.2, 4.5). Conclusions Parents are enthusiastic about PHRs. Future work should focus on engaging them as members of the healthcare team. PMID:25567294

Neuropsychological Characteristics and Their Association with Higher-Level Functional Capacity in Parkinson's Disease

PubMed Central

Miura, Kayoko; Matsui, Mie; Takashima, Shutaro; Tanaka, Kortaro

2015-01-01

Background/Aims Little is known about the relationship between cognitive functions and higher-level functional capacity (e.g. intellectual activity, social role, and social participation) in Parkinson's disease (PD). The purpose of this study was to clarify neuropsychological characteristics and their association with higher-level functional capacity in PD patients. Methods Participants were 31 PD patients and 23 demographically matched healthy controls. Neuropsychological tests were conducted. One year later, a questionnaire survey evaluated higher-level functional capacity in daily living. Results The PD group scored significantly lower than the control group in all cognitive domains, particularly executive function and processing. Executive function, processing speed, language, and memory were significantly correlated with higher-level functional capacity in PD patients. Stepwise regression showed that only executive function (Trail Making Test-B), together with disease severity (HY stage), predicted the higher-level functional capacity. Conclusion Our findings provide evidence of a relationship between executive function and higher-level functional capacity in patients with PD. PMID:26273243

Factors associated with mouth breathing in children with -developmental -disabilities.

PubMed

de Castilho, Lia Silva; Abreu, Mauro Henrique Nogueira Guimarães; de Oliveira, Renata Batista; Souza E Silva, Maria Elisa; Resende, Vera Lúcia Silva

2016-01-01

To investigate the prevalence and factors associated with mouth breathing among patients with developmental disabilities of a dental service. We analyzed 408 dental records. Mouth breathing was reported by the patients' parents and from direct observation. Other variables were as -follows: history of asthma, bronchitis, palate shape, pacifier use, thumb -sucking, nail biting, use of medications, gastroesophageal reflux, bruxism, gender, age, and diagnosis of the patient. Statistical analysis included descriptive analysis with ratio calculation and multiple logistic regression. Variables with p < 0.25 were included in the model to estimate the adjusted OR (95% CI), calculated by the forward stepwise method. Variables with p ​​< 0.05 were kept in the model. Being male (p = 0.016) and use of centrally acting drugs (p = 0.001) were the variables that remained in the model. Among patients with -developmental disabilities, boys and psychotropic drug users had a greater chance of being mouth breathers. © 2016 Special Care Dentistry Association and Wiley Periodicals, Inc.

Creativity and inductive reasoning: the relationship between divergent thinking and performance on Wasons 246 task.

PubMed

Vartanian, Oshin; Martindale, Colin; Kwiatkowski, Jonna

2003-05-01

This study was an investigation of the relationship between potential creativityas measured by fluency scores on the Alternate Uses Testand performance on Wasons 246 task. As hypothesized, participants who were successful in discovering the rule had significantly higher fluency scores. Successful participants also generated higher frequencies of confirmatory and disconfirmatory hypotheses, but a multiple regression analysis using the stepwise method revealed that the frequency of generating disconfirmatory hypotheses and fluency scores were the only two significant factors in task outcome. The results also supported earlier studies where disconfirmation was shown to play a more important role in the later stages of hypothesis testing. This was especially true of successful participants, who employed a higher frequency of disconfirmatory hypotheses after receiving feedback on the first announcement. These results imply that successful participants benefited from the provision of feedback on the first announcement by switching to a more successful strategy in the hypothesis-testing sequence.

Mood and Balance are Associated with Free-Living Physical Activity of People after Stroke Residing in the community

PubMed Central

Alzahrani, Matar A.; Dean, Catherine M.; Ada, Louise; Dorsch, Simone; Canning, Colleen G.

2012-01-01

Purpose. To determine which characteristics are most associated with free-living physical activity in community-dwelling ambulatory people after stroke. Method. Factors (age, gender, side of stroke, time since stroke, BMI, and spouse), sensory-motor impairments (weakness, contracture, spasticity, coordination, proprioception, and balance), and non-sensory-motor impairments (cognition, language, perception, mood, and confidence) were collected on 42 people with chronic stroke. Free-living physical activity was measured using an activity monitor and reported as time on feet and activity counts. Results. Univariate analysis showed that balance and mood were correlated with time on feet (r = 0.42, 0.43, P < 0.01) and also with activity counts (r = 0.52, 0.54, P < 0.01). Stepwise multiple regression showed that mood and balance accounted for 25% of the variance in time on feet and 40% of the variance in activity counts. Conclusions. Mood and balance are associated with free-living physical activity in ambulatory people after stroke residing in the community. PMID:22013550

The QSAR study of flavonoid-metal complexes scavenging rad OH free radical

NASA Astrophysics Data System (ADS)

Wang, Bo-chu; Qian, Jun-zhen; Fan, Ying; Tan, Jun

2014-10-01

Flavonoid-metal complexes have antioxidant activities. However, quantitative structure-activity relationships (QSAR) of flavonoid-metal complexes and their antioxidant activities has still not been tackled. On the basis of 21 structures of flavonoid-metal complexes and their antioxidant activities for scavenging rad OH free radical, we optimised their structures using Gaussian 03 software package and we subsequently calculated and chose 18 quantum chemistry descriptors such as dipole, charge and energy. Then we chose several quantum chemistry descriptors that are very important to the IC50 of flavonoid-metal complexes for scavenging rad OH free radical through method of stepwise linear regression, Meanwhile we obtained 4 new variables through the principal component analysis. Finally, we built the QSAR models based on those important quantum chemistry descriptors and the 4 new variables as the independent variables and the IC50 as the dependent variable using an Artificial Neural Network (ANN), and we validated the two models using experimental data. These results show that the two models in this paper are reliable and predictable.

Using the theory of planned behavior to predict two types of snack food consumption among Midwestern upper elementary children: implications for practice.

PubMed

Branscum, Paul; Sharma, Manoj

This study examined the extent to which constructs of the theory of planned behavior (TPB) can predict the consumption of two types of snack foods among elementary school children. A 15-item instrument tested for validity and reliability measuring TPB constructs was developed and administered to 167 children. Snack foods were evaluated using a modified 24-hour recall method. On average, children consumed 302 calories from snack foods per day. Stepwise multiple regression found that attitudes, subjective norms, and perceived control accounted for 44.7% of the variance for intentions. Concurrently, intentions accounted for 11.3% of the variance for calorically-dense snack food consumption and 8.9% of the variance for fruit and vegetable snack consumption. Results suggest that the theory of planned behavior is an efficacious theory for these two behaviors. Future interventions should consider using this theoretical framework and aim to enhance children's attitudes, perceived control, and subjective norms towards snack food consumption.

Academic Procrastination: The Relationship Between Causal Attribution Styles and Behavioral Postponement

PubMed Central

Badri Gargari, Rahim; Sabouri, Hossein; Norzad, Fatemeh

2011-01-01

Objective: This research was conducted to study the relationship between attribution and academic procrastination in University Students. Methods: The subjects were 203 undergraduate students, 55 males and 148 females, selected from English and French language and literature students of Tabriz University. Data were gathered through Procrastination Assessment Scale-student (PASS) and Causal Dimension Scale (CDA) and were analyzed by multiple regression analysis (stepwise). Results: The results showed that there was a meaningful and negative relation between the locus of control and controllability in success context and academic procrastination. Besides, a meaningful and positive relation was observed between the locus of control and stability in failure context and procrastination. It was also found that 17% of the variance of procrastination was accounted by linear combination of attributions. Conclusion: We believe that causal attribution is a key in understanding procrastination in academic settings and is used by those who have the knowledge of Causal Attribution styles to organize their learning. PMID:24644450

BREASTFEEDING AND MATERNAL ALCOHOL USE: PREVALENCE AND EFFECTS ON CHILD OUTCOMES AND FETAL ALCOHOL SPECTRUM DISORDERS

PubMed Central

May, Philip A.; Hasken, Julie M.; Blankenship, Jason; Marais, Anna-Susan; Joubert, Belinda; Cloete, Marise; de Vries, Marlene M.; Barnard, Ronel; Botha, Isobel; Roux, Sumien; Doms, Cate; Gossage, J. Phillip; Kalberg, Wendy O.; Buckley, David; Robinson, Luther K.; Adnams, Colleen M.; Manning, Melanie A.; Parry, Charles D.H.; Hoyme, H. Eugene; Tabachnick, Barbara; Seedat, Soraya

2016-01-01

Objective Determine any effects that maternal alcohol consumption during the breastfeeding period has on child outcomes. Methods Population-based samples of children with fetal alcohol spectrum disorders (FASD), normally-developing children, and their mothers were analyzed for differences in child outcomes. Results Ninety percent (90%) of mothers breastfed for an average of 19.9 months. Of mothers who drank postpartum and breastfed (MDPB), 47% breastfed for 12 months or more. In case control analyses, children of MDPB were significantly lighter, had lower verbal IQ scores, and more anomalies in comparisons controlling for prenatal alcohol exposure and final FASD diagnosis. Utilizing a stepwise logistic regression model adjusting for nine confounders of prenatal drinking and other maternal risks, MDPB were 6.4 times more likely to have a child with FASD than breastfeeding mothers who abstained from alcohol while breastfeeding. Conclusions Alcohol use during the period of breastfeeding was found to significantly compromise a child’s development. PMID:27174445

REASONS AND CONSEQUENCES OF APPLIED LEADERSHIP STYLES IN ETHICAL DILEMMAS WHEN NURSE MANAGERS MAKE DECISIONS.

PubMed

Zydziunaite, V; Suominen, T

2014-09-21

Abstract Background: Understanding the reasons and consequences of leadership styles in ethical dilemmas is fundamental to exploring nurse managers' abilities to influence outcomes for patients and nursing personnel. Purpose: To explain the associations between different leadership styles, reasons for their application and its consequences when nurse managers make decisions in ethical dilemmas. Methods: The data were collected between 15 October 2011 and 30 April 2012 by statistically validated questionnaire. The respondents (n=278) were nurse managers. The data were analyzed using SPSS 20.0, calculating Spearman's correlations, the Stepwise Regression and ANOVA. Results: The reasons for applying different leadership styles in ethical dilemmas include personal characteristics, years in work position, institutional factors, and the professional authority of nurse managers. The applied leadership styles in ethical dilemmas are associated with the consequences regarding the satisfaction of patients', relatives' and nurse managers' needs. Conclusions: Nurse managers exhibited leadership styles oriented to maintenance, focusing more on the "doing the job" than on managing the decision-making in ethical dilemmas.

Predictors of Long-Term School-Based Behavioral Outcomes in the Multimodal Treatment Study of Children with Attention-Deficit/Hyperactivity Disorder.

PubMed

Reed, Margot O; Jakubovski, Ewgeni; Johnson, Jessica A; Bloch, Michael H

2017-05-01

To explore predictors of 8-year school-based behavioral outcomes in attention-deficit/hyperactivity disorder (ADHD). We examined potential baseline predictors of school-based behavioral outcomes in children who completed the 8-year follow-up in the multimodal treatment study of children with ADHD. Stepwise logistic regression and receiver operating characteristic (ROC) analysis identified baseline predictors that were associated with a higher risk of truancy, school discipline, and in-school fights. Stepwise regression analysis explained between 8.1% (in-school fights) and 12.0% (school discipline) of the total variance in school-based behavioral outcomes. Logistic regression identified several baseline characteristics that were associated with school-based behavioral difficulties 8 years later, including being male (associated with truancy and school discipline), African American (school discipline, in-school fights), increased conduct disorder (CD) symptoms (truancy), decreased affection from parents (school discipline), ADHD severity (in-school fights), and study site (truancy and school discipline). ROC analyses identified the most discriminative predictors of truancy, school discipline, and in-school fights, which were Aggression and Conduct Problem Scale Total score, family income, and race, respectively. A modest, but nontrivial portion of school-based behavioral outcomes, was predicted by baseline childhood characteristics. Exploratory analyses identified modifiable (lack of paternal involvement, lower parental knowledge of behavioral principles, and parental use of physical punishment), somewhat modifiable (income and having comorbid CD), and nonmodifiable (African American and male) factors that were associated with school-based behavioral difficulties. Future research should confirm that the associations between earlier specific parenting behaviors and poor subsequent school-based behavioral outcomes are, indeed, causally related and independent cooccurring childhood psychopathology. Future research might target increasing paternal involvement and parental knowledge of behavioral principles and reducing use of physical punishment to improve school-based behavioral outcomes in children with ADHD.

[Correlations Between Joint Proprioception, Muscle Strength, and Functional Ability in Patients with Knee Osteoarthritis].

PubMed

Chen, Yoa; Yu, Yong; He, Cheng-qi

2015-11-01

To establish correlations between joint proprioception, muscle flexion and extension peak torque, and functional ability in patients with knee osteoarthritis (OA). Fifty-six patients with symptomatic knee OA were recruited in this study. Both proprioceptive acuity and muscle strength were measured using the isomed-2000 isokinetic dynamometer. Proprioceptive acuity was evaluated by establishing the joint motion detection threshold (JMDT). Muscle strength was evaluated by Max torque (Nm) and Max torque/weight (Nm/ kg). Functional ability was assessed by the Western Ontario and McMaster Universities Osteoarthritis Index physical function (WOMAC-PF) questionnaire. Correlational analyses were performed between proprioception, muscle strength, and functional ability. A multiple stepwise regression model was established, with WOMAC-PF as dependent variable and patient age, body mass index (BMI), visual analogue scale (VAS)-score, mean grade for Kellgren-Lawrance of both knees, mean strength for quadriceps and hamstring muscles of both knees, and mean JMDT of both knees as independent variables. Poor proprioception (high JMDT) was negatively correlated with muscle strength (P<0.05). There was no significant correlation between knee proprioception (high JMDT) and joint pain (WOMAC pain score), and between knee proprioception (high JMDT) and joint stiffness (WOMAC stiffness score). Poor proprioception (high JMDT) was correlated with limitation in functional ability (WOMAC physical function score r=0.659, P<0.05). WOMAC score was correlated with poor muscle strength (quadriceps muscle strength r = -0.511, P<0.05, hamstring muscle strength r = -0.408, P<0.05). The multiple stepwise regression model showed that high JMDT C standard partial regression coefficient (B) = 0.385, P<0.50 and high VAS-scale score (B=0.347, P<0.05) were significant predictors of WOMAC-PF score. Patients with poor proprioception is associated with poor muscle strength and limitation in functional ability. Patients with symptomatic OA of knees commonly endure with moderate to considerable dysfunction, which is associated with poor proprioception (high JMDT) and high VAS-scale score.

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

NASA Technical Reports Server (NTRS)

Banse, Karl; Yong, Marina

1990-01-01

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

Examining the long-term racial disparities in health and economic conditions among Hurricane Katrina survivors: policy implications for Gulf Coast recovery.

PubMed

Toldson, Ivory A; Ray, Kilynda; Hatcher, Schnavia Smith; Louis, Laura Straughn

2011-01-01

This study examines disparities in the long-term health, emotional well-being, and economic consequences of the 2005 Gulf Coast hurricanes. Researchers analyzed the responses of 216 Black and 508 White Hurricane Katrina survivors who participated in the ABC News Hurricane Katrina Anniversary Poll in 2006. Self-reported data of the long-term negative impact of the hurricane on personal health, emotional well-being, and finances were regressed on race, income, and measures of loss, injury, family mortality, anxiety, and confidence in the government. Descriptive analyses, stepwise logistic regression, and analyses of variance revealed that Black hurricane survivors more frequently reported hurricane-related problems with personal health, emotional well-being, and finances. In addition, Blacks were more likely than Whites to report the loss of friends, relatives, and personal property.

Impact of multicollinearity on small sample hydrologic regression models

NASA Astrophysics Data System (ADS)

Kroll, Charles N.; Song, Peter

2013-06-01

Often hydrologic regression models are developed with ordinary least squares (OLS) procedures. The use of OLS with highly correlated explanatory variables produces multicollinearity, which creates highly sensitive parameter estimators with inflated variances and improper model selection. It is not clear how to best address multicollinearity in hydrologic regression models. Here a Monte Carlo simulation is developed to compare four techniques to address multicollinearity: OLS, OLS with variance inflation factor screening (VIF), principal component regression (PCR), and partial least squares regression (PLS). The performance of these four techniques was observed for varying sample sizes, correlation coefficients between the explanatory variables, and model error variances consistent with hydrologic regional regression models. The negative effects of multicollinearity are magnified at smaller sample sizes, higher correlations between the variables, and larger model error variances (smaller R2). The Monte Carlo simulation indicates that if the true model is known, multicollinearity is present, and the estimation and statistical testing of regression parameters are of interest, then PCR or PLS should be employed. If the model is unknown, or if the interest is solely on model predictions, is it recommended that OLS be employed since using more complicated techniques did not produce any improvement in model performance. A leave-one-out cross-validation case study was also performed using low-streamflow data sets from the eastern United States. Results indicate that OLS with stepwise selection generally produces models across study regions with varying levels of multicollinearity that are as good as biased regression techniques such as PCR and PLS.

Fatigue loading and R-curve behavior of a dental glass-ceramic with multiple flaw distributions

PubMed Central

Joshi, Gaurav V.; Duan, Yuanyuan; Bona, Alvaro Della; Hill, Thomas J.; John, Kenneth St.; Griggs, Jason A.

2013-01-01

Objectives To determine the effects of surface finish and mechanical loading on the rising toughness curve (R-curve) behavior of a fluorapatite glass-ceramic (IPS e.max ZirPress) and to determine a statistical model for fitting fatigue lifetime data with multiple flaw distributions. Materials and Methods Rectangular beam specimens were fabricated by pressing. Two groups of specimens (n=30) with polished (15 μm) or air abraded surface were tested under rapid monotonic loading in oil. Additional polished specimens were subjected to cyclic loading at 2 Hz (n=44) and 10 Hz (n=36). All fatigue tests were performed using a fully articulated four-point flexure fixture in 37°C water. Fractography was used to determine the critical flaw size and estimate fracture toughness. To prove the presence of R-curve behavior, non-linear regression was used. Forward stepwise regression was performed to determine the effects on fracture toughness of different variables, such as initial flaw type, critical flaw size, critical flaw eccentricity, cycling frequency, peak load, and number of cycles. Fatigue lifetime data were fit to an exclusive flaw model. Results There was an increase in fracture toughness values with increasing critical flaw size for both loading methods (rapid monotonic loading and fatigue). The values for the fracture toughness ranged from 0.75 to 1.1 MPa·m1/2 reaching a plateau at different critical flaw sizes based on loading method. Significance Cyclic loading had a significant effect on the R-curve behavior. The fatigue lifetime distribution was dependent on the flaw distribution, and it fit well to an exclusive flaw model. PMID:24034441

Predicting non-familial major physical violent crime perpetration in the U.S. Army from administrative data

PubMed Central

Rosellini, Anthony J.; Monahan, John; Street, Amy E.; Heeringa, Steven G.; Hill, Eric D.; Petukhova, Maria; Reis, Ben Y.; Sampson, Nancy A.; Bliese, Paul; Schoenbaum, Michael; Stein, Murray B.; Ursano, Robert; Kessler, Ronald C.

2016-01-01

BACKGROUND Although interventions exist to reduce violent crime, optimal implementation requires accurate targeting. We report the results of an attempt to develop an actuarial model using machine learning methods to predict future violent crimes among U.S. Army soldiers. METHODS A consolidated administrative database for all 975,057 soldiers in the U.S. Army in 2004-2009 was created in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). 5,771 of these soldiers committed a first founded major physical violent crime (murder-manslaughter, kidnapping, aggravated arson, aggravated assault, robbery) over that time period. Temporally prior administrative records measuring socio-demographic, Army career, criminal justice, medical/pharmacy, and contextual variables were used to build an actuarial model for these crimes separately among men and women using machine learning methods (cross-validated stepwise regression; random forests; penalized regressions). The model was then validated in an independent 2011-2013 sample. RESULTS Key predictors were indicators of disadvantaged social/socio-economic status, early career stage, prior crime, and mental disorder treatment. Area under the receiver operating characteristic curve was .80-.82 in 2004-2009 and .77 in a 2011-2013 validation sample. 36.2-33.1% (male-female) of all administratively-recorded crimes were committed by the 5% of soldiers having highest predicted risk in 2004-2009 and an even higher proportion (50.5%) in the 2011-2013 validation sample. CONCLUSIONS Although these results suggest that the models could be used to target soldiers at high risk of violent crime perpetration for preventive interventions, final implementation decisions would require further validation and weighing of predicted effectiveness against intervention costs and competing risks. PMID:26436603

The Schaake shuffle: A method for reconstructing space-time variability in forecasted precipitation and temperature fields

USGS Publications Warehouse

Clark, M.R.; Gangopadhyay, S.; Hay, L.; Rajagopalan, B.; Wilby, R.

2004-01-01

A number of statistical methods that are used to provide local-scale ensemble forecasts of precipitation and temperature do not contain realistic spatial covariability between neighboring stations or realistic temporal persistence for subsequent forecast lead times. To demonstrate this point, output from a global-scale numerical weather prediction model is used in a stepwise multiple linear regression approach to downscale precipitation and temperature to individual stations located in and around four study basins in the United States. Output from the forecast model is downscaled for lead times up to 14 days. Residuals in the regression equation are modeled stochastically to provide 100 ensemble forecasts. The precipitation and temperature ensembles from this approach have a poor representation of the spatial variability and temporal persistence. The spatial correlations for downscaled output are considerably lower than observed spatial correlations at short forecast lead times (e.g., less than 5 days) when there is high accuracy in the forecasts. At longer forecast lead times, the downscaled spatial correlations are close to zero. Similarly, the observed temporal persistence is only partly present at short forecast lead times. A method is presented for reordering the ensemble output in order to recover the space-time variability in precipitation and temperature fields. In this approach, the ensemble members for a given forecast day are ranked and matched with the rank of precipitation and temperature data from days randomly selected from similar dates in the historical record. The ensembles are then reordered to correspond to the original order of the selection of historical data. Using this approach, the observed intersite correlations, intervariable correlations, and the observed temporal persistence are almost entirely recovered. This reordering methodology also has applications for recovering the space-time variability in modeled streamflow. ?? 2004 American Meteorological Society.

A Comparison of Two Balance Calibration Model Building Methods

NASA Technical Reports Server (NTRS)

DeLoach, Richard; Ulbrich, Norbert

2007-01-01

Simulated strain-gage balance calibration data is used to compare the accuracy of two balance calibration model building methods for different noise environments and calibration experiment designs. The first building method obtains a math model for the analysis of balance calibration data after applying a candidate math model search algorithm to the calibration data set. The second building method uses stepwise regression analysis in order to construct a model for the analysis. Four balance calibration data sets were simulated in order to compare the accuracy of the two math model building methods. The simulated data sets were prepared using the traditional One Factor At a Time (OFAT) technique and the Modern Design of Experiments (MDOE) approach. Random and systematic errors were introduced in the simulated calibration data sets in order to study their influence on the math model building methods. Residuals of the fitted calibration responses and other statistical metrics were compared in order to evaluate the calibration models developed with different combinations of noise environment, experiment design, and model building method. Overall, predicted math models and residuals of both math model building methods show very good agreement. Significant differences in model quality were attributable to noise environment, experiment design, and their interaction. Generally, the addition of systematic error significantly degraded the quality of calibration models developed from OFAT data by either method, but MDOE experiment designs were more robust with respect to the introduction of a systematic component of the unexplained variance.

Comparison of Nine Statistical Model Based Warfarin Pharmacogenetic Dosing Algorithms Using the Racially Diverse International Warfarin Pharmacogenetic Consortium Cohort Database

PubMed Central

Liu, Rong; Li, Xi; Zhang, Wei; Zhou, Hong-Hao

2015-01-01

Objective Multiple linear regression (MLR) and machine learning techniques in pharmacogenetic algorithm-based warfarin dosing have been reported. However, performances of these algorithms in racially diverse group have never been objectively evaluated and compared. In this literature-based study, we compared the performances of eight machine learning techniques with those of MLR in a large, racially-diverse cohort. Methods MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied in warfarin dose algorithms in a cohort from the International Warfarin Pharmacogenetics Consortium database. Covariates obtained by stepwise regression from 80% of randomly selected patients were used to develop algorithms. To compare the performances of these algorithms, the mean percentage of patients whose predicted dose fell within 20% of the actual dose (mean percentage within 20%) and the mean absolute error (MAE) were calculated in the remaining 20% of patients. The performances of these techniques in different races, as well as the dose ranges of therapeutic warfarin were compared. Robust results were obtained after 100 rounds of resampling. Results BART, MARS and SVR were statistically indistinguishable and significantly out performed all the other approaches in the whole cohort (MAE: 8.84–8.96 mg/week, mean percentage within 20%: 45.88%–46.35%). In the White population, MARS and BART showed higher mean percentage within 20% and lower mean MAE than those of MLR (all p values < 0.05). In the Asian population, SVR, BART, MARS and LAR performed the same as MLR. MLR and LAR optimally performed among the Black population. When patients were grouped in terms of warfarin dose range, all machine learning techniques except ANN and LAR showed significantly higher mean percentage within 20%, and lower MAE (all p values < 0.05) than MLR in the low- and high- dose ranges. Conclusion Overall, machine learning-based techniques, BART, MARS and SVR performed superior than MLR in warfarin pharmacogenetic dosing. Differences of algorithms’ performances exist among the races. Moreover, machine learning-based algorithms tended to perform better in the low- and high- dose ranges than MLR. PMID:26305568

A comparative study on the level of satisfaction among regular and contractual health-care workers in a Northern city of India.

PubMed

Dixit, Jyoti; Goel, Sonu; Sharma, Vijaylakshmi

2017-01-01

Job satisfaction greatly determines the productivity and efficiency of human resources for health. The current study aims to assess the level of satisfaction and factors influencing the job satisfaction among regular and contractual health-care workers. A cross-sectional quantitative study was conducted from January to June 2015 among health care workers ( n = 354) at all levels of public health-care facilities of Chandigarh. The correlation between variables with overall level of satisfaction was computed for regular and contractual health-care workers. Stepwise multiple linear regression was done to elucidate the major factors influencing job satisfaction. Majority of the regular health-care staff was highly satisfied (86.9%) as compared to contractual staff (10.5%), which however was moderately satisfied (55.9%). Stepwise regression model showed that work-related matters (β = 1.370, P < 0.01), organizational facilities (β = 1.586, P < 0.01), privileges attached to the job (β = 0.530, P < 0.01), attention to the suggestions (β = 0.515, P < 0.01), chance of promotion (β = 0.703, P < 0.01), and human resource issues (β = 1.0721, P < 0.01) are strong predictors of overall satisfaction level. Under the National Rural Health Mission, contract appointments have improved the overall availability of health-care staff at all levels of public health facilities. However, there are concerns regarding their level of motivation with various aspects related to the job, which need to be urgently addressed so as to improve the effectiveness and efficiency of health services.

Relationships between Lifestyle, Living Environments, and Incidence of Hypertension in Japan (in Men): Based on Participant’s Data from the Nationwide Medical Check-Up

PubMed Central

Oka, Mayumi; Yamamoto, Mio; Mure, Kanae; Takeshita, Tatsuya; Arita, Mikio

2016-01-01

This study aims to investigate factors that contribute to the differences in incidence of hypertension between different regions in Japan, by accounting for not only individual lifestyles, but also their living environments. The target participants of this survey were individuals who received medical treatment for hypertension, as well as hypertension patients who have not received any treatment. The objective variable for analysis was the incidence of hypertension as data aggregated per prefecture. We used data (in men) including obesity, salt intake, vegetable intake, habitual alcohol consumption, habitual smoking, and number of steps walked per day. The variables within living environment included number of rail stations, standard/light vehicle usage, and slope of habitable land. In addition, we analyzed data for the variables related to medical environment including, participation rate in medical check-ups and number of hospitals. We performed multiple stepwise regression analyses to elucidate the correlation of these variables by using hypertension incidence as the objective variable. Hypertension incidence showed a significant negative correlation with walking and medical check-ups, and a significant positive correlation with light-vehicle usage and slope. Between the number of steps and variables related to the living environment, number of rail stations showed a significant positive correlation, while, standard- and light-vehicle usage showed significant negative correlation. Moreover, with stepwise multiple regression analysis, walking showed the strongest effect. The differences in daily walking based on living environment were associated with the disparities in the hypertension incidence in Japan. PMID:27788198

Exploring the association between parental rearing styles and medical students' critical thinking disposition in China.

PubMed

Huang, Lei; Wang, Zhaoxin; Yao, Yuhong; Shan, Chang; Wang, Haojie; Zhu, Mengyi; Lu, Yuan; Sun, Pengfei; Zhao, Xudong

2015-05-14

Critical thinking is an essential ability for medical students. However, the relationship between parental rearing styles and medical students' critical thinking disposition has rarely been considered. The aim of this study was to investigate whether parental rearing styles were significant predictors of critical thinking disposition among Chinese medical students. 1,075 medical students from the first year to the fifth year attending one of three medical schools in China were recruited via multistage stratified cluster sampling. The Chinese Critical Thinking Disposition Inventory(CTDI-CV) and The Egna Minnen av Barndoms Uppfostran (EMBU) questionnaire were applied to collect data and to conduct descriptive analysis. Stepwise multiple linear regression was used to analyze the data. The critical thinking disposition average mean score was 287.44 with 632 participants (58.79%) demonstrating positive critical thinking disposition. Stepwise multiple linear regression analysis revealed that the rearing styles of fathers, including "overprotection", "emotional warmth and understanding", "rejection" and "over-interference" were significant predictors of medical students' critical thinking disposition that explained 79.0% of the variance in critical thinking ability. Rearing styles of mothers including "emotional warmth and understanding", "punishing" and "rejection" were also found to be significant predictors, and explained 77.0% of the variance. Meaningful association has been evidenced between parental rearing styles and Chinese medical students' critical thinking disposition. Parental rearing styles should be considered as one of the many potential determinant factors that contribute to the cultivation of medical students' critical thinking capability. Positive parental rearing styles should be encouraged in the cultivation of children's critical thinking skills.

Relationship between masticatory performance using a gummy jelly and masticatory movement.

PubMed

Uesugi, Hanako; Shiga, Hiroshi

2017-10-01

The purpose of this study was to clarify the relationship between masticatory performance using a gummy jelly and masticatory movement. Thirty healthy males were asked to chew a gummy jelly on their habitual chewing side for 20s, and the parameters of masticatory performance and masticatory movement were calculated as follows. For evaluating the masticatory performance, the amount of glucose extraction during chewing of a gummy jelly was measured. For evaluating the masticatory movement, the movement of the mandibular incisal point was recorded using the MKG K6-I, and ten parameters of the movement path (opening distance and masticatory width), movement rhythm (opening time, closing time, occluding time, and cycle time), stability of movement (stability of path and stability of rhythm), and movement velocity (opening maximum velocity and closing maximum velocity) were calculated from 10 cycles of chewing beginning with the fifth cycle. The relationship between the amount of glucose extraction and parameters representing masticatory movement was investigated and then stepwise multiple linear regression analysis was performed. The amount of glucose extraction was associated with 7 parameters representing the masticatory movement. Stepwise multiple linear regression analysis showed that the opening distance, closing time, stability of rhythm, and closing maximum velocity were the most important factors affecting the glucose extraction. From these results it was suggested that there was a close relation between masticatory performance and masticatory movement, and that the masticatory performance could be increased by rhythmic, rapid and stable mastication with a large opening distance. Copyright © 2017 Japan Prosthodontic Society. Published by Elsevier Ltd. All rights reserved.

The relationship between personality traits and sexual self-esteem and its components.

PubMed

Firoozi, Mahbobe; Azmoude, Elham; Asgharipoor, Negar

2016-01-01

Women's sexual self-esteem is one of the most important factors that affect women's sexual satisfaction and their sexual anxiety. Various aspects of sexual life are blended with the entire personality. Determining the relationship between personality traits and self-concept aspects such as sexual self-esteem leads to better understanding of sexual behavior in people with different personality traits and helps in identifying the psychological variables affecting their sexual performance. The aim this study was to determine the relationship between personality traits and sexual self-esteem. This correlation study was performed on 127 married women who referred to selected health care centers of Mashhad in 2014-2015. Data collection tools included NEO personality inventory dimensions and Zeanah and Schwarz sexual self-esteem questionnaire. Data were analyzed through Pearson correlation coefficient test and stepwise regression model. The results of Pearson correlation test showed a significant relationship between neuroticism personality dimension (r = -0.414), extroversion (r = 0.363), agreeableness (r = 0.420), and conscientiousness (r = 0.364) with sexual self-esteem (P < 0.05). The relationship between openness with sexual self-esteem was not significant (P > 0.05). In addition, based on the results of the stepwise regression model, three dimensions of agreeableness, neuroticism, and extraversion could predict 27% of the women's sexual self-esteem variance. The results showed a correlation between women's personality characteristics and their sexual self-esteem. Paying attention to personality characteristics may be important to identify at-risk group or the women having low sexual self-esteem in premarital and family counseling.

Lithium might be associated with better decision-making performance in euthymic bipolar patients.

PubMed

Adida, Marc; Jollant, Fabrice; Clark, Luke; Guillaume, Sebastien; Goodwin, Guy M; Azorin, Jean-Michel; Courtet, Philippe

2015-06-01

Bipolar disorder is associated with impaired decision-making. Little is known about how treatment, especially lithium, influences decision-making abilities in bipolar patients when euthymic. We aimed at testing for an association between lithium medication and decision-making performance in remitted bipolar patients. Decision-making was measured using the Iowa Gambling Task in 3 groups of subjects: 34 and 56 euthymic outpatients with bipolar disorder, treated with lithium (monotherapy and lithium combined with anticonvulsant or antipsychotic) and without lithium (anticonvulsant, antipsychotic and combination treatment), respectively, and 152 matched healthy controls. Performance was compared between the 3 groups. In the 90 euthymic patients, the relationship between different sociodemographic and clinical variables and decision-making was assessed by stepwise multivariate regression analysis. Euthymic patients with lithium (p=0.007) and healthy controls (p=0.001) selected significantly more cards from the safe decks than euthymic patients without lithium, with no significant difference between euthymic patients with lithium and healthy controls (p=0.9). In the 90 euthymic patients, the stepwise linear multivariate regression revealed that decision-making was significantly predicted (p<0.001) by lithium dose, level of education and no family history of bipolar disorder (all p≤0.01). Because medication was not randomized, it was not possible to discriminate the effect of different medications. Lithium medication might be associated with better decision-making in remitted bipolar patients. A randomized trial is required to test for the hypothesis that lithium, but not other mood stabilizers, may specifically improve decision-making abilities in bipolar disorder. Copyright © 2015 Elsevier B.V. and ECNP. All rights reserved.

Bone stress in runners with tibial stress fracture.

PubMed

Meardon, Stacey A; Willson, John D; Gries, Samantha R; Kernozek, Thomas W; Derrick, Timothy R

2015-11-01

Combinations of smaller bone geometry and greater applied loads may contribute to tibial stress fracture. We examined tibial bone stress, accounting for geometry and applied loads, in runners with stress fracture. 23 runners with a history of tibial stress fracture & 23 matched controls ran over a force platform while 3-D kinematic and kinetic data were collected. An elliptical model of the distal 1/3 tibia cross section was used to estimate stress at 4 locations (anterior, posterior, medial and lateral). Inner and outer radii for the model were obtained from 2 planar x-ray images. Bone stress differences were assessed using two-factor ANOVA (α=0.05). Key contributors to observed stress differences between groups were examined using stepwise regression. Runners with tibial stress fracture experienced greater anterior tension and posterior compression at the distal tibia. Location, but not group, differences in shear stress were observed. Stepwise regression revealed that anterior-posterior outer diameter of the tibia and the sagittal plane bending moment explained >80% of the variance in anterior and posterior bone stress. Runners with tibial stress fracture displayed greater stress anteriorly and posteriorly at the distal tibia. Elevated tibial stress was associated with smaller bone geometry and greater bending moments about the medial-lateral axis of the tibia. Future research needs to identify key running mechanics associated with the sagittal plane bending moment at the distal tibia as well as to identify ways to improve bone geometry in runners in order to better guide preventative and rehabilitative efforts. Copyright © 2015 Elsevier Ltd. All rights reserved.

The impact of recreational boat traffic on Marbled Murrelets (Brachyramphus marmoratus).

PubMed

Bellefleur, Danielle; Lee, Philip; Ronconi, Robert A

2009-01-01

This study evaluated the impact of small boat traffic on reaction distances of Marbled Murrelets (Brachyramphus marmoratus), in the marine waters of Pacific Rim National Park Reserve, British Columbia, Canada. Observers on moving boats recorded the minimum distance the boat came to murrelets on the water, and any disturbance reaction (fly, dive, no reaction). Out of the 7500 interactions 11.7% flew, 30.8% dove and 58.1% exhibited no flushing reaction. Using a product-limit analysis, we developed curves for the proportion of Marbled Murrelets flushing (dive or flight) as a function of reaction distance. Overall, the majority of Marbled Murrelets waited until boats were within 40 m before reacting, with 25% of the population reacting at 29.2m. A stepwise Cox regression indicated that age, boat speed, and boat density (loaded in that order), significantly affected flushing response. More juveniles flushed than adults (70.1 versus 51.7%), but at closer distances. Faster boats caused a greater proportion of birds to flush, and at further distances (25% of birds flushed at 40 m at speeds > 29 kph versus 28m at speeds <12kph). A stepwise logistic regression on diving and flight responses indicated that birds tended to fly completely out of feeding areas at the approach of boats travelling >28.8 kph and later in the season (July and August). Other secondary variables included; boat density and time of day. Discussion focused on possible management actions such as the application of speed limits, set back distances, and exclusion of boat traffic to protect Marbled Murrelets.

Attitudes Toward Obese Persons and Weight Locus of Control in Chinese Nurses: A Cross-sectional Survey.

PubMed

Wang, Yan; Ding, Ye; Song, Daoping; Zhu, Daqiao; Wang, Jianrong

2016-01-01

Obese individuals frequently experience weight-related bias or discrimination-even in healthcare settings. Although obesity bias has been associated with several demographic factors, little is known about the association of weight locus of control with bias against overweight persons or about weight bias among Chinese health professionals. The aim of the study was to examine attitudes toward obese patients in a sample of Chinese registered nurses (RNs) and the relationship between weight bias and nurses' weight locus of control. RNs working in nine community health service centers across Shanghai, China, answered three self-report questionnaires: The Attitudes Toward Obese Persons Scale (ATOP), the External Weight Locus of Control Subscale (eWLOC) from the Dieting Belief Scale, and a sociodemographic profile. Hierarchical, stepwise, multiple regression was used to predict ATOP scores. From among 385 invited, a total of 297 RNs took part in the study (77.1% response rate). Participants scored an average of 71.04 on the ATOP, indicating slightly positive attitudes toward obese persons, and 30.08 on the eWLOC, indicating a belief in the uncontrollability of body weight. Using hierarchical, stepwise, multiple regression, two predictors of ATOP scores were statistically significant (eWLOC scores and status as a specialist rather than generalist nurse), but explained variance was low. Chinese RNs seemed to have relatively neutral or even slightly positive attitudes toward obese persons. Those nurses who believed that obesity was beyond the individual's control or worked in specialties were more likely to have positive attitudes toward obese people. Improved understanding of the comprehensive etiology of obesity is needed.

Remote Sensing as a Landscape Epidemiologic Tool to Identify Villages at High Risk for Malaria Transmission

NASA Technical Reports Server (NTRS)

Beck, Louisa R.; Rodriquez, Mario H.; Dister, Sheri W.; Rodriquez, Americo D.; Rejmankova, Eliska; Ulloa, Armando; Meza, Rosa A.; Roberts, Donald R.; Paris, Jack F.; Spanner, Michael A.;

Multiple balance tests improve the assessment of postural stability in subjects with Parkinson's disease

PubMed Central

Jacobs, J V; Horak, F B; Tran, V K; Nutt, J G

2006-01-01

Objectives Clinicians often base the implementation of therapies on the presence of postural instability in subjects with Parkinson's disease (PD). These decisions are frequently based on the pull test from the Unified Parkinson's Disease Rating Scale (UPDRS). We sought to determine whether combining the pull test, the one‐leg stance test, the functional reach test, and UPDRS items 27–29 (arise from chair, posture, and gait) predicts balance confidence and falling better than any test alone. Methods The study included 67 subjects with PD. Subjects performed the one‐leg stance test, the functional reach test, and the UPDRS motor exam. Subjects also responded to the Activities‐specific Balance Confidence (ABC) scale and reported how many times they fell during the previous year. Regression models determined the combination of tests that optimally predicted mean ABC scores or categorised fall frequency. Results When all tests were included in a stepwise linear regression, only gait (UPDRS item 29), the pull test (UPDRS item 30), and the one‐leg stance test, in combination, represented significant predictor variables for mean ABC scores (r2 = 0.51). A multinomial logistic regression model including the one‐leg stance test and gait represented the model with the fewest significant predictor variables that correctly identified the most subjects as fallers or non‐fallers (85% of subjects were correctly identified). Conclusions Multiple balance tests (including the one‐leg stance test, and the gait and pull test items of the UPDRS) that assess different types of postural stress provide an optimal assessment of postural stability in subjects with PD. PMID:16484639

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

PubMed

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

2012-02-01

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

Which Frail Older People Are Dehydrated? The UK DRIE Study

PubMed Central

Bunn, Diane K.; Downing, Alice; Jimoh, Florence O.; Groves, Joyce; Free, Carol; Cowap, Vicky; Potter, John F.; Hunter, Paul R.; Shepstone, Lee

2016-01-01

Background: Water-loss dehydration in older people is associated with increased mortality and disability. We aimed to assess the prevalence of dehydration in older people living in UK long-term care and associated cognitive, functional, and health characteristics. Methods: The Dehydration Recognition In our Elders (DRIE) cohort study included people aged 65 or older living in long-term care without heart or renal failure. In a cross-sectional baseline analysis, we assessed serum osmolality, previously suggested dehydration risk factors, general health, markers of continence, cognitive and functional health, nutrition status, and medications. Univariate linear regression was used to assess relationships between participant characteristics and serum osmolality, then associated characteristics entered into stepwise backwards multivariate linear regression. Results: DRIE included 188 residents (mean age 86 years, 66% women) of whom 20% were dehydrated (serum osmolality >300 mOsm/kg). Linear and logistic regression suggested that renal, cognitive, and diabetic status were consistently associated with serum osmolality and odds of dehydration, while potassium-sparing diuretics, sex, number of recent health contacts, and bladder incontinence were sometimes associated. Thirst was not associated with hydration status. Conclusions: DRIE found high prevalence of dehydration in older people living in UK long-term care, reinforcing the proposed association between cognitive and renal function and hydration. Dehydration is associated with increased mortality and disability in older people, but trials to assess effects of interventions to support healthy fluid intakes in older people living in residential care are needed to enable us to formally assess causal direction and any health benefits of increasing fluid intakes. PMID:26553658

Chronic atrophic gastritis in association with hair mercury level.

PubMed

Xue, Zeyun; Xue, Huiping; Jiang, Jianlan; Lin, Bing; Zeng, Si; Huang, Xiaoyun; An, Jianfu

2014-11-01

The objective of this study was to explore hair mercury level in association with chronic atrophic gastritis, a precancerous stage of gastric cancer (GC), and thus provide a brand new angle of view on the timely intervention of precancerous stage of GC. We recruited 149 healthy volunteers as controls and 152 patients suffering from chronic gastritis as cases. The controls denied upper gastrointestinal discomforts, and the cases were diagnosed as chronic superficial gastritis (n=68) or chronic atrophic gastritis (n=84). We utilized Mercury Automated Analyzer (NIC MA-3000) to detect hair mercury level of both healthy controls and cases of chronic gastritis. The statistic of measurement data was expressed as mean ± standard deviation, which was analyzed using Levene variance equality test and t test. Pearson correlation analysis was employed to determine associated factors affecting hair mercury levels, and multiple stepwise regression analysis was performed to deduce regression equations. Statistical significance is considered if p value is less than 0.05. The overall hair mercury level was 0.908949 ± 0.8844490 ng/g [mean ± standard deviation (SD)] in gastritis cases and 0.460198 ± 0.2712187 ng/g (mean±SD) in healthy controls; the former level was significantly higher than the latter one (p=0.000<0.01). The hair mercury level in chronic atrophic gastritis subgroup was 1.155220 ± 0.9470246 ng/g (mean ± SD) and that in chronic superficial gastritis subgroup was 0.604732 ± 0.6942509 ng/g (mean ± SD); the former level was significantly higher than the latter level (p<0.01). The hair mercury level in chronic superficial gastritis cases was significantly higher than that in healthy controls (p<0.05). The hair mercury level in chronic atrophic gastritis cases was significantly higher than that in healthy controls (p<0.01). Stratified analysis indicated that the hair mercury level in healthy controls with eating seafood was significantly higher than that in healthy controls without eating seafood (p<0.01) and that the hair mercury level in chronic atrophic gastritis cases was significantly higher than that in chronic superficial gastritis cases (p<0.01). Pearson correlation analysis indicated that eating seafood was most correlated with hair mercury level and positively correlated in the healthy controls and that the severity of gastritis was most correlated with hair mercury level and positively correlated in the gastritis cases. Multiple stepwise regression analysis indicated that the regression equation of hair mercury level in controls could be expressed as 0.262 multiplied the value of eating seafood plus 0.434, the model that was statistically significant (p<0.01). Multiple stepwise regression analysis also indicated that the regression equation of hair mercury level in gastritis cases could be expressed as 0.305 multiplied the severity of gastritis, the model that was also statistically significant (p<0.01). The graphs of regression standardized residual for both controls and cases conformed to normal distribution. The main positively correlated factor affecting the hair mercury level is eating seafood in healthy people whereas the predominant positively correlated factor affecting the hair mercury level is the severity of gastritis in chronic gastritis patients. That is to say, the severity of chronic gastritis is positively correlated with the level of hair mercury. The incessantly increased level of hair mercury possibly reflects the development of gastritis from normal stomach to superficial gastritis and to atrophic gastritis. The detection of hair mercury is potentially a means to predict the severity of chronic gastritis and possibly to insinuate the environmental mercury threat to human health in terms of gastritis or even carcinogenesis.

Graphene Nanopores for Protein Sequencing.

PubMed

Wilson, James; Sloman, Leila; He, Zhiren; Aksimentiev, Aleksei

2016-07-19

An inexpensive, reliable method for protein sequencing is essential to unraveling the biological mechanisms governing cellular behavior and disease. Current protein sequencing methods suffer from limitations associated with the size of proteins that can be sequenced, the time, and the cost of the sequencing procedures. Here, we report the results of all-atom molecular dynamics simulations that investigated the feasibility of using graphene nanopores for protein sequencing. We focus our study on the biologically significant phenylalanine-glycine repeat peptides (FG-nups)-parts of the nuclear pore transport machinery. Surprisingly, we found FG-nups to behave similarly to single stranded DNA: the peptides adhere to graphene and exhibit step-wise translocation when subject to a transmembrane bias or a hydrostatic pressure gradient. Reducing the peptide's charge density or increasing the peptide's hydrophobicity was found to decrease the translocation speed. Yet, unidirectional and stepwise translocation driven by a transmembrane bias was observed even when the ratio of charged to hydrophobic amino acids was as low as 1:8. The nanopore transport of the peptides was found to produce stepwise modulations of the nanopore ionic current correlated with the type of amino acids present in the nanopore, suggesting that protein sequencing by measuring ionic current blockades may be possible.

Influence of the mechanical properties of resilient denture liners on the retention of overdenture attachments.

PubMed

Kubo, Keitaro; Koike, Takashi; Ueda, Takayuki; Sakurai, Kaoru

2018-03-15

Information is lacking about the selection criteria for silicone resilient denture liners applied as a matrix material for attachments on overdentures. The purpose of this in vitro study was to investigate the mechanical properties of silicone resilient denture liners and their influence on the initial retention force of overdenture attachments and the reduction in retention force over time. Nine types of silicone resilient denture liner were injected and fixed to the matrix section of an experimental denture base. They were then fitted to an epoxy resin model that simulated the residual ridge with a patrix ball attachment (n=10). The retention force of the denture was measured with a digital force gauge, and the maximum force of traction (N) was regarded as the initial retention force. The retention force reduction (N) after repeated insertion and removal (n=5) was calculated by subtracting the retention force after 3348 cycles (3-year simulated insertion and removal) from the initial retention force. The intaglio of the matrix was observed with a scanning electron microscope (SEM) before and after the 3348 cycles. Four mechanical properties (hardness, strain-in-compression, tensile strength, and arithmetic mean roughness) of the resilient denture liners were measured. One-way ANOVA of the initial retention force of each lining material was performed, followed by the Scheffe test (α=.05). Pearson correlation analysis was used (α=.05) to analyze correlations of the initial retention force with the retention force reduction after insertion and removal and the mechanical properties of each material. Multiple regression analysis with the stepwise method extracted the initial retention force and the retention force reduction as dependent variables, and the resilient denture liner mechanical properties as explanatory variables (α=.05). The initial retention force of the resilient denture liners was 1.3 to 5.4 N. Multiple comparisons showed significant differences in some groups (P<.05). The retention force reduction of the resilient denture liners was 0.2 to 1.9 N. Multiple regression analysis with the stepwise method extracted hardness and strain-in-compression as explanatory variables for the initial retention force and the retention force reduction. Within the limitations of this in vitro study, we found that hardness influenced the initial retention force of the overdenture, and that strain-in-compression influenced the retention force reduction in the 3-year simulation. Copyright © 2017 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.

Methods for presentation and display of multivariate data

NASA Technical Reports Server (NTRS)

Myers, R. H.

1981-01-01

Methods for the presentation and display of multivariate data are discussed with emphasis placed on the multivariate analysis of variance problems and the Hotelling T(2) solution in the two-sample case. The methods utilize the concepts of stepwise discrimination analysis and the computation of partial correlation coefficients.

Breakfast Frequency and Development of Metabolic Risk

PubMed Central

Odegaard, Andrew O.; Jacobs, David R.; Steffen, Lyn M.; Van Horn, Linda; Ludwig, David S.; Pereira, Mark A.

2013-01-01

OBJECTIVE The relation of breakfast intake frequency to metabolic health is not well studied. The aim of this study was to examine breakfast intake frequency with incidence of metabolic conditions. RESEARCH DESIGN AND METHODS We performed an analysis of 3,598 participants from the community-based Coronary Artery Risk Development in Young Adults (CARDIA) study who were free of diabetes in the year 7 examination when breakfast and dietary habits were assessed (1992–1993) and participated in at least one of the five subsequent follow-up examinations over 18 years. RESULTS Relative to those with infrequent breakfast consumption (0–3 days/week), participants who reported eating breakfast daily gained 1.9 kg less weight over 18 years (P = 0.001). In a Cox regression analysis, there was a stepwise decrease in risk across conditions in frequent breakfast consumers (4–6 days/week) and daily consumers. The results for incidence of abdominal obesity, obesity, metabolic syndrome, and hypertension remained significant after adjustment for baseline measures of adiposity (waist circumference or BMI) in daily breakfast consumers. Hazard ratios (HRs) and 95% CIs for daily breakfast consumption were as follows: abdominal obesity HR 0.78 (95% CI 0.66–0.91), obesity 0.80 (0.67–0.96), metabolic syndrome 0.82 (0.69–0.98), and hypertension 0.84 (0.72–0.99). For type 2 diabetes, the corresponding estimate was 0.81 (0.63–1.05), with a significant stepwise inverse association in black men and white men and women but no association in black women. There was no evidence of differential results for high versus low overall dietary quality. CONCLUSIONS Daily breakfast intake is strongly associated with reduced risk of a spectrum of metabolic conditions. PMID:23775814

Updated Value of Service Reliability Estimates for Electric Utility Customers in the United States

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

Sullivan, Michael; Schellenberg, Josh; Blundell, Marshall

2015-01-01

This report updates the 2009 meta-analysis that provides estimates of the value of service reliability for electricity customers in the United States (U.S.). The meta-dataset now includes 34 different datasets from surveys fielded by 10 different utility companies between 1989 and 2012. Because these studies used nearly identical interruption cost estimation or willingness-to-pay/accept methods, it was possible to integrate their results into a single meta-dataset describing the value of electric service reliability observed in all of them. Once the datasets from the various studies were combined, a two-part regression model was used to estimate customer damage functions that can bemore » generally applied to calculate customer interruption costs per event by season, time of day, day of week, and geographical regions within the U.S. for industrial, commercial, and residential customers. This report focuses on the backwards stepwise selection process that was used to develop the final revised model for all customer classes. Across customer classes, the revised customer interruption cost model has improved significantly because it incorporates more data and does not include the many extraneous variables that were in the original specification from the 2009 meta-analysis. The backwards stepwise selection process led to a more parsimonious model that only included key variables, while still achieving comparable out-of-sample predictive performance. In turn, users of interruption cost estimation tools such as the Interruption Cost Estimate (ICE) Calculator will have less customer characteristics information to provide and the associated inputs page will be far less cumbersome. The upcoming new version of the ICE Calculator is anticipated to be released in 2015.« less

What are we missing? Risk behaviors among Arab-American adolescents and emerging adults

PubMed Central

Munro-Kramer, Michelle L.; Fava, Nicole M.; Saftner, Melissa A.; Darling-Fisher, Cynthia S.; Tate, Nutrena H.; Stoddard, Sarah A.; Martyn, Kristy K.

2016-01-01

Background and Purpose Research on Arab-Americans as a distinct ethnic group is limited, especially when considering the health of Arab-American youth. This study describes health risk (substance use, violence); health promotive behaviors (hope, spirituality); and sexual activity (oral, vaginal, anal sex) of Arab-American adolescents and emerging adults (15-23 years old) within their life context, as well as the association between these behaviors. Methods A secondary analysis of data on a subset of Arab-American participants obtained from a randomized control trial were utilized to conduct mixed methods analyses. Qualitative analyses completed on the open-ended questions used the constant comparative method for a subsample (n=24) of participants. Descriptive quantitative analyses of survey data utilized bivariate analyses and stepwise logistic regression to explore the relation between risk behaviors and sexual activity among the full sample (n=57). Conclusions Qualitative analyses revealed two groups of participants: (a) multiple risk behaviors and negative life events, and (b) minimal risk behaviors and positive life events. Quantitative analyses indicated older youth, smokers, and those with higher hope pathways were more likely to report vaginal sex. Implications for Practice The unique cultural and social contexts of Arab-American youth provide a framework for recommendations for the prevention of risk behaviors. PMID:26990394

Multi-resolution analysis using integrated microscopic configuration with local patterns for benign-malignant mass classification

NASA Astrophysics Data System (ADS)

Rabidas, Rinku; Midya, Abhishek; Chakraborty, Jayasree; Sadhu, Anup; Arif, Wasim

2018-02-01

In this paper, Curvelet based local attributes, Curvelet-Local configuration pattern (C-LCP), is introduced for the characterization of mammographic masses as benign or malignant. Amid different anomalies such as micro- calcification, bilateral asymmetry, architectural distortion, and masses, the reason for targeting the mass lesions is due to their variation in shape, size, and margin which makes the diagnosis a challenging task. Being efficient in classification, multi-resolution property of the Curvelet transform is exploited and local information is extracted from the coefficients of each subband using Local configuration pattern (LCP). The microscopic measures in concatenation with the local textural information provide more discriminating capability than individual. The measures embody the magnitude information along with the pixel-wise relationships among the neighboring pixels. The performance analysis is conducted with 200 mammograms of the DDSM database containing 100 mass cases of each benign and malignant. The optimal set of features is acquired via stepwise logistic regression method and the classification is carried out with Fisher linear discriminant analysis. The best area under the receiver operating characteristic curve and accuracy of 0.95 and 87.55% are achieved with the proposed method, which is further compared with some of the state-of-the-art competing methods.

Optimization of a Solid-Phase Microextraction method for the Gas Chromatography-Mass Spectrometry analysis of blackberry (Rubus ulmifolius Schott) fruit volatiles.

PubMed

D'Agostino, M F; Sanz, J; Sanz, M L; Giuffrè, A M; Sicari, V; Soria, A C

2015-07-01

A Solid-Phase Microextraction method for the Gas Chromatography-Mass Spectrometry analysis of blackberry (Rubus sp.) volatiles has been fully optimized by means of a Box-Behnken experimental design. The optimized operating conditions (Carboxen/Polydimethylsiloxane fiber coating, 66°C, 20 min equilibrium time and 16 min extraction time) have been applied to the characterization for the first time of the volatile composition of Rubus ulmifolius Schott blackberries collected in Italy and Spain. A total of 74 volatiles of different functionality were identified; esters and aliphatic alcohols were the predominant classes in both sample types. Methylbutanal (2.02-25.70%), ethanol (9.84-68.21%), 2,3-butanedione (2.31-14.71%), trans-2-hexenal (0.49-17.49%), 3-hydroxy-2-butanone (0.08-7.39%), 1-hexanol (0.56-16.39%), 1-octanol (0.49-10.86%) and methylbutanoic acid (0.53-21.48%) were the major compounds in most blackberries analyzed. Stepwise multiple regression analysis of semiquantitative data showed that only two variables (ethyl decanoate and ethyl acetate) were necessary for a successful differentiation of blackberries according to their harvest location. Copyright © 2015 Elsevier Ltd. All rights reserved.

Structure-based predictions of 13C-NMR chemical shifts for a series of 2-functionalized 5-(methylsulfonyl)-1-phenyl-1H-indoles derivatives using GA-based MLR method

NASA Astrophysics Data System (ADS)

Ghavami, Raouf; Sadeghi, Faridoon; Rasouli, Zolikha; Djannati, Farhad

2012-12-01

Experimental values for the 13C NMR chemical shifts (ppm, TMS = 0) at 300 K ranging from 96.28 ppm (C4' of indole derivative 17) to 159.93 ppm (C4' of indole derivative 23) relative to deuteride chloroform (CDCl3, 77.0 ppm) or dimethylsulfoxide (DMSO, 39.50 ppm) as internal reference in CDCl3 or DMSO-d6 solutions have been collected from literature for thirty 2-functionalized 5-(methylsulfonyl)-1-phenyl-1H-indole derivatives containing different substituted groups. An effective quantitative structure-property relationship (QSPR) models were built using hybrid method combining genetic algorithm (GA) based on stepwise selection multiple linear regression (SWS-MLR) as feature-selection tools and correlation models between each carbon atom of indole derivative and calculated descriptors. Each compound was depicted by molecular structural descriptors that encode constitutional, topological, geometrical, electrostatic, and quantum chemical features. The accuracy of all developed models were confirmed using different types of internal and external procedures and various statistical tests. Furthermore, the domain of applicability for each model which indicates the area of reliable predictions was defined.

Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation

PubMed Central

2018-01-01

Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site. PMID:29370230

Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation.

PubMed

Illias, Hazlee Azil; Zhao Liang, Wee

2018-01-01

Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site.

Variable selection for zero-inflated and overdispersed data with application to health care demand in Germany.

PubMed

Wang, Zhu; Ma, Shuangge; Wang, Ching-Yun

2015-09-01

In health services and outcome research, count outcomes are frequently encountered and often have a large proportion of zeros. The zero-inflated negative binomial (ZINB) regression model has important applications for this type of data. With many possible candidate risk factors, this paper proposes new variable selection methods for the ZINB model. We consider maximum likelihood function plus a penalty including the least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD), and minimax concave penalty (MCP). An EM (expectation-maximization) algorithm is proposed for estimating the model parameters and conducting variable selection simultaneously. This algorithm consists of estimating penalized weighted negative binomial models and penalized logistic models via the coordinated descent algorithm. Furthermore, statistical properties including the standard error formulae are provided. A simulation study shows that the new algorithm not only has more accurate or at least comparable estimation, but also is more robust than the traditional stepwise variable selection. The proposed methods are applied to analyze the health care demand in Germany using the open-source R package mpath. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Fishing for drifts: detecting buoyancy changes of a top marine predator using a step-wise filtering method

PubMed Central

Gordine, Samantha Alex; Fedak, Michael; Boehme, Lars

2015-01-01

ABSTRACT In southern elephant seals (Mirounga leonina), fasting- and foraging-related fluctuations in body composition are reflected by buoyancy changes. Such buoyancy changes can be monitored by measuring changes in the rate at which a seal drifts passively through the water column, i.e. when all active swimming motion ceases. Here, we present an improved knowledge-based method for detecting buoyancy changes from compressed and abstracted dive profiles received through telemetry. By step-wise filtering of the dive data, the developed algorithm identifies fragments of dives that correspond to times when animals drift. In the dive records of 11 southern elephant seals from South Georgia, this filtering method identified 0.8–2.2% of all dives as drift dives, indicating large individual variation in drift diving behaviour. The obtained drift rate time series exhibit that, at the beginning of each migration, all individuals were strongly negatively buoyant. Over the following 75–150 days, the buoyancy of all individuals peaked close to or at neutral buoyancy, indicative of a seal's foraging success. Independent verification with visually inspected detailed high-resolution dive data confirmed that this method is capable of reliably detecting buoyancy changes in the dive records of drift diving species using abstracted data. This also affirms that abstracted dive profiles convey the geometric shape of drift dives in sufficient detail for them to be identified. Further, it suggests that, using this step-wise filtering method, buoyancy changes could be detected even in old datasets with compressed dive information, for which conventional drift dive classification previously failed. PMID:26486362

Impact of the Japanese 5S management method on patients’ and caretakers’ satisfaction: a quasi-experimental study in Senegal

PubMed Central

Kanamori, Shogo; Castro, Marcia C.; Sow, Seydou; Matsuno, Rui; Cissokho, Alioune; Jimba, Masamine

2016-01-01

Background The 5S method is a lean management tool for workplace organization, with 5S being an abbreviation for five Japanese words that translate to English as Sort, Set in Order, Shine, Standardize, and Sustain. In Senegal, the 5S intervention program was implemented in 10 health centers in two regions between 2011 and 2014. Objective To identify the impact of the 5S intervention program on the satisfaction of clients (patients and caretakers) who visited the health centers. Design A standardized 5S intervention protocol was implemented in the health centers using a quasi-experimental separate pre-post samples design (four intervention and three control health facilities). A questionnaire with 10 five-point Likert items was used to measure client satisfaction. Linear regression analysis was conducted to identify the intervention's effect on the client satisfaction scores, represented by an equally weighted average of the 10 Likert items (Cronbach's alpha=0.83). Additional regression analyses were conducted to identify the intervention's effect on the scores of each Likert item. Results Backward stepwise linear regression (n=1,928) indicated a statistically significant effect of the 5S intervention, represented by an increase of 0.19 points in the client satisfaction scores in the intervention group, 6 to 8 months after the intervention (p=0.014). Additional regression analyses showed significant score increases of 0.44 (p=0.002), 0.14 (p=0.002), 0.06 (p=0.019), and 0.17 (p=0.044) points on four items, which, respectively were healthcare staff members’ communication, explanations about illnesses or cases, and consultation duration, and clients’ overall satisfaction. Conclusions The 5S has the potential to improve client satisfaction at resource-poor health facilities and could therefore be recommended as a strategic option for improving the quality of healthcare service in low- and middle-income countries. To explore more effective intervention modalities, further studies need to address the mechanisms by which 5S leads to attitude changes in healthcare staff. PMID:27900932

FEMALE SEX AND DISCONTINUATION OF ISONIAZID DUE TO ADVERSE EFFECTS DURING THE TREATMENT OF LATENT TUBERCULOSIS

PubMed Central

Pettit, April C.; Bethel, James; Hirsch-Moverman, Yael; Colson, Paul W.; Sterling, Timothy R.

2013-01-01

SUMMARY Objectives To determine the rate of and risk factors for discontinuation of isoniazid due to adverse effects during the treatment of latent tuberculosis infection in a large, multi-site study. Methods The Tuberculosis Epidemiologic Studies Consortium (TBESC) conducted a prospective study from March 2007–September 2008 among adults initiating isoniazid for treatment of LTBI at 12 sites in the US and Canada. The relative risk for isoniazid discontinuation due to adverse effects was determined using negative binomial regression. Adjusted models were constructed using forward stepwise regression. Results Of 1,306 persons initiating isoniazid, 617 (47.2%, 95% CI 44.5–50.0%) completed treatment and 196 (15.0%, 95% CI 13.1–17.1%) discontinued due to adverse effects. In multivariable analysis, female sex (RR 1.67, 95% CI 1.32–2.10, p<0.001) and current alcohol use (RR 1.41, 95% CI 1.13–1.77, p=0.003) were independently associated with isoniazid discontinuation due to adverse effects. Conclusions The rate of discontinuation of isoniazid due to adverse effects was substantially higher than reported earlier. Women were at increased risk of discontinuing isoniazid due to adverse effects; close monitoring of women for adverse effects may be warranted. Current alcohol use was also associated with isoniazid discontinuation; counseling patients to abstain from alcohol could decrease discontinuation due to adverse effects. PMID:23845828

Application of remote sensing for fishery resources assessment and monitoring. [Gulf of Mexico

NASA Technical Reports Server (NTRS)

Savastano, K. J. (Principal Investigator)

1975-01-01

The author has identified the following significant results. The distribution and abundance of white marlin correlated with the chlorophyll, water temperature, and Secchi depth sea truth measurements. Results of correlation analyses for dolphin were inconclusive. Predicition models for white marlin were developed using stepwise multiple regression and discriminant function analysis techniques which demonstrated a potential for increasing the probability of game fishing success. The S190A and B imagery was density sliced/color enhanced with white marlin location superimposed on the image, but no density/white marlin relationship could be established.

Prediction of health levels by remote sensing

NASA Technical Reports Server (NTRS)

Rush, M.; Vernon, S.

1975-01-01

Measures of the environment derived from remote sensing were compared to census population/housing measures in their ability to discriminate among health status areas in two urban communities. Three hypotheses were developed to explore the relationships between environmental and health data. Univariate and multiple step-wise linear regression analyses were performed on data from two sample areas in Houston and Galveston, Texas. Environmental data gathered by remote sensing were found to equal or surpass census data in predicting rates of health outcomes. Remote sensing offers the advantages of data collection for any chosen area or time interval, flexibilities not allowed by the decennial census.

Relationships between locus of control and paranormal beliefs.

PubMed

Newby, Robert W; Davis, Jessica Boyette

2004-06-01

The present study investigated the associations between scores on paranormal beliefs, locus of control, and certain psychological processes such as affect and cognitions as measured by the Linguistic Inquiry and Word Count. Analysis yielded significant correlations between scores on Locus of Control and two subscales of Tobacyk's (1988) Revised Paranormal Beliefs Scale, New Age Philosophy and Traditional Paranormal Beliefs. A step-wise multiple regression analysis indicated that Locus of Control was significantly related to New Age Philosophy. Other correlations were found between Tobacyk's subscales, Locus of Control, and three processes measured by the Linguistic Inquiry and Word Count.

Lateral stability analysis for X-29A drop model using system identification methodology

NASA Technical Reports Server (NTRS)

Raney, David L.; Batterson, James G.

1989-01-01

A 22-percent dynamically scaled replica of the X-29A forward-swept-wing airplane has been flown in radio-controlled drop tests at the NASA Langley Research Center. A system identification study of the recorded data was undertaken to examine the stability and control derivatives that influence the lateral behavior of this vehicle with particular emphasis on an observed wing rock phenomenon. All major lateral stability derivatives and the damping-in-roll derivative were identified for angles of attack from 5 to 80 degrees by using a data-partitioning methodology and a modified stepwise regression algorithm.

Neurocognition and community outcome in schizophrenia: long-term predictive validity.

PubMed

Fujii, Daryl E; Wylie, A Michael

2003-02-01

The present study examined the predictive validity of neuropsychological measures to functional outcome in 26 schizophrenic patients 15-plus year post-testing. Outcome measures included score on the Resource Associated Functional Level Scale (RAFLS), number of state hospital admissions, and total duration of state hospital inpatient stay. Results of several stepwise multiple regressions revealed that verbal memory significantly predicted RAFLS score, accounting for nearly half of the variance. Trails B significantly predicted duration of state hospital inpatient status. Discussion focused on the utility of these measures for clinicians and system planners. Copyright 2002 Elsevier Science B.V.

Estimation of stature from the foot and its segments in a sub-adult female population of North India.

PubMed

Krishan, Kewal; Kanchan, Tanuj; Passi, Neelam

2011-11-21

Establishing personal identity is one of the main concerns in forensic investigations. Estimation of stature forms a basic domain of the investigation process in unknown and co-mingled human remains in forensic anthropology case work. The objective of the present study was to set up standards for estimation of stature from the foot and its segments in a sub-adult female population. The sample for the study constituted 149 young females from the Northern part of India. The participants were aged between 13 and 18 years. Besides stature, seven anthropometric measurements that included length of the foot from each toe (T1, T2, T3, T4, and T5 respectively), foot breadth at ball (BBAL) and foot breadth at heel (BHEL) were measured on both feet in each participant using standard methods and techniques. The results indicated that statistically significant differences (p < 0.05) between left and right feet occur in both the foot breadth measurements (BBAL and BHEL). Foot length measurements (T1 to T5 lengths) did not show any statistically significant bilateral asymmetry. The correlation between stature and all the foot measurements was found to be positive and statistically significant (p-value < 0.001). Linear regression models and multiple regression models were derived for estimation of stature from the measurements of the foot. The present study indicates that anthropometric measurements of foot and its segments are valuable in the estimation of stature. Foot length measurements estimate stature with greater accuracy when compared to foot breadth measurements. The present study concluded that foot measurements have a strong relationship with stature in the sub-adult female population of North India. Hence, the stature of an individual can be successfully estimated from the foot and its segments using different regression models derived in the study. The regression models derived in the study may be applied successfully for the estimation of stature in sub-adult females, whenever foot remains are brought for forensic examination. Stepwise multiple regression models tend to estimate stature more accurately than linear regression models in female sub-adults.

Stepwise Bay Annulation of Indigo for the Synthesis of Desymmetrized Electron Acceptors and Donor–Acceptor Constructs

DOE PAGES

Kolaczkowski, Matthew A.; He, Bo; Liu, Yi

2016-10-10

In this work, a selective stepwise annulation of indigo has been demonstrated as a means of providing both monoannulated and differentially double-annulated indigo derivatives. Disparate substitution of the electron accepting bay-annulated indigo system allows for fine control over both the electronic properties as well as donor-acceptor structural architectures. Optical and electronic properties were characterized computationally as well as through UV-vis absorption spectroscopy and cyclic voltammetry. Finally, this straightforward method provides a modular approach for the design of indigo-based materials with tailored optoelectronic properties.

Non-Destructive Evaluation of the Leaf Nitrogen Concentration by In-Field Visible/Near-Infrared Spectroscopy in Pear Orchards.

PubMed

Wang, Jie; Shen, Changwei; Liu, Na; Jin, Xin; Fan, Xueshan; Dong, Caixia; Xu, Yangchun

2017-03-08

Non-destructive and timely determination of leaf nitrogen (N) concentration is urgently needed for N management in pear orchards. A two-year field experiment was conducted in a commercial pear orchard with five N application rates: 0 (N0), 165 (N1), 330 (N2), 660 (N3), and 990 (N4) kg·N·ha -1 . The mid-portion leaves on the year's shoot were selected for the spectral measurement first and then N concentration determination in the laboratory at 50 and 80 days after full bloom (DAB). Three methods of in-field spectral measurement (25° bare fibre under solar conditions, black background attached to plant probe, and white background attached to plant probe) were compared. We also investigated the modelling performances of four chemometric techniques (principal components regression, PCR; partial least squares regression, PLSR; stepwise multiple linear regression, SMLR; and back propagation neural network, BPNN) and three vegetation indices (difference spectral index, normalized difference spectral index, and ratio spectral index). Due to the low correlation of reflectance obtained by the 25° field of view method, all of the modelling was performed on two spectral datasets-both acquired by a plant probe. Results showed that the best modelling and prediction accuracy were found in the model established by PLSR and spectra measured with a black background. The randomly-separated subsets of calibration ( n = 1000) and validation ( n = 420) of this model resulted in high R² values of 0.86 and 0.85, respectively, as well as a low mean relative error (<6%). Furthermore, a higher coefficient of determination between the leaf N concentration and fruit yield was found at 50 DAB samplings in both 2015 (R² = 0.77) and 2014 (R² = 0.59). Thus, the leaf N concentration was suggested to be determined at 50 DAB by visible/near-infrared spectroscopy and the threshold should be 24-27 g/kg.

Chelant-aided enhancement of lead mobilization in residential soils.

PubMed

Sarkar, Dibyendu; Andra, Syam S; Saminathan, Sumathi K M; Datta, Rupali

2008-12-01

Chelation of metals is an important factor in enhancing solubility and hence, availability to plants to promote phytoremediation. We compared the effects of two chelants, namely, ethylenediaminetetraacetic acid (EDTA) and ethylenediaminedisuccinic acid (EDDS) in enhancing mobilized lead (Pb) in Pb-based paint contaminated residential soils collected from San Antonio, Texas and Baltimore, Maryland. Batch incubation studies were performed to investigate the effectiveness of the two chelants in enhancing mobilized Pb, at various concentrations and treatment durations. Over a period of 1 month, the mobilized Pb pool in the San Antonio study soils increased from 52 mg kg(-1) to 287 and 114 mg kg(-1) in the presence of 15 mM kg(-1) EDTA and EDDS, respectively. Stepwise linear regression analysis demonstrated that pH and organic matter content significantly affected the mobilized Pb fraction. The regression models explained a large percentage, from 83 to 99%, of the total variation in mobilized Pb concentrations.

MMI: Multimodel inference or models with management implications?

USGS Publications Warehouse

Fieberg, J.; Johnson, Douglas H.

2015-01-01

We consider a variety of regression modeling strategies for analyzing observational data associated with typical wildlife studies, including all subsets and stepwise regression, a single full model, and Akaike's Information Criterion (AIC)-based multimodel inference. Although there are advantages and disadvantages to each approach, we suggest that there is no unique best way to analyze data. Further, we argue that, although multimodel inference can be useful in natural resource management, the importance of considering causality and accurately estimating effect sizes is greater than simply considering a variety of models. Determining causation is far more valuable than simply indicating how the response variable and explanatory variables covaried within a data set, especially when the data set did not arise from a controlled experiment. Understanding the causal mechanism will provide much better predictions beyond the range of data observed. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

Modeling the language learning strategies and English language proficiency of pre-university students in UMS: A case study

NASA Astrophysics Data System (ADS)

Kiram, J. J.; Sulaiman, J.; Swanto, S.; Din, W. A.

2015-10-01

This study aims to construct a mathematical model of the relationship between a student's Language Learning Strategy usage and English Language proficiency. Fifty-six pre-university students of University Malaysia Sabah participated in this study. A self-report questionnaire called the Strategy Inventory for Language Learning was administered to them to measure their language learning strategy preferences before they sat for the Malaysian University English Test (MUET), the results of which were utilised to measure their English language proficiency. We attempted the model assessment specific to Multiple Linear Regression Analysis subject to variable selection using Stepwise regression. We conducted various assessments to the model obtained, including the Global F-test, Root Mean Square Error and R-squared. The model obtained suggests that not all language learning strategies should be included in the model in an attempt to predict Language Proficiency.

A stepwise protocol for the treatment of refractory gastroesophageal reflux-induced chronic cough

PubMed Central

Xu, Xianghuai; Lv, Hanjing; Yu, Li; Chen, Qiang; Liang, Siwei

2016-01-01

Background Refractory gastroesophageal reflux-induced chronic cough (GERC) is difficult to manage. The purpose of the study is to evaluate the efficacy of a novel stepwise protocol for treating this condition. Methods A total of 103 consecutive patients with suspected refractory reflux-induced chronic cough failing to a standard anti-reflux therapy were treated with a stepwise therapy. Treatment commences with high-dose omeprazole and, if necessary, is escalated to subsequent sequential treatment with ranitidine and finally baclofen. The primary end-point was overall cough resolution, and the secondary end-point was cough resolution after each treatment step. Results High-dose omeprazole eliminated or improved cough in 28.1% of patients (n=29). Further stepwise of treatment with the addition of ranitide yielded a favorable response in an additional 12.6% (n=13) of patients, and subsequent escalation to baclofen provoked response in another 36.9% (n=38) of patients. Overall, this stepwise protocol was successful in 77.6% (n=80) of patients. The diurnal cough symptom score fell from 3 [1] to 1 [0] (Z=6.316, P=0.000), and the nocturnal cough symptom score decreased from 1 [1] to 0 [1] (Z=–4.511, P=0.000), with a corresponding reduction in the Gastroesophageal Reflux Diagnostic Questionnaire score from 8.6±1.7 to 6.8±0.7 (t=3.612, P=0.000). Conversely, the cough threshold C2 to capsaicin was increased from 0.49 (0.49) µmol/L to 1.95 (2.92) µmol/L (Z=–5.892, P=0.000), and the cough threshold C5 was increased from 1.95 (2.92) µmol/L to 7.8 (5.85) µmol/L (Z=–5.171, P=0.000). Conclusions Sequential stepwise anti-reflux therapy is a useful therapeutic strategy for refractory reflux-induced chronic cough. PMID:26904227

Climate Prediction for Brazil's Nordeste: Performance of Empirical and Numerical Modeling Methods.

NASA Astrophysics Data System (ADS)

Moura, Antonio Divino; Hastenrath, Stefan

2004-07-01

Comparisons of performance of climate forecast methods require consistency in the predictand and a long common reference period. For Brazil's Nordeste, empirical methods developed at the University of Wisconsin use preseason (October January) rainfall and January indices of the fields of meridional wind component and sea surface temperature (SST) in the tropical Atlantic and the equatorial Pacific as input to stepwise multiple regression and neural networking. These are used to predict the March June rainfall at a network of 27 stations. An experiment at the International Research Institute for Climate Prediction, Columbia University, with a numerical model (ECHAM4.5) used global SST information through February to predict the March June rainfall at three grid points in the Nordeste. The predictands for the empirical and numerical model forecasts are correlated at +0.96, and the period common to the independent portion of record of the empirical prediction and the numerical modeling is 1968 99. Over this period, predicted versus observed rainfall are evaluated in terms of correlation, root-mean-square error, absolute error, and bias. Performance is high for both approaches. Numerical modeling produces a correlation of +0.68, moderate errors, and strong negative bias. For the empirical methods, errors and bias are small, and correlations of +0.73 and +0.82 are reached between predicted and observed rainfall.

Shuffling cross-validation-bee algorithm as a new descriptor selection method for retention studies of pesticides in biopartitioning micellar chromatography.

PubMed

Zarei, Kobra; Atabati, Morteza; Ahmadi, Monire

2017-05-04

Bee algorithm (BA) is an optimization algorithm inspired by the natural foraging behaviour of honey bees to find the optimal solution which can be proposed to feature selection. In this paper, shuffling cross-validation-BA (CV-BA) was applied to select the best descriptors that could describe the retention factor (log k) in the biopartitioning micellar chromatography (BMC) of 79 heterogeneous pesticides. Six descriptors were obtained using BA and then the selected descriptors were applied for model development using multiple linear regression (MLR). The descriptor selection was also performed using stepwise, genetic algorithm and simulated annealing methods and MLR was applied to model development and then the results were compared with those obtained from shuffling CV-BA. The results showed that shuffling CV-BA can be applied as a powerful descriptor selection method. Support vector machine (SVM) was also applied for model development using six selected descriptors by BA. The obtained statistical results using SVM were better than those obtained using MLR, as the root mean square error (RMSE) and correlation coefficient (R) for whole data set (training and test), using shuffling CV-BA-MLR, were obtained as 0.1863 and 0.9426, respectively, while these amounts for the shuffling CV-BA-SVM method were obtained as 0.0704 and 0.9922, respectively.

Trends in evaporation of a large subtropical lake

NASA Astrophysics Data System (ADS)

Hu, Cheng; Wang, Yongwei; Wang, Wei; Liu, Shoudong; Piao, Meihua; Xiao, Wei; Lee, Xuhui

2017-07-01

How rising temperature and changing solar radiation affect evaporation of natural water bodies remains poor understood. In this study, evaporation from Lake Taihu, a large (area 2400 km2) freshwater lake in the Yangtze River Delta, China, was simulated by the CLM4-LISSS offline lake model and estimated with pan evaporation data. Both methods were calibrated against lake evaporation measured directly with eddy covariance in 2012. Results show a significant increasing trend of annual lake evaporation from 1979 to 2013, at a rate of 29.6 mm decade-1 according to the lake model and 25.4 mm decade-1 according to the pan method. The mean annual evaporation during this period shows good agreement between these two methods (977 mm according to the model and 1007 mm according to the pan method). A stepwise linear regression reveals that downward shortwave radiation was the most significant contributor to the modeled evaporation trend, while air temperature was the most significant contributor to the pan evaporation trend. Wind speed had little impact on the modeled lake evaporation but had a negative contribution to the pan evaporation trend offsetting some of the temperature effect. Reference evaporation was not a good proxy for the lake evaporation because it was on average 20.6 % too high and its increasing trend was too large (56.5 mm decade-1).

Psychological factors influence the gastroesophageal reflux disease (GERD) and their effect on quality of life among firefighters in South Korea.

PubMed

Jang, Seung-Ho; Ryu, Han-Seung; Choi, Suck-Chei; Lee, Sang-Yeol

2016-10-01

The purpose of this study was to examine psychosocial factors related to gastroesophageal reflux disease (GERD) and their effects on quality of life (QOL) in firefighters. Data were collected from 1217 firefighters in a Korean province. We measured psychological symptoms using the scale. In order to observe the influence of the high-risk group on occupational stress, we conduct logistic multiple linear regression. The correlation between psychological factors and QOL was also analyzed and performed a hierarchical regression analysis. GERD was observed in 32.2% of subjects. Subjects with GERD showed higher depressive symptom, anxiety and occupational stress scores, and lower self-esteem and QOL scores relative to those observed in GERD - negative subject. GERD risk was higher for the following occupational stress subcategories: job demand, lack of reward, interpersonal conflict, and occupational climate. The stepwise regression analysis showed that depressive symptoms, occupational stress, self-esteem, and anxiety were the best predictors of QOL. The results suggest that psychological and medical approaches should be combined in GERD assessment.

Impact of “Sick” and “Recovery” Roles on Brain Injury Rehabilitation Outcomes

PubMed Central

Barclay, David A.

2012-01-01

This study utilizes a multivariate, correlational, expost facto research design to examine Parsons' “sick role” as a dynamic, time-sensitive process of “sick role” and “recovery role” and the impact of this process on goal attainment (H1) and psychosocial distress (H2) of adult survivors of acquired brain injury. Measures used include the Brief Symptom Inventory-18, a Goal Attainment Scale, and an original instrument to measure sick role process. 60 survivors of ABI enrolled in community reentry rehabilitation participated. Stepwise regression analyses did not fully support the multivariate hypotheses. Two models emerged from the stepwise analyses. Goal attainment, gender, and postrehab responsibilities accounted for 40% of the shared variance of psychosocial distress. Anxiety and depression accounted for 22% of the shared variance of goal attainment with anxiety contributing to the majority of the explained variance. Bivariate analysis found sick role variables, anxiety, somatization, depression, gender, and goal attainment as significant. The study has implications for ABI rehabilitation in placing greater emphasis on sick role processes, anxiety, gender, and goal attainment in guiding program planning and future research with survivors of ABI. PMID:23119164

Suicidal Ideation and Schizophrenia: Contribution of Appraisal, Stigmatization, and Cognition.

PubMed

Stip, Emmanuel; Caron, Jean; Tousignant, Michel; Lecomte, Yves

2017-10-01

To predict suicidal ideation in people with schizophrenia, certain studies have measured its relationship with the variables of defeat and entrapment. The relationships are positive, but their interactions remain undefined. To further their understanding, this research sought to measure the relationship between suicidal ideation with the variables of loss, entrapment, and humiliation. The convenience sample included 30 patients with schizophrenia spectrum disorders. The study was prospective (3 measurement times) during a 6-month period. Results were analyzed by stepwise multiple regression. The contribution of the 3 variables to the variance of suicidal ideation was not significant at any of the 3 times (T1: 16.2%, P = 0.056; T2: 19.9%, P = 0.117; T3: 11.2%, P = 0.109). Further analyses measured the relationship between the variables of stigmatization, perceived cognitive dysfunction, symptoms, depression, self-esteem, reason to live, spirituality, social provision, and suicidal ideation. Stepwise multiple regression demonstrated that the contribution of the variables of stigmatization and perceived cognitive dysfunction to the variance of suicidal ideation was significant at all 3 times (T1: 41.7.5%, P = 0.000; T2: 35.2%, P = 0.001; T3: 21.5%, P = 0.012). Yet, over time, the individual contribution of the variables changed: T1, stigmatization (β = 0.518; P = 0.002); T2, stigmatization (β = 0.394; P = 0.025) and perceived cognitive dysfunction (β = 0.349; P = 0.046). Then, at T3, only perceived cognitive dysfunction contributed significantly to suicidal ideation (β = 0.438; P = 0.016). The results highlight the importance of the contribution of the variables of perceived cognitive dysfunction and stigmatization in the onset of suicidal ideation in people with schizophrenia spectrum disorders.

Baastrup's Disease Is Associated with Recurrent of Sciatica after Posterior Lumbar Spinal Decompressions Utilizing Floating Spinous Process Procedures.

PubMed

Koda, Masao; Mannoji, Chikato; Murakami, Masazumi; Kinoshita, Tomoaki; Hirayama, Jiro; Miyashita, Tomohiro; Eguchi, Yawara; Yamazaki, Masashi; Suzuki, Takane; Aramomi, Masaaki; Ota, Mitsutoshi; Maki, Satoshi; Takahashi, Kazuhisa; Furuya, Takeo

2016-12-01

Retrospective case-control study. To determine whether kissing spine is a risk factor for recurrence of sciatica after lumbar posterior decompression using a spinous process floating approach. Kissing spine is defined by apposition and sclerotic change of the facing spinous processes as shown in X-ray images, and is often accompanied by marked disc degeneration and decrement of disc height. If kissing spine significantly contributes to weight bearing and the stability of the lumbar spine, trauma to the spinous process might induce a breakdown of lumbar spine stability after posterior decompression surgery in cases of kissing spine. The present study included 161 patients who had undergone posterior decompression surgery for lumbar canal stenosis using a spinous process floating approaches. We defined recurrence of sciatica as that resolved after initial surgery and then recurred. Kissing spine was defined as sclerotic change and the apposition of the spinous process in a plain radiogram. Preoperative foraminal stenosis was determined by the decrease of perineural fat intensity detected by parasagittal T1-weighted magnetic resonance imaging. Preoperative percentage slip, segmental range of motion, and segmental scoliosis were analyzed in preoperative radiographs. Univariate analysis followed by stepwise logistic regression analysis determined factors independently associated with recurrence of sciatica. Stepwise logistic regression revealed kissing spine ( p =0.024; odds ratio, 3.80) and foraminal stenosis ( p <0.01; odds ratio, 17.89) as independent risk factors for the recurrence of sciatica after posterior lumbar spinal decompression with spinous process floating procedures for lumbar spinal canal stenosis. When a patient shows kissing spine and concomitant subclinical foraminal stenosis at the affected level, we should sufficiently discuss the selection of an appropriate surgical procedure.

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

PubMed

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

2018-05-26

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

Emergency department blood transfusion: the first two units are free.

PubMed

Ley, Eric J; Liou, Douglas Z; Singer, Matthew B; Mirocha, James; Melo, Nicolas; Chung, Rex; Bukur, Marko; Salim, Ali

2013-09-01

Studies on blood product transfusions after trauma recommend targeting specific ratios to reduce mortality. Although crystalloid volumes as little as 1.5 L predict increased mortality after trauma, little data is available regarding the threshold of red blood cell (RBC) transfusion volume that predicts increased mortality. Data from a level I trauma center between January 2000 and December 2008 were reviewed. Trauma patients who received at least 100 mL RBC in the emergency department (ED) were included. Each unit of RBC was defined as 300 mL. Demographics, RBC transfusion volume, and mortality were analyzed in the nonelderly (<70 y) and elderly (≥70 y). Multivariate logistic regression was performed at various volume cutoffs to determine whether there was a threshold transfusion volume that independently predicted mortality. A total of 560 patients received ≥100 mL RBC in the ED. Overall mortality was 24.3%, with 22.5% (104 deaths) in the nonelderly and 32.7% (32 deaths) in the elderly. Multivariate logistic regression demonstrated that RBC transfusion of ≥900 mL was associated with increased mortality in both the nonelderly (adjusted odds ratio 2.06, P = 0.008) and elderly (adjusted odds ratio 5.08, P = 0.006). Although transfusion of greater than 2 units in the ED was an independent predictor of mortality, transfusion of 2 units or less was not. Interestingly, unlike crystalloid volume, stepwise increases in blood volume were not associated with stepwise increases in mortality. The underlying etiology for mortality discrepancies, such as transfusion ratios, hypothermia, or immunosuppression, needs to be better delineated. Copyright © 2013 Elsevier Inc. All rights reserved.

Determinants of brain-derived neurotrophic factor (BDNF) in umbilical cord and maternal serum.

PubMed

Flöck, A; Weber, S K; Ferrari, N; Fietz, C; Graf, C; Fimmers, R; Gembruch, U; Merz, W M

2016-01-01

Brain-derived neurotrophic factor (BDNF) plays a fundamental role in brain development; additionally, it is involved in various aspects of cerebral function, including neurodegenerative and psychiatric diseases. Involvement of BDNF in parturition has not been investigated. The aim of our study was to analyze determinants of umbilical cord BDNF (UC-BDNF) concentrations of healthy, term newborns and their respective mothers. This cross-sectional prospective study was performed at a tertiary referral center. Maternal venous blood samples were taken on admission to labor ward; newborn venous blood samples were drawn from the umbilical cord (UC), before delivery of the placenta. Analysis was performed with a commercially available immunoassay. Univariate analyses and stepwise multivariate regression models were applied. 120 patients were recruited. UC-BDNF levels were lower than maternal serum concentrations (median 641 ng/mL, IQR 506 vs. median 780 ng/mL, IQR 602). Correlation between UC- and maternal BDNF was low (R=0.251, p=0.01). In univariate analysis, mode of delivery (MoD), gestational age (GA), body mass index at delivery, and gestational diabetes were determinants of UC-BDNF (MoD and smoking for maternal BDNF, respectively). Stepwise multivariate regression analysis revealed a model with MoD and GA as determinants for UC-BDNF (MoD for maternal BDNF). MoD and GA at delivery are determinants of circulating BDNF in the mother and newborn. We hypothesize that BDNF, like other neuroendocrine factors, is involved in the neuroendocrine cascade of delivery. Timing and mode of delivery may exert BDNF-induced effects on the cerebral function of newborns and their mothers. Copyright © 2015 Elsevier Ltd. All rights reserved.

A comparison of logistic regression analysis and an artificial neural network using the BI-RADS lexicon for ultrasonography in conjunction with introbserver variability.

PubMed

Kim, Sun Mi; Han, Heon; Park, Jeong Mi; Choi, Yoon Jung; Yoon, Hoi Soo; Sohn, Jung Hee; Baek, Moon Hee; Kim, Yoon Nam; Chae, Young Moon; June, Jeon Jong; Lee, Jiwon; Jeon, Yong Hwan

2012-10-01

To determine which Breast Imaging Reporting and Data System (BI-RADS) descriptors for ultrasound are predictors for breast cancer using logistic regression (LR) analysis in conjunction with interobserver variability between breast radiologists, and to compare the performance of artificial neural network (ANN) and LR models in differentiation of benign and malignant breast masses. Five breast radiologists retrospectively reviewed 140 breast masses and described each lesion using BI-RADS lexicon and categorized final assessments. Interobserver agreements between the observers were measured by kappa statistics. The radiologists' responses for BI-RADS were pooled. The data were divided randomly into train (n = 70) and test sets (n = 70). Using train set, optimal independent variables were determined by using LR analysis with forward stepwise selection. The LR and ANN models were constructed with the optimal independent variables and the biopsy results as dependent variable. Performances of the models and radiologists were evaluated on the test set using receiver-operating characteristic (ROC) analysis. Among BI-RADS descriptors, margin and boundary were determined as the predictors according to stepwise LR showing moderate interobserver agreement. Area under the ROC curves (AUC) for both of LR and ANN were 0.87 (95% CI, 0.77-0.94). AUCs for the five radiologists ranged 0.79-0.91. There was no significant difference in AUC values among the LR, ANN, and radiologists (p > 0.05). Margin and boundary were found as statistically significant predictors with good interobserver agreement. Use of the LR and ANN showed similar performance to that of the radiologists for differentiation of benign and malignant breast masses.

Factors associated with enrollment of African Americans into a clinical trial: results from the African American study of kidney disease and hypertension.

PubMed

Gadegbeku, Crystal A; Stillman, Phyllis Kreger; Huffman, Mark D; Jackson, James S; Kusek, John W; Jamerson, Kenneth A

2008-11-01

Recruitment of diverse populations into clinical trials remains challenging but is needed to fully understand disease processes and benefit the general public. Greater knowledge of key factors among ethnic and racial minority populations associated with the decision to participate in clinical research studies may facilitate recruitment and enhance the generalizibility of study results. Therefore, during the recruitment phase of the African American Study of Kidney Disease and Hypertension (AASK) trial, we conducted a telephone survey, using validated questions, to explore potential facilitators and barriers of research participation among eligible candidates residing in seven U.S. locations. Survey responses included a range of characteristics and perceptions among participants and non-participants and were compared using bivariate and step-wise logistic regression analyses. One-hundred forty-one respondents in the one-hundred forty (70 trial participants and 71 non-participants) completed the survey. Trial participants and non-participants were similar in multiple demographic characteristics and shared similar views on discrimination, physician mistrust, and research integrity. Key group differences were related to their perceptions of the impact of their research participation. Participants associated enrollment with personal and societal health benefits, while non-participants were influenced by the health risks. In a step-wise linear regression analysis, the most powerful significant positive predictors of participation were acknowledgement of health status as important in the enrollment decision (OR=4.54, p=0.006), employment (OR=3.12, p = 0.05) and healthcare satisfaction (OR=2.12, p<0.01). Racially-based mistrust did not emerge as a negative predictor and subjects' decisions were not influenced by the race of the research staff. In conclusion, these results suggest that health-related factors, and not psychosocial perceptions, have predominant influence on research participation among African Americans.

[Recommendations for the Stepwise Occupational Reintegration: Can the Characteristic of the Patients Explain the Differences Between the Rehabilitation Centers?].

PubMed

Schmid, L; Jankowiak, S; Kaluscha, R; Krischak, G

2016-06-01

The first step to initiate a stepwise occupational reintegration (SOR) is the recommendation of the rehabilitation centers. Therefore rehabilitation centers have a significant impact on the use of SOR. There is evidence that the recommendation rate between the rehabilitation centers differs clearly. The present survey therefore analyses in detail the differences of the recommendation rate and examines which patient-related factors could explain the differences. This study is based on analysis of routine data provided by the German pension insurance in Baden-Württemberg (Rehabilitationsstatistikdatenbasis 2013; RSD). In the analyses rehabilitation measures were included if they were conducted by employed patients (18-64 years) with a muscular-skeletal system disease or a disorder of the connective tissue. Logistic regression models were performed to explain the differences in the recommendation rate of the rehabilitation centers. The data of 134 853 rehabilitation measures out of 32 rehabilitation centers were available. The recommendation rate differed between the rehabilitation centers from 1.36-18.53%. The logistic regression analysis showed that the period of working incapacity 12 month before the rehabilitation and the working capacity on the current job were the most important predictors for the recommendation of a SOR by the rehabilitation centers. Also the rehabilitation centers themselves have an important influence. The results of this survey indicate that the characteristic of the patients is an important factor for the recommendation of SOR. Additionally the rehabilitation centers themselves have an influence on the recommendation of SOR. The results point to the fact that the rehabilitation centers use different criteria by making a recommendation. © Georg Thieme Verlag KG Stuttgart · New York.

Nutritional and biochemical parameters associated with 6-year change in bone mineral density in community-dwelling Japanese women aged 69 years and older: The Muramatsu Study.

PubMed

Nakamura, Kazutoshi; Oyama, Mari; Saito, Toshiko; Oshiki, Rieko; Kobayashi, Ryosaku; Nishiwaki, Tomoko; Nashimoto, Mitsue; Tsuchiya, Yasuo

2012-04-01

Predictors of bone loss in elderly Asian women have been unclear. This cohort study aimed to assess lifestyle, nutritional, and biochemical predictors of bone loss in elderly Japanese women. Subjects included 389 community-dwelling women aged 69 y and older from the Muramatsu cohort initiated in 2003; follow-up ended in 2009. We obtained data on physical characteristics, osteoporosis treatment (with bisphosphonates or selective estrogen receptor modulators), physical activity, calcium intake, serum 25-hydroxyvitamin D, undercarboxylated osteocalcin, serum albumin, and bone turnover markers as predictors. The outcome was a 6-y change in forearm BMD (ΔBMD). Osteoporosis treatment was coded as 0 for none, 1 for sometimes, and 2 for always during the follow-up period. Stepwise multiple linear regression analysis was used to identify independent predictors of ΔBMD. Mean age of the subjects was 73.3 y. Mean values of ΔBMD and Δweight were -0.019 g/cm(2) (-5.8%) and -2.2 kg, respectively. Stepwise multiple linear regression analysis revealed baseline BMD (β = -0.137, P < 0.0001), osteoporosis treatment (β = 0.0068, P = 0.0105), serum albumin levels (β = 0.0122, P = 0.0319), and Δweight (β = 0.0015, P = 0.0009) as significant independent predictors of ΔBMD. However, none of the other nutritional or biochemical indices were found to be significant predictors of ΔBMD. Our findings indicate that adequate general nutrition and appropriate osteoporosis medication, rather than specific nutritional regimens, may be effective in preventing bone loss in elderly women. Copyright © 2012 Elsevier Inc. All rights reserved.

Developing screening services for colorectal cancer on Android smartphones.

PubMed

Wu, Hui-Ching; Chang, Chiao-Jung; Lin, Chun-Che; Tsai, Ming-Chang; Chang, Che-Chia; Tseng, Ming-Hseng

2014-08-01

Colorectal cancer (CRC) is an important health problem in Western countries and also in Asia. It is the third leading cause of cancer deaths in both men and women in Taiwan. According to the well-known adenoma-to-carcinoma sequence, the majority of CRC develops from colorectal adenomatous polyps. This concept provides the rationale for screening and prevention of CRC. Removal of colorectal adenoma could reduce the mortality and incidence of CRC. Mobile phones are now playing an ever more crucial role in people's daily lives. The latest generation of smartphones is increasingly viewed as hand-held computers rather than as phones, because of their powerful on-board computing capability, capacious memories, large screens, and open operating systems that encourage development of applications (apps). If we can detect the potential CRC patients early and offer them appropriate treatments and services, this would not only promote the quality of life, but also reduce the possible serious complications and medical costs. In this study, an intelligent CRC screening app on Android™ (Google™, Mountain View, CA) smartphones has been developed based on a data mining approach using decision tree algorithms. For comparison, the stepwise backward multivariate logistic regression model and the fecal occult blood test were also used. Compared with the stepwise backward multivariate logistic regression model and the fecal occult blood test, the proposed app system not only provides an easy and efficient way to quickly detect high-risk groups of potential CRC patients, but also brings more information about CRC to customer-oriented services. We developed and implemented an app system on Android platforms for ubiquitous healthcare services for CRC screening. It can assist people in achieving early screening, diagnosis, and treatment purposes, prevent the occurrence of complications, and thus reach the goal of preventive medicine.