Principal components and iterative regression analysis of geophysical series: Application to Sunspot number (1750 2004)

NASA Astrophysics Data System (ADS)

Nordemann, D. J. R.; Rigozo, N. R.; de Souza Echer, M. P.; Echer, E.

2008-11-01

We present here an implementation of a least squares iterative regression method applied to the sine functions embedded in the principal components extracted from geophysical time series. This method seems to represent a useful improvement for the non-stationary time series periodicity quantitative analysis. The principal components determination followed by the least squares iterative regression method was implemented in an algorithm written in the Scilab (2006) language. The main result of the method is to obtain the set of sine functions embedded in the series analyzed in decreasing order of significance, from the most important ones, likely to represent the physical processes involved in the generation of the series, to the less important ones that represent noise components. Taking into account the need of a deeper knowledge of the Sun's past history and its implication to global climate change, the method was applied to the Sunspot Number series (1750-2004). With the threshold and parameter values used here, the application of the method leads to a total of 441 explicit sine functions, among which 65 were considered as being significant and were used for a reconstruction that gave a normalized mean squared error of 0.146.

Oil and gas pipeline construction cost analysis and developing regression models for cost estimation

NASA Astrophysics Data System (ADS)

Thaduri, Ravi Kiran

In this study, cost data for 180 pipelines and 136 compressor stations have been analyzed. On the basis of the distribution analysis, regression models have been developed. Material, Labor, ROW and miscellaneous costs make up the total cost of a pipeline construction. The pipelines are analyzed based on different pipeline lengths, diameter, location, pipeline volume and year of completion. In a pipeline construction, labor costs dominate the total costs with a share of about 40%. Multiple non-linear regression models are developed to estimate the component costs of pipelines for various cross-sectional areas, lengths and locations. The Compressor stations are analyzed based on the capacity, year of completion and location. Unlike the pipeline costs, material costs dominate the total costs in the construction of compressor station, with an average share of about 50.6%. Land costs have very little influence on the total costs. Similar regression models are developed to estimate the component costs of compressor station for various capacities and locations.

Advancing individual tree biomass prediction: assessment and alternatives to the component ratio method

Treesearch

Aaron Weiskittel; Jereme Frank; David Walker; Phil Radtke; David Macfarlane; James Westfall

2015-01-01

Prediction of forest biomass and carbon is becoming important issues in the United States. However, estimating forest biomass and carbon is difficult and relies on empirically-derived regression equations. Based on recent findings from a national gap analysis and comprehensive assessment of the USDA Forest Service Forest Inventory and Analysis (USFS-FIA) component...

Multivariate Analysis of Seismic Field Data

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

Alam, M. Kathleen

1999-06-01

This report includes the details of the model building procedure and prediction of seismic field data. Principal Components Regression, a multivariate analysis technique, was used to model seismic data collected as two pieces of equipment were cycled on and off. Models built that included only the two pieces of equipment of interest had trouble predicting data containing signals not included in the model. Evidence for poor predictions came from the prediction curves as well as spectral F-ratio plots. Once the extraneous signals were included in the model, predictions improved dramatically. While Principal Components Regression performed well for the present datamore » sets, the present data analysis suggests further work will be needed to develop more robust modeling methods as the data become more complex.« less

The Breslow estimator of the nonparametric baseline survivor function in Cox's regression model: some heuristics.

PubMed

Hanley, James A

2008-01-01

Most survival analysis textbooks explain how the hazard ratio parameters in Cox's life table regression model are estimated. Fewer explain how the components of the nonparametric baseline survivor function are derived. Those that do often relegate the explanation to an "advanced" section and merely present the components as algebraic or iterative solutions to estimating equations. None comment on the structure of these estimators. This note brings out a heuristic representation that may help to de-mystify the structure.

Introduction to uses and interpretation of principal component analyses in forest biology.

Treesearch

J. G. Isebrands; Thomas R. Crow

1975-01-01

The application of principal component analysis for interpretation of multivariate data sets is reviewed with emphasis on (1) reduction of the number of variables, (2) ordination of variables, and (3) applications in conjunction with multiple regression.

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

NASA Astrophysics Data System (ADS)

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

2008-04-01

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

Development and Validation of the Work-Related Well-Being Index: Analysis of the Federal Employee Viewpoint Survey.

PubMed

Eaton, Jennifer L; Mohr, David C; Hodgson, Michael J; McPhaul, Kathleen M

2018-02-01

To describe development and validation of the work-related well-being (WRWB) index. Principal components analysis was performed using Federal Employee Viewpoint Survey (FEVS) data (N = 392,752) to extract variables representing worker well-being constructs. Confirmatory factor analysis was performed to verify factor structure. To validate the WRWB index, we used multiple regression analysis to examine relationships with burnout associated outcomes. Principal Components Analysis identified three positive psychology constructs: "Work Positivity", "Co-worker Relationships", and "Work Mastery". An 11 item index explaining 63.5% of variance was achieved. The structural equation model provided a very good fit to the data. Higher WRWB scores were positively associated with all three employee experience measures examined in regression models. The new WRWB index shows promise as a valid and widely accessible instrument to assess worker well-being.

Estimating regression coefficients from clustered samples: Sampling errors and optimum sample allocation

NASA Technical Reports Server (NTRS)

Kalton, G.

1983-01-01

A number of surveys were conducted to study the relationship between the level of aircraft or traffic noise exposure experienced by people living in a particular area and their annoyance with it. These surveys generally employ a clustered sample design which affects the precision of the survey estimates. Regression analysis of annoyance on noise measures and other variables is often an important component of the survey analysis. Formulae are presented for estimating the standard errors of regression coefficients and ratio of regression coefficients that are applicable with a two- or three-stage clustered sample design. Using a simple cost function, they also determine the optimum allocation of the sample across the stages of the sample design for the estimation of a regression coefficient.

Patient phenotypes associated with outcomes after aneurysmal subarachnoid hemorrhage: a principal component analysis.

PubMed

Ibrahim, George M; Morgan, Benjamin R; Macdonald, R Loch

2014-03-01

Predictors of outcome after aneurysmal subarachnoid hemorrhage have been determined previously through hypothesis-driven methods that often exclude putative covariates and require a priori knowledge of potential confounders. Here, we apply a data-driven approach, principal component analysis, to identify baseline patient phenotypes that may predict neurological outcomes. Principal component analysis was performed on 120 subjects enrolled in a prospective randomized trial of clazosentan for the prevention of angiographic vasospasm. Correlation matrices were created using a combination of Pearson, polyserial, and polychoric regressions among 46 variables. Scores of significant components (with eigenvalues>1) were included in multivariate logistic regression models with incidence of severe angiographic vasospasm, delayed ischemic neurological deficit, and long-term outcome as outcomes of interest. Sixteen significant principal components accounting for 74.6% of the variance were identified. A single component dominated by the patients' initial hemodynamic status, World Federation of Neurosurgical Societies score, neurological injury, and initial neutrophil/leukocyte counts was significantly associated with poor outcome. Two additional components were associated with angiographic vasospasm, of which one was also associated with delayed ischemic neurological deficit. The first was dominated by the aneurysm-securing procedure, subarachnoid clot clearance, and intracerebral hemorrhage, whereas the second had high contributions from markers of anemia and albumin levels. Principal component analysis, a data-driven approach, identified patient phenotypes that are associated with worse neurological outcomes. Such data reduction methods may provide a better approximation of unique patient phenotypes and may inform clinical care as well as patient recruitment into clinical trials. http://www.clinicaltrials.gov. Unique identifier: NCT00111085.

Modeling Governance KB with CATPCA to Overcome Multicollinearity in the Logistic Regression

NASA Astrophysics Data System (ADS)

Khikmah, L.; Wijayanto, H.; Syafitri, U. D.

2017-04-01

The problem often encounters in logistic regression modeling are multicollinearity problems. Data that have multicollinearity between explanatory variables with the result in the estimation of parameters to be bias. Besides, the multicollinearity will result in error in the classification. In general, to overcome multicollinearity in regression used stepwise regression. They are also another method to overcome multicollinearity which involves all variable for prediction. That is Principal Component Analysis (PCA). However, classical PCA in only for numeric data. Its data are categorical, one method to solve the problems is Categorical Principal Component Analysis (CATPCA). Data were used in this research were a part of data Demographic and Population Survey Indonesia (IDHS) 2012. This research focuses on the characteristic of women of using the contraceptive methods. Classification results evaluated using Area Under Curve (AUC) values. The higher the AUC value, the better. Based on AUC values, the classification of the contraceptive method using stepwise method (58.66%) is better than the logistic regression model (57.39%) and CATPCA (57.39%). Evaluation of the results of logistic regression using sensitivity, shows the opposite where CATPCA method (99.79%) is better than logistic regression method (92.43%) and stepwise (92.05%). Therefore in this study focuses on major class classification (using a contraceptive method), then the selected model is CATPCA because it can raise the level of the major class model accuracy.

Classical Testing in Functional Linear Models.

PubMed

Kong, Dehan; Staicu, Ana-Maria; Maity, Arnab

2016-01-01

We extend four tests common in classical regression - Wald, score, likelihood ratio and F tests - to functional linear regression, for testing the null hypothesis, that there is no association between a scalar response and a functional covariate. Using functional principal component analysis, we re-express the functional linear model as a standard linear model, where the effect of the functional covariate can be approximated by a finite linear combination of the functional principal component scores. In this setting, we consider application of the four traditional tests. The proposed testing procedures are investigated theoretically for densely observed functional covariates when the number of principal components diverges. Using the theoretical distribution of the tests under the alternative hypothesis, we develop a procedure for sample size calculation in the context of functional linear regression. The four tests are further compared numerically for both densely and sparsely observed noisy functional data in simulation experiments and using two real data applications.

Classical Testing in Functional Linear Models

PubMed Central

Kong, Dehan; Staicu, Ana-Maria; Maity, Arnab

2016-01-01

We extend four tests common in classical regression - Wald, score, likelihood ratio and F tests - to functional linear regression, for testing the null hypothesis, that there is no association between a scalar response and a functional covariate. Using functional principal component analysis, we re-express the functional linear model as a standard linear model, where the effect of the functional covariate can be approximated by a finite linear combination of the functional principal component scores. In this setting, we consider application of the four traditional tests. The proposed testing procedures are investigated theoretically for densely observed functional covariates when the number of principal components diverges. Using the theoretical distribution of the tests under the alternative hypothesis, we develop a procedure for sample size calculation in the context of functional linear regression. The four tests are further compared numerically for both densely and sparsely observed noisy functional data in simulation experiments and using two real data applications. PMID:28955155

Prediction by regression and intrarange data scatter in surface-process studies

USGS Publications Warehouse

Toy, T.J.; Osterkamp, W.R.; Renard, K.G.

1993-01-01

Modeling is a major component of contemporary earth science, and regression analysis occupies a central position in the parameterization, calibration, and validation of geomorphic and hydrologic models. Although this methodology can be used in many ways, we are primarily concerned with the prediction of values for one variable from another variable. Examination of the literature reveals considerable inconsistency in the presentation of the results of regression analysis and the occurrence of patterns in the scatter of data points about the regression line. Both circumstances confound utilization and evaluation of the models. Statisticians are well aware of various problems associated with the use of regression analysis and offer improved practices; often, however, their guidelines are not followed. After a review of the aforementioned circumstances and until standard criteria for model evaluation become established, we recommend, as a minimum, inclusion of scatter diagrams, the standard error of the estimate, and sample size in reporting the results of regression analyses for most surface-process studies. ?? 1993 Springer-Verlag.

Symplectic geometry spectrum regression for prediction of noisy time series

NASA Astrophysics Data System (ADS)

Xie, Hong-Bo; Dokos, Socrates; Sivakumar, Bellie; Mengersen, Kerrie

2016-05-01

We present the symplectic geometry spectrum regression (SGSR) technique as well as a regularized method based on SGSR for prediction of nonlinear time series. The main tool of analysis is the symplectic geometry spectrum analysis, which decomposes a time series into the sum of a small number of independent and interpretable components. The key to successful regularization is to damp higher order symplectic geometry spectrum components. The effectiveness of SGSR and its superiority over local approximation using ordinary least squares are demonstrated through prediction of two noisy synthetic chaotic time series (Lorenz and Rössler series), and then tested for prediction of three real-world data sets (Mississippi River flow data and electromyographic and mechanomyographic signal recorded from human body).

On Using the Average Intercorrelation Among Predictor Variables and Eigenvector Orientation to Choose a Regression Solution.

ERIC Educational Resources Information Center

Mugrage, Beverly; And Others

Three ridge regression solutions are compared with ordinary least squares regression and with principal components regression using all components. Ridge regression, particularly the Lawless-Wang solution, out-performed ordinary least squares regression and the principal components solution on the criteria of stability of coefficient and closeness…

Application of third molar development and eruption models in estimating dental age in Malay sub-adults.

PubMed

Mohd Yusof, Mohd Yusmiaidil Putera; Cauwels, Rita; Deschepper, Ellen; Martens, Luc

2015-08-01

The third molar development (TMD) has been widely utilized as one of the radiographic method for dental age estimation. By using the same radiograph of the same individual, third molar eruption (TME) information can be incorporated to the TMD regression model. This study aims to evaluate the performance of dental age estimation in individual method models and the combined model (TMD and TME) based on the classic regressions of multiple linear and principal component analysis. A sample of 705 digital panoramic radiographs of Malay sub-adults aged between 14.1 and 23.8 years was collected. The techniques described by Gleiser and Hunt (modified by Kohler) and Olze were employed to stage the TMD and TME, respectively. The data was divided to develop three respective models based on the two regressions of multiple linear and principal component analysis. The trained models were then validated on the test sample and the accuracy of age prediction was compared between each model. The coefficient of determination (R²) and root mean square error (RMSE) were calculated. In both genders, adjusted R² yielded an increment in the linear regressions of combined model as compared to the individual models. The overall decrease in RMSE was detected in combined model as compared to TMD (0.03-0.06) and TME (0.2-0.8). In principal component regression, low value of adjusted R(2) and high RMSE except in male were exhibited in combined model. Dental age estimation is better predicted using combined model in multiple linear regression models. Copyright © 2015 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Chandra X-ray Center Science Data Systems Regression Testing of CIAO

NASA Astrophysics Data System (ADS)

Lee, N. P.; Karovska, M.; Galle, E. C.; Bonaventura, N. R.

2011-07-01

The Chandra Interactive Analysis of Observations (CIAO) is a software system developed for the analysis of Chandra X-ray Observatory observations. An important component of a successful CIAO release is the repeated testing of the tools across various platforms to ensure consistent and scientifically valid results. We describe the procedures of the scientific regression testing of CIAO and the enhancements made to the testing system to increase the efficiency of run time and result validation.

Spectroscopic and Chemometric Analysis of Binary and Ternary Edible Oil Mixtures: Qualitative and Quantitative Study.

PubMed

Jović, Ozren; Smolić, Tomislav; Primožič, Ines; Hrenar, Tomica

2016-04-19

The aim of this study was to investigate the feasibility of FTIR-ATR spectroscopy coupled with the multivariate numerical methodology for qualitative and quantitative analysis of binary and ternary edible oil mixtures. Four pure oils (extra virgin olive oil, high oleic sunflower oil, rapeseed oil, and sunflower oil), as well as their 54 binary and 108 ternary mixtures, were analyzed using FTIR-ATR spectroscopy in combination with principal component and discriminant analysis, partial least-squares, and principal component regression. It was found that the composition of all 166 samples can be excellently represented using only the first three principal components describing 98.29% of total variance in the selected spectral range (3035-2989, 1170-1140, 1120-1100, 1093-1047, and 930-890 cm(-1)). Factor scores in 3D space spanned by these three principal components form a tetrahedral-like arrangement: pure oils being at the vertices, binary mixtures at the edges, and ternary mixtures on the faces of a tetrahedron. To confirm the validity of results, we applied several cross-validation methods. Quantitative analysis was performed by minimization of root-mean-square error of cross-validation values regarding the spectral range, derivative order, and choice of method (partial least-squares or principal component regression), which resulted in excellent predictions for test sets (R(2) > 0.99 in all cases). Additionally, experimentally more demanding gas chromatography analysis of fatty acid content was carried out for all specimens, confirming the results obtained by FTIR-ATR coupled with principal component analysis. However, FTIR-ATR provided a considerably better model for prediction of mixture composition than gas chromatography, especially for high oleic sunflower oil.

Changes of choroidal neovascularization in indocyanine green angiography after intravitreal ranibizumab injection.

PubMed

Lee, Ji Eun; Kim, Hyun Woong; Lee, Sang Joon; Lee, Joo Eun

2015-05-01

To investigate vascular structural changes of choroidal neovascularization (CNV) followed by intravitreal ranibizumab injections using indocyanine green angiography. A total of 31 patients with exudative age-related macular degeneration and CNV whose structures were identifiable in indocyanine green angiography were included. Ranibizumab was injected into the vitreous cavity once a month for 3 months and then as needed for the next 3 months prospectively. Indocyanine green angiography was performed at baseline, 3, and 6 months. Early to midphase images of the indocyanine green angiography in the details of vascular structure of the CNV were discerned the best were used in the image analysis. Vascular structures of CNV were described as arteriovenular and capillary components, and structural changes were assessed. Arteriovenular components were observed in 29 eyes (94%). Regression of the capillary components was observed in most cases. Although regression of arteriovenular component was noted in 14 eyes (48%), complete resolution was not observed. The eyes were categorized into 3 groups according to CNV structural changes: the regressed (Group R, 10 eyes, 31%), the matured (Group M, 7 eyes, 23%), and the growing (Group G, 14 eyes, 45%). In Group R, there was no regrowth of CNV found at 6 months. In Group M, distinct vascular structures were observed at 3 months and persisted without apparent changes at 6 months. In Group G, growth or reperfusion of capillary components from the persisting arteriovenular components was noted at 6 months. Both capillary and arteriovenular components were regressed during monthly ranibizumab injections. However, CNV regrowth was observed in a group of patients during the as-needed treatment phase.

Quantifying components of the hydrologic cycle in Virginia using chemical hydrograph separation and multiple regression analysis

USGS Publications Warehouse

Sanford, Ward E.; Nelms, David L.; Pope, Jason P.; Selnick, David L.

2012-01-01

This study by the U.S. Geological Survey, prepared in cooperation with the Virginia Department of Environmental Quality, quantifies the components of the hydrologic cycle across the Commonwealth of Virginia. Long-term, mean fluxes were calculated for precipitation, surface runoff, infiltration, total evapotranspiration (ET), riparian ET, recharge, base flow (or groundwater discharge) and net total outflow. Fluxes of these components were first estimated on a number of real-time-gaged watersheds across Virginia. Specific conductance was used to distinguish and separate surface runoff from base flow. Specific-conductance data were collected every 15 minutes at 75 real-time gages for approximately 18 months between March 2007 and August 2008. Precipitation was estimated for 1971–2000 using PRISM climate data. Precipitation and temperature from the PRISM data were used to develop a regression-based relation to estimate total ET. The proportion of watershed precipitation that becomes surface runoff was related to physiographic province and rock type in a runoff regression equation. Component flux estimates from the watersheds were transferred to flux estimates for counties and independent cities using the ET and runoff regression equations. Only 48 of the 75 watersheds yielded sufficient data, and data from these 48 were used in the final runoff regression equation. The base-flow proportion for the 48 watersheds averaged 72 percent using specific conductance, a value that was substantially higher than the 61 percent average calculated using a graphical-separation technique (the USGS program PART). Final results for the study are presented as component flux estimates for all counties and independent cities in Virginia.

Discrimination of serum Raman spectroscopy between normal and colorectal cancer

NASA Astrophysics Data System (ADS)

Li, Xiaozhou; Yang, Tianyue; Yu, Ting; Li, Siqi

2011-07-01

Raman spectroscopy of tissues has been widely studied for the diagnosis of various cancers, but biofluids were seldom used as the analyte because of the low concentration. Herein, serum of 30 normal people, 46 colon cancer, and 44 rectum cancer patients were measured Raman spectra and analyzed. The information of Raman peaks (intensity and width) and that of the fluorescence background (baseline function coefficients) were selected as parameters for statistical analysis. Principal component regression (PCR) and partial least square regression (PLSR) were used on the selected parameters separately to see the performance of the parameters. PCR performed better than PLSR in our spectral data. Then linear discriminant analysis (LDA) was used on the principal components (PCs) of the two regression method on the selected parameters, and a diagnostic accuracy of 88% and 83% were obtained. The conclusion is that the selected features can maintain the information of original spectra well and Raman spectroscopy of serum has the potential for the diagnosis of colorectal cancer.

A use of regression analysis in acoustical diagnostics of gear drives

NASA Technical Reports Server (NTRS)

Balitskiy, F. Y.; Genkin, M. D.; Ivanova, M. A.; Kobrinskiy, A. A.; Sokolova, A. G.

1973-01-01

A study is presented of components of the vibration spectrum as the filtered first and second harmonics of the tooth frequency which permits information to be obtained on the physical characteristics of the vibration excitation process, and an approach to be made to comparison of models of the gearing. Regression analysis of two random processes has shown a strong dependence of the second harmonic on the first, and independence of the first from the second. The nature of change in the regression line, with change in loading moment, gives rise to the idea of a variable phase shift between the first and second harmonics.

Quantile regression in the presence of monotone missingness with sensitivity analysis

PubMed Central

Liu, Minzhao; Daniels, Michael J.; Perri, Michael G.

2016-01-01

In this paper, we develop methods for longitudinal quantile regression when there is monotone missingness. In particular, we propose pattern mixture models with a constraint that provides a straightforward interpretation of the marginal quantile regression parameters. Our approach allows sensitivity analysis which is an essential component in inference for incomplete data. To facilitate computation of the likelihood, we propose a novel way to obtain analytic forms for the required integrals. We conduct simulations to examine the robustness of our approach to modeling assumptions and compare its performance to competing approaches. The model is applied to data from a recent clinical trial on weight management. PMID:26041008

Logistic Regression Analysis of the Response of Winter Wheat to Components of Artificial Freezing Episodes

USDA-ARS?s Scientific Manuscript database

Improvement of cold tolerance of winter wheat (Triticum aestivum L.) through breeding methods has been problematic. A better understanding of how individual wheat cultivars respond to components of the freezing process may provide new information that can be used to develop more cold tolerance culti...

Application of Semiparametric Spline Regression Model in Analyzing Factors that In uence Population Density in Central Java

NASA Astrophysics Data System (ADS)

Sumantari, Y. D.; Slamet, I.; Sugiyanto

2017-06-01

Semiparametric regression is a statistical analysis method that consists of parametric and nonparametric regression. There are various approach techniques in nonparametric regression. One of the approach techniques is spline. Central Java is one of the most densely populated province in Indonesia. Population density in this province can be modeled by semiparametric regression because it consists of parametric and nonparametric component. Therefore, the purpose of this paper is to determine the factors that in uence population density in Central Java using the semiparametric spline regression model. The result shows that the factors which in uence population density in Central Java is Family Planning (FP) active participants and district minimum wage.

Component analysis and initial validity of the exercise fear avoidance scale.

PubMed

Wingo, Brooks C; Baskin, Monica; Ard, Jamy D; Evans, Retta; Roy, Jane; Vogtle, Laura; Grimley, Diane; Snyder, Scott

2013-01-01

To develop the Exercise Fear Avoidance Scale (EFAS) to measure fear of exercise-induced discomfort. We conducted principal component analysis to determine component structure and Cronbach's alpha to assess internal consistency of the EFAS. Relationships between EFAS scores, BMI, physical activity, and pain were analyzed using multivariate regression. The best fit was a 3-component structure: weight-specific fears, cardiorespiratory fears, and musculoskeletal fears. Cronbach's alpha for the EFAS was α=.86. EFAS scores significantly predicted BMI, physical activity, and PDI scores. Psychometric properties of this scale suggest it may be useful for tailoring exercise prescriptions to address fear of exercise-related discomfort.

Improving Cluster Analysis with Automatic Variable Selection Based on Trees

DTIC Science & Technology

2014-12-01

regression trees Daisy DISsimilAritY PAM partitioning around medoids PMA penalized multivariate analysis SPC sparse principal components UPGMA unweighted...unweighted pair-group average method ( UPGMA ). This method measures dissimilarities between all objects in two clusters and takes the average value

Principal components analysis in clinical studies.

PubMed

Zhang, Zhongheng; Castelló, Adela

2017-09-01

In multivariate analysis, independent variables are usually correlated to each other which can introduce multicollinearity in the regression models. One approach to solve this problem is to apply principal components analysis (PCA) over these variables. This method uses orthogonal transformation to represent sets of potentially correlated variables with principal components (PC) that are linearly uncorrelated. PCs are ordered so that the first PC has the largest possible variance and only some components are selected to represent the correlated variables. As a result, the dimension of the variable space is reduced. This tutorial illustrates how to perform PCA in R environment, the example is a simulated dataset in which two PCs are responsible for the majority of the variance in the data. Furthermore, the visualization of PCA is highlighted.

Understanding software faults and their role in software reliability modeling

NASA Technical Reports Server (NTRS)

Munson, John C.

1994-01-01

This study is a direct result of an on-going project to model the reliability of a large real-time control avionics system. In previous modeling efforts with this system, hardware reliability models were applied in modeling the reliability behavior of this system. In an attempt to enhance the performance of the adapted reliability models, certain software attributes were introduced in these models to control for differences between programs and also sequential executions of the same program. As the basic nature of the software attributes that affect software reliability become better understood in the modeling process, this information begins to have important implications on the software development process. A significant problem arises when raw attribute measures are to be used in statistical models as predictors, for example, of measures of software quality. This is because many of the metrics are highly correlated. Consider the two attributes: lines of code, LOC, and number of program statements, Stmts. In this case, it is quite obvious that a program with a high value of LOC probably will also have a relatively high value of Stmts. In the case of low level languages, such as assembly language programs, there might be a one-to-one relationship between the statement count and the lines of code. When there is a complete absence of linear relationship among the metrics, they are said to be orthogonal or uncorrelated. Usually the lack of orthogonality is not serious enough to affect a statistical analysis. However, for the purposes of some statistical analysis such as multiple regression, the software metrics are so strongly interrelated that the regression results may be ambiguous and possibly even misleading. Typically, it is difficult to estimate the unique effects of individual software metrics in the regression equation. The estimated values of the coefficients are very sensitive to slight changes in the data and to the addition or deletion of variables in the regression equation. Since most of the existing metrics have common elements and are linear combinations of these common elements, it seems reasonable to investigate the structure of the underlying common factors or components that make up the raw metrics. The technique we have chosen to use to explore this structure is a procedure called principal components analysis. Principal components analysis is a decomposition technique that may be used to detect and analyze collinearity in software metrics. When confronted with a large number of metrics measuring a single construct, it may be desirable to represent the set by some smaller number of variables that convey all, or most, of the information in the original set. Principal components are linear transformations of a set of random variables that summarize the information contained in the variables. The transformations are chosen so that the first component accounts for the maximal amount of variation of the measures of any possible linear transform; the second component accounts for the maximal amount of residual variation; and so on. The principal components are constructed so that they represent transformed scores on dimensions that are orthogonal. Through the use of principal components analysis, it is possible to have a set of highly related software attributes mapped into a small number of uncorrelated attribute domains. This definitively solves the problem of multi-collinearity in subsequent regression analysis. There are many software metrics in the literature, but principal component analysis reveals that there are few distinct sources of variation, i.e. dimensions, in this set of metrics. It would appear perfectly reasonable to characterize the measurable attributes of a program with a simple function of a small number of orthogonal metrics each of which represents a distinct software attribute domain.

Sparse modeling of spatial environmental variables associated with asthma

PubMed Central

Chang, Timothy S.; Gangnon, Ronald E.; Page, C. David; Buckingham, William R.; Tandias, Aman; Cowan, Kelly J.; Tomasallo, Carrie D.; Arndt, Brian G.; Hanrahan, Lawrence P.; Guilbert, Theresa W.

2014-01-01

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin’s Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5–50 years over a three-year period. Each patient’s home address was geocoded to one of 3,456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin’s geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. PMID:25533437

Sparse modeling of spatial environmental variables associated with asthma.

PubMed

Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W

2015-02-01

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.

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

PubMed

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

2011-12-01

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

Estimation of diffusion coefficients from voltammetric signals by support vector and gaussian process regression

PubMed Central

2014-01-01

Background Support vector regression (SVR) and Gaussian process regression (GPR) were used for the analysis of electroanalytical experimental data to estimate diffusion coefficients. Results For simulated cyclic voltammograms based on the EC, Eqr, and EqrC mechanisms these regression algorithms in combination with nonlinear kernel/covariance functions yielded diffusion coefficients with higher accuracy as compared to the standard approach of calculating diffusion coefficients relying on the Nicholson-Shain equation. The level of accuracy achieved by SVR and GPR is virtually independent of the rate constants governing the respective reaction steps. Further, the reduction of high-dimensional voltammetric signals by manual selection of typical voltammetric peak features decreased the performance of both regression algorithms compared to a reduction by downsampling or principal component analysis. After training on simulated data sets, diffusion coefficients were estimated by the regression algorithms for experimental data comprising voltammetric signals for three organometallic complexes. Conclusions Estimated diffusion coefficients closely matched the values determined by the parameter fitting method, but reduced the required computational time considerably for one of the reaction mechanisms. The automated processing of voltammograms according to the regression algorithms yields better results than the conventional analysis of peak-related data. PMID:24987463

A single determinant dominates the rate of yeast protein evolution.

PubMed

Drummond, D Allan; Raval, Alpan; Wilke, Claus O

2006-02-01

A gene's rate of sequence evolution is among the most fundamental evolutionary quantities in common use, but what determines evolutionary rates has remained unclear. Here, we carry out the first combined analysis of seven predictors (gene expression level, dispensability, protein abundance, codon adaptation index, gene length, number of protein-protein interactions, and the gene's centrality in the interaction network) previously reported to have independent influences on protein evolutionary rates. Strikingly, our analysis reveals a single dominant variable linked to the number of translation events which explains 40-fold more variation in evolutionary rate than any other, suggesting that protein evolutionary rate has a single major determinant among the seven predictors. The dominant variable explains nearly half the variation in the rate of synonymous and protein evolution. We show that the two most commonly used methods to disentangle the determinants of evolutionary rate, partial correlation analysis and ordinary multivariate regression, produce misleading or spurious results when applied to noisy biological data. We overcome these difficulties by employing principal component regression, a multivariate regression of evolutionary rate against the principal components of the predictor variables. Our results support the hypothesis that translational selection governs the rate of synonymous and protein sequence evolution in yeast.

Application of principal component analysis (PCA) as a sensory assessment tool for fermented food products.

PubMed

Ghosh, Debasree; Chattopadhyay, Parimal

2012-06-01

The objective of the work was to use the method of quantitative descriptive analysis (QDA) to describe the sensory attributes of the fermented food products prepared with the incorporation of lactic cultures. Panellists were selected and trained to evaluate various attributes specially color and appearance, body texture, flavor, overall acceptability and acidity of the fermented food products like cow milk curd and soymilk curd, idli, sauerkraut and probiotic ice cream. Principal component analysis (PCA) identified the six significant principal components that accounted for more than 90% of the variance in the sensory attribute data. Overall product quality was modelled as a function of principal components using multiple least squares regression (R (2) = 0.8). The result from PCA was statistically analyzed by analysis of variance (ANOVA). These findings demonstrate the utility of quantitative descriptive analysis for identifying and measuring the fermented food product attributes that are important for consumer acceptability.

A guide to understanding meta-analysis.

PubMed

Israel, Heidi; Richter, Randy R

2011-07-01

With the focus on evidence-based practice in healthcare, a well-conducted systematic review that includes a meta-analysis where indicated represents a high level of evidence for treatment effectiveness. The purpose of this commentary is to assist clinicians in understanding meta-analysis as a statistical tool using both published articles and explanations of components of the technique. We describe what meta-analysis is, what heterogeneity is, and how it affects meta-analysis, effect size, the modeling techniques of meta-analysis, and strengths and weaknesses of meta-analysis. Common components like forest plot interpretation, software that may be used, special cases for meta-analysis, such as subgroup analysis, individual patient data, and meta-regression, and a discussion of criticisms, are included.

Structured functional additive regression in reproducing kernel Hilbert spaces.

PubMed

Zhu, Hongxiao; Yao, Fang; Zhang, Hao Helen

2014-06-01

Functional additive models (FAMs) provide a flexible yet simple framework for regressions involving functional predictors. The utilization of data-driven basis in an additive rather than linear structure naturally extends the classical functional linear model. However, the critical issue of selecting nonlinear additive components has been less studied. In this work, we propose a new regularization framework for the structure estimation in the context of Reproducing Kernel Hilbert Spaces. The proposed approach takes advantage of the functional principal components which greatly facilitates the implementation and the theoretical analysis. The selection and estimation are achieved by penalized least squares using a penalty which encourages the sparse structure of the additive components. Theoretical properties such as the rate of convergence are investigated. The empirical performance is demonstrated through simulation studies and a real data application.

Combining multiple regression and principal component analysis for accurate predictions for column ozone in Peninsular Malaysia

NASA Astrophysics Data System (ADS)

Rajab, Jasim M.; MatJafri, M. Z.; Lim, H. S.

2013-06-01

This study encompasses columnar ozone modelling in the peninsular Malaysia. Data of eight atmospheric parameters [air surface temperature (AST), carbon monoxide (CO), methane (CH4), water vapour (H2Ovapour), skin surface temperature (SSKT), atmosphere temperature (AT), relative humidity (RH), and mean surface pressure (MSP)] data set, retrieved from NASA's Atmospheric Infrared Sounder (AIRS), for the entire period (2003-2008) was employed to develop models to predict the value of columnar ozone (O3) in study area. The combined method, which is based on using both multiple regressions combined with principal component analysis (PCA) modelling, was used to predict columnar ozone. This combined approach was utilized to improve the prediction accuracy of columnar ozone. Separate analysis was carried out for north east monsoon (NEM) and south west monsoon (SWM) seasons. The O3 was negatively correlated with CH4, H2Ovapour, RH, and MSP, whereas it was positively correlated with CO, AST, SSKT, and AT during both the NEM and SWM season periods. Multiple regression analysis was used to fit the columnar ozone data using the atmospheric parameter's variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to acquire subsets of the predictor variables to be comprised in the linear regression model of the atmospheric parameter's variables. It was found that the increase in columnar O3 value is associated with an increase in the values of AST, SSKT, AT, and CO and with a drop in the levels of CH4, H2Ovapour, RH, and MSP. The result of fitting the best models for the columnar O3 value using eight of the independent variables gave about the same values of the R (≈0.93) and R2 (≈0.86) for both the NEM and SWM seasons. The common variables that appeared in both regression equations were SSKT, CH4 and RH, and the principal precursor of the columnar O3 value in both the NEM and SWM seasons was SSKT.

Principal component analysis-based pattern analysis of dose-volume histograms and influence on rectal toxicity.

PubMed

Söhn, Matthias; Alber, Markus; Yan, Di

2007-09-01

The variability of dose-volume histogram (DVH) shapes in a patient population can be quantified using principal component analysis (PCA). We applied this to rectal DVHs of prostate cancer patients and investigated the correlation of the PCA parameters with late bleeding. PCA was applied to the rectal wall DVHs of 262 patients, who had been treated with a four-field box, conformal adaptive radiotherapy technique. The correlated changes in the DVH pattern were revealed as "eigenmodes," which were ordered by their importance to represent data set variability. Each DVH is uniquely characterized by its principal components (PCs). The correlation of the first three PCs and chronic rectal bleeding of Grade 2 or greater was investigated with uni- and multivariate logistic regression analyses. Rectal wall DVHs in four-field conformal RT can primarily be represented by the first two or three PCs, which describe approximately 94% or 96% of the DVH shape variability, respectively. The first eigenmode models the total irradiated rectal volume; thus, PC1 correlates to the mean dose. Mode 2 describes the interpatient differences of the relative rectal volume in the two- or four-field overlap region. Mode 3 reveals correlations of volumes with intermediate doses ( approximately 40-45 Gy) and volumes with doses >70 Gy; thus, PC3 is associated with the maximal dose. According to univariate logistic regression analysis, only PC2 correlated significantly with toxicity. However, multivariate logistic regression analysis with the first two or three PCs revealed an increased probability of bleeding for DVHs with more than one large PC. PCA can reveal the correlation structure of DVHs for a patient population as imposed by the treatment technique and provide information about its relationship to toxicity. It proves useful for augmenting normal tissue complication probability modeling approaches.

Non-destructive evaluation of chlorophyll content in quinoa and amaranth leaves by simple and multiple regression analysis of RGB image components.

PubMed

Riccardi, M; Mele, G; Pulvento, C; Lavini, A; d'Andria, R; Jacobsen, S-E

2014-06-01

Leaf chlorophyll content provides valuable information about physiological status of plants; it is directly linked to photosynthetic potential and primary production. In vitro assessment by wet chemical extraction is the standard method for leaf chlorophyll determination. This measurement is expensive, laborious, and time consuming. Over the years alternative methods, rapid and non-destructive, have been explored. The aim of this work was to evaluate the applicability of a fast and non-invasive field method for estimation of chlorophyll content in quinoa and amaranth leaves based on RGB components analysis of digital images acquired with a standard SLR camera. Digital images of leaves from different genotypes of quinoa and amaranth were acquired directly in the field. Mean values of each RGB component were evaluated via image analysis software and correlated to leaf chlorophyll provided by standard laboratory procedure. Single and multiple regression models using RGB color components as independent variables have been tested and validated. The performance of the proposed method was compared to that of the widely used non-destructive SPAD method. Sensitivity of the best regression models for different genotypes of quinoa and amaranth was also checked. Color data acquisition of the leaves in the field with a digital camera was quick, more effective, and lower cost than SPAD. The proposed RGB models provided better correlation (highest R (2)) and prediction (lowest RMSEP) of the true value of foliar chlorophyll content and had a lower amount of noise in the whole range of chlorophyll studied compared with SPAD and other leaf image processing based models when applied to quinoa and amaranth.

Functional Data Analysis Applied to Modeling of Severe Acute Mucositis and Dysphagia Resulting From Head and Neck Radiation Therapy.

PubMed

Dean, Jamie A; Wong, Kee H; Gay, Hiram; Welsh, Liam C; Jones, Ann-Britt; Schick, Ulrike; Oh, Jung Hun; Apte, Aditya; Newbold, Kate L; Bhide, Shreerang A; Harrington, Kevin J; Deasy, Joseph O; Nutting, Christopher M; Gulliford, Sarah L

2016-11-15

Current normal tissue complication probability modeling using logistic regression suffers from bias and high uncertainty in the presence of highly correlated radiation therapy (RT) dose data. This hinders robust estimates of dose-response associations and, hence, optimal normal tissue-sparing strategies from being elucidated. Using functional data analysis (FDA) to reduce the dimensionality of the dose data could overcome this limitation. FDA was applied to modeling of severe acute mucositis and dysphagia resulting from head and neck RT. Functional partial least squares regression (FPLS) and functional principal component analysis were used for dimensionality reduction of the dose-volume histogram data. The reduced dose data were input into functional logistic regression models (functional partial least squares-logistic regression [FPLS-LR] and functional principal component-logistic regression [FPC-LR]) along with clinical data. This approach was compared with penalized logistic regression (PLR) in terms of predictive performance and the significance of treatment covariate-response associations, assessed using bootstrapping. The area under the receiver operating characteristic curve for the PLR, FPC-LR, and FPLS-LR models was 0.65, 0.69, and 0.67, respectively, for mucositis (internal validation) and 0.81, 0.83, and 0.83, respectively, for dysphagia (external validation). The calibration slopes/intercepts for the PLR, FPC-LR, and FPLS-LR models were 1.6/-0.67, 0.45/0.47, and 0.40/0.49, respectively, for mucositis (internal validation) and 2.5/-0.96, 0.79/-0.04, and 0.79/0.00, respectively, for dysphagia (external validation). The bootstrapped odds ratios indicated significant associations between RT dose and severe toxicity in the mucositis and dysphagia FDA models. Cisplatin was significantly associated with severe dysphagia in the FDA models. None of the covariates was significantly associated with severe toxicity in the PLR models. Dose levels greater than approximately 1.0 Gy/fraction were most strongly associated with severe acute mucositis and dysphagia in the FDA models. FPLS and functional principal component analysis marginally improved predictive performance compared with PLR and provided robust dose-response associations. FDA is recommended for use in normal tissue complication probability modeling. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

Mixed-effects Gaussian process functional regression models with application to dose-response curve prediction.

PubMed

Shi, J Q; Wang, B; Will, E J; West, R M

2012-11-20

We propose a new semiparametric model for functional regression analysis, combining a parametric mixed-effects model with a nonparametric Gaussian process regression model, namely a mixed-effects Gaussian process functional regression model. The parametric component can provide explanatory information between the response and the covariates, whereas the nonparametric component can add nonlinearity. We can model the mean and covariance structures simultaneously, combining the information borrowed from other subjects with the information collected from each individual subject. We apply the model to dose-response curves that describe changes in the responses of subjects for differing levels of the dose of a drug or agent and have a wide application in many areas. We illustrate the method for the management of renal anaemia. An individual dose-response curve is improved when more information is included by this mechanism from the subject/patient over time, enabling a patient-specific treatment regime. Copyright © 2012 John Wiley & Sons, Ltd.

Functional mixture regression.

PubMed

Yao, Fang; Fu, Yuejiao; Lee, Thomas C M

2011-04-01

In functional linear models (FLMs), the relationship between the scalar response and the functional predictor process is often assumed to be identical for all subjects. Motivated by both practical and methodological considerations, we relax this assumption and propose a new class of functional regression models that allow the regression structure to vary for different groups of subjects. By projecting the predictor process onto its eigenspace, the new functional regression model is simplified to a framework that is similar to classical mixture regression models. This leads to the proposed approach named as functional mixture regression (FMR). The estimation of FMR can be readily carried out using existing software implemented for functional principal component analysis and mixture regression. The practical necessity and performance of FMR are illustrated through applications to a longevity analysis of female medflies and a human growth study. Theoretical investigations concerning the consistent estimation and prediction properties of FMR along with simulation experiments illustrating its empirical properties are presented in the supplementary material available at Biostatistics online. Corresponding results demonstrate that the proposed approach could potentially achieve substantial gains over traditional FLMs.

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

PubMed

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

2011-10-01

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

Subacute casemix classification for stroke rehabilitation in Australia. How well does AN-SNAP v2 explain variance in outcomes?

PubMed

Kohler, Friedbert; Renton, Roger; Dickson, Hugh G; Estell, John; Connolly, Carol E

2011-02-01

We sought the best predictors for length of stay, discharge destination and functional improvement for inpatients undergoing rehabilitation following a stroke and compared these predictors against AN-SNAP v2. The Oxfordshire classification subgroup, sociodemographic data and functional data were collected for patients admitted between 1997 and 2007, with a diagnosis of recent stroke. The data were factor analysed using Principal Components Analysis for categorical data (CATPCA). Categorical regression analyses was performed to determine the best predictors of length of stay, discharge destination, and functional improvement. A total of 1154 patients were included in the study. Principal components analysis indicated that the data were effectively unidimensional, with length of stay being the most important component. Regression analysis demonstrated that the best predictor was the admission motor FIM score, explaining 38.9% of variance for length of stay, 37.4%.of variance for functional improvement and 16% of variance for discharge destination. The best explanatory variable in our inpatient rehabilitation service is the admission motor FIM. AN- SNAP v2 classification is a less effective explanatory variable. This needs to be taken into account when using AN-SNAP v2 classification for clinical or funding purposes.

Prediction of Knee Joint Contact Forces From External Measures Using Principal Component Prediction and Reconstruction.

PubMed

Saliba, Christopher M; Clouthier, Allison L; Brandon, Scott C E; Rainbow, Michael J; Deluzio, Kevin J

2018-05-29

Abnormal loading of the knee joint contributes to the pathogenesis of knee osteoarthritis. Gait retraining is a non-invasive intervention that aims to reduce knee loads by providing audible, visual, or haptic feedback of gait parameters. The computational expense of joint contact force prediction has limited real-time feedback to surrogate measures of the contact force, such as the knee adduction moment. We developed a method to predict knee joint contact forces using motion analysis and a statistical regression model that can be implemented in near real-time. Gait waveform variables were deconstructed using principal component analysis and a linear regression was used to predict the principal component scores of the contact force waveforms. Knee joint contact force waveforms were reconstructed using the predicted scores. We tested our method using a heterogenous population of asymptomatic controls and subjects with knee osteoarthritis. The reconstructed contact force waveforms had mean (SD) RMS differences of 0.17 (0.05) bodyweight compared to the contact forces predicted by a musculoskeletal model. Our method successfully predicted subject-specific shape features of contact force waveforms and is a potentially powerful tool in biofeedback and clinical gait analysis.

Construction of mathematical model for measuring material concentration by colorimetric method

NASA Astrophysics Data System (ADS)

Liu, Bing; Gao, Lingceng; Yu, Kairong; Tan, Xianghua

2018-06-01

This paper use the method of multiple linear regression to discuss the data of C problem of mathematical modeling in 2017. First, we have established a regression model for the concentration of 5 substances. But only the regression model of the substance concentration of urea in milk can pass through the significance test. The regression model established by the second sets of data can pass the significance test. But this model exists serious multicollinearity. We have improved the model by principal component analysis. The improved model is used to control the system so that it is possible to measure the concentration of material by direct colorimetric method.

Associations between Caries among Children and Household Sugar Procurement, Exposure to Fluoridated Water and Socioeconomic Indicators in the Brazilian Capital Cities

PubMed Central

Gonçalves, Michele Martins; Leles, Cláudio Rodrigues; Freire, Maria do Carmo Matias

2013-01-01

The objective of this ecological study was to investigate the association between caries experience in 5- and 12-year-old Brazilian children in 2010 and household sugar procurement in 2003 and the effects of exposure to water fluoridation and socioeconomic indicators. Sample units were all 27 Brazilian capital cities. Data were obtained from the National Surveys of Oral Health; the National Household Food Budget Survey; and the United Nations Program for Development. Data analysis included correlation coefficients, exploratory factor analysis, and linear regression. There were significant negative associations between caries experience and procurement of confectionery, fluoridated water, HDI, and per capita income. Procurement of confectionery and soft drinks was positively associated with HDI and per capita income. Exploratory factor analysis grouped the independent variables by reducing highly correlated variables into two uncorrelated component variables that explained 86.1% of total variance. The first component included income, HDI, water fluoridation, and procurement of confectionery, while the second included free sugar and procurement of soft drinks. Multiple regression analysis showed that caries is associated with the first component. Caries experience was associated with better socioeconomic indicators of a city and exposure to fluoridated water, which may affect the impact of sugars on the disease. PMID:24307900

A novel approach combining self-organizing map and parallel factor analysis for monitoring water quality of watersheds under non-point source pollution

PubMed Central

Zhang, Yixiang; Liang, Xinqiang; Wang, Zhibo; Xu, Lixian

2015-01-01

High content of organic matter in the downstream of watersheds underscored the severity of non-point source (NPS) pollution. The major objectives of this study were to characterize and quantify dissolved organic matter (DOM) in watersheds affected by NPS pollution, and to apply self-organizing map (SOM) and parallel factor analysis (PARAFAC) to assess fluorescence properties as proxy indicators for NPS pollution and labor-intensive routine water quality indicators. Water from upstreams and downstreams was sampled to measure dissolved organic carbon (DOC) concentrations and excitation-emission matrix (EEM). Five fluorescence components were modeled with PARAFAC. The regression analysis between PARAFAC intensities (Fmax) and raw EEM measurements indicated that several raw fluorescence measurements at target excitation-emission wavelength region could provide similar DOM information to massive EEM measurements combined with PARAFAC. Regression analysis between DOC concentration and raw EEM measurements suggested that some regions in raw EEM could be used as surrogates for labor-intensive routine indicators. SOM can be used to visualize the occurrence of pollution. Relationship between DOC concentration and PARAFAC components analyzed with SOM suggested that PARAFAC component 2 might be the major part of bulk DOC and could be recognized as a proxy indicator to predict the DOC concentration. PMID:26526140

Acoustic-articulatory mapping in vowels by locally weighted regression

PubMed Central

McGowan, Richard S.; Berger, Michael A.

2009-01-01

A method for mapping between simultaneously measured articulatory and acoustic data is proposed. The method uses principal components analysis on the articulatory and acoustic variables, and mapping between the domains by locally weighted linear regression, or loess [Cleveland, W. S. (1979). J. Am. Stat. Assoc. 74, 829–836]. The latter method permits local variation in the slopes of the linear regression, assuming that the function being approximated is smooth. The methodology is applied to vowels of four speakers in the Wisconsin X-ray Microbeam Speech Production Database, with formant analysis. Results are examined in terms of (1) examples of forward (articulation-to-acoustics) mappings and inverse mappings, (2) distributions of local slopes and constants, (3) examples of correlations among slopes and constants, (4) root-mean-square error, and (5) sensitivity of formant frequencies to articulatory change. It is shown that the results are qualitatively correct and that loess performs better than global regression. The forward mappings show different root-mean-square error properties than the inverse mappings indicating that this method is better suited for the forward mappings than the inverse mappings, at least for the data chosen for the current study. Some preliminary results on sensitivity of the first two formant frequencies to the two most important articulatory principal components are presented. PMID:19813812

Obscure phenomena in statistical analysis of quantitative structure-activity relationships. Part 1: Multicollinearity of physicochemical descriptors.

PubMed

Mager, P P; Rothe, H

1990-10-01

Multicollinearity of physicochemical descriptors leads to serious consequences in quantitative structure-activity relationship (QSAR) analysis, such as incorrect estimators and test statistics of regression coefficients of the ordinary least-squares (OLS) model applied usually to QSARs. Beside the diagnosis of the known simple collinearity, principal component regression analysis (PCRA) also allows the diagnosis of various types of multicollinearity. Only if the absolute values of PCRA estimators are order statistics that decrease monotonically, the effects of multicollinearity can be circumvented. Otherwise, obscure phenomena may be observed, such as good data recognition but low predictive model power of a QSAR model.

Short wavelength Raman spectroscopy applied to the discrimination and characterization of three cultivars of extra virgin olive oils in different maturation stages.

PubMed

Gouvinhas, Irene; Machado, Nelson; Carvalho, Teresa; de Almeida, José M M M; Barros, Ana I R N A

2015-01-01

Extra virgin olive oils produced from three cultivars on different maturation stages were characterized using Raman spectroscopy. Chemometric methods (principal component analysis, discriminant analysis, principal component regression and partial least squares regression) applied to Raman spectral data were utilized to evaluate and quantify the statistical differences between cultivars and their ripening process. The models for predicting the peroxide value and free acidity of olive oils showed good calibration and prediction values and presented high coefficients of determination (>0.933). Both the R(2), and the correlation equations between the measured chemical parameters, and the values predicted by each approach are presented; these comprehend both PCR and PLS, used to assess SNV normalized Raman data, as well as first and second derivative of the spectra. This study demonstrates that a combination of Raman spectroscopy with multivariate analysis methods can be useful to predict rapidly olive oil chemical characteristics during the maturation process. Copyright © 2014 Elsevier B.V. All rights reserved.

Structured functional additive regression in reproducing kernel Hilbert spaces

PubMed Central

Zhu, Hongxiao; Yao, Fang; Zhang, Hao Helen

2013-01-01

Summary Functional additive models (FAMs) provide a flexible yet simple framework for regressions involving functional predictors. The utilization of data-driven basis in an additive rather than linear structure naturally extends the classical functional linear model. However, the critical issue of selecting nonlinear additive components has been less studied. In this work, we propose a new regularization framework for the structure estimation in the context of Reproducing Kernel Hilbert Spaces. The proposed approach takes advantage of the functional principal components which greatly facilitates the implementation and the theoretical analysis. The selection and estimation are achieved by penalized least squares using a penalty which encourages the sparse structure of the additive components. Theoretical properties such as the rate of convergence are investigated. The empirical performance is demonstrated through simulation studies and a real data application. PMID:25013362

ECOPASS - a multivariate model used as an index of growth performance of poplar clones

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

Ceulemans, R.; Impens, I.

The model (ECOlogical PASSport) reported was constructed by principal component analysis from a combination of biochemical, anatomical/morphological and ecophysiological gas exchange parameters measured on 5 fast growing poplar clones. Productivity data were 10 selected trees in 3 plantations in Belgium and given as m.a.i.(b.a.). The model is shown to be able to reflect not only genetic origin and the relative effects of the different parameters of the clones, but also their production potential. Multiple regression analysis of the 4 principal components showed a high cumulative correlation (96%) between the 3 components related to ecophysiological, biochemical and morphological parameters, and productivity;more » the ecophysiological component alone correlated 85% with productivity.« less

Cognitive, emotive, and cognitive-behavioral correlates of suicidal ideation among Chinese adolescents in Hong Kong.

PubMed

Kwok, Sylvia Lai Yuk Ching; Shek, Daniel Tan Lei

2010-03-05

Utilizing Daniel Goleman's theory of emotional competence, Beck's cognitive theory, and Rudd's cognitive-behavioral theory of suicidality, the relationships between hopelessness (cognitive component), social problem solving (cognitive-behavioral component), emotional competence (emotive component), and adolescent suicidal ideation were examined. Based on the responses of 5,557 Secondary 1 to Secondary 4 students from 42 secondary schools in Hong Kong, results showed that suicidal ideation was positively related to adolescent hopelessness, but negatively related to emotional competence and social problem solving. While standard regression analyses showed that all the above variables were significant predictors of suicidal ideation, hierarchical regression analyses showed that hopelessness was the most important predictor of suicidal ideation, followed by social problem solving and emotional competence. Further regression analyses found that all four subscales of emotional competence, i.e., empathy, social skills, self-management of emotions, and utilization of emotions, were important predictors of male adolescent suicidal ideation. However, the subscale of social skills was not a significant predictor of female adolescent suicidal ideation. Standard regression analysis also revealed that all three subscales of social problem solving, i.e., negative problem orientation, rational problem solving, and impulsiveness/carelessness style, were important predictors of suicidal ideation. Theoretical and practice implications of the findings are discussed.

An EM-based semi-parametric mixture model approach to the regression analysis of competing-risks data.

PubMed

Ng, S K; McLachlan, G J

2003-04-15

We consider a mixture model approach to the regression analysis of competing-risks data. Attention is focused on inference concerning the effects of factors on both the probability of occurrence and the hazard rate conditional on each of the failure types. These two quantities are specified in the mixture model using the logistic model and the proportional hazards model, respectively. We propose a semi-parametric mixture method to estimate the logistic and regression coefficients jointly, whereby the component-baseline hazard functions are completely unspecified. Estimation is based on maximum likelihood on the basis of the full likelihood, implemented via an expectation-conditional maximization (ECM) algorithm. Simulation studies are performed to compare the performance of the proposed semi-parametric method with a fully parametric mixture approach. The results show that when the component-baseline hazard is monotonic increasing, the semi-parametric and fully parametric mixture approaches are comparable for mildly and moderately censored samples. When the component-baseline hazard is not monotonic increasing, the semi-parametric method consistently provides less biased estimates than a fully parametric approach and is comparable in efficiency in the estimation of the parameters for all levels of censoring. The methods are illustrated using a real data set of prostate cancer patients treated with different dosages of the drug diethylstilbestrol. Copyright 2003 John Wiley & Sons, Ltd.

Who Gets Market Supplements? Gender Differences within a Large Canadian University

ERIC Educational Resources Information Center

Doucet, Christine; Durand, Claire; Smith, Michael

2008-01-01

This study examines the gender pay gap among university faculty by analyzing gender differences in one component of faculty members' salaries--"market premiums." The data were collected during the Fall of 2002 using a survey of faculty at a single Canadian research university. Correspondence analysis and logistic regression analysis were…

A Developmental Model of Cross-Cultural Competence at the Tactical Level

DTIC Science & Technology

2010-11-01

components of 3C and describe how 3C develops in Soldiers. Five components of 3C were identified: Cultural Maturity , Cognitive Flexibility, Cultural...a result of the data analysis: Cultural Maturity , Cognitive Flexibility, Cultural Knowledge, Cultural Acuity, and Interpersonal Skills. These five...create regressions in the 3C development process. In short, KSAAs mature interdependently and simultaneously. Thus, development and transitions across

Membrane Introduction Mass Spectrometry Combined with an Orthogonal Partial-Least Squares Calibration Model for Mixture Analysis.

PubMed

Li, Min; Zhang, Lu; Yao, Xiaolong; Jiang, Xingyu

2017-01-01

The emerging membrane introduction mass spectrometry technique has been successfully used to detect benzene, toluene, ethyl benzene and xylene (BTEX), while overlapped spectra have unfortunately hindered its further application to the analysis of mixtures. Multivariate calibration, an efficient method to analyze mixtures, has been widely applied. In this paper, we compared univariate and multivariate analyses for quantification of the individual components of mixture samples. The results showed that the univariate analysis creates poor models with regression coefficients of 0.912, 0.867, 0.440 and 0.351 for BTEX, respectively. For multivariate analysis, a comparison to the partial-least squares (PLS) model shows that the orthogonal partial-least squares (OPLS) regression exhibits an optimal performance with regression coefficients of 0.995, 0.999, 0.980 and 0.976, favorable calibration parameters (RMSEC and RMSECV) and a favorable validation parameter (RMSEP). Furthermore, the OPLS exhibits a good recovery of 73.86 - 122.20% and relative standard deviation (RSD) of the repeatability of 1.14 - 4.87%. Thus, MIMS coupled with the OPLS regression provides an optimal approach for a quantitative BTEX mixture analysis in monitoring and predicting water pollution.

Protein-coding genes combined with long noncoding RNA as a novel transcriptome molecular staging model to predict the survival of patients with esophageal squamous cell carcinoma.

PubMed

Guo, Jin-Cheng; Wu, Yang; Chen, Yang; Pan, Feng; Wu, Zhi-Yong; Zhang, Jia-Sheng; Wu, Jian-Yi; Xu, Xiu-E; Zhao, Jian-Mei; Li, En-Min; Zhao, Yi; Xu, Li-Yan

2018-04-09

Esophageal squamous cell carcinoma (ESCC) is the predominant subtype of esophageal carcinoma in China. This study was to develop a staging model to predict outcomes of patients with ESCC. Using Cox regression analysis, principal component analysis (PCA), partitioning clustering, Kaplan-Meier analysis, receiver operating characteristic (ROC) curve analysis, and classification and regression tree (CART) analysis, we mined the Gene Expression Omnibus database to determine the expression profiles of genes in 179 patients with ESCC from GSE63624 and GSE63622 dataset. Univariate cox regression analysis of the GSE63624 dataset revealed that 2404 protein-coding genes (PCGs) and 635 long non-coding RNAs (lncRNAs) were associated with the survival of patients with ESCC. PCA categorized these PCGs and lncRNAs into three principal components (PCs), which were used to cluster the patients into three groups. ROC analysis demonstrated that the predictive ability of PCG-lncRNA PCs when applied to new patients was better than that of the tumor-node-metastasis staging (area under ROC curve [AUC]: 0.69 vs. 0.65, P < 0.05). Accordingly, we constructed a molecular disaggregated model comprising one lncRNA and two PCGs, which we designated as the LSB staging model using CART analysis in the GSE63624 dataset. This LSB staging model classified the GSE63622 dataset of patients into three different groups, and its effectiveness was validated by analysis of another cohort of 105 patients. The LSB staging model has clinical significance for the prognosis prediction of patients with ESCC and may serve as a three-gene staging microarray.

Physiological and anthropometric determinants of rhythmic gymnastics performance.

PubMed

Douda, Helen T; Toubekis, Argyris G; Avloniti, Alexandra A; Tokmakidis, Savvas P

2008-03-01

To identify the physiological and anthropometric predictors of rhythmic gymnastics performance, which was defined from the total ranking score of each athlete in a national competition. Thirty-four rhythmic gymnasts were divided into 2 groups, elite (n = 15) and nonelite (n = 19), and they underwent a battery of anthropometric, physical fitness, and physiological measurements. The principal-components analysis extracted 6 components: anthropometric, flexibility, explosive strength, aerobic capacity, body dimensions, and anaerobic metabolism. These were used in a simultaneous multiple-regression procedure to determine which best explain the variance in rhythmic gymnastics performance. Based on the principal-component analysis, the anthropometric component explained 45% of the total variance, flexibility 12.1%, explosive strength 9.2%, aerobic capacity 7.4%, body dimensions 6.8%, and anaerobic metabolism 4.6%. Components of anthropometric (r = .50) and aerobic capacity (r = .49) were significantly correlated with performance (P < .01). When the multiple-regression model-y = 10.708 + (0.0005121 x VO2max) + (0.157 x arm span) + (0.814 x midthigh circumference) - (0.293 x body mass)-was applied to elite gymnasts, 92.5% of the variation was explained by VO2max (58.9%), arm span (12%), midthigh circumference (13.1%), and body mass (8.5%). Selected anthropometric characteristics, aerobic power, flexibility, and explosive strength are important determinants of successful performance. These findings might have practical implications for both training and talent identification in rhythmic gymnastics.

Use of multivariate analysis for determining sources of solutes found in wet atmospheric deposition in the United States

USGS Publications Warehouse

Hooper, R.P.; Peters, N.E.

1989-01-01

A principal-components analysis was performed on the major solutes in wet deposition collected from 194 stations in the United States and its territories. Approximately 90% of the components derived could be interpreted as falling into one of three categories - acid, salt, or an agricultural/soil association. The total mass, or the mass of any one solute, was apportioned among these components by multiple linear regression techniques. The use of multisolute components for determining trends or spatial distribution represents a substantial improvement over single-solute analysis in that these components are more directly related to the sources of the deposition. The geographic patterns displayed by the components in this analysis indicate a far more important role for acid deposition in the Southeast and intermountain regions of the United States than would be indicated by maps of sulfate or nitrate deposition alone. In the Northeast and Midwest, the acid component is not declining at most stations, as would be expected from trends in sulfate deposition, but is holding constant or increasing. This is due, in part, to a decline in the agriculture/soil factor throughout this region, which would help to neutralize the acidity.

Protein quantification on dendrimer-activated surfaces by using time-of-flight secondary ion mass spectrometry and principal component regression

NASA Astrophysics Data System (ADS)

Kim, Young-Pil; Hong, Mi-Young; Shon, Hyun Kyong; Chegal, Won; Cho, Hyun Mo; Moon, Dae Won; Kim, Hak-Sung; Lee, Tae Geol

2008-12-01

Interaction between streptavidin and biotin on poly(amidoamine) (PAMAM) dendrimer-activated surfaces and on self-assembled monolayers (SAMs) was quantitatively studied by using time-of-flight secondary ion mass spectrometry (ToF-SIMS). The surface protein density was systematically varied as a function of protein concentration and independently quantified using the ellipsometry technique. Principal component analysis (PCA) and principal component regression (PCR) were used to identify a correlation between the intensities of the secondary ion peaks and the surface protein densities. From the ToF-SIMS and ellipsometry results, a good linear correlation of protein density was found. Our study shows that surface protein densities are higher on dendrimer-activated surfaces than on SAMs surfaces due to the spherical property of the dendrimer, and that these surface protein densities can be easily quantified with high sensitivity in a label-free manner by ToF-SIMS.

Analysis of the Importance of Oxides and Clays in Cd, Cr, Cu, Ni, Pb and Zn Adsorption and Retention with Regression Trees

PubMed Central

González-Costa, Juan José; Reigosa, Manuel Joaquín; Matías, José María; Fernández-Covelo, Emma

2017-01-01

This study determines the influence of the different soil components and of the cation-exchange capacity on the adsorption and retention of different heavy metals: cadmium, chromium, copper, nickel, lead and zinc. In order to do so, regression models were created through decision trees and the importance of soil components was assessed. Used variables were: humified organic matter, specific cation-exchange capacity, percentages of sand and silt, proportions of Mn, Fe and Al oxides and hematite, and the proportion of quartz, plagioclase and mica, and the proportions of the different clays: kaolinite, vermiculite, gibbsite and chlorite. The most important components in the obtained models were vermiculite and gibbsite, especially for the adsorption of cadmium and zinc, while clays were less relevant. Oxides are less important than clays, especially for the adsorption of chromium and lead and the retention of chromium, copper and lead. PMID:28072849

On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP.

PubMed

Winkler, Irene; Debener, Stefan; Müller, Klaus-Robert; Tangermann, Michael

2015-01-01

Standard artifact removal methods for electroencephalographic (EEG) signals are either based on Independent Component Analysis (ICA) or they regress out ocular activity measured at electrooculogram (EOG) channels. Successful ICA-based artifact reduction relies on suitable pre-processing. Here we systematically evaluate the effects of high-pass filtering at different frequencies. Offline analyses were based on event-related potential data from 21 participants performing a standard auditory oddball task and an automatic artifactual component classifier method (MARA). As a pre-processing step for ICA, high-pass filtering between 1-2 Hz consistently produced good results in terms of signal-to-noise ratio (SNR), single-trial classification accuracy and the percentage of `near-dipolar' ICA components. Relative to no artifact reduction, ICA-based artifact removal significantly improved SNR and classification accuracy. This was not the case for a regression-based approach to remove EOG artifacts.

Different brain activations between own- and other-race face categorization: an fMRI study using group independent component analysis

NASA Astrophysics Data System (ADS)

Wei, Wenjuan; Liu, Jiangang; Dai, Ruwei; Feng, Lu; Li, Ling; Tian, Jie

2014-03-01

Previous behavioral research has proved that individuals process own- and other-race faces differently. One well-known effect is the other-race effect (ORE), which indicates that individuals categorize other-race faces more accurately and faster than own-race faces. The existed functional magnetic resonance imaging (fMRI) studies of the other-race effect mainly focused on the racial prejudice and the socio-affective differences towards own- and other-race face. In the present fMRI study, we adopted a race-categorization task to determine the activation level differences between categorizing own- and other-race faces. Thirty one Chinese participants who live in China with Chinese as the majority and who had no direct contact with Caucasian individual were recruited in the present study. We used the group independent component analysis (ICA), which is a method of blind source signal separation that has proven to be promising for analysis of fMRI data. We separated the entail data into 56 components which is estimated based on one subject using the Minimal Description Length (MDL) criteria. The components sorted based on the multiple linear regression temporal sorting criteria, and the fit regression parameters were used in performing statistical test to evaluate the task-relatedness of the components. The one way anova was performed to test the significance of the component time course in different conditions. Our result showed that the areas, which coordinates is similar to the right FFA coordinates that previous studies reported, were greater activated for own-race faces than other-race faces, while the precuneus showed greater activation for other-race faces than own-race faces.

Poisson Mixture Regression Models for Heart Disease Prediction.

PubMed

Mufudza, Chipo; Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

Poisson Mixture Regression Models for Heart Disease Prediction

PubMed Central

Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

Weight estimation techniques for composite airplanes in general aviation industry

NASA Technical Reports Server (NTRS)

Paramasivam, T.; Horn, W. J.; Ritter, J.

1986-01-01

Currently available weight estimation methods for general aviation airplanes were investigated. New equations with explicit material properties were developed for the weight estimation of aircraft components such as wing, fuselage and empennage. Regression analysis was applied to the basic equations for a data base of twelve airplanes to determine the coefficients. The resulting equations can be used to predict the component weights of either metallic or composite airplanes.

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.

Cenesthopathy and Subjective Cognitive Complaints: An Exploratory Study in Schizophrenia.

PubMed

Jimeno, Natalia; Vargas, Martin L

2018-01-01

Cenesthopathy is mainly associated with schizophrenia; however, its neurobiological basis is nowadays unclear. The general objective was to explore clinical correlates of cenesthopathy and subjective cognitive complaints in schizophrenia. Participants (n = 30) meeting DSM-IV criteria for psychotic disorder were recruited from a psychiatry unit and assessed with: Association for Methodology and Documentation in Psychiatry (AMDP) system, Positive and Negative Syndrome Scale, Frankfurt Complaint Questionnaire (FCQ), and the Bonn Scale for the Assessment of Basic Symptoms (BSABS). For quantitative variables, means and Spearman correlation coefficients were calculated. Linear regression following backward method and principal component analysis with varimax rotation were used. 83.3% of subjects (73.3% male, mean age, 31.5 years) presented any type of cenesthopathy; all types of cenesthetic basic symptoms were found. Cenesthetic basic symptoms significantly correlated with the AMDP category "fear and anancasm," FCQ total score, and BSABS cognitive thought disturbances. In the regression analysis only 1 predictor, cognitive thought disturbances, entered the model. In the principal component analysis, a main component which accounted for 22.69% of the variance was found. Cenesthopathy, as assessed with the Bonn Scale (BSABS), is mainly associated with cog-nitive abnormalities including disturbances of thought initiative and mental intentionality, of receptive speech, and subjective retardation or pressure of thoughts. © 2018 S. Karger AG, Basel.

Serum Folate Shows an Inverse Association with Blood Pressure in a Cohort of Chinese Women of Childbearing Age: A Cross-Sectional Study

PubMed Central

Shen, Minxue; Tan, Hongzhuan; Zhou, Shujin; Retnakaran, Ravi; Smith, Graeme N.; Davidge, Sandra T.; Trasler, Jacquetta; Walker, Mark C.; Wen, Shi Wu

2016-01-01

Background It has been reported that higher folate intake from food and supplementation is associated with decreased blood pressure (BP). The association between serum folate concentration and BP has been examined in few studies. We aim to examine the association between serum folate and BP levels in a cohort of young Chinese women. Methods We used the baseline data from a pre-conception cohort of women of childbearing age in Liuyang, China, for this study. Demographic data were collected by structured interview. Serum folate concentration was measured by immunoassay, and homocysteine, blood glucose, triglyceride and total cholesterol were measured through standardized clinical procedures. Multiple linear regression and principal component regression model were applied in the analysis. Results A total of 1,532 healthy normotensive non-pregnant women were included in the final analysis. The mean concentration of serum folate was 7.5 ± 5.4 nmol/L and 55% of the women presented with folate deficiency (< 6.8 nmol/L). Multiple linear regression and principal component regression showed that serum folate levels were inversely associated with systolic and diastolic BP, after adjusting for demographic, anthropometric, and biochemical factors. Conclusions Serum folate is inversely associated with BP in non-pregnant women of childbearing age with high prevalence of folate deficiency. PMID:27182603

HYBRID NEURAL NETWORK AND SUPPORT VECTOR MACHINE METHOD FOR OPTIMIZATION

NASA Technical Reports Server (NTRS)

Rai, Man Mohan (Inventor)

2005-01-01

System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

Hybrid Neural Network and Support Vector Machine Method for Optimization

NASA Technical Reports Server (NTRS)

Rai, Man Mohan (Inventor)

2007-01-01

System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

Rank estimation and the multivariate analysis of in vivo fast-scan cyclic voltammetric data

PubMed Central

Keithley, Richard B.; Carelli, Regina M.; Wightman, R. Mark

2010-01-01

Principal component regression has been used in the past to separate current contributions from different neuromodulators measured with in vivo fast-scan cyclic voltammetry. Traditionally, a percent cumulative variance approach has been used to determine the rank of the training set voltammetric matrix during model development, however this approach suffers from several disadvantages including the use of arbitrary percentages and the requirement of extreme precision of training sets. Here we propose that Malinowski’s F-test, a method based on a statistical analysis of the variance contained within the training set, can be used to improve factor selection for the analysis of in vivo fast-scan cyclic voltammetric data. These two methods of rank estimation were compared at all steps in the calibration protocol including the number of principal components retained, overall noise levels, model validation as determined using a residual analysis procedure, and predicted concentration information. By analyzing 119 training sets from two different laboratories amassed over several years, we were able to gain insight into the heterogeneity of in vivo fast-scan cyclic voltammetric data and study how differences in factor selection propagate throughout the entire principal component regression analysis procedure. Visualizing cyclic voltammetric representations of the data contained in the retained and discarded principal components showed that using Malinowski’s F-test for rank estimation of in vivo training sets allowed for noise to be more accurately removed. Malinowski’s F-test also improved the robustness of our criterion for judging multivariate model validity, even though signal-to-noise ratios of the data varied. In addition, pH change was the majority noise carrier of in vivo training sets while dopamine prediction was more sensitive to noise. PMID:20527815

Detection of increase in corneal irregularity due to pterygium using Fourier series harmonic analyses with multiple diameters.

PubMed

Minami, Keiichiro; Miyata, Kazunori; Otani, Atsushi; Tokunaga, Tadatoshi; Tokuda, Shouta; Amano, Shiro

2018-05-01

To determine steep increase of corneal irregularity induced by advancement of pterygium. A total of 456 eyes from 456 consecutive patients with primary pterygia were examined for corneal topography and advancement of pterygium with respect to the corneal diameter. Corneal irregularity induced by the pterygium advancement was evaluated by Fourier harmonic analyses of the topographic data that were modified for a series of analysis diameters from 1 mm to 6 mm. Incidences of steep increases in the asymmetry or higher-order irregularity components (inflection points) were determined by using segmented regression analysis for each analysis diameter. The pterygium advancement ranged from 2% to 57%, with a mean of 22.0%. Both components showed steep increases from the inflection points. The inflection points in the higher-order irregularity component altered with the analysis diameter (14.0%-30.6%), while there was no alternation in the asymmetry components (35.5%-36.8%). For the former component, the values at the inflection points were obtained in a range of 0.16 to 0.25 D. The Fourier harmonic analyses for a series of analysis diameters revealed that the higher-order irregularity component increased with the pterygium advancement. The analysis results confirmed the precedence of corneal irregularity due to pterygium advancement.

Wastewater-Based Epidemiology of Stimulant Drugs: Functional Data Analysis Compared to Traditional Statistical Methods.

PubMed

Salvatore, Stefania; Bramness, Jørgen Gustav; Reid, Malcolm J; Thomas, Kevin Victor; Harman, Christopher; Røislien, Jo

2015-01-01

Wastewater-based epidemiology (WBE) is a new methodology for estimating the drug load in a population. Simple summary statistics and specification tests have typically been used to analyze WBE data, comparing differences between weekday and weekend loads. Such standard statistical methods may, however, overlook important nuanced information in the data. In this study, we apply functional data analysis (FDA) to WBE data and compare the results to those obtained from more traditional summary measures. We analysed temporal WBE data from 42 European cities, using sewage samples collected daily for one week in March 2013. For each city, the main temporal features of two selected drugs were extracted using functional principal component (FPC) analysis, along with simpler measures such as the area under the curve (AUC). The individual cities' scores on each of the temporal FPCs were then used as outcome variables in multiple linear regression analysis with various city and country characteristics as predictors. The results were compared to those of functional analysis of variance (FANOVA). The three first FPCs explained more than 99% of the temporal variation. The first component (FPC1) represented the level of the drug load, while the second and third temporal components represented the level and the timing of a weekend peak. AUC was highly correlated with FPC1, but other temporal characteristic were not captured by the simple summary measures. FANOVA was less flexible than the FPCA-based regression, and even showed concordance results. Geographical location was the main predictor for the general level of the drug load. FDA of WBE data extracts more detailed information about drug load patterns during the week which are not identified by more traditional statistical methods. Results also suggest that regression based on FPC results is a valuable addition to FANOVA for estimating associations between temporal patterns and covariate information.

Support vector regression and artificial neural network models for stability indicating analysis of mebeverine hydrochloride and sulpiride mixtures in pharmaceutical preparation: A comparative study

NASA Astrophysics Data System (ADS)

Naguib, Ibrahim A.; Darwish, Hany W.

2012-02-01

A comparison between support vector regression (SVR) and Artificial Neural Networks (ANNs) multivariate regression methods is established showing the underlying algorithm for each and making a comparison between them to indicate the inherent advantages and limitations. In this paper we compare SVR to ANN with and without variable selection procedure (genetic algorithm (GA)). To project the comparison in a sensible way, the methods are used for the stability indicating quantitative analysis of mixtures of mebeverine hydrochloride and sulpiride in binary mixtures as a case study in presence of their reported impurities and degradation products (summing up to 6 components) in raw materials and pharmaceutical dosage form via handling the UV spectral data. For proper analysis, a 6 factor 5 level experimental design was established resulting in a training set of 25 mixtures containing different ratios of the interfering species. An independent test set consisting of 5 mixtures was used to validate the prediction ability of the suggested models. The proposed methods (linear SVR (without GA) and linear GA-ANN) were successfully applied to the analysis of pharmaceutical tablets containing mebeverine hydrochloride and sulpiride mixtures. The results manifest the problem of nonlinearity and how models like the SVR and ANN can handle it. The methods indicate the ability of the mentioned multivariate calibration models to deconvolute the highly overlapped UV spectra of the 6 components' mixtures, yet using cheap and easy to handle instruments like the UV spectrophotometer.

A diagnostic analysis of the VVP single-doppler retrieval technique

NASA Technical Reports Server (NTRS)

Boccippio, Dennis J.

1995-01-01

A diagnostic analysis of the VVP (volume velocity processing) retrieval method is presented, with emphasis on understanding the technique as a linear, multivariate regression. Similarities and differences to the velocity-azimuth display and extended velocity-azimuth display retrieval techniques are discussed, using this framework. Conventional regression diagnostics are then employed to quantitatively determine situations in which the VVP technique is likely to fail. An algorithm for preparation and analysis of a robust VVP retrieval is developed and applied to synthetic and actual datasets with high temporal and spatial resolution. A fundamental (but quantifiable) limitation to some forms of VVP analysis is inadequate sampling dispersion in the n space of the multivariate regression, manifest as a collinearity between the basis functions of some fitted parameters. Such collinearity may be present either in the definition of these basis functions or in their realization in a given sampling configuration. This nonorthogonality may cause numerical instability, variance inflation (decrease in robustness), and increased sensitivity to bias from neglected wind components. It is shown that these effects prevent the application of VVP to small azimuthal sectors of data. The behavior of the VVP regression is further diagnosed over a wide range of sampling constraints, and reasonable sector limits are established.

Identification of milk origin and process-induced changes in milk by stable isotope ratio mass spectrometry.

PubMed

Scampicchio, Matteo; Mimmo, Tanja; Capici, Calogero; Huck, Christian; Innocente, Nadia; Drusch, Stephan; Cesco, Stefano

2012-11-14

Stable isotope values were used to develop a new analytical approach enabling the simultaneous identification of milk samples either processed with different heating regimens or from different geographical origins. The samples consisted of raw, pasteurized (HTST), and ultrapasteurized (UHT) milk from different Italian origins. The approach consisted of the analysis of the isotope ratio of δ¹³C and δ¹⁵N for the milk samples and their fractions (fat, casein, and whey). The main finding of this work is that as the heat processing affects the composition of the milk fractions, changes in δ¹³C and δ¹⁵N were also observed. These changes were used as markers to develop pattern recognition maps based on principal component analysis and supervised classification models, such as linear discriminant analysis (LDA), multivariate regression (MLR), principal component regression (PCR), and partial least-squares (PLS). The results give proof of the concept that isotope ratio mass spectroscopy can discriminate simultaneously between milk samples according to their geographical origin and type of processing.

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.

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

PubMed

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

2015-03-01

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

Has the Bologna Process Been Worthwhile? An Analysis of the Learning Society-Adapted Outcome Index through Quantile Regression

ERIC Educational Resources Information Center

Fernandez-Sainz, A.; García-Merino, J. D.; Urionabarrenetxea, S.

2016-01-01

This paper seeks to discover whether the performance of university students has improved in the wake of the changes in higher education introduced by the Bologna Declaration of 1999 and the construction of the European Higher Education Area. A principal component analysis is used to construct a multi-dimensional performance variable called the…

Improving the accuracy of Density Functional Theory (DFT) calculation for homolysis bond dissociation energies of Y-NO bond: generalized regression neural network based on grey relational analysis and principal component analysis.

PubMed

Li, Hong Zhi; Tao, Wei; Gao, Ting; Li, Hui; Lu, Ying Hua; Su, Zhong Min

2011-01-01

We propose a generalized regression neural network (GRNN) approach based on grey relational analysis (GRA) and principal component analysis (PCA) (GP-GRNN) to improve the accuracy of density functional theory (DFT) calculation for homolysis bond dissociation energies (BDE) of Y-NO bond. As a demonstration, this combined quantum chemistry calculation with the GP-GRNN approach has been applied to evaluate the homolysis BDE of 92 Y-NO organic molecules. The results show that the ull-descriptor GRNN without GRA and PCA (F-GRNN) and with GRA (G-GRNN) approaches reduce the root-mean-square (RMS) of the calculated homolysis BDE of 92 organic molecules from 5.31 to 0.49 and 0.39 kcal mol(-1) for the B3LYP/6-31G (d) calculation. Then the newly developed GP-GRNN approach further reduces the RMS to 0.31 kcal mol(-1). Thus, the GP-GRNN correction on top of B3LYP/6-31G (d) can improve the accuracy of calculating the homolysis BDE in quantum chemistry and can predict homolysis BDE which cannot be obtained experimentally.

Binding affinity toward human prion protein of some anti-prion compounds - Assessment based on QSAR modeling, molecular docking and non-parametric ranking.

PubMed

Kovačević, Strahinja; Karadžić, Milica; Podunavac-Kuzmanović, Sanja; Jevrić, Lidija

2018-01-01

The present study is based on the quantitative structure-activity relationship (QSAR) analysis of binding affinity toward human prion protein (huPrP C ) of quinacrine, pyridine dicarbonitrile, diphenylthiazole and diphenyloxazole analogs applying different linear and non-linear chemometric regression techniques, including univariate linear regression, multiple linear regression, partial least squares regression and artificial neural networks. The QSAR analysis distinguished molecular lipophilicity as an important factor that contributes to the binding affinity. Principal component analysis was used in order to reveal similarities or dissimilarities among the studied compounds. The analysis of in silico absorption, distribution, metabolism, excretion and toxicity (ADMET) parameters was conducted. The ranking of the studied analogs on the basis of their ADMET parameters was done applying the sum of ranking differences, as a relatively new chemometric method. The main aim of the study was to reveal the most important molecular features whose changes lead to the changes in the binding affinities of the studied compounds. Another point of view on the binding affinity of the most promising analogs was established by application of molecular docking analysis. The results of the molecular docking were proven to be in agreement with the experimental outcome. Copyright © 2017 Elsevier B.V. All rights reserved.

Aroma profile and sensory characteristics of a sulfur dioxide-free mulberry (Morus nigra) wine subjected to non-thermal accelerating aging techniques.

PubMed

Tchabo, William; Ma, Yongkun; Kwaw, Emmanuel; Zhang, Haining; Xiao, Lulu; Tahir, Haroon Elrasheid

2017-10-01

The present study was undertaken to assess accelerating aging effects of high pressure, ultrasound and manosonication on the aromatic profile and sensorial attributes of aged mulberry wines (AMW). A total of 166 volatile compounds were found amongst the AMW. The outcomes of the investigation were presented by means of geometric mean (GM), cluster analysis (CA), principal component analysis (PCA), partial least squares regressions (PLSR) and principal component regression (PCR). GM highlighted 24 organoleptic attributes responsible for the sensorial profile of the AMW. Moreover, CA revealed that the volatile composition of the non-thermal accelerated aged wines differs from that of the conventional aged wines. Besides, PCA discriminated the AMW on the basis of their main sensorial characteristics. Furthermore, PLSR identified 75 aroma compounds which were mainly responsible for the olfactory notes of the AMW. Finally, the overall quality of the AMW was noted to be better predicted by PLSR than PCR. Copyright © 2017 Elsevier Ltd. All rights reserved.

A hybrid sales forecasting scheme by combining independent component analysis with K-means clustering and support vector regression.

PubMed

Lu, Chi-Jie; Chang, Chi-Chang

2014-01-01

Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA) with K-means clustering and support vector regression (SVR). The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT) product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting.

A Hybrid Sales Forecasting Scheme by Combining Independent Component Analysis with K-Means Clustering and Support Vector Regression

PubMed Central

2014-01-01

Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA) with K-means clustering and support vector regression (SVR). The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT) product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting. PMID:25045738

Toward the International Classification of Functioning, Disability and Health (ICF) Rehabilitation Set: A Minimal Generic Set of Domains for Rehabilitation as a Health Strategy.

PubMed

Prodinger, Birgit; Cieza, Alarcos; Oberhauser, Cornelia; Bickenbach, Jerome; Üstün, Tevfik Bedirhan; Chatterji, Somnath; Stucki, Gerold

2016-06-01

To develop a comprehensive set of the International Classification of Functioning, Disability and Health (ICF) categories as a minimal standard for reporting and assessing functioning and disability in clinical populations along the continuum of care. The specific aims were to specify the domains of functioning recommended for an ICF Rehabilitation Set and to identify a minimal set of environmental factors (EFs) to be used alongside the ICF Rehabilitation Set when describing disability across individuals and populations with various health conditions. Secondary analysis of existing data sets using regression methods (Random Forests and Group Lasso regression) and expert consultations. Along the continuum of care, including acute, early postacute, and long-term and community rehabilitation settings. Persons (N=9863) with various health conditions participated in primary studies. The number of respondents for whom the dependent variable data were available and used in this analysis was 9264. Not applicable. For regression analyses, self-reported general health was used as a dependent variable. The ICF categories from the functioning component and the EF component were used as independent variables for the development of the ICF Rehabilitation Set and the minimal set of EFs, respectively. Thirty ICF categories to be complemented with 12 EFs were identified as relevant to the identified ICF sets. The ICF Rehabilitation Set constitutes of 9 ICF categories from the component body functions and 21 from the component activities and participation. The minimal set of EFs contains 12 categories spanning all chapters of the EF component of the ICF. The identified sets proposed serve as minimal generic sets of aspects of functioning in clinical populations for reporting data within and across heath conditions, time, clinical settings including rehabilitation, and countries. These sets present a reference framework for harmonizing existing information on disability across general and clinical populations. Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Structure and content components of self-management interventions that improve health-related quality of life in people with inflammatory bowel disease: a systematic review, meta-analysis and meta-regression.

PubMed

Tu, Wenjing; Xu, Guihua; Du, Shizheng

2015-10-01

The purpose of this review was to identify and categorise the components of the content and structure of effective self-management interventions for patients with inflammatory bowel disease. Inflammatory bowel diseases are chronic gastrointestinal disorders impacting health-related quality of life. Although the efficacy of self-management interventions has been demonstrated in previous studies, the most effective components of the content and structure of these interventions remain unknown. A systematic review, meta-analysis and meta-regression of randomised controlled trials was used. A systematic search of six electronic databases, including Pubmed, Embase, Cochrane central register of controlled trials, Web of Science, Cumulative Index of Nursing and Allied Health Literature and Chinese Biomedical Literature Database, was conducted. Content analysis was used to categorise the components of the content and structure of effective self-management interventions for inflammatory bowel disease. Clinically important and statistically significant beneficial effects on health-related quality of life were explored, by comparing the association between effect sizes and various components of self-management interventions such as the presence or absence of specific content and different delivery methods. Fifteen randomised controlled trials were included in this review. Distance or remote self-management interventions demonstrated a larger effect size. However, there is no evidence for a positive effect associated with specific content component of self-management interventions in adult patients with inflammatory bowel disease in general. The results showed that self-management interventions have positive effects on health-related quality of life in patients with inflammatory bowel disease, and distance or remote self-management programmes had better outcomes than other types of interventions. This review provides useful information to clinician and researchers when determining components of effective self-management programmes for patients with inflammatory bowel disease. More high-quality randomised controlled trials are needed to test the results. © 2015 John Wiley & Sons Ltd.

Comparison of vertical E × B drift velocities and ground-based magnetometer observations of DELTA H in the low latitude under geomagnetically disturbed conditions

NASA Astrophysics Data System (ADS)

Prabhu, M.; Unnikrishnan, K.

2018-04-01

In the present work, we analyzed the daytime vertical E × B drift velocities obtained from Jicamarca Unattended Long-term Ionosphere Atmosphere (JULIA) radar and ΔH component of geomagnetic field measured as the difference between the magnitudes of the horizontal (H) components between two magnetometers deployed at two different locations Jicamarca, and Piura in Peru for 22 geomagnetically disturbed events in which either SC has occurred or Dstmax < -50 nT during the period 2006-2011. The ΔH component of geomagnetic field is measured as the differences in the magnitudes of horizontal H component between magnetometer placed directly on the magnetic equator and one displaced 6-9° away. It will provide a direct measure of the daytime electrojet current, due to the eastward electric field. This will in turn gives the magnitude of vertical E × B drift velocity in the F region. A positive correlation exists between peak values of daytime vertical E × B drift velocity and peak value of ΔH for the three consecutive days of the events. It was observed that 45% of the events have daytime vertical E × B drift velocity peak in the magnitude range 10-20 m/s and 20-30 m/s and 20% have peak ΔH in the magnitude range 50-60 nT and 80-90 nT. It was observed that the time of occurrence of the peak value of both the vertical E × B drift velocity and the ΔH have a maximum (40%) probability in the same time range 11:00-13:00 LT. We also investigated the correlation between E × B drift velocity and Dst index and the correlation between delta H and Dst index. A strong positive correlation is found between E × B drift and Dst index as well as between delta H and Dst Index. Three different techniques of data analysis - linear, polynomial (order 2), and polynomial (order 3) regression analysis were considered. The regression parameters in all the three cases were calculated using the Least Square Method (LSM), using the daytime vertical E × B drift velocity and ΔH. A formula was developed which indicates the relationship between daytime vertical E × B drift velocity and ΔH, for the disturbed periods. The E × B drift velocity was then evaluated using the formulae thus found for the three regression analysis and validated for the 'disturbed periods' of 3 selected events. The E × B drift velocities estimated by the three regression analysis have a fairly good agreement with JULIA radar observed values under different seasons and solar activity conditions. Root Mean Square (RMS) errors calculated for each case suggest that polynomial (order 3) regression analysis provides a better agreement with the observations from among the three.

Quantitative genetic analysis of the body composition and blood pressure association in two ethnically diverse populations.

PubMed

Ghosh, Sudipta; Dosaev, Tasbulat; Prakash, Jai; Livshits, Gregory

2017-04-01

The major aim of this study was to conduct comparative quantitative-genetic analysis of the body composition (BCP) and somatotype (STP) variation, as well as their correlations with blood pressure (BP) in two ethnically, culturally and geographically different populations: Santhal, indigenous ethnic group from India and Chuvash, indigenous population from Russia. Correspondently two pedigree-based samples were collected from 1,262 Santhal and1,558 Chuvash individuals, respectively. At the first stage of the study, descriptive statistics and a series of univariate regression analyses were calculated. Finally, multiple and multivariate regression (MMR) analyses, with BP measurements as dependent variables and age, sex, BCP and STP as independent variables were carried out in each sample separately. The significant and independent covariates of BP were identified and used for re-examination in pedigree-based variance decomposition analysis. Despite clear and significant differences between the populations in BCP/STP, both Santhal and Chuvash were found to be predominantly mesomorphic irrespective of their sex. According to MMR analyses variation of BP significantly depended on age and mesomorphic component in both samples, and in addition on sex, ectomorphy and fat mass index in Santhal and on fat free mass index in Chuvash samples, respectively. Additive genetic component contributes to a substantial proportion of blood pressure and body composition variance. Variance component analysis in addition to above mentioned results suggests that additive genetic factors influence BP and BCP/STP associations significantly. © 2017 Wiley Periodicals, Inc.

Household Food Waste: Multivariate Regression and Principal Components Analyses of Awareness and Attitudes among U.S. Consumers

PubMed Central

2016-01-01

We estimate models of consumer food waste awareness and attitudes using responses from a national survey of U.S. residents. Our models are interpreted through the lens of several theories that describe how pro-social behaviors relate to awareness, attitudes and opinions. Our analysis of patterns among respondents’ food waste attitudes yields a model with three principal components: one that represents perceived practical benefits households may lose if food waste were reduced, one that represents the guilt associated with food waste, and one that represents whether households feel they could be doing more to reduce food waste. We find our respondents express significant agreement that some perceived practical benefits are ascribed to throwing away uneaten food, e.g., nearly 70% of respondents agree that throwing away food after the package date has passed reduces the odds of foodborne illness, while nearly 60% agree that some food waste is necessary to ensure meals taste fresh. We identify that these attitudinal responses significantly load onto a single principal component that may represent a key attitudinal construct useful for policy guidance. Further, multivariate regression analysis reveals a significant positive association between the strength of this component and household income, suggesting that higher income households most strongly agree with statements that link throwing away uneaten food to perceived private benefits. PMID:27441687

Men's Alcohol Expectancies at Selected Community Colleges

ERIC Educational Resources Information Center

Derby, Dustin C.

2011-01-01

Men's alcohol expectancies are an important cognitive-behavioral component of their consumption; yet, sparse research details such behaviors for men in two-year colleges. Selected for inclusion with the current study were 563 men from seven Illinois community colleges. Logistic regression analysis indicated four significant, positive relationships…

Prediction of sweetness and amino acid content in soybean crops from hyperspectral imagery

NASA Astrophysics Data System (ADS)

Monteiro, Sildomar Takahashi; Minekawa, Yohei; Kosugi, Yukio; Akazawa, Tsuneya; Oda, Kunio

Hyperspectral image data provides a powerful tool for non-destructive crop analysis. This paper investigates a hyperspectral image data-processing method to predict the sweetness and amino acid content of soybean crops. Regression models based on artificial neural networks were developed in order to calculate the level of sucrose, glucose, fructose, and nitrogen concentrations, which can be related to the sweetness and amino acid content of vegetables. A performance analysis was conducted comparing regression models obtained using different preprocessing methods, namely, raw reflectance, second derivative, and principal components analysis. This method is demonstrated using high-resolution hyperspectral data of wavelengths ranging from the visible to the near infrared acquired from an experimental field of green vegetable soybeans. The best predictions were achieved using a nonlinear regression model of the second derivative transformed dataset. Glucose could be predicted with greater accuracy, followed by sucrose, fructose and nitrogen. The proposed method provides the possibility to provide relatively accurate maps predicting the chemical content of soybean crop fields.

Gene set analysis using variance component tests.

PubMed

Huang, Yen-Tsung; Lin, Xihong

2013-06-28

Gene set analyses have become increasingly important in genomic research, as many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional repertoire, e.g., a biological pathway/network and are highly correlated. However, most of the existing gene set analysis methods do not fully account for the correlation among the genes. Here we propose to tackle this important feature of a gene set to improve statistical power in gene set analyses. We propose to model the effects of an independent variable, e.g., exposure/biological status (yes/no), on multiple gene expression values in a gene set using a multivariate linear regression model, where the correlation among the genes is explicitly modeled using a working covariance matrix. We develop TEGS (Test for the Effect of a Gene Set), a variance component test for the gene set effects by assuming a common distribution for regression coefficients in multivariate linear regression models, and calculate the p-values using permutation and a scaled chi-square approximation. We show using simulations that type I error is protected under different choices of working covariance matrices and power is improved as the working covariance approaches the true covariance. The global test is a special case of TEGS when correlation among genes in a gene set is ignored. Using both simulation data and a published diabetes dataset, we show that our test outperforms the commonly used approaches, the global test and gene set enrichment analysis (GSEA). We develop a gene set analyses method (TEGS) under the multivariate regression framework, which directly models the interdependence of the expression values in a gene set using a working covariance. TEGS outperforms two widely used methods, GSEA and global test in both simulation and a diabetes microarray data.

Pathways to the Bachelor's Degree for Latino Students. Latino Students & the Educational Pipeline, Part III

ERIC Educational Resources Information Center

Swail, Watson Scott; Cabrera, Alberto F.; Lee, Chul; Williams, Adriane

2005-01-01

The third component of our three-part series focuses on students who attained a bachelor's degree and what it took to get there. We used multiple regression analysis to determine the factors that seemed to matter on the pathway to the BA. The appendix of this report provides methodological details to this analysis. (Contains 2 footnotes and 7…

Identifying Nanoscale Structure-Function Relationships Using Multimodal Atomic Force Microscopy, Dimensionality Reduction, and Regression Techniques.

PubMed

Kong, Jessica; Giridharagopal, Rajiv; Harrison, Jeffrey S; Ginger, David S

2018-05-31

Correlating nanoscale chemical specificity with operational physics is a long-standing goal of functional scanning probe microscopy (SPM). We employ a data analytic approach combining multiple microscopy modes, using compositional information in infrared vibrational excitation maps acquired via photoinduced force microscopy (PiFM) with electrical information from conductive atomic force microscopy. We study a model polymer blend comprising insulating poly(methyl methacrylate) (PMMA) and semiconducting poly(3-hexylthiophene) (P3HT). We show that PiFM spectra are different from FTIR spectra, but can still be used to identify local composition. We use principal component analysis to extract statistically significant principal components and principal component regression to predict local current and identify local polymer composition. In doing so, we observe evidence of semiconducting P3HT within PMMA aggregates. These methods are generalizable to correlated SPM data and provide a meaningful technique for extracting complex compositional information that are impossible to measure from any one technique.

Identifying changes in dissolved organic matter content and characteristics by fluorescence spectroscopy coupled with self-organizing map and classification and regression tree analysis during wastewater treatment.

PubMed

Yu, Huibin; Song, Yonghui; Liu, Ruixia; Pan, Hongwei; Xiang, Liancheng; Qian, Feng

2014-10-01

The stabilization of latent tracers of dissolved organic matter (DOM) of wastewater was analyzed by three-dimensional excitation-emission matrix (EEM) fluorescence spectroscopy coupled with self-organizing map and classification and regression tree analysis (CART) in wastewater treatment performance. DOM of water samples collected from primary sedimentation, anaerobic, anoxic, oxic and secondary sedimentation tanks in a large-scale wastewater treatment plant contained four fluorescence components: tryptophan-like (C1), tyrosine-like (C2), microbial humic-like (C3) and fulvic-like (C4) materials extracted by self-organizing map. These components showed good positive linear correlations with dissolved organic carbon of DOM. C1 and C2 were representative components in the wastewater, and they were removed to a higher extent than those of C3 and C4 in the treatment process. C2 was a latent parameter determined by CART to differentiate water samples of oxic and secondary sedimentation tanks from the successive treatment units, indirectly proving that most of tyrosine-like material was degraded by anaerobic microorganisms. C1 was an accurate parameter to comprehensively separate the samples of the five treatment units from each other, indirectly indicating that tryptophan-like material was decomposed by anaerobic and aerobic bacteria. EEM fluorescence spectroscopy in combination with self-organizing map and CART analysis can be a nondestructive effective method for characterizing structural component of DOM fractions and monitoring organic matter removal in wastewater treatment process. Copyright © 2014 Elsevier Ltd. All rights reserved.

Linear regression analysis and its application to multivariate chromatographic calibration for the quantitative analysis of two-component mixtures.

PubMed

Dinç, Erdal; Ozdemir, Abdil

2005-01-01

Multivariate chromatographic calibration technique was developed for the quantitative analysis of binary mixtures enalapril maleate (EA) and hydrochlorothiazide (HCT) in tablets in the presence of losartan potassium (LST). The mathematical algorithm of multivariate chromatographic calibration technique is based on the use of the linear regression equations constructed using relationship between concentration and peak area at the five-wavelength set. The algorithm of this mathematical calibration model having a simple mathematical content was briefly described. This approach is a powerful mathematical tool for an optimum chromatographic multivariate calibration and elimination of fluctuations coming from instrumental and experimental conditions. This multivariate chromatographic calibration contains reduction of multivariate linear regression functions to univariate data set. The validation of model was carried out by analyzing various synthetic binary mixtures and using the standard addition technique. Developed calibration technique was applied to the analysis of the real pharmaceutical tablets containing EA and HCT. The obtained results were compared with those obtained by classical HPLC method. It was observed that the proposed multivariate chromatographic calibration gives better results than classical HPLC.

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

PubMed

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

2018-01-01

Functional linear regression models are effectively used in gene-based association analysis of complex traits. These models combine information about individual genetic variants, taking into account their positions and reducing the influence of noise and/or observation errors. To increase the power of methods, where several differently informative components are combined, weights are introduced to give the advantage to more informative components. Allele-specific weights have been introduced to collapsing and kernel-based approaches to gene-based association analysis. Here we have for the first time introduced weights to functional linear regression models adapted for both independent and family samples. Using data simulated on the basis of GAW17 genotypes and weights defined by allele frequencies via the beta distribution, we demonstrated that type I errors correspond to declared values and that increasing the weights of causal variants allows the power of functional linear models to be increased. We applied the new method to real data on blood pressure from the ORCADES sample. Five of the six known genes with P < 0.1 in at least one analysis had lower P values with weighted models. Moreover, we found an association between diastolic blood pressure and the VMP1 gene (P = 8.18×10-6), when we used a weighted functional model. For this gene, the unweighted functional and weighted kernel-based models had P = 0.004 and 0.006, respectively. The new method has been implemented in the program package FREGAT, which is freely available at https://cran.r-project.org/web/packages/FREGAT/index.html.

SPATIAL ANALYSIS OF AIR POLLUTION AND DEVELOPMENT OF A LAND-USE REGRESSION ( LUR ) MODEL IN AN URBAN AIRSHED

EPA Science Inventory

The Detroit Children's Health Study is an epidemiologic study examining associations between chronic ambient environmental exposures to gaseous air pollutants and respiratory health outcomes among elementary school-age children in an urban airshed. The exposure component of this...

A Genetic Analysis of Individual Differences in Dissociative Behaviors in Childhood and Adolescence

ERIC Educational Resources Information Center

Becker-Blease, Kathryn A.; Deater-Deckard, Kirby; Eley, Thalia; Freyd, Jennifer J.; Stevenson, Jim; Plomin, Robert

2004-01-01

Background: Dissociation--a pattern of general disruption in memory and consciousness--has been found to be an important cognitive component of children's and adults' coping with severe trauma. Dissociative experiences include amnesia, identity disturbance, age regression, difficulty with concentration, and trance states. Stable individual…

Explaining Relationships among Student Outcomes and the School's Physical Environment

ERIC Educational Resources Information Center

Tanner, C. Kenneth

2008-01-01

This descriptive study investigated the possible effects of selected school design patterns on third-grade students' academic achievement. A reduced regression analysis revealed the effects of school design components (patterns) on ITBS achievement data, after including control variables, for a sample of third-grade students drawn from 24…

Biomass estimates of eastern red cedar tree components

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

Schnell, R.L.

1976-02-01

Fresh and dry-weight relationships of species of the eastern red cedar (Juniperus virginiana L.) found in the Tennessee Valley are presented. Both wood and bark were analyzed. All fresh and dry weights tabulated were computed from predicting equations developed by multiple regression analysis of field data. (JGB)

Application and interpretation of functional data analysis techniques to differential scanning calorimetry data from lupus patients.

PubMed

Kendrick, Sarah K; Zheng, Qi; Garbett, Nichola C; Brock, Guy N

2017-01-01

DSC is used to determine thermally-induced conformational changes of biomolecules within a blood plasma sample. Recent research has indicated that DSC curves (or thermograms) may have different characteristics based on disease status and, thus, may be useful as a monitoring and diagnostic tool for some diseases. Since thermograms are curves measured over a range of temperature values, they are considered functional data. In this paper we apply functional data analysis techniques to analyze differential scanning calorimetry (DSC) data from individuals from the Lupus Family Registry and Repository (LFRR). The aim was to assess the effect of lupus disease status as well as additional covariates on the thermogram profiles, and use FD analysis methods to create models for classifying lupus vs. control patients on the basis of the thermogram curves. Thermograms were collected for 300 lupus patients and 300 controls without lupus who were matched with diseased individuals based on sex, race, and age. First, functional regression with a functional response (DSC) and categorical predictor (disease status) was used to determine how thermogram curve structure varied according to disease status and other covariates including sex, race, and year of birth. Next, functional logistic regression with disease status as the response and functional principal component analysis (FPCA) scores as the predictors was used to model the effect of thermogram structure on disease status prediction. The prediction accuracy for patients with Osteoarthritis and Rheumatoid Arthritis but without Lupus was also calculated to determine the ability of the classifier to differentiate between Lupus and other diseases. Data were divided 1000 times into separate 2/3 training and 1/3 test data for evaluation of predictions. Finally, derivatives of thermogram curves were included in the models to determine whether they aided in prediction of disease status. Functional regression with thermogram as a functional response and disease status as predictor showed a clear separation in thermogram curve structure between cases and controls. The logistic regression model with FPCA scores as the predictors gave the most accurate results with a mean 79.22% correct classification rate with a mean sensitivity = 79.70%, and specificity = 81.48%. The model correctly classified OA and RA patients without Lupus as controls at a rate of 75.92% on average with a mean sensitivity = 79.70% and specificity = 77.6%. Regression models including FPCA scores for derivative curves did not perform as well, nor did regression models including covariates. Changes in thermograms observed in the disease state likely reflect covalent modifications of plasma proteins or changes in large protein-protein interacting networks resulting in the stabilization of plasma proteins towards thermal denaturation. By relating functional principal components from thermograms to disease status, our Functional Principal Component Analysis model provides results that are more easily interpretable compared to prior studies. Further, the model could also potentially be coupled with other biomarkers to improve diagnostic classification for lupus.

Experimental investigation of fuel regression rate in a HTPB based lab-scale hybrid rocket motor

NASA Astrophysics Data System (ADS)

Li, Xintian; Tian, Hui; Yu, Nanjia; Cai, Guobiao

2014-12-01

The fuel regression rate is an important parameter in the design process of the hybrid rocket motor. Additives in the solid fuel may have influences on the fuel regression rate, which will affect the internal ballistics of the motor. A series of firing experiments have been conducted on lab-scale hybrid rocket motors with 98% hydrogen peroxide (H2O2) oxidizer and hydroxyl terminated polybutadiene (HTPB) based fuels in this paper. An innovative fuel regression rate analysis method is established to diminish the errors caused by start and tailing stages in a short time firing test. The effects of the metal Mg, Al, aromatic hydrocarbon anthracene (C14H10), and carbon black (C) on the fuel regression rate are investigated. The fuel regression rate formulas of different fuel components are fitted according to the experiment data. The results indicate that the influence of C14H10 on the fuel regression rate of HTPB is not evident. However, the metal additives in the HTPB fuel can increase the fuel regression rate significantly.

Determination of the chemical parameters and manufacturer of divins from their broadband transmission spectra

NASA Astrophysics Data System (ADS)

Khodasevich, M. A.; Sinitsyn, G. V.; Skorbanova, E. A.; Rogovaya, M. V.; Kambur, E. I.; Aseev, V. A.

2016-06-01

Analysis of multiparametric data on transmission spectra of 24 divins (Moldovan cognacs) in the 190-2600 nm range allows identification of outliers and their removal from a sample under study in the following consideration. The principal component analysis and classification tree with a single-rank predictor constructed in the 2D space of principal components allow classification of divin manufacturers. It is shown that the accuracy of syringaldehyde, ethyl acetate, vanillin, and gallic acid concentrations in divins calculated with the regression to latent structures depends on the sample volume and is 3, 6, 16, and 20%, respectively, which is acceptable for the application.

[Optimization of processing technology for semen cuscuta by uniform and regression analysis].

PubMed

Li, Chun-yu; Luo, Hui-yu; Wang, Shu; Zhai, Ya-nan; Tian, Shu-hui; Zhang, Dan-shen

2011-02-01

To optimize the best preparation technology for the contains of total flavornoids, polysaccharides, the percentage of water and alcohol-soluble components in Semen Cuscuta herb processing. UV-spectrophotometry was applied to determine the contains of total flavornoids and polysaccharides, which were extracted from Semen Cuscuta. And the processing was optimized by the way of uniform design and contour map. The best preparation technology was satisfied with some conditions as follows: baking temperature 150 degrees C, baking time 140 seconds. The regression models are notable and reasonable, which can forecast results precisely.

Comprehensive investigation into historical pipeline construction costs and engineering economic analysis of Alaska in-state gas pipeline

NASA Astrophysics Data System (ADS)

Rui, Zhenhua

This study analyzes historical cost data of 412 pipelines and 220 compressor stations. On the basis of this analysis, the study also evaluates the feasibility of an Alaska in-state gas pipeline using Monte Carlo simulation techniques. Analysis of pipeline construction costs shows that component costs, shares of cost components, and learning rates for material and labor costs vary by diameter, length, volume, year, and location. Overall average learning rates for pipeline material and labor costs are 6.1% and 12.4%, respectively. Overall average cost shares for pipeline material, labor, miscellaneous, and right of way (ROW) are 31%, 40%, 23%, and 7%, respectively. Regression models are developed to estimate pipeline component costs for different lengths, cross-sectional areas, and locations. An analysis of inaccuracy in pipeline cost estimation demonstrates that the cost estimation of pipeline cost components is biased except for in the case of total costs. Overall overrun rates for pipeline material, labor, miscellaneous, ROW, and total costs are 4.9%, 22.4%, -0.9%, 9.1%, and 6.5%, respectively, and project size, capacity, diameter, location, and year of completion have different degrees of impacts on cost overruns of pipeline cost components. Analysis of compressor station costs shows that component costs, shares of cost components, and learning rates for material and labor costs vary in terms of capacity, year, and location. Average learning rates for compressor station material and labor costs are 12.1% and 7.48%, respectively. Overall average cost shares of material, labor, miscellaneous, and ROW are 50.6%, 27.2%, 21.5%, and 0.8%, respectively. Regression models are developed to estimate compressor station component costs in different capacities and locations. An investigation into inaccuracies in compressor station cost estimation demonstrates that the cost estimation for compressor stations is biased except for in the case of material costs. Overall average overrun rates for compressor station material, labor, miscellaneous, land, and total costs are 3%, 60%, 2%, -14%, and 11%, respectively, and cost overruns for cost components are influenced by location and year of completion to different degrees. Monte Carlo models are developed and simulated to evaluate the feasibility of an Alaska in-state gas pipeline by assigning triangular distribution of the values of economic parameters. Simulated results show that the construction of an Alaska in-state natural gas pipeline is feasible at three scenarios: 500 million cubic feet per day (mmcfd), 750 mmcfd, and 1000 mmcfd.

[HPLC fingerprint of flavonoids in Sophora flavescens and determination of five components].

PubMed

Ma, Hong-Yan; Zhou, Wan-Shan; Chu, Fu-Jiang; Wang, Dong; Liang, Sheng-Wang; Li, Shao

2013-08-01

A simple and reliable method of high-performance liquid chromatography with photodiode array detection (HPLC-DAD) was developed to evaluate the quality of a traditional Chinese medicine Sophora flavescens through establishing chromatographic fingerprint and simultaneous determination of five flavonoids, including trifolirhizin, maackiain, kushenol I, kurarinone and sophoraflavanone G. The optimal conditions of separation and detection were achieved on an ULTIMATE XB-C18 column (4.6 mm x 250 mm, 5 microm) with a gradient of acetonitrile and water, detected at 295 nm. In the chromatographic fingerprint, 13 peaks were selected as the characteristic peaks to assess the similarities of different samples collected from different origins in China according to similarity evaluation for chromatographic fingerprint of traditional chinese medicine (2004AB) and principal component analysis (PCA) were used in data analysis. There were significant differences in the fingerprint chromatograms between S. flavescens and S. tonkinensis. Principal component analysis showed that kurarinone and sophoraflavanone G were the most important component. In quantitative analysis, the five components showed good regression (R > 0.999) with linear ranges, and their recoveries were in the range of 96.3% - 102.3%. This study indicated that the combination of quantitative and chromatographic fingerprint analysis can be readily utilized as a quality control method for S. flavescens and its related traditional Chinese medicinal preparations.

Functional Data Analysis Applied to Modeling of Severe Acute Mucositis and Dysphagia Resulting From Head and Neck Radiation Therapy

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

Dean, Jamie A., E-mail: jamie.dean@icr.ac.uk; Wong, Kee H.; Gay, Hiram

Purpose: Current normal tissue complication probability modeling using logistic regression suffers from bias and high uncertainty in the presence of highly correlated radiation therapy (RT) dose data. This hinders robust estimates of dose-response associations and, hence, optimal normal tissue–sparing strategies from being elucidated. Using functional data analysis (FDA) to reduce the dimensionality of the dose data could overcome this limitation. Methods and Materials: FDA was applied to modeling of severe acute mucositis and dysphagia resulting from head and neck RT. Functional partial least squares regression (FPLS) and functional principal component analysis were used for dimensionality reduction of the dose-volume histogrammore » data. The reduced dose data were input into functional logistic regression models (functional partial least squares–logistic regression [FPLS-LR] and functional principal component–logistic regression [FPC-LR]) along with clinical data. This approach was compared with penalized logistic regression (PLR) in terms of predictive performance and the significance of treatment covariate–response associations, assessed using bootstrapping. Results: The area under the receiver operating characteristic curve for the PLR, FPC-LR, and FPLS-LR models was 0.65, 0.69, and 0.67, respectively, for mucositis (internal validation) and 0.81, 0.83, and 0.83, respectively, for dysphagia (external validation). The calibration slopes/intercepts for the PLR, FPC-LR, and FPLS-LR models were 1.6/−0.67, 0.45/0.47, and 0.40/0.49, respectively, for mucositis (internal validation) and 2.5/−0.96, 0.79/−0.04, and 0.79/0.00, respectively, for dysphagia (external validation). The bootstrapped odds ratios indicated significant associations between RT dose and severe toxicity in the mucositis and dysphagia FDA models. Cisplatin was significantly associated with severe dysphagia in the FDA models. None of the covariates was significantly associated with severe toxicity in the PLR models. Dose levels greater than approximately 1.0 Gy/fraction were most strongly associated with severe acute mucositis and dysphagia in the FDA models. Conclusions: FPLS and functional principal component analysis marginally improved predictive performance compared with PLR and provided robust dose-response associations. FDA is recommended for use in normal tissue complication probability modeling.« less

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.

Association of sleep quality components and wake time with metabolic syndrome: The Qazvin Metabolic Diseases Study (QMDS), Iran.

PubMed

Zohal, Mohammadali; Ghorbani, Azam; Esmailzadehha, Neda; Ziaee, Amir; Mohammadi, Zahrasadat

2017-11-01

The aim of this study was to determine the association of sleep quality and sleep quantity with metabolic syndrome in Qazvin, Iran. this cross sectional study was conducted in 1079 residents of Qazvin selected by multistage cluster random sampling method in 2011. Metabolic syndrome was defined according to the criteria proposed by the national cholesterol education program third Adult treatment panel. Sleep was assessed using the Pittsburgh sleep quality index (PSQI). A logistic regression analysis was used to examine the association of sleep status and metabolic syndrome. Mean age was 40.08±10.33years. Of 1079, 578 (52.2%) were female, and 30.6% had metabolic syndrome. The total global PSQI score in the subjects with metabolic syndrome was significantly higher than subjects without metabolic syndrome (6.30±3.20 vs. 5.83±2.76, P=0.013). In logistic regression analysis, sleep disturbances was associated with 1.388 fold increased risk of metabolic syndrome after adjustment for age, gender, and body mass index. Sleep disturbances component was a predictor of metabolic syndrome in the present study. More longitudinal studies are necessary to understand the association of sleep quality and its components with metabolic syndrome. Copyright © 2017 Diabetes India. Published by Elsevier Ltd. All rights reserved.

Case study on prediction of remaining methane potential of landfilled municipal solid waste by statistical analysis of waste composition data.

PubMed

Sel, İlker; Çakmakcı, Mehmet; Özkaya, Bestamin; Suphi Altan, H

2016-10-01

Main objective of this study was to develop a statistical model for easier and faster Biochemical Methane Potential (BMP) prediction of landfilled municipal solid waste by analyzing waste composition of excavated samples from 12 sampling points and three waste depths representing different landfilling ages of closed and active sections of a sanitary landfill site located in İstanbul, Turkey. Results of Principal Component Analysis (PCA) were used as a decision support tool to evaluation and describe the waste composition variables. Four principal component were extracted describing 76% of data set variance. The most effective components were determined as PCB, PO, T, D, W, FM, moisture and BMP for the data set. Multiple Linear Regression (MLR) models were built by original compositional data and transformed data to determine differences. It was observed that even residual plots were better for transformed data the R(2) and Adjusted R(2) values were not improved significantly. The best preliminary BMP prediction models consisted of D, W, T and FM waste fractions for both versions of regressions. Adjusted R(2) values of the raw and transformed models were determined as 0.69 and 0.57, respectively. Copyright © 2016 Elsevier Ltd. All rights reserved.

Reliability analysis of structural ceramic components using a three-parameter Weibull distribution

NASA Technical Reports Server (NTRS)

Duffy, Stephen F.; Powers, Lynn M.; Starlinger, Alois

1992-01-01

Described here are nonlinear regression estimators for the three-Weibull distribution. Issues relating to the bias and invariance associated with these estimators are examined numerically using Monte Carlo simulation methods. The estimators were used to extract parameters from sintered silicon nitride failure data. A reliability analysis was performed on a turbopump blade utilizing the three-parameter Weibull distribution and the estimates from the sintered silicon nitride data.

Failure of Standard Training Sets in the Analysis of Fast-Scan Cyclic Voltammetry Data.

PubMed

Johnson, Justin A; Rodeberg, Nathan T; Wightman, R Mark

2016-03-16

The use of principal component regression, a multivariate calibration method, in the analysis of in vivo fast-scan cyclic voltammetry data allows for separation of overlapping signal contributions, permitting evaluation of the temporal dynamics of multiple neurotransmitters simultaneously. To accomplish this, the technique relies on information about current-concentration relationships across the scan-potential window gained from analysis of training sets. The ability of the constructed models to resolve analytes depends critically on the quality of these data. Recently, the use of standard training sets obtained under conditions other than those of the experimental data collection (e.g., with different electrodes, animals, or equipment) has been reported. This study evaluates the analyte resolution capabilities of models constructed using this approach from both a theoretical and experimental viewpoint. A detailed discussion of the theory of principal component regression is provided to inform this discussion. The findings demonstrate that the use of standard training sets leads to misassignment of the current-concentration relationships across the scan-potential window. This directly results in poor analyte resolution and, consequently, inaccurate quantitation, which may lead to erroneous conclusions being drawn from experimental data. Thus, it is strongly advocated that training sets be obtained under the experimental conditions to allow for accurate data analysis.

Factors Controlling Sediment Load in The Central Anatolia Region of Turkey: Ankara River Basin.

PubMed

Duru, Umit; Wohl, Ellen; Ahmadi, Mehdi

2017-05-01

Better understanding of the factors controlling sediment load at a catchment scale can facilitate estimation of soil erosion and sediment transport rates. The research summarized here enhances understanding of correlations between potential control variables on suspended sediment loads. The Soil and Water Assessment Tool was used to simulate flow and sediment at the Ankara River basin. Multivariable regression analysis and principal component analysis were then performed between sediment load and controlling variables. The physical variables were either directly derived from a Digital Elevation Model or from field maps or computed using established equations. Mean observed sediment rate is 6697 ton/year and mean sediment yield is 21 ton/y/km² from the gage. Soil and Water Assessment Tool satisfactorily simulated observed sediment load with Nash-Sutcliffe efficiency, relative error, and coefficient of determination (R²) values of 0.81, -1.55, and 0.93, respectively in the catchment. Therefore, parameter values from the physically based model were applied to the multivariable regression analysis as well as principal component analysis. The results indicate that stream flow, drainage area, and channel width explain most of the variability in sediment load among the catchments. The implications of the results, efficient siltation management practices in the catchment should be performed to stream flow, drainage area, and channel width.

Factors Controlling Sediment Load in The Central Anatolia Region of Turkey: Ankara River Basin

NASA Astrophysics Data System (ADS)

Duru, Umit; Wohl, Ellen; Ahmadi, Mehdi

2017-05-01

Better understanding of the factors controlling sediment load at a catchment scale can facilitate estimation of soil erosion and sediment transport rates. The research summarized here enhances understanding of correlations between potential control variables on suspended sediment loads. The Soil and Water Assessment Tool was used to simulate flow and sediment at the Ankara River basin. Multivariable regression analysis and principal component analysis were then performed between sediment load and controlling variables. The physical variables were either directly derived from a Digital Elevation Model or from field maps or computed using established equations. Mean observed sediment rate is 6697 ton/year and mean sediment yield is 21 ton/y/km² from the gage. Soil and Water Assessment Tool satisfactorily simulated observed sediment load with Nash-Sutcliffe efficiency, relative error, and coefficient of determination ( R²) values of 0.81, -1.55, and 0.93, respectively in the catchment. Therefore, parameter values from the physically based model were applied to the multivariable regression analysis as well as principal component analysis. The results indicate that stream flow, drainage area, and channel width explain most of the variability in sediment load among the catchments. The implications of the results, efficient siltation management practices in the catchment should be performed to stream flow, drainage area, and channel width.

Tensile properties of cooked meat sausages and their correlation with texture profile analysis (TPA) parameters and physico-chemical characteristics.

PubMed

Herrero, A M; de la Hoz, L; Ordóñez, J A; Herranz, B; Romero de Ávila, M D; Cambero, M I

2008-11-01

The possibilities of using breaking strength (BS) and energy to fracture (EF) for monitoring textural properties of some cooked meat sausages (chopped, mortadella and galantines) were studied. Texture profile analysis (TPA), folding test and physico-chemical measurements were also performed. Principal component analysis enabled these meat products to be grouped into three textural profiles which showed significant (p<0.05) differences mainly for BS, hardness, adhesiveness and cohesiveness. Multivariate analysis indicated that BS, EF and TPA parameters were correlated (p<0.05) for every individual meat product (chopped, mortadella and galantines) and all products together. On the basis of these results, TPA parameters could be used for constructing regression models to predict BS. The resulting regression model for all cooked meat products was BS=-0.160+6.600∗cohesiveness-1.255∗adhesiveness+0.048∗hardness-506.31∗springiness (R(2)=0.745, p<0.00005). Simple linear regression analysis showed significant coefficients of determination between BS (R(2)=0.586, p<0.0001) versus folding test grade (FG) and EF versus FG (R(2)=0.564, p<0.0001).

Orthogonal decomposition of left ventricular remodeling in myocardial infarction

PubMed Central

Zhang, Xingyu; Medrano-Gracia, Pau; Ambale-Venkatesh, Bharath; Bluemke, David A.; Cowan, Brett R; Finn, J. Paul; Kadish, Alan H.; Lee, Daniel C.; Lima, Joao A. C.; Young, Alistair A.; Suinesiaputra, Avan

2017-01-01

Abstract Left ventricular size and shape are important for quantifying cardiac remodeling in response to cardiovascular disease. Geometric remodeling indices have been shown to have prognostic value in predicting adverse events in the clinical literature, but these often describe interrelated shape changes. We developed a novel method for deriving orthogonal remodeling components directly from any (moderately independent) set of clinical remodeling indices. Results: Six clinical remodeling indices (end-diastolic volume index, sphericity, relative wall thickness, ejection fraction, apical conicity, and longitudinal shortening) were evaluated using cardiac magnetic resonance images of 300 patients with myocardial infarction, and 1991 asymptomatic subjects, obtained from the Cardiac Atlas Project. Partial least squares (PLS) regression of left ventricular shape models resulted in remodeling components that were optimally associated with each remodeling index. A Gram–Schmidt orthogonalization process, by which remodeling components were successively removed from the shape space in the order of shape variance explained, resulted in a set of orthonormal remodeling components. Remodeling scores could then be calculated that quantify the amount of each remodeling component present in each case. A one-factor PLS regression led to more decoupling between scores from the different remodeling components across the entire cohort, and zero correlation between clinical indices and subsequent scores. Conclusions: The PLS orthogonal remodeling components had similar power to describe differences between myocardial infarction patients and asymptomatic subjects as principal component analysis, but were better associated with well-understood clinical indices of cardiac remodeling. The data and analyses are available from www.cardiacatlas.org. PMID:28327972

Optimizing methods for linking cinematic features to fMRI data.

PubMed

Kauttonen, Janne; Hlushchuk, Yevhen; Tikka, Pia

2015-04-15

One of the challenges of naturalistic neurosciences using movie-viewing experiments is how to interpret observed brain activations in relation to the multiplicity of time-locked stimulus features. As previous studies have shown less inter-subject synchronization across viewers of random video footage than story-driven films, new methods need to be developed for analysis of less story-driven contents. To optimize the linkage between our fMRI data collected during viewing of a deliberately non-narrative silent film 'At Land' by Maya Deren (1944) and its annotated content, we combined the method of elastic-net regularization with the model-driven linear regression and the well-established data-driven independent component analysis (ICA) and inter-subject correlation (ISC) methods. In the linear regression analysis, both IC and region-of-interest (ROI) time-series were fitted with time-series of a total of 36 binary-valued and one real-valued tactile annotation of film features. The elastic-net regularization and cross-validation were applied in the ordinary least-squares linear regression in order to avoid over-fitting due to the multicollinearity of regressors, the results were compared against both the partial least-squares (PLS) regression and the un-regularized full-model regression. Non-parametric permutation testing scheme was applied to evaluate the statistical significance of regression. We found statistically significant correlation between the annotation model and 9 ICs out of 40 ICs. Regression analysis was also repeated for a large set of cubic ROIs covering the grey matter. Both IC- and ROI-based regression analyses revealed activations in parietal and occipital regions, with additional smaller clusters in the frontal lobe. Furthermore, we found elastic-net based regression more sensitive than PLS and un-regularized regression since it detected a larger number of significant ICs and ROIs. Along with the ISC ranking methods, our regression analysis proved a feasible method for ordering the ICs based on their functional relevance to the annotated cinematic features. The novelty of our method is - in comparison to the hypothesis-driven manual pre-selection and observation of some individual regressors biased by choice - in applying data-driven approach to all content features simultaneously. We found especially the combination of regularized regression and ICA useful when analyzing fMRI data obtained using non-narrative movie stimulus with a large set of complex and correlated features. Copyright © 2015. Published by Elsevier Inc.

A regression approach to the mapping of bio-physical characteristics of surface sediment using in situ and airborne hyperspectral acquisitions

NASA Astrophysics Data System (ADS)

Ibrahim, Elsy; Kim, Wonkook; Crawford, Melba; Monbaliu, Jaak

2017-02-01

Remote sensing has been successfully utilized to distinguish and quantify sediment properties in the intertidal environment. Classification approaches of imagery are popular and powerful yet can lead to site- and case-specific results. Such specificity creates challenges for temporal studies. Thus, this paper investigates the use of regression models to quantify sediment properties instead of classifying them. Two regression approaches, namely multiple regression (MR) and support vector regression (SVR), are used in this study for the retrieval of bio-physical variables of intertidal surface sediment of the IJzermonding, a Belgian nature reserve. In the regression analysis, mud content, chlorophyll a concentration, organic matter content, and soil moisture are estimated using radiometric variables of two airborne sensors, namely airborne hyperspectral sensor (AHS) and airborne prism experiment (APEX) and and using field hyperspectral acquisitions by analytical spectral device (ASD). The performance of the two regression approaches is best for the estimation of moisture content. SVR attains the highest accuracy without feature reduction while MR achieves good results when feature reduction is carried out. Sediment property maps are successfully obtained using the models and hyperspectral imagery where SVR used with all bands achieves the best performance. The study also involves the extraction of weights identifying the contribution of each band of the images in the quantification of each sediment property when MR and principal component analysis are used.

Investigation of carbon dioxide emission in China by primary component analysis.

PubMed

Zhang, Jing; Wang, Cheng-Ming; Liu, Lian; Guo, Hang; Liu, Guo-Dong; Li, Yuan-Wei; Deng, Shi-Huai

2014-02-15

Principal component analysis (PCA) is employed to investigate the relationship between CO2 emissions (COEs) stemming from fossil fuel burning and cement manufacturing and their affecting factors. Eight affecting factors, namely, Population (P), Urban Population (UP); the Output Values of Primary Industry (PIOV), Secondary Industry (SIOV), and Tertiary Industry (TIOV); and the Proportions of Primary Industry's Output Value (PPIOV), Secondary Industry's Output Value (PSIOV), and Tertiary Industry's Output Value (PTIOV), are chosen. PCA is employed to eliminate the multicollinearity of the affecting factors. Two principal components, which can explain 92.86% of the variance of the eight affecting factors, are chosen as variables in the regression analysis. Ordinary least square regression is used to estimate multiple linear regression models, in which COEs and the principal components serve as dependent and independent variables, respectively. The results are given in the following. (1) Theoretically, the carbon intensities of PIOV, SIOV, and TIOV are 2573.4693, 552.7036, and 606.0791 kt per one billion $, respectively. The incomplete statistical data, the different statistical standards, and the ideology of self sufficiency and peasantry appear to show that the carbon intensity of PIOV is higher than those of SIOV and TIOV in China. (2) PPIOV, PSIOV, and PTIOV influence the fluctuations of COE. The parameters of PPIOV, PSIOV, and PTIOV are -2706946.7564, 2557300.5450, and 3924767.9807 kt, respectively. As the economic structure of China is strongly tied to technology level, the period when PIOV plays the leading position is characterized by lagging technology and economic developing. Thus, the influence of PPIOV has a negative value. As the increase of PSIOV and PTIOV is always followed by technological innovation and economic development, PSIOV and PTIOV have the opposite influence. (3) The carbon intensities of P and UP are 1.1029 and 1.7862 kt per thousand people, respectively. The carbon intensity of the rural population can be inferred to be lower than 1.1029 kt per thousand people. The characteristics of poverty and the use of bio-energy in rural areas result in a carbon intensity of the rural population that is lower than that of P. Copyright © 2013 Elsevier B.V. All rights reserved.

Development and Validation of the Work-Related Well-Being Index: Analysis of the Federal Employee Viewpoint Survey (FEVS).

PubMed

Eaton, Jennifer L; Mohr, David C; Hodgson, Michael J; McPhaul, Kathleen M

2017-10-11

To describe development and validation of the Work-Related Well-Being (WRWB) Index. Principal Components Analysis was performed using Federal Employee Viewpoint Survey (FEVS) data (N = 392,752) to extract variables representing worker well-being constructs. Confirmatory factor analysis was performed to verify factor structure. To validate the WRWB index, we used multiple regression analysis to examine relationships with burnout associated outcomes. PCA identified three positive psychology constructs: "Work Positivity", "Co-worker Relationships", and "Work Mastery". An 11 item index explaining 63.5% of variance was achieved. The structural equation model provided a very good fit to the data. Higher WRWB scores were positively associated with all 3 employee experience measures examined in regression models. The new WRWB index shows promise as a valid and widely accessible instrument to assess worker well-being.

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

PubMed

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

2018-02-01

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

Relationship of Heath and Carter's Second Component to Lean Body Mass and Height in College Women

ERIC Educational Resources Information Center

Slaughter, M. H.; And Others

1977-01-01

The Heath and Carter approach to determining somatotypes is less accurate than is regression analysis, mainly because of the lack of association between skeletal widths and lean body mass as measured by body density and whole-body fat percentage, holding constant muscle circumference. (Author)

Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models

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

Nee, K.; Bryan, S.; Levitskaia, T.

The reliability of chemical processes can be greatly improved by implementing inline monitoring systems. Combining multivariate analysis with non-destructive sensors can enhance the process without interfering with the operation. Here, we present here hierarchical models using both principal component analysis and partial least square analysis developed for different chemical components representative of solvent extraction process streams. A training set of 380 samples and an external validation set of 95 samples were prepared and Near infrared and Raman spectral data as well as conductivity under variable temperature conditions were collected. The results from the models indicate that careful selection of themore » spectral range is important. By compressing the data through Principal Component Analysis (PCA), we lower the rank of the data set to its most dominant features while maintaining the key principal components to be used in the regression analysis. Within the studied data set, concentration of five chemical components were modeled; total nitrate (NO 3 -), total acid (H +), neodymium (Nd 3+), sodium (Na +), and ionic strength (I.S.). The best overall model prediction for each of the species studied used a combined data set comprised of complementary techniques including NIR, Raman, and conductivity. Finally, our study shows that chemometric models are powerful but requires significant amount of carefully analyzed data to capture variations in the chemistry.« less

Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models

DOE PAGES

Nee, K.; Bryan, S.; Levitskaia, T.; ...

2017-12-28

The reliability of chemical processes can be greatly improved by implementing inline monitoring systems. Combining multivariate analysis with non-destructive sensors can enhance the process without interfering with the operation. Here, we present here hierarchical models using both principal component analysis and partial least square analysis developed for different chemical components representative of solvent extraction process streams. A training set of 380 samples and an external validation set of 95 samples were prepared and Near infrared and Raman spectral data as well as conductivity under variable temperature conditions were collected. The results from the models indicate that careful selection of themore » spectral range is important. By compressing the data through Principal Component Analysis (PCA), we lower the rank of the data set to its most dominant features while maintaining the key principal components to be used in the regression analysis. Within the studied data set, concentration of five chemical components were modeled; total nitrate (NO 3 -), total acid (H +), neodymium (Nd 3+), sodium (Na +), and ionic strength (I.S.). The best overall model prediction for each of the species studied used a combined data set comprised of complementary techniques including NIR, Raman, and conductivity. Finally, our study shows that chemometric models are powerful but requires significant amount of carefully analyzed data to capture variations in the chemistry.« less

Crude oil price forecasting based on hybridizing wavelet multiple linear regression model, particle swarm optimization techniques, and principal component analysis.

PubMed

Shabri, Ani; Samsudin, Ruhaidah

2014-01-01

Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.

Watch what happens: using a web-based multimedia platform to enhance intraoperative learning and development of clinical reasoning.

PubMed

Fingeret, Abbey L; Martinez, Rebecca H; Hsieh, Christine; Downey, Peter; Nowygrod, Roman

2016-02-01

We aim to determine whether observed operations or internet-based video review predict improved performance in the surgery clerkship. A retrospective review of students' usage of surgical videos, observed operations, evaluations, and examination scores were used to construct an exploratory principal component analysis. Multivariate regression was used to determine factors predictive of clerkship performance. Case log data for 231 students revealed a median of 25 observed cases. Students accessed the web-based video platform a median of 15 times. Principal component analysis yielded 4 factors contributing 74% of the variability with a Kaiser-Meyer-Olkin coefficient of .83. Multivariate regression predicted shelf score (P < .0001), internal clinical skills examination score (P < .0001), subjective evaluations (P < .001), and video website utilization (P < .001) but not observed cases to be significantly associated with overall performance. Utilization of a web-based operative video platform during a surgical clerkship is an independently associated with improved clinical reasoning, fund of knowledge, and overall evaluation. Thus, this modality can serve as a useful adjunct to live observation. Copyright © 2016 Elsevier Inc. All rights reserved.

Analysis of nonlinear relationships in dual epidemics, and its application to the management of grapevine downy and powdery mildews.

PubMed

Savary, Serge; Delbac, Lionel; Rochas, Amélie; Taisant, Guillaume; Willocquet, Laetitia

2009-08-01

Dual epidemics are defined as epidemics developing on two or several plant organs in the course of a cropping season. Agricultural pathosystems where such epidemics develop are often very important, because the harvestable part is one of the organs affected. These epidemics also are often difficult to manage, because the linkage between epidemiological components occurring on different organs is poorly understood, and because prediction of the risk toward the harvestable organs is difficult. In the case of downy mildew (DM) and powdery mildew (PM) of grapevine, nonlinear modeling and logistic regression indicated nonlinearity in the foliage-cluster relationships. Nonlinear modeling enabled the parameterization of a transmission coefficient that numerically links the two components, leaves and clusters, in DM and PM epidemics. Logistic regression analysis yielded a series of probabilistic models that enabled predicting preset levels of cluster infection risks based on DM and PM severities on the foliage at successive crop stages. The usefulness of this framework for tactical decision-making for disease control is discussed.

Crude Oil Price Forecasting Based on Hybridizing Wavelet Multiple Linear Regression Model, Particle Swarm Optimization Techniques, and Principal Component Analysis

PubMed Central

Shabri, Ani; Samsudin, Ruhaidah

2014-01-01

Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series. PMID:24895666

Inferring the use of forelimb suspensory locomotion by extinct primate species via shape exploration of the ulna.

PubMed

Rein, Thomas R; Harvati, Katerina; Harrison, Terry

2015-01-01

Uncovering links between skeletal morphology and locomotor behavior is an essential component of paleobiology because it allows researchers to infer the locomotor repertoire of extinct species based on preserved fossils. In this study, we explored ulnar shape in anthropoid primates using 3D geometric morphometrics to discover novel aspects of shape variation that correspond to observed differences in the relative amount of forelimb suspensory locomotion performed by species. The ultimate goal of this research was to construct an accurate predictive model that can be applied to infer the significance of these behaviors. We studied ulnar shape variation in extant species using principal component analysis. Species mainly clustered into phylogenetic groups along the first two principal components. Upon closer examination, the results showed that the position of species within each major clade corresponded closely with the proportion of forelimb suspensory locomotion that they have been observed to perform in nature. We used principal component regression to construct a predictive model for the proportion of these behaviors that would be expected to occur in the locomotor repertoire of anthropoid primates. We then applied this regression analysis to Pliopithecus vindobonensis, a stem catarrhine from the Miocene of central Europe, and found strong evidence that this species was adapted to perform a proportion of forelimb suspensory locomotion similar to that observed in the extant woolly monkey, Lagothrix lagothricha. Copyright © 2014 Elsevier Ltd. All rights reserved.

Comparison between Two Linear Supervised Learning Machines' Methods with Principle Component Based Methods for the Spectrofluorimetric Determination of Agomelatine and Its Degradants.

PubMed

Elkhoudary, Mahmoud M; Naguib, Ibrahim A; Abdel Salam, Randa A; Hadad, Ghada M

2017-05-01

Four accurate, sensitive and reliable stability indicating chemometric methods were developed for the quantitative determination of Agomelatine (AGM) whether in pure form or in pharmaceutical formulations. Two supervised learning machines' methods; linear artificial neural networks (PC-linANN) preceded by principle component analysis and linear support vector regression (linSVR), were compared with two principle component based methods; principle component regression (PCR) as well as partial least squares (PLS) for the spectrofluorimetric determination of AGM and its degradants. The results showed the benefits behind using linear learning machines' methods and the inherent merits of their algorithms in handling overlapped noisy spectral data especially during the challenging determination of AGM alkaline and acidic degradants (DG1 and DG2). Relative mean squared error of prediction (RMSEP) for the proposed models in the determination of AGM were 1.68, 1.72, 0.68 and 0.22 for PCR, PLS, SVR and PC-linANN; respectively. The results showed the superiority of supervised learning machines' methods over principle component based methods. Besides, the results suggested that linANN is the method of choice for determination of components in low amounts with similar overlapped spectra and narrow linearity range. Comparison between the proposed chemometric models and a reported HPLC method revealed the comparable performance and quantification power of the proposed models.

The Relationship of Social Engagement and Social Support With Sense of Community.

PubMed

Tang, Fengyan; Chi, Iris; Dong, Xinqi

2017-07-01

We aimed to investigate the relationship of engagement in social and cognitive activities and social support with the sense of community (SOC) and its components among older Chinese Americans. The Sense of Community Index (SCI) was used to measure SOC and its four component factors: membership, influence, needs fulfillment, and emotional connection. Social engagement was assessed with 16 questions. Social support included positive support and negative strain. Principal component analysis was used to identify the SCI components. Linear regression analysis was used to detect the contribution of social engagement and social support to SOC and its components. After controlling for sociodemographics and self-rated health, social activity engagement and positive social support were positively related to SOC and its components. This study points to the importance of social activity engagement and positive support from family and friends in increasing the sense of community. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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

PubMed Central

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

2010-01-01

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

Wind Tunnel Strain-Gage Balance Calibration Data Analysis Using a Weighted Least Squares Approach

NASA Technical Reports Server (NTRS)

Ulbrich, N.; Volden, T.

2017-01-01

A new approach is presented that uses a weighted least squares fit to analyze wind tunnel strain-gage balance calibration data. The weighted least squares fit is specifically designed to increase the influence of single-component loadings during the regression analysis. The weighted least squares fit also reduces the impact of calibration load schedule asymmetries on the predicted primary sensitivities of the balance gages. A weighting factor between zero and one is assigned to each calibration data point that depends on a simple count of its intentionally loaded load components or gages. The greater the number of a data point's intentionally loaded load components or gages is, the smaller its weighting factor becomes. The proposed approach is applicable to both the Iterative and Non-Iterative Methods that are used for the analysis of strain-gage balance calibration data in the aerospace testing community. The Iterative Method uses a reasonable estimate of the tare corrected load set as input for the determination of the weighting factors. The Non-Iterative Method, on the other hand, uses gage output differences relative to the natural zeros as input for the determination of the weighting factors. Machine calibration data of a six-component force balance is used to illustrate benefits of the proposed weighted least squares fit. In addition, a detailed derivation of the PRESS residuals associated with a weighted least squares fit is given in the appendices of the paper as this information could not be found in the literature. These PRESS residuals may be needed to evaluate the predictive capabilities of the final regression models that result from a weighted least squares fit of the balance calibration data.

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.

Low-Level Stratus Prediction Using Binary Statistical Regression: A Progress Report Using Moffett Field Data.

DTIC Science & Technology

1983-12-01

analysis; such work is not reported here. It seems pos- sible that a robust principle component analysis may he informa- tive (see Gnanadesikan (1977...Statistics in Atmospheric Sciences, American Meteorological Soc., Boston, Mass. (1979) pp. 46-48. a Gnanadesikan , R., Methods for Statistical Data...North Carolina Chapel Hill, NC 20742 Dr. R. Gnanadesikan Bell Telephone Lab Murray Hill, NJ 07733 -%.. *5%a: *1 *15 I ,, - . . , ,, ... . . . . . . NO

Comparative study of human blood Raman spectra and biochemical analysis of patients with cancer

NASA Astrophysics Data System (ADS)

Shamina, Lyudmila A.; Bratchenko, Ivan A.; Artemyev, Dmitry N.; Myakinin, Oleg O.; Moryatov, Alexander A.; Orlov, Andrey E.; Kozlov, Sergey V.; Zakharov, Valery P.

2018-04-01

In this study we measured spectral features of blood by Raman spectroscopy. Correlation of the obtained spectral data and biochemical studies results is investigated. Analysis of specific spectra allows for identification of informative spectral bands proportional to components whose content is associated with body fluids homeostasis changes at various pathological conditions. Regression analysis of the obtained spectral data allows for discriminating the lung cancer from other tumors with a posteriori probability of 88.3%. The potentiality of applying surface-enhanced Raman spectroscopy with utilized experimental setup for further studies of the body fluids component composition was estimated. The greatest signal amplification was achieved for the gold substrate with a surface roughness of 1 μm. In general, the developed approach of body fluids analysis provides the basis of a useful and minimally invasive method of pathologies screening.

Quantitative analysis of the mixtures of illicit drugs using terahertz time-domain spectroscopy

NASA Astrophysics Data System (ADS)

Jiang, Dejun; Zhao, Shusen; Shen, Jingling

2008-03-01

A method was proposed to quantitatively inspect the mixtures of illicit drugs with terahertz time-domain spectroscopy technique. The mass percentages of all components in a mixture can be obtained by linear regression analysis, on the assumption that all components in the mixture and their absorption features be known. For illicit drugs were scarce and expensive, firstly we used common chemicals, Benzophenone, Anthraquinone, Pyridoxine hydrochloride and L-Ascorbic acid in the experiment. Then illicit drugs and a common adulterant, methamphetamine and flour, were selected for our experiment. Experimental results were in significant agreement with actual content, which suggested that it could be an effective method for quantitative identification of illicit drugs.

Rhythms of life: antecedents and outcomes of work-family balance in employed parents.

PubMed

Aryee, Samuel; Srinivas, E S; Tan, Hwee Hoon

2005-01-01

This study examined antecedents and outcomes of a fourfold taxonomy of work-family balance in terms of the direction of influence (work-family vs. family-work) and type of effect (conflict vs. facilitation). Respondents were full-time employed parents in India. Confirmatory factor analysis results provided evidence for the discriminant validity of M. R. Frone's (2003) fourfold taxonomy of work-family balance. Results of moderated regression analysis revealed that different processes underlie the conflict and facilitation components. Furthermore, gender had only a limited moderating influence on the relationships between the antecedents and the components of work-family balance. Last, work-family facilitation was related to the work outcomes of job satisfaction and organizational commitment.

Restricted maximum likelihood estimation of genetic principal components and smoothed covariance matrices

PubMed Central

Meyer, Karin; Kirkpatrick, Mark

2005-01-01

Principal component analysis is a widely used 'dimension reduction' technique, albeit generally at a phenotypic level. It is shown that we can estimate genetic principal components directly through a simple reparameterisation of the usual linear, mixed model. This is applicable to any analysis fitting multiple, correlated genetic effects, whether effects for individual traits or sets of random regression coefficients to model trajectories. Depending on the magnitude of genetic correlation, a subset of the principal component generally suffices to capture the bulk of genetic variation. Corresponding estimates of genetic covariance matrices are more parsimonious, have reduced rank and are smoothed, with the number of parameters required to model the dispersion structure reduced from k(k + 1)/2 to m(2k - m + 1)/2 for k effects and m principal components. Estimation of these parameters, the largest eigenvalues and pertaining eigenvectors of the genetic covariance matrix, via restricted maximum likelihood using derivatives of the likelihood, is described. It is shown that reduced rank estimation can reduce computational requirements of multivariate analyses substantially. An application to the analysis of eight traits recorded via live ultrasound scanning of beef cattle is given. PMID:15588566

An augmented classical least squares method for quantitative Raman spectral analysis against component information loss.

PubMed

Zhou, Yan; Cao, Hui

2013-01-01

We propose an augmented classical least squares (ACLS) calibration method for quantitative Raman spectral analysis against component information loss. The Raman spectral signals with low analyte concentration correlations were selected and used as the substitutes for unknown quantitative component information during the CLS calibration procedure. The number of selected signals was determined by using the leave-one-out root-mean-square error of cross-validation (RMSECV) curve. An ACLS model was built based on the augmented concentration matrix and the reference spectral signal matrix. The proposed method was compared with partial least squares (PLS) and principal component regression (PCR) using one example: a data set recorded from an experiment of analyte concentration determination using Raman spectroscopy. A 2-fold cross-validation with Venetian blinds strategy was exploited to evaluate the predictive power of the proposed method. The one-way variance analysis (ANOVA) was used to access the predictive power difference between the proposed method and existing methods. Results indicated that the proposed method is effective at increasing the robust predictive power of traditional CLS model against component information loss and its predictive power is comparable to that of PLS or PCR.

Proteoglycan concentrations in healthy and diseased articular cartilage by Fourier transform infrared imaging and principal component regression

NASA Astrophysics Data System (ADS)

Yin, Jianhua; Xia, Yang

2014-12-01

Fourier transform infrared imaging (FTIRI) combining with principal component regression (PCR) analysis were used to determine the reduction of proteoglycan (PG) in articular cartilage after the transection of the anterior cruciate ligament (ACL). A number of canine knee cartilage sections were harvested from the meniscus-covered and meniscus-uncovered medial tibial locations from the control joints, the ACL joints at three time points after the surgery, and their contralateral joints. The PG loss in the ACL cartilage was related positively to the durations after the surgery. The PG loss in the contralateral knees was less than that of the ACL knees. The PG loss in the meniscus-covered cartilage was less than that of the meniscus-uncovered tissue in both ACL and contralateral knees. The quantitative mapping of PG loss could monitor the disease progression and repair processes in arthritis.

Isolating and Examining Sources of Suppression and Multicollinearity in Multiple Linear Regression.

PubMed

Beckstead, Jason W

2012-03-30

The presence of suppression (and multicollinearity) in multiple regression analysis complicates interpretation of predictor-criterion relationships. The mathematical conditions that produce suppression in regression analysis have received considerable attention in the methodological literature but until now nothing in the way of an analytic strategy to isolate, examine, and remove suppression effects has been offered. In this article such an approach, rooted in confirmatory factor analysis theory and employing matrix algebra, is developed. Suppression is viewed as the result of criterion-irrelevant variance operating among predictors. Decomposition of predictor variables into criterion-relevant and criterion-irrelevant components using structural equation modeling permits derivation of regression weights with the effects of criterion-irrelevant variance omitted. Three examples with data from applied research are used to illustrate the approach: the first assesses child and parent characteristics to explain why some parents of children with obsessive-compulsive disorder accommodate their child's compulsions more so than do others, the second examines various dimensions of personal health to explain individual differences in global quality of life among patients following heart surgery, and the third deals with quantifying the relative importance of various aptitudes for explaining academic performance in a sample of nursing students. The approach is offered as an analytic tool for investigators interested in understanding predictor-criterion relationships when complex patterns of intercorrelation among predictors are present and is shown to augment dominance analysis.

Discrimination of rectal cancer through human serum using surface-enhanced Raman spectroscopy

NASA Astrophysics Data System (ADS)

Li, Xiaozhou; Yang, Tianyue; Li, Siqi; Zhang, Su; Jin, Lili

2015-05-01

In this paper, surface-enhanced Raman spectroscopy (SERS) was used to detect the changes in blood serum components that accompany rectal cancer. The differences in serum SERS data between rectal cancer patients and healthy controls were examined. Postoperative rectal cancer patients also participated in the comparison to monitor the effects of cancer treatments. The results show that there are significant variations at certain wavenumbers which indicates alteration of corresponding biological substances. Principal component analysis (PCA) and parameters of intensity ratios were used on the original SERS spectra for the extraction of featured variables. These featured variables then underwent linear discriminant analysis (LDA) and classification and regression tree (CART) for the discrimination analysis. Accuracies of 93.5 and 92.4 % were obtained for PCA-LDA and parameter-CART, respectively.

Drought Patterns Forecasting using an Auto-Regressive Logistic Model

NASA Astrophysics Data System (ADS)

del Jesus, M.; Sheffield, J.; Méndez Incera, F. J.; Losada, I. J.; Espejo, A.

2014-12-01

Drought is characterized by a water deficit that may manifest across a large range of spatial and temporal scales. Drought may create important socio-economic consequences, many times of catastrophic dimensions. A quantifiable definition of drought is elusive because depending on its impacts, consequences and generation mechanism, different water deficit periods may be identified as a drought by virtue of some definitions but not by others. Droughts are linked to the water cycle and, although a climate change signal may not have emerged yet, they are also intimately linked to climate.In this work we develop an auto-regressive logistic model for drought prediction at different temporal scales that makes use of a spatially explicit framework. Our model allows to include covariates, continuous or categorical, to improve the performance of the auto-regressive component.Our approach makes use of dimensionality reduction (principal component analysis) and classification techniques (K-Means and maximum dissimilarity) to simplify the representation of complex climatic patterns, such as sea surface temperature (SST) and sea level pressure (SLP), while including information on their spatial structure, i.e. considering their spatial patterns. This procedure allows us to include in the analysis multivariate representation of complex climatic phenomena, as the El Niño-Southern Oscillation. We also explore the impact of other climate-related variables such as sun spots. The model allows to quantify the uncertainty of the forecasts and can be easily adapted to make predictions under future climatic scenarios. The framework herein presented may be extended to other applications such as flash flood analysis, or risk assessment of natural hazards.

Separation of the long-term thermal effects from the strain measurements in the Geodynamics Laboratory of Lanzarote

NASA Astrophysics Data System (ADS)

Venedikov, A. P.; Arnoso, J.; Cai, W.; Vieira, R.; Tan, S.; Velez, E. J.

2006-01-01

A 12-year series (1992-2004) of strain measurements recorded in the Geodynamics Laboratory of Lanzarote is investigated. Through a tidal analysis the non-tidal component of the data is separated in order to use it for studying signals, useful for monitoring of the volcanic activity on the island. This component contains various perturbations of meteorological and oceanic origin, which should be eliminated in order to make the useful signals discernible. The paper is devoted to the estimation and elimination of the effect of the air temperature inside the station, which strongly dominates the strainmeter data. For solving this task, a regression model is applied, which includes a linear relation with the temperature and time-dependant polynomials. The regression includes nonlinearly a set of parameters, which are estimated by a properly applied Bayesian approach. The results obtained are: the regression coefficient of the strain data on temperature is equal to (-367.4 ± 0.8) × 10 -9 °C -1, the curve of the non-tidal component reduced by the effect of the temperature and a polynomial approximation of the reduced curve. The technique used here can be helpful to investigators in the domain of the earthquake and volcano monitoring. However, the fundamental and extremely difficult problem of what kind of signals in the reduced curves might be useful in this field is not considered here.

Economist intelligence unit democracy index in relation to health services accessibility: a regression analysis.

PubMed

Walker, Mary Ellen; Anonson, June; Szafron, Michael

2015-01-01

The relationship between political environment and health services accessibility (HSA) has not been the focus of any specific studies. The purpose of this study was to address this gap in the literature by examining the relationship between political environment and HSA. This relationship that HSA indicators (physicians, nurses and hospital beds per 10 000 people) has with political environment was analyzed with multiple least-squares regression using the components of democracy (electoral processes and pluralism, functioning of government, political participation, political culture, and civil liberties). The components of democracy were represented by the 2011 Economist Intelligence Unit Democracy Index (EIUDI) sub-scores. The EIUDI sub-scores and the HSA indicators were evaluated for significant relationships with multiple least-squares regression. While controlling for a country's geographic location and level of democracy, we found that two components of a nation's political environment: functioning of government and political participation, and their interaction had significant relationships with the three HSA indicators. These study findings are of significance to health professionals because they examine the political contexts in which citizens access health services, they come from research that is the first of its kind, and they help explain the effect political environment has on health. © The Author 2014. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Wavelet regression model in forecasting crude oil price

NASA Astrophysics Data System (ADS)

Hamid, Mohd Helmie; Shabri, Ani

2017-05-01

This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.

Architectural measures of the cancellous bone of the mandibular condyle identified by principal components analysis.

PubMed

Giesen, E B W; Ding, M; Dalstra, M; van Eijden, T M G J

2003-09-01

As several morphological parameters of cancellous bone express more or less the same architectural measure, we applied principal components analysis to group these measures and correlated these to the mechanical properties. Cylindrical specimens (n = 24) were obtained in different orientations from embalmed mandibular condyles; the angle of the first principal direction and the axis of the specimen, expressing the orientation of the trabeculae, ranged from 10 degrees to 87 degrees. Morphological parameters were determined by a method based on Archimedes' principle and by micro-CT scanning, and the mechanical properties were obtained by mechanical testing. The principal components analysis was used to obtain a set of independent components to describe the morphology. This set was entered into linear regression analyses for explaining the variance in mechanical properties. The principal components analysis revealed four components: amount of bone, number of trabeculae, trabecular orientation, and miscellaneous. They accounted for about 90% of the variance in the morphological variables. The component loadings indicated that a higher amount of bone was primarily associated with more plate-like trabeculae, and not with more or thicker trabeculae. The trabecular orientation was most determinative (about 50%) in explaining stiffness, strength, and failure energy. The amount of bone was second most determinative and increased the explained variance to about 72%. These results suggest that trabecular orientation and amount of bone are important in explaining the anisotropic mechanical properties of the cancellous bone of the mandibular condyle.

Orthogonal decomposition of left ventricular remodeling in myocardial infarction.

PubMed

Zhang, Xingyu; Medrano-Gracia, Pau; Ambale-Venkatesh, Bharath; Bluemke, David A; Cowan, Brett R; Finn, J Paul; Kadish, Alan H; Lee, Daniel C; Lima, Joao A C; Young, Alistair A; Suinesiaputra, Avan

2017-03-01

Left ventricular size and shape are important for quantifying cardiac remodeling in response to cardiovascular disease. Geometric remodeling indices have been shown to have prognostic value in predicting adverse events in the clinical literature, but these often describe interrelated shape changes. We developed a novel method for deriving orthogonal remodeling components directly from any (moderately independent) set of clinical remodeling indices. Six clinical remodeling indices (end-diastolic volume index, sphericity, relative wall thickness, ejection fraction, apical conicity, and longitudinal shortening) were evaluated using cardiac magnetic resonance images of 300 patients with myocardial infarction, and 1991 asymptomatic subjects, obtained from the Cardiac Atlas Project. Partial least squares (PLS) regression of left ventricular shape models resulted in remodeling components that were optimally associated with each remodeling index. A Gram-Schmidt orthogonalization process, by which remodeling components were successively removed from the shape space in the order of shape variance explained, resulted in a set of orthonormal remodeling components. Remodeling scores could then be calculated that quantify the amount of each remodeling component present in each case. A one-factor PLS regression led to more decoupling between scores from the different remodeling components across the entire cohort, and zero correlation between clinical indices and subsequent scores. The PLS orthogonal remodeling components had similar power to describe differences between myocardial infarction patients and asymptomatic subjects as principal component analysis, but were better associated with well-understood clinical indices of cardiac remodeling. The data and analyses are available from www.cardiacatlas.org. © The Author 2017. Published by Oxford University Press.

Multicomponent analysis of a digital Trail Making Test.

PubMed

Fellows, Robert P; Dahmen, Jessamyn; Cook, Diane; Schmitter-Edgecombe, Maureen

2017-01-01

The purpose of the current study was to use a newly developed digital tablet-based variant of the TMT to isolate component cognitive processes underlying TMT performance. Similar to the paper-based trail making test, this digital variant consists of two conditions, Part A and Part B. However, this digital version automatically collects additional data to create component subtest scores to isolate cognitive abilities. Specifically, in addition to the total time to completion and number of errors, the digital Trail Making Test (dTMT) records several unique components including the number of pauses, pause duration, lifts, lift duration, time inside each circle, and time between circles. Participants were community-dwelling older adults who completed a neuropsychological evaluation including measures of processing speed, inhibitory control, visual working memory/sequencing, and set-switching. The abilities underlying TMT performance were assessed through regression analyses of component scores from the dTMT with traditional neuropsychological measures. Results revealed significant correlations between paper and digital variants of Part A (r s  = .541, p < .001) and paper and digital versions of Part B (r s  = .799, p < .001). Regression analyses with traditional neuropsychological measures revealed that Part A components were best predicted by speeded processing, while inhibitory control and visual/spatial sequencing were predictors of specific components of Part B. Exploratory analyses revealed that specific dTMT-B components were associated with a performance-based medication management task. Taken together, these results elucidate specific cognitive abilities underlying TMT performance, as well as the utility of isolating digital components.

Metabolic syndrome: An independent risk factor for erectile dysfunction

PubMed Central

Sanjay, Saran; Bharti, Gupta Sona; Manish, Gutch; Rajeev, Philip; Pankaj, Agrawal; Puspalata, Agroiya; Keshavkumar, Gupta

2015-01-01

Objective: The objective was to determine the role of various components of metabolic syndrome (MetS) as independent risk factor for erectile dysfunction (ED). Materials and Methods: A total of 113 subjects of MetS, as recommended by recent IDF and AHA/NHLBI joint interim statement were selected for study who presented for ED. After doing Anthropometric examination, fasting laboratory assay for fasting plasma glucose (FPG), fasting insulin, hemoglobin A1c, triglyceride (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), and 2 h oral glucose tolerance test (OGTT) was done. Erectile function was assessed by completing questions one through five of the International Index of Erectile Function (IIEF-5). A multiple linear regression analysis was carried out on 66 subjects with IIEF-5 score as dependent variable and components of MetS FPG, 2 h OGTT, TG, HDL, and waist circumference as independent variables. Results: Using a multiple linear regression analysis, we observed that presence of the various components of MetS was associated with ED and a decrease IIEF-5 score and this effect was greater than the effect associated with any of the individual components. Of the individual components of the MetS, HDL (B = 0.136; P = 0.004) and FPG (B = −0.069; P = 0.007) conferred the strongest effect on IIEF-5 score. However, overall age had most significant effect on IIEF-5 score. Conclusion: It is crucial to formulate strategies and implement them to prevent or control the epidemic of the MetS and its consequences. The early identification and treatment of risk factors might be helpful to prevent ED and secondary cardiovascular disease, including diet and lifestyle interventions. PMID:25729692

Metabolic syndrome: An independent risk factor for erectile dysfunction.

PubMed

Sanjay, Saran; Bharti, Gupta Sona; Manish, Gutch; Rajeev, Philip; Pankaj, Agrawal; Puspalata, Agroiya; Keshavkumar, Gupta

2015-01-01

The objective was to determine the role of various components of metabolic syndrome (MetS) as independent risk factor for erectile dysfunction (ED). A total of 113 subjects of MetS, as recommended by recent IDF and AHA/NHLBI joint interim statement were selected for study who presented for ED. After doing Anthropometric examination, fasting laboratory assay for fasting plasma glucose (FPG), fasting insulin, hemoglobin A1c, triglyceride (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), and 2 h oral glucose tolerance test (OGTT) was done. Erectile function was assessed by completing questions one through five of the International Index of Erectile Function (IIEF-5). A multiple linear regression analysis was carried out on 66 subjects with IIEF-5 score as dependent variable and components of MetS FPG, 2 h OGTT, TG, HDL, and waist circumference as independent variables. Using a multiple linear regression analysis, we observed that presence of the various components of MetS was associated with ED and a decrease IIEF-5 score and this effect was greater than the effect associated with any of the individual components. Of the individual components of the MetS, HDL (B = 0.136; P = 0.004) and FPG (B = -0.069; P = 0.007) conferred the strongest effect on IIEF-5 score. However, overall age had most significant effect on IIEF-5 score. It is crucial to formulate strategies and implement them to prevent or control the epidemic of the MetS and its consequences. The early identification and treatment of risk factors might be helpful to prevent ED and secondary cardiovascular disease, including diet and lifestyle interventions.

Forest dynamics to precipitation and temperature in the Gulf of Mexico coastal region.

PubMed

Li, Tianyu; Meng, Qingmin

2017-05-01

The forest is one of the most significant components of the Gulf of Mexico (GOM) coast. It provides livelihood to inhabitant and is known to be sensitive to climatic fluctuations. This study focuses on examining the impacts of temperature and precipitation variations on coastal forest. Two different regression methods, ordinary least squares (OLS) and geographically weighted regression (GWR), were employed to reveal the relationship between meteorological variables and forest dynamics. OLS regression analysis shows that changes in precipitation and temperature, over a span of 12 months, are responsible for 56% of NDVI variation. The forest, which is not particularly affected by the average monthly precipitation in most months, is observed to be affected by cumulative seasonal and annual precipitation explicitly. Temperature and precipitation almost equally impact on NDVI changes; about 50% of the NDVI variations is explained in OLS modeling, and about 74% of the NDVI variations is explained in GWR modeling. GWR analysis indicated that both precipitation and temperature characterize the spatial heterogeneity patterns of forest dynamics.

Forest dynamics to precipitation and temperature in the Gulf of Mexico coastal region

NASA Astrophysics Data System (ADS)

Li, Tianyu; Meng, Qingmin

2017-05-01

The forest is one of the most significant components of the Gulf of Mexico (GOM) coast. It provides livelihood to inhabitant and is known to be sensitive to climatic fluctuations. This study focuses on examining the impacts of temperature and precipitation variations on coastal forest. Two different regression methods, ordinary least squares (OLS) and geographically weighted regression (GWR), were employed to reveal the relationship between meteorological variables and forest dynamics. OLS regression analysis shows that changes in precipitation and temperature, over a span of 12 months, are responsible for 56% of NDVI variation. The forest, which is not particularly affected by the average monthly precipitation in most months, is observed to be affected by cumulative seasonal and annual precipitation explicitly. Temperature and precipitation almost equally impact on NDVI changes; about 50% of the NDVI variations is explained in OLS modeling, and about 74% of the NDVI variations is explained in GWR modeling. GWR analysis indicated that both precipitation and temperature characterize the spatial heterogeneity patterns of forest dynamics.

Rainfall and streamflow from small tree-covered and fern-covered and burned watersheds in Hawaii

Treesearch

H. W. Anderson; P. D. Duffy; Teruo Yamamoto

1966-01-01

Streamflow from two 30-acre watersheds near Honolulu was studied by using principal components regression analysis. Models using data on monthly, storm, and peak discharges were tested against several variables expressing amount and intensity of rainfall, and against variables expressing antecedent rainfall. Explained variation ranged from 78 to 94 percent. The...

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...

Principal component analysis for surface reflection components and structure in facial images and synthesis of facial images for various ages

NASA Astrophysics Data System (ADS)

Hirose, Misa; Toyota, Saori; Ojima, Nobutoshi; Ogawa-Ochiai, Keiko; Tsumura, Norimichi

2017-08-01

In this paper, principal component analysis is applied to the distribution of pigmentation, surface reflectance, and landmarks in whole facial images to obtain feature values. The relationship between the obtained feature vectors and the age of the face is then estimated by multiple regression analysis so that facial images can be modulated for woman aged 10-70. In a previous study, we analyzed only the distribution of pigmentation, and the reproduced images appeared to be younger than the apparent age of the initial images. We believe that this happened because we did not modulate the facial structures and detailed surfaces, such as wrinkles. By considering landmarks and surface reflectance over the entire face, we were able to analyze the variation in the distributions of facial structures and fine asperity, and pigmentation. As a result, our method is able to appropriately modulate the appearance of a face so that it appears to be the correct age.

Resolving the percentage of component terrains within single resolution elements

NASA Technical Reports Server (NTRS)

Marsh, S. E.; Switzer, P.; Kowalik, W. S.; Lyon, R. J. P.

1980-01-01

An approximate maximum likelihood technique employing a widely available discriminant analysis program is discussed that has been developed for resolving the percentage of component terrains within single resolution elements. The method uses all four channels of Landsat data simultaneously and does not require prior knowledge of the percentage of components in mixed pixels. It was tested in five cases that were chosen to represent mixtures of outcrop, soil and vegetation which would typically be encountered in geologic studies with Landsat data. For all five cases, the method proved to be superior to single band weighted average and linear regression techniques and permitted an estimate of the total area occupied by component terrains to within plus or minus 6% of the true area covered. Its major drawback is a consistent overestimation of the pixel component percent of the darker materials (vegetation) and an underestimation of the pixel component percent of the brighter materials (sand).

Human allometry: adult bodies are more nearly geometrically similar than regression analysis has suggested.

PubMed

Burton, Richard F

2010-01-01

It is almost a matter of dogma that human body mass in adults tends to vary roughly in proportion to the square of height (stature), as Quetelet stated in 1835. As he realised, perfect isometry or geometric similarity requires that body mass varies with height cubed, so there seems to be a trend for tall adults to be relatively much lighter than short ones. Much evidence regarding component tissues and organs seems to accord with this idea. However, the hypothesis is presented that the proportions of the body are actually very much less size-dependent. Past evidence has mostly been obtained by least-squares regression analysis, but this cannot generally give a true picture of the allometric relationships. This is because there is considerable scatter in the data (leading to a low correlation between mass and height) and because neither variable causally determines the other. The relevant regression equations, though often formulated in logarithmic terms, effectively treat the masses as proportional to (body height)(b). Values of b estimated by regression must usually underestimate the true functional values, doing so especially when mass and height are poorly correlated. It is therefore telling support for the hypothesis that published estimates of b both for the whole body (which range between 1.0 and 2.5) and for its component tissues and organs (which vary even more) correlate with the corresponding correlation coefficients for mass and height. There is no simple statistical technique for establishing the true functional relationships, but Monte Carlo modelling has shown that the results obtained for total body mass are compatible with a true height exponent of three. Other data, on relationships between body mass and the girths of various body parts such as the thigh and chest, are also more consistent with isometry than regression analysis has suggested. This too is demonstrated by modelling. It thus seems that much of anthropometry needs to be re-evaluated. It is not suggested that all organs and tissues scale equally with whole body size.

Quantitative structure-activity relationship study of P2X7 receptor inhibitors using combination of principal component analysis and artificial intelligence methods.

PubMed

Ahmadi, Mehdi; Shahlaei, Mohsen

2015-01-01

P2X7 antagonist activity for a set of 49 molecules of the P2X7 receptor antagonists, derivatives of purine, was modeled with the aid of chemometric and artificial intelligence techniques. The activity of these compounds was estimated by means of combination of principal component analysis (PCA), as a well-known data reduction method, genetic algorithm (GA), as a variable selection technique, and artificial neural network (ANN), as a non-linear modeling method. First, a linear regression, combined with PCA, (principal component regression) was operated to model the structure-activity relationships, and afterwards a combination of PCA and ANN algorithm was employed to accurately predict the biological activity of the P2X7 antagonist. PCA preserves as much of the information as possible contained in the original data set. Seven most important PC's to the studied activity were selected as the inputs of ANN box by an efficient variable selection method, GA. The best computational neural network model was a fully-connected, feed-forward model with 7-7-1 architecture. The developed ANN model was fully evaluated by different validation techniques, including internal and external validation, and chemical applicability domain. All validations showed that the constructed quantitative structure-activity relationship model suggested is robust and satisfactory.

Quantitative structure–activity relationship study of P2X7 receptor inhibitors using combination of principal component analysis and artificial intelligence methods

PubMed Central

Ahmadi, Mehdi; Shahlaei, Mohsen

2015-01-01

P2X7 antagonist activity for a set of 49 molecules of the P2X7 receptor antagonists, derivatives of purine, was modeled with the aid of chemometric and artificial intelligence techniques. The activity of these compounds was estimated by means of combination of principal component analysis (PCA), as a well-known data reduction method, genetic algorithm (GA), as a variable selection technique, and artificial neural network (ANN), as a non-linear modeling method. First, a linear regression, combined with PCA, (principal component regression) was operated to model the structure–activity relationships, and afterwards a combination of PCA and ANN algorithm was employed to accurately predict the biological activity of the P2X7 antagonist. PCA preserves as much of the information as possible contained in the original data set. Seven most important PC's to the studied activity were selected as the inputs of ANN box by an efficient variable selection method, GA. The best computational neural network model was a fully-connected, feed-forward model with 7−7−1 architecture. The developed ANN model was fully evaluated by different validation techniques, including internal and external validation, and chemical applicability domain. All validations showed that the constructed quantitative structure–activity relationship model suggested is robust and satisfactory. PMID:26600858

Impact of covariate models on the assessment of the air pollution-mortality association in a single- and multipollutant context.

PubMed

Sacks, Jason D; Ito, Kazuhiko; Wilson, William E; Neas, Lucas M

2012-10-01

With the advent of multicity studies, uniform statistical approaches have been developed to examine air pollution-mortality associations across cities. To assess the sensitivity of the air pollution-mortality association to different model specifications in a single and multipollutant context, the authors applied various regression models developed in previous multicity time-series studies of air pollution and mortality to data from Philadelphia, Pennsylvania (May 1992-September 1995). Single-pollutant analyses used daily cardiovascular mortality, fine particulate matter (particles with an aerodynamic diameter ≤2.5 µm; PM(2.5)), speciated PM(2.5), and gaseous pollutant data, while multipollutant analyses used source factors identified through principal component analysis. In single-pollutant analyses, risk estimates were relatively consistent across models for most PM(2.5) components and gaseous pollutants. However, risk estimates were inconsistent for ozone in all-year and warm-season analyses. Principal component analysis yielded factors with species associated with traffic, crustal material, residual oil, and coal. Risk estimates for these factors exhibited less sensitivity to alternative regression models compared with single-pollutant models. Factors associated with traffic and crustal material showed consistently positive associations in the warm season, while the coal combustion factor showed consistently positive associations in the cold season. Overall, mortality risk estimates examined using a source-oriented approach yielded more stable and precise risk estimates, compared with single-pollutant analyses.

Neighborhood social capital and crime victimization: comparison of spatial regression analysis and hierarchical regression analysis.

PubMed

Takagi, Daisuke; Ikeda, Ken'ichi; Kawachi, Ichiro

2012-11-01

Crime is an important determinant of public health outcomes, including quality of life, mental well-being, and health behavior. A body of research has documented the association between community social capital and crime victimization. The association between social capital and crime victimization has been examined at multiple levels of spatial aggregation, ranging from entire countries, to states, metropolitan areas, counties, and neighborhoods. In multilevel analysis, the spatial boundaries at level 2 are most often drawn from administrative boundaries (e.g., Census tracts in the U.S.). One problem with adopting administrative definitions of neighborhoods is that it ignores spatial spillover. We conducted a study of social capital and crime victimization in one ward of Tokyo city, using a spatial Durbin model with an inverse-distance weighting matrix that assigned each respondent a unique level of "exposure" to social capital based on all other residents' perceptions. The study is based on a postal questionnaire sent to 20-69 years old residents of Arakawa Ward, Tokyo. The response rate was 43.7%. We examined the contextual influence of generalized trust, perceptions of reciprocity, two types of social network variables, as well as two principal components of social capital (constructed from the above four variables). Our outcome measure was self-reported crime victimization in the last five years. In the spatial Durbin model, we found that neighborhood generalized trust, reciprocity, supportive networks and two principal components of social capital were each inversely associated with crime victimization. By contrast, a multilevel regression performed with the same data (using administrative neighborhood boundaries) found generally null associations between neighborhood social capital and crime. Spatial regression methods may be more appropriate for investigating the contextual influence of social capital in homogeneous cultural settings such as Japan. Copyright © 2012 Elsevier Ltd. All rights reserved.

Soccer and sexual health education: a promising approach for reducing adolescent births in Haiti.

PubMed

Kaplan, Kathryn C; Lewis, Judy; Gebrian, Bette; Theall, Katherine

2015-05-01

To explore the effect of an innovative, integrative program in female sexual reproductive health (SRH) and soccer (or fútbol, in Haitian Creole) in rural Haiti by measuring the rate of births among program participants 15-19 years old and their nonparticipant peers. A retrospective cohort study using 2006-2009 data from the computerized data-tracking system of the Haitian Health Foundation (HHF), a U.S.-based nongovernmental organization serving urban and rural populations in Haiti, was used to assess births among girls 15-19 years old who participated in HHF's GenNext program, a combination education-soccer program for youth, based on SRH classes HHF nurses and community workers had been conducting in Haiti for mothers, fathers, and youth; girl-centered health screenings; and an all-female summer soccer league, during 2006-2009 (n = 4 251). Bivariate and multiple logistic regression analyses were carried out to assess differences in the rate of births among program participants according to their level of participation (SRH component only ("EDU") versus both the SRH and soccer components ("SO") compared to their village peers who did not participate. Hazard ratios (HRs) of birth rates were estimated using Cox regression analysis of childbearing data for the three different groups. In the multiple logistic regression analysis, only the girls in the "EDU" group had significantly fewer births than the nonparticipants after adjusting for confounders (odds ratio = 0.535; 95% confidence interval (CI) = 0.304, 0.940). The Cox regression analysis demonstrated that those in the EDU group (HR = 0.893; 95% CI = 0.802, 0.994) and to a greater degree those in the SO group (HR = 0.631; 95% CI = 0.558, 0.714) were significantly protected against childbearing between the ages of 15 and 19 years. HHF's GenNext program demonstrates the effectiveness of utilizing nurse educators, community mobilization, and youth participation in sports, education, and structured youth groups to promote and sustain health for adolescent girls and young women.

Influence factors and forecast of carbon emission in China: structure adjustment for emission peak

NASA Astrophysics Data System (ADS)

Wang, B.; Cui, C. Q.; Li, Z. P.

2018-02-01

This paper introduced Principal Component Analysis and Multivariate Linear Regression Model to verify long-term balance relationships between Carbon Emissions and the impact factors. The integrated model of improved PCA and multivariate regression analysis model is attainable to figure out the pattern of carbon emission sources. Main empirical results indicate that among all selected variables, the role of energy consumption scale was largest. GDP and Population follow and also have significant impacts on carbon emission. Industrialization rate and fossil fuel proportion, which is the indicator of reflecting the economic structure and energy structure, have a higher importance than the factor of urbanization rate and the dweller consumption level of urban areas. In this way, some suggestions are put forward for government to achieve the peak of carbon emissions.

Implementation of an integrating sphere for the enhancement of noninvasive glucose detection using quantum cascade laser spectroscopy

NASA Astrophysics Data System (ADS)

Werth, Alexandra; Liakat, Sabbir; Dong, Anqi; Woods, Callie M.; Gmachl, Claire F.

2018-05-01

An integrating sphere is used to enhance the collection of backscattered light in a noninvasive glucose sensor based on quantum cascade laser spectroscopy. The sphere enhances signal stability by roughly an order of magnitude, allowing us to use a thermoelectrically (TE) cooled detector while maintaining comparable glucose prediction accuracy levels. Using a smaller TE-cooled detector reduces form factor, creating a mobile sensor. Principal component analysis has predicted principal components of spectra taken from human subjects that closely match the absorption peaks of glucose. These principal components are used as regressors in a linear regression algorithm to make glucose concentration predictions, over 75% of which are clinically accurate.

Simultaneous determination of rifampicin, isoniazid and pyrazinamide in tablet preparations by multivariate spectrophotometric calibration.

PubMed

Goicoechea, H C; Olivieri, A C

1999-08-01

The use of multivariate spectrophotometric calibration is presented for the simultaneous determination of the active components of tablets used in the treatment of pulmonary tuberculosis. The resolution of ternary mixtures of rifampicin, isoniazid and pyrazinamide has been accomplished by using partial least squares (PLS-1) regression analysis. Although the components show an important degree of spectral overlap, they have been simultaneously determined with high accuracy and precision, rapidly and with no need of nonaqueous solvents for dissolving the samples. No interference has been observed from the tablet excipients. A comparison is presented with the related multivariate method of classical least squares (CLS) analysis, which is shown to yield less reliable results due to the severe spectral overlap among the studied compounds. This is highlighted in the case of isoniazid, due to the small absorbances measured for this component.

A classical regression framework for mediation analysis: fitting one model to estimate mediation effects.

PubMed

Saunders, Christina T; Blume, Jeffrey D

2017-10-26

Mediation analysis explores the degree to which an exposure's effect on an outcome is diverted through a mediating variable. We describe a classical regression framework for conducting mediation analyses in which estimates of causal mediation effects and their variance are obtained from the fit of a single regression model. The vector of changes in exposure pathway coefficients, which we named the essential mediation components (EMCs), is used to estimate standard causal mediation effects. Because these effects are often simple functions of the EMCs, an analytical expression for their model-based variance follows directly. Given this formula, it is instructive to revisit the performance of routinely used variance approximations (e.g., delta method and resampling methods). Requiring the fit of only one model reduces the computation time required for complex mediation analyses and permits the use of a rich suite of regression tools that are not easily implemented on a system of three equations, as would be required in the Baron-Kenny framework. Using data from the BRAIN-ICU study, we provide examples to illustrate the advantages of this framework and compare it with the existing approaches. © The Author 2017. Published by Oxford University Press.

An Objective Screening Method for Major Depressive Disorder Using Logistic Regression Analysis of Heart Rate Variability Data Obtained in a Mental Task Paradigm.

PubMed

Sun, Guanghao; Shinba, Toshikazu; Kirimoto, Tetsuo; Matsui, Takemi

2016-01-01

Heart rate variability (HRV) has been intensively studied as a promising biological marker of major depressive disorder (MDD). Our previous study confirmed that autonomic activity and reactivity in depression revealed by HRV during rest and mental task (MT) conditions can be used as diagnostic measures and in clinical evaluation. In this study, logistic regression analysis (LRA) was utilized for the classification and prediction of MDD based on HRV data obtained in an MT paradigm. Power spectral analysis of HRV on R-R intervals before, during, and after an MT (random number generation) was performed in 44 drug-naïve patients with MDD and 47 healthy control subjects at Department of Psychiatry in Shizuoka Saiseikai General Hospital. Logit scores of LRA determined by HRV indices and heart rates discriminated patients with MDD from healthy subjects. The high frequency (HF) component of HRV and the ratio of the low frequency (LF) component to the HF component (LF/HF) correspond to parasympathetic and sympathovagal balance, respectively. The LRA achieved a sensitivity and specificity of 80.0 and 79.0%, respectively, at an optimum cutoff logit score (0.28). Misclassifications occurred only when the logit score was close to the cutoff score. Logit scores also correlated significantly with subjective self-rating depression scale scores ( p  < 0.05). HRV indices recorded during a MT may be an objective tool for screening patients with MDD in psychiatric practice. The proposed method appears promising for not only objective and rapid MDD screening but also evaluation of its severity.

Assessing Principal Component Regression Prediction of Neurochemicals Detected with Fast-Scan Cyclic Voltammetry

PubMed Central

2011-01-01

Principal component regression is a multivariate data analysis approach routinely used to predict neurochemical concentrations from in vivo fast-scan cyclic voltammetry measurements. This mathematical procedure can rapidly be employed with present day computer programming languages. Here, we evaluate several methods that can be used to evaluate and improve multivariate concentration determination. The cyclic voltammetric representation of the calculated regression vector is shown to be a valuable tool in determining whether the calculated multivariate model is chemically appropriate. The use of Cook’s distance successfully identified outliers contained within in vivo fast-scan cyclic voltammetry training sets. This work also presents the first direct interpretation of a residual color plot and demonstrated the effect of peak shifts on predicted dopamine concentrations. Finally, separate analyses of smaller increments of a single continuous measurement could not be concatenated without substantial error in the predicted neurochemical concentrations due to electrode drift. Taken together, these tools allow for the construction of more robust multivariate calibration models and provide the first approach to assess the predictive ability of a procedure that is inherently impossible to validate because of the lack of in vivo standards. PMID:21966586

Assessing principal component regression prediction of neurochemicals detected with fast-scan cyclic voltammetry.

PubMed

Keithley, Richard B; Wightman, R Mark

2011-06-07

Principal component regression is a multivariate data analysis approach routinely used to predict neurochemical concentrations from in vivo fast-scan cyclic voltammetry measurements. This mathematical procedure can rapidly be employed with present day computer programming languages. Here, we evaluate several methods that can be used to evaluate and improve multivariate concentration determination. The cyclic voltammetric representation of the calculated regression vector is shown to be a valuable tool in determining whether the calculated multivariate model is chemically appropriate. The use of Cook's distance successfully identified outliers contained within in vivo fast-scan cyclic voltammetry training sets. This work also presents the first direct interpretation of a residual color plot and demonstrated the effect of peak shifts on predicted dopamine concentrations. Finally, separate analyses of smaller increments of a single continuous measurement could not be concatenated without substantial error in the predicted neurochemical concentrations due to electrode drift. Taken together, these tools allow for the construction of more robust multivariate calibration models and provide the first approach to assess the predictive ability of a procedure that is inherently impossible to validate because of the lack of in vivo standards.

[Association between the use of blood components and the five-year mortality after liver transplant].

PubMed

de Morais, Bruno Salomé; Sanches, Marcelo Dias; Ribeiro, Daniel Dias; Lima, Agnaldo Soares; de Abreu Ferrari, Teresa Cristina; Duarte, Malvina Maria de Freitas; Cançado, Guilherme Henrique Gomes Moreira

2011-01-01

Liver transplant (LT) surgery is associated with significant bleeding in 20% of cases, and several authors have demonstrated the risks related to blood components. The objective of the present study was to evaluate the impact of using blood components during hospitalization in five-year survival of patients undergoing LT. One hundred and thirteen patients were evaluated retrospectively. Several variables, including the use of blood components intraoperatively and throughout hospitalization, were categorized and evaluated by univariate analysis using Fisher's test. A level of significance of 5% was adopted. Results with p < 0.2 underwent multivariate analysis using multinomial logistic regression. Parenchymal diseases, preoperative renal dysfunction, and longer stay in hospital and ICU are associated with greater five-year mortality after LT (p < 0.05). Unlike the intraoperative use of blood components, the accumulated transfusion of packed red blood cell, frozen fresh plasma, and platelets during the entire hospitalization was associated with greater five-year mortality after liver transplantation (p < 0.01). This study emphasizes the relationship between the use of blood components during hospitalization and increased mortality in five years after LT. 2011 Elsevier Editora Ltda. All rights reserved.

Resolving and quantifying overlapped chromatographic bands by transmutation

PubMed

Malinowski

2000-09-15

A new chemometric technique called "transmutation" is developed for the purpose of sharpening overlapped chromatographic bands in order to quantify the components. The "transmutation function" is created from the chromatogram of the pure component of interest, obtained from the same instrument, operating under the same experimental conditions used to record the unresolved chromatogram of the sample mixture. The method is used to quantify mixtures containing toluene, ethylbenzene, m-xylene, naphthalene, and biphenyl from unresolved chromatograms previously reported. The results are compared to those obtained using window factor analysis, rank annihilation factor analysis, and matrix regression analysis. Unlike the latter methods, the transmutation method is not restricted to two-dimensional arrays of data, such as those obtained from HPLC/DAD, but is also applicable to chromatograms obtained from single detector experiments. Limitations of the method are discussed.

"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…

Investigation of noise in gear transmissions by the method of mathematical smoothing of experiments

NASA Technical Reports Server (NTRS)

Sheftel, B. T.; Lipskiy, G. K.; Ananov, P. P.; Chernenko, I. K.

1973-01-01

A rotatable central component smoothing method is used to analyze rotating gear noise spectra. A matrix is formulated in which the randomized rows correspond to various tests and the columns to factor values. Canonical analysis of the obtained regression equation permits the calculation of optimal speed and load at a previous assigned noise level.

Sugar and acid content of Citrus prediction modeling using FT-IR fingerprinting in combination with multivariate statistical analysis.

PubMed

Song, Seung Yeob; Lee, Young Koung; Kim, In-Jung

2016-01-01

A high-throughput screening system for Citrus lines were established with higher sugar and acid contents using Fourier transform infrared (FT-IR) spectroscopy in combination with multivariate analysis. FT-IR spectra confirmed typical spectral differences between the frequency regions of 950-1100 cm(-1), 1300-1500 cm(-1), and 1500-1700 cm(-1). Principal component analysis (PCA) and subsequent partial least square-discriminant analysis (PLS-DA) were able to discriminate five Citrus lines into three separate clusters corresponding to their taxonomic relationships. The quantitative predictive modeling of sugar and acid contents from Citrus fruits was established using partial least square regression algorithms from FT-IR spectra. The regression coefficients (R(2)) between predicted values and estimated sugar and acid content values were 0.99. These results demonstrate that by using FT-IR spectra and applying quantitative prediction modeling to Citrus sugar and acid contents, excellent Citrus lines can be early detected with greater accuracy. Copyright © 2015 Elsevier Ltd. All rights reserved.

Optimisation of the formulation of a bubble bath by a chemometric approach market segmentation and optimisation.

PubMed

Marengo, Emilio; Robotti, Elisa; Gennaro, Maria Carla; Bertetto, Mariella

2003-03-01

The optimisation of the formulation of a commercial bubble bath was performed by chemometric analysis of Panel Tests results. A first Panel Test was performed to choose the best essence, among four proposed to the consumers; the best essence chosen was used in the revised commercial bubble bath. Afterwards, the effect of changing the amount of four components (the amount of primary surfactant, the essence, the hydratant and the colouring agent) of the bubble bath was studied by a fractional factorial design. The segmentation of the bubble bath market was performed by a second Panel Test, in which the consumers were requested to evaluate the samples coming from the experimental design. The results were then treated by Principal Component Analysis. The market had two segments: people preferring a product with a rich formulation and people preferring a poor product. The final target, i.e. the optimisation of the formulation for each segment, was obtained by the calculation of regression models relating the subjective evaluations given by the Panel and the compositions of the samples. The regression models allowed to identify the best formulations for the two segments ofthe market.

A SAR and QSAR study of new artemisinin compounds with antimalarial activity.

PubMed

Santos, Cleydson Breno R; Vieira, Josinete B; Lobato, Cleison C; Hage-Melim, Lorane I S; Souto, Raimundo N P; Lima, Clarissa S; Costa, Elizabeth V M; Brasil, Davi S B; Macêdo, Williams Jorge C; Carvalho, José Carlos T

2013-12-30

The Hartree-Fock method and the 6-31G** basis set were employed to calculate the molecular properties of artemisinin and 20 derivatives with antimalarial activity. Maps of molecular electrostatic potential (MEPs) and molecular docking were used to investigate the interaction between ligands and the receptor (heme). Principal component analysis and hierarchical cluster analysis were employed to select the most important descriptors related to activity. The correlation between biological activity and molecular properties was obtained using the partial least squares and principal component regression methods. The regression PLS and PCR models built in this study were also used to predict the antimalarial activity of 30 new artemisinin compounds with unknown activity. The models obtained showed not only statistical significance but also predictive ability. The significant molecular descriptors related to the compounds with antimalarial activity were the hydration energy (HE), the charge on the O11 oxygen atom (QO11), the torsion angle O1-O2-Fe-N2 (D2) and the maximum rate of R/Sanderson Electronegativity (RTe+). These variables led to a physical and structural explanation of the molecular properties that should be selected for when designing new ligands to be used as antimalarial agents.

Applying different independent component analysis algorithms and support vector regression for IT chain store sales forecasting.

PubMed

Dai, Wensheng; Wu, Jui-Yu; Lu, Chi-Jie

2014-01-01

Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting.

Applying Different Independent Component Analysis Algorithms and Support Vector Regression for IT Chain Store Sales Forecasting

PubMed Central

Dai, Wensheng

2014-01-01

Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting. PMID:25165740

Multivariate classification of small order watersheds in the Quabbin Reservoir Basin, Massachusetts

USGS Publications Warehouse

Lent, R.M.; Waldron, M.C.; Rader, J.C.

1998-01-01

A multivariate approach was used to analyze hydrologic, geologic, geographic, and water-chemistry data from small order watersheds in the Quabbin Reservoir Basin in central Massachusetts. Eighty three small order watersheds were delineated and landscape attributes defining hydrologic, geologic, and geographic features of the watersheds were compiled from geographic information system data layers. Principal components analysis was used to evaluate 11 chemical constituents collected bi-weekly for 1 year at 15 surface-water stations in order to subdivide the basin into subbasins comprised of watersheds with similar water quality characteristics. Three principal components accounted for about 90 percent of the variance in water chemistry data. The principal components were defined as a biogeochemical variable related to wetland density, an acid-neutralization variable, and a road-salt variable related to density of primary roads. Three subbasins were identified. Analysis of variance and multiple comparisons of means were used to identify significant differences in stream water chemistry and landscape attributes among subbasins. All stream water constituents were significantly different among subbasins. Multiple regression techniques were used to relate stream water chemistry to landscape attributes. Important differences in landscape attributes were related to wetlands, slope, and soil type.A multivariate approach was used to analyze hydrologic, geologic, geographic, and water-chemistry data from small order watersheds in the Quabbin Reservoir Basin in central Massachusetts. Eighty three small order watersheds were delineated and landscape attributes defining hydrologic, geologic, and geographic features of the watersheds were compiled from geographic information system data layers. Principal components analysis was used to evaluate 11 chemical constituents collected bi-weekly for 1 year at 15 surface-water stations in order to subdivide the basin into subbasins comprised of watersheds with similar water quality characteristics. Three principal components accounted for about 90 percent of the variance in water chemistry data. The principal components were defined as a biogeochemical variable related to wetland density, an acid-neutralization variable, and a road-salt variable related to density of primary roads. Three subbasins were identified. Analysis of variance and multiple comparisons of means were used to identify significant differences in stream water chemistry and landscape attributes among subbasins. All stream water constituents were significantly different among subbasins. Multiple regression techniques were used to relate stream water chemistry to landscape attributes. Important differences in landscape attributes were related to wetlands, slope, and soil type.

Exploring relationships between Dairy Herd Improvement monitors of performance and the Transition Cow Index in Wisconsin dairy herds.

PubMed

Schultz, K K; Bennett, T B; Nordlund, K V; Döpfer, D; Cook, N B

2016-09-01

Transition cow management has been tracked via the Transition Cow Index (TCI; AgSource Cooperative Services, Verona, WI) since 2006. Transition Cow Index was developed to measure the difference between actual and predicted milk yield at first test day to evaluate the relative success of the transition period program. This project aimed to assess TCI in relation to all commonly used Dairy Herd Improvement (DHI) metrics available through AgSource Cooperative Services. Regression analysis was used to isolate variables that were relevant to TCI, and then principal components analysis and network analysis were used to determine the relative strength and relatedness among variables. Finally, cluster analysis was used to segregate herds based on similarity of relevant variables. The DHI data were obtained from 2,131 Wisconsin dairy herds with test-day mean ≥30 cows, which were tested ≥10 times throughout the 2014 calendar year. The original list of 940 DHI variables was reduced through expert-driven selection and regression analysis to 23 variables. The K-means cluster analysis produced 5 distinct clusters. Descriptive statistics were calculated for the 23 variables per cluster grouping. Using principal components analysis, cluster analysis, and network analysis, 4 parameters were isolated as most relevant to TCI; these were energy-corrected milk, 3 measures of intramammary infection (dry cow cure rate, linear somatic cell count score in primiparous cows, and new infection rate), peak ratio, and days in milk at peak milk production. These variables together with cow and newborn calf survival measures form a group of metrics that can be used to assist in the evaluation of overall transition period performance. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

The PX-EM algorithm for fast stable fitting of Henderson's mixed model

PubMed Central

Foulley, Jean-Louis; Van Dyk, David A

2000-01-01

This paper presents procedures for implementing the PX-EM algorithm of Liu, Rubin and Wu to compute REML estimates of variance covariance components in Henderson's linear mixed models. The class of models considered encompasses several correlated random factors having the same vector length e.g., as in random regression models for longitudinal data analysis and in sire-maternal grandsire models for genetic evaluation. Numerical examples are presented to illustrate the procedures. Much better results in terms of convergence characteristics (number of iterations and time required for convergence) are obtained for PX-EM relative to the basic EM algorithm in the random regression. PMID:14736399

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

NASA Astrophysics Data System (ADS)

Asavaskulkiet, Krissada

2018-04-01

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

Partial Least Squares Regression Can Aid in Detecting Differential Abundance of Multiple Features in Sets of Metagenomic Samples

PubMed Central

Libiger, Ondrej; Schork, Nicholas J.

2015-01-01

It is now feasible to examine the composition and diversity of microbial communities (i.e., “microbiomes”) that populate different human organs and orifices using DNA sequencing and related technologies. To explore the potential links between changes in microbial communities and various diseases in the human body, it is essential to test associations involving different species within and across microbiomes, environmental settings and disease states. Although a number of statistical techniques exist for carrying out relevant analyses, it is unclear which of these techniques exhibit the greatest statistical power to detect associations given the complexity of most microbiome datasets. We compared the statistical power of principal component regression, partial least squares regression, regularized regression, distance-based regression, Hill's diversity measures, and a modified test implemented in the popular and widely used microbiome analysis methodology “Metastats” across a wide range of simulated scenarios involving changes in feature abundance between two sets of metagenomic samples. For this purpose, simulation studies were used to change the abundance of microbial species in a real dataset from a published study examining human hands. Each technique was applied to the same data, and its ability to detect the simulated change in abundance was assessed. We hypothesized that a small subset of methods would outperform the rest in terms of the statistical power. Indeed, we found that the Metastats technique modified to accommodate multivariate analysis and partial least squares regression yielded high power under the models and data sets we studied. The statistical power of diversity measure-based tests, distance-based regression and regularized regression was significantly lower. Our results provide insight into powerful analysis strategies that utilize information on species counts from large microbiome data sets exhibiting skewed frequency distributions obtained on a small to moderate number of samples. PMID:26734061

[Quantitative analysis of nucleotide mixtures with terahertz time domain spectroscopy].

PubMed

Zhang, Zeng-yan; Xiao, Ti-qiao; Zhao, Hong-wei; Yu, Xiao-han; Xi, Zai-jun; Xu, Hong-jie

2008-09-01

Adenosine, thymidine, guanosine, cytidine and uridine form the building blocks of ribose nucleic acid (RNA) and deoxyribose nucleic acid (DNA). Nucleosides and their derivants are all have biological activities. Some of them can be used as medicine directly or as materials to synthesize other medicines. It is meaningful to detect the component and content in nucleosides mixtures. In the present paper, components and contents of the mixtures of adenosine, thymidine, guanosine, cytidine and uridine were analyzed. THz absorption spectra of pure nucleosides were set as standard spectra. The mixture's absorption spectra were analyzed by linear regression with non-negative constraint to identify the components and their relative content in the mixtures. The experimental and analyzing results show that it is simple and effective to get the components and their relative percentage in the mixtures by terahertz time domain spectroscopy with a relative error less than 10%. Component which is absent could be excluded exactly by this method, and the error sources were also analyzed. All the experiments and analysis confirms that this method is of no damage or contamination to the sample. This means that it will be a simple, effective and new method in biochemical materials analysis, which extends the application field of THz-TDS.

Predictors of unsuccessful outcome in cemented femoral revisions using bone impaction grafting; Cox regression analysis of 208 cases.

PubMed

Te Stroet, Martijn A J; Rijnen, Wim H C; Gardeniers, Jean W M; Schreurs, B Willem; Hannink, Gerjon

2016-09-29

Despite improvements in the technique of femoral impaction bone grafting, reconstruction failures still can occur. Therefore, the aim of our study was to determine risk factors for the endpoint re-revision for any reason. We used prospectively collected demographic, clinical and surgical data of all 202 patients who underwent 208 femoral revisions using the X-change Femoral Revision System (Stryker-Howmedica), fresh-frozen morcellised allograft and a cemented polished Exeter stem in our department from 1991 to 2007. Univariable and multivariable Cox regression analyses were performed to identify potential factors associated with re-revision. The mean follow-up was 10.6 (5-21) years. The cumulative re-revision rate was 6.3% (13/208). After univariable selection, sex, age, body mass index (BMI), American Association of Anesthesiologists (ASA) classification, type of removed femoral component, and mesh used for reconstruction were included in multivariable regression analysis.In the multivariable analysis, BMI was the only factor that was significantly associated with the risk of re-revision after bone impaction grafting (BMI ≥30 vs. BMI <30, HR = 6.54 [95% CI 1.89-22.65]; p = 0.003). BMI was the only factor associated with the risk of re-revision for any reason. Besides BMI also other factors, such as Endoklinik score and the type of removed femoral component, can provide guidance in the process of preclinical decision making. With the knowledge obtained from this study, preoperative patient selection, informed consent, and treatment protocols can be better adjusted to the individual patient who needs to undergo a femoral revision with impaction bone grafting.

Just a fad? Gamification in health and fitness apps.

PubMed

Lister, Cameron; West, Joshua H; Cannon, Ben; Sax, Tyler; Brodegard, David

2014-08-04

Gamification has been a predominant focus of the health app industry in recent years. However, to our knowledge, there has yet to be a review of gamification elements in relation to health behavior constructs, or insight into the true proliferation of gamification in health apps. The objective of this study was to identify the extent to which gamification is used in health apps, and analyze gamification of health and fitness apps as a potential component of influence on a consumer's health behavior. An analysis of health and fitness apps related to physical activity and diet was conducted among apps in the Apple App Store in the winter of 2014. This analysis reviewed a sample of 132 apps for the 10 effective game elements, the 6 core components of health gamification, and 13 core health behavior constructs. A regression analysis was conducted in order to measure the correlation between health behavior constructs, gamification components, and effective game elements. This review of the most popular apps showed widespread use of gamification principles, but low adherence to any professional guidelines or industry standard. Regression analysis showed that game elements were associated with gamification (P<.001). Behavioral theory was associated with gamification (P<.05), but not game elements, and upon further analysis gamification was only associated with composite motivational behavior scores (P<.001), and not capacity or opportunity/trigger. This research, to our knowledge, represents the first comprehensive review of gamification use in health and fitness apps, and the potential to impact health behavior. The results show that use of gamification in health and fitness apps has become immensely popular, as evidenced by the number of apps found in the Apple App Store containing at least some components of gamification. This shows a lack of integrating important elements of behavioral theory from the app industry, which can potentially impact the efficacy of gamification apps to change behavior. Apps represent a very promising, burgeoning market and landscape in which to disseminate health behavior change interventions. Initial results show an abundant use of gamification in health and fitness apps, which necessitates the in-depth study and evaluation of the potential of gamification to change health behaviors.

Temporal trend and climate factors of hemorrhagic fever with renal syndrome epidemic in Shenyang City, China

PubMed Central

2011-01-01

Background Hemorrhagic fever with renal syndrome (HFRS) is an important infectious disease caused by different species of hantaviruses. As a rodent-borne disease with a seasonal distribution, external environmental factors including climate factors may play a significant role in its transmission. The city of Shenyang is one of the most seriously endemic areas for HFRS. Here, we characterized the dynamic temporal trend of HFRS, and identified climate-related risk factors and their roles in HFRS transmission in Shenyang, China. Methods The annual and monthly cumulative numbers of HFRS cases from 2004 to 2009 were calculated and plotted to show the annual and seasonal fluctuation in Shenyang. Cross-correlation and autocorrelation analyses were performed to detect the lagged effect of climate factors on HFRS transmission and the autocorrelation of monthly HFRS cases. Principal component analysis was constructed by using climate data from 2004 to 2009 to extract principal components of climate factors to reduce co-linearity. The extracted principal components and autocorrelation terms of monthly HFRS cases were added into a multiple regression model called principal components regression model (PCR) to quantify the relationship between climate factors, autocorrelation terms and transmission of HFRS. The PCR model was compared to a general multiple regression model conducted only with climate factors as independent variables. Results A distinctly declining temporal trend of annual HFRS incidence was identified. HFRS cases were reported every month, and the two peak periods occurred in spring (March to May) and winter (November to January), during which, nearly 75% of the HFRS cases were reported. Three principal components were extracted with a cumulative contribution rate of 86.06%. Component 1 represented MinRH0, MT1, RH1, and MWV1; component 2 represented RH2, MaxT3, and MAP3; and component 3 represented MaxT2, MAP2, and MWV2. The PCR model was composed of three principal components and two autocorrelation terms. The association between HFRS epidemics and climate factors was better explained in the PCR model (F = 446.452, P < 0.001, adjusted R2 = 0.75) than in the general multiple regression model (F = 223.670, P < 0.000, adjusted R2 = 0.51). Conclusion The temporal distribution of HFRS in Shenyang varied in different years with a distinctly declining trend. The monthly trends of HFRS were significantly associated with local temperature, relative humidity, precipitation, air pressure, and wind velocity of the different previous months. The model conducted in this study will make HFRS surveillance simpler and the control of HFRS more targeted in Shenyang. PMID:22133347

Hybrid ICA-Regression: Automatic Identification and Removal of Ocular Artifacts from Electroencephalographic Signals.

PubMed

Mannan, Malik M Naeem; Jeong, Myung Y; Kamran, Muhammad A

2016-01-01

Electroencephalography (EEG) is a portable brain-imaging technique with the advantage of high-temporal resolution that can be used to record electrical activity of the brain. However, it is difficult to analyze EEG signals due to the contamination of ocular artifacts, and which potentially results in misleading conclusions. Also, it is a proven fact that the contamination of ocular artifacts cause to reduce the classification accuracy of a brain-computer interface (BCI). It is therefore very important to remove/reduce these artifacts before the analysis of EEG signals for applications like BCI. In this paper, a hybrid framework that combines independent component analysis (ICA), regression and high-order statistics has been proposed to identify and eliminate artifactual activities from EEG data. We used simulated, experimental and standard EEG signals to evaluate and analyze the effectiveness of the proposed method. Results demonstrate that the proposed method can effectively remove ocular artifacts as well as it can preserve the neuronal signals present in EEG data. A comparison with four methods from literature namely ICA, regression analysis, wavelet-ICA (wICA), and regression-ICA (REGICA) confirms the significantly enhanced performance and effectiveness of the proposed method for removal of ocular activities from EEG, in terms of lower mean square error and mean absolute error values and higher mutual information between reconstructed and original EEG.

Hybrid ICA—Regression: Automatic Identification and Removal of Ocular Artifacts from Electroencephalographic Signals

PubMed Central

Mannan, Malik M. Naeem; Jeong, Myung Y.; Kamran, Muhammad A.

2016-01-01

Electroencephalography (EEG) is a portable brain-imaging technique with the advantage of high-temporal resolution that can be used to record electrical activity of the brain. However, it is difficult to analyze EEG signals due to the contamination of ocular artifacts, and which potentially results in misleading conclusions. Also, it is a proven fact that the contamination of ocular artifacts cause to reduce the classification accuracy of a brain-computer interface (BCI). It is therefore very important to remove/reduce these artifacts before the analysis of EEG signals for applications like BCI. In this paper, a hybrid framework that combines independent component analysis (ICA), regression and high-order statistics has been proposed to identify and eliminate artifactual activities from EEG data. We used simulated, experimental and standard EEG signals to evaluate and analyze the effectiveness of the proposed method. Results demonstrate that the proposed method can effectively remove ocular artifacts as well as it can preserve the neuronal signals present in EEG data. A comparison with four methods from literature namely ICA, regression analysis, wavelet-ICA (wICA), and regression-ICA (REGICA) confirms the significantly enhanced performance and effectiveness of the proposed method for removal of ocular activities from EEG, in terms of lower mean square error and mean absolute error values and higher mutual information between reconstructed and original EEG. PMID:27199714

Patterns in longitudinal growth of refraction in Southern Chinese children: cluster and principal component analysis.

PubMed

Chen, Yanxian; Chang, Billy Heung Wing; Ding, Xiaohu; He, Mingguang

2016-11-22

In the present study we attempt to use hypothesis-independent analysis in investigating the patterns in refraction growth in Chinese children, and to explore the possible risk factors affecting the different components of progression, as defined by Principal Component Analysis (PCA). A total of 637 first-born twins in Guangzhou Twin Eye Study with 6-year annual visits (baseline age 7-15 years) were available in the analysis. Cluster 1 to 3 were classified after a partitioning clustering, representing stable, slow and fast progressing groups of refraction respectively. Baseline age and refraction, paternal refraction, maternal refraction and proportion of two myopic parents showed significant differences across the three groups. Three major components of progression were extracted using PCA: "Average refraction", "Acceleration" and the combination of "Myopia stabilization" and "Late onset of refraction progress". In regression models, younger children with more severe myopia were associated with larger "Acceleration". The risk factors of "Acceleration" included change of height and weight, near work, and parental myopia, while female gender, change of height and weight were associated with "Stabilization", and increased outdoor time was related to "Late onset of refraction progress". We therefore concluded that genetic and environmental risk factors have different impacts on patterns of refraction progression.

Patterns in longitudinal growth of refraction in Southern Chinese children: cluster and principal component analysis

PubMed Central

Chen, Yanxian; Chang, Billy Heung Wing; Ding, Xiaohu; He, Mingguang

2016-01-01

In the present study we attempt to use hypothesis-independent analysis in investigating the patterns in refraction growth in Chinese children, and to explore the possible risk factors affecting the different components of progression, as defined by Principal Component Analysis (PCA). A total of 637 first-born twins in Guangzhou Twin Eye Study with 6-year annual visits (baseline age 7–15 years) were available in the analysis. Cluster 1 to 3 were classified after a partitioning clustering, representing stable, slow and fast progressing groups of refraction respectively. Baseline age and refraction, paternal refraction, maternal refraction and proportion of two myopic parents showed significant differences across the three groups. Three major components of progression were extracted using PCA: “Average refraction”, “Acceleration” and the combination of “Myopia stabilization” and “Late onset of refraction progress”. In regression models, younger children with more severe myopia were associated with larger “Acceleration”. The risk factors of “Acceleration” included change of height and weight, near work, and parental myopia, while female gender, change of height and weight were associated with “Stabilization”, and increased outdoor time was related to “Late onset of refraction progress”. We therefore concluded that genetic and environmental risk factors have different impacts on patterns of refraction progression. PMID:27874105

Identification of molecular markers associated with mite resistance in coconut (Cocos nucifera L.).

PubMed

Shalini, K V; Manjunatha, S; Lebrun, P; Berger, A; Baudouin, L; Pirany, N; Ranganath, R M; Prasad, D Theertha

2007-01-01

Coconut mite (Aceria guerreronis 'Keifer') has become a major threat to Indian coconut (Coçcos nucifera L.) cultivators and the processing industry. Chemical and biological control measures have proved to be costly, ineffective, and ecologically undesirable. Planting mite-resistant coconut cultivars is the most effective method of preventing yield loss and should form a major component of any integrated pest management stratagem. Coconut genotypes, and mite-resistant and -susceptible accessions were collected from different parts of South India. Thirty-two simple sequence repeat (SSR) and 7 RAPD primers were used for molecular analyses. In single-marker analysis, 9 SSR and 4 RAPD markers associated with mite resistance were identified. In stepwise multiple regression analysis of SSRs, a combination of 6 markers showed 100% association with mite infestation. Stepwise multiple regression analysis for RAPD data revealed that a combination of 3 markers accounted for 83.86% of mite resistance in the selected materials. Combined stepwise multiple regression analysis of RAPD and SSR data showed that a combination of 5 markers explained 100% of the association with mite resistance in coconut. Markers associated with mite resistance are important in coconut breeding programs and will facilitate the selection of mite-resistant plants at an early stage as well as mother plants for breeding programs.

A Quantile Regression Approach to Understanding the Relations Between Morphological Awareness, Vocabulary, and Reading Comprehension in Adult Basic Education Students

PubMed Central

Tighe, Elizabeth L.; Schatschneider, Christopher

2015-01-01

The purpose of this study was to investigate the joint and unique contributions of morphological awareness and vocabulary knowledge at five reading comprehension levels in Adult Basic Education (ABE) students. We introduce the statistical technique of multiple quantile regression, which enabled us to assess the predictive utility of morphological awareness and vocabulary knowledge at multiple points (quantiles) along the continuous distribution of reading comprehension. To demonstrate the efficacy of our multiple quantile regression analysis, we compared and contrasted our results with a traditional multiple regression analytic approach. Our results indicated that morphological awareness and vocabulary knowledge accounted for a large portion of the variance (82-95%) in reading comprehension skills across all quantiles. Morphological awareness exhibited the greatest unique predictive ability at lower levels of reading comprehension whereas vocabulary knowledge exhibited the greatest unique predictive ability at higher levels of reading comprehension. These results indicate the utility of using multiple quantile regression to assess trajectories of component skills across multiple levels of reading comprehension. The implications of our findings for ABE programs are discussed. PMID:25351773

Food adulteration analysis without laboratory prepared or determined reference food adulterant values.

PubMed

Kalivas, John H; Georgiou, Constantinos A; Moira, Marianna; Tsafaras, Ilias; Petrakis, Eleftherios A; Mousdis, George A

2014-04-01

Quantitative analysis of food adulterants is an important health and economic issue that needs to be fast and simple. Spectroscopy has significantly reduced analysis time. However, still needed are preparations of analyte calibration samples matrix matched to prediction samples which can be laborious and costly. Reported in this paper is the application of a newly developed pure component Tikhonov regularization (PCTR) process that does not require laboratory prepared or reference analysis methods, and hence, is a greener calibration method. The PCTR method requires an analyte pure component spectrum and non-analyte spectra. As a food analysis example, synchronous fluorescence spectra of extra virgin olive oil samples adulterated with sunflower oil is used. Results are shown to be better than those obtained using ridge regression with reference calibration samples. The flexibility of PCTR allows including reference samples and is generic for use with other instrumental methods and food products. Copyright © 2013 Elsevier Ltd. All rights reserved.

Engine With Regression and Neural Network Approximators Designed

NASA Technical Reports Server (NTRS)

Patnaik, Surya N.; Hopkins, Dale A.

2001-01-01

At the NASA Glenn Research Center, the NASA engine performance program (NEPP, ref. 1) and the design optimization testbed COMETBOARDS (ref. 2) with regression and neural network analysis-approximators have been coupled to obtain a preliminary engine design methodology. The solution to a high-bypass-ratio subsonic waverotor-topped turbofan engine, which is shown in the preceding figure, was obtained by the simulation depicted in the following figure. This engine is made of 16 components mounted on two shafts with 21 flow stations. The engine is designed for a flight envelope with 47 operating points. The design optimization utilized both neural network and regression approximations, along with the cascade strategy (ref. 3). The cascade used three algorithms in sequence: the method of feasible directions, the sequence of unconstrained minimizations technique, and sequential quadratic programming. The normalized optimum thrusts obtained by the three methods are shown in the following figure: the cascade algorithm with regression approximation is represented by a triangle, a circle is shown for the neural network solution, and a solid line indicates original NEPP results. The solutions obtained from both approximate methods lie within one standard deviation of the benchmark solution for each operating point. The simulation improved the maximum thrust by 5 percent. The performance of the linear regression and neural network methods as alternate engine analyzers was found to be satisfactory for the analysis and operation optimization of air-breathing propulsion engines (ref. 4).

Effective psychological and psychosocial approaches to reduce repetition of self-harm: a systematic review, meta-analysis and meta-regression

PubMed Central

Robinson, Jo; Spittal, Matthew J; Carter, Greg

2016-01-01

Objective To examine the efficacy of psychological and psychosocial interventions for reductions in repeated self-harm. Design We conducted a systematic review, meta-analysis and meta-regression to examine the efficacy of psychological and psychosocial interventions to reduce repeat self-harm in adults. We included a sensitivity analysis of studies with a low risk of bias for the meta-analysis. For the meta-regression, we examined whether the type, intensity (primary analyses) and other components of intervention or methodology (secondary analyses) modified the overall intervention effect. Data sources A comprehensive search of MEDLINE, PsycInfo and EMBASE (from 1999 to June 2016) was performed. Eligibility criteria for selecting studies Randomised controlled trials of psychological and psychosocial interventions for adult self-harm patients. Results Forty-five trials were included with data available from 36 (7354 participants) for the primary analysis. Meta-analysis showed a significant benefit of all psychological and psychosocial interventions combined (risk ratio 0.84; 95% CI 0.74 to 0.96; number needed to treat=33); however, sensitivity analyses showed that this benefit was non-significant when restricted to a limited number of high-quality studies. Meta-regression showed that the type of intervention did not modify the treatment effects. Conclusions Consideration of a psychological or psychosocial intervention over and above treatment as usual is worthwhile; with the public health benefits of ensuring that this practice is widely adopted potentially worth the investment. However, the specific type and nature of the intervention that should be delivered is not yet clear. Cognitive–behavioural therapy or interventions with an interpersonal focus and targeted on the precipitants to self-harm may be the best candidates on the current evidence. Further research is required. PMID:27660314

Revealing the underlying drivers of disaster risk: a global analysis

NASA Astrophysics Data System (ADS)

Peduzzi, Pascal

2017-04-01

Disasters events are perfect examples of compound events. Disaster risk lies at the intersection of several independent components such as hazard, exposure and vulnerability. Understanding the weight of each component requires extensive standardisation. Here, I show how footprints of past disastrous events were generated using GIS modelling techniques and used for extracting population and economic exposures based on distribution models. Using past event losses, it was possible to identify and quantify a wide range of socio-politico-economic drivers associated with human vulnerability. The analysis was applied to about nine thousand individual past disastrous events covering earthquakes, floods and tropical cyclones. Using a multiple regression analysis on these individual events it was possible to quantify each risk component and assess how vulnerability is influenced by various hazard intensities. The results show that hazard intensity, exposure, poverty, governance as well as other underlying factors (e.g. remoteness) can explain the magnitude of past disasters. Analysis was also performed to highlight the role of future trends in population and climate change and how this may impacts exposure to tropical cyclones in the future. GIS models combined with statistical multiple regression analysis provided a powerful methodology to identify, quantify and model disaster risk taking into account its various components. The same methodology can be applied to various types of risk at local to global scale. This method was applied and developed for the Global Risk Analysis of the Global Assessment Report on Disaster Risk Reduction (GAR). It was first applied on mortality risk in GAR 2009 and GAR 2011. New models ranging from global assets exposure and global flood hazard models were also recently developed to improve the resolution of the risk analysis and applied through CAPRA software to provide probabilistic economic risk assessments such as Average Annual Losses (AAL) and Probable Maximum Losses (PML) in GAR 2013 and GAR 2015. In parallel similar methodologies were developed to highlitght the role of ecosystems for Climate Change Adaptation (CCA) and Disaster Risk Reduction (DRR). New developments may include slow hazards (such as e.g. soil degradation and droughts), natech hazards (by intersecting with georeferenced critical infrastructures) The various global hazard, exposure and risk models can be visualized and download through the PREVIEW Global Risk Data Platform.

Analysis of Moisture Content in Beetroot using Fourier Transform Infrared Spectroscopy and by Principal Component Analysis.

PubMed

Nesakumar, Noel; Baskar, Chanthini; Kesavan, Srinivasan; Rayappan, John Bosco Balaguru; Alwarappan, Subbiah

2018-05-22

The moisture content of beetroot varies during long-term cold storage. In this work, we propose a strategy to identify the moisture content and age of beetroot using principal component analysis coupled Fourier transform infrared spectroscopy (FTIR). Frequent FTIR measurements were recorded directly from the beetroot sample surface over a period of 34 days for analysing its moisture content employing attenuated total reflectance in the spectral ranges of 2614-4000 and 1465-1853 cm -1 with a spectral resolution of 8 cm -1 . In order to estimate the transmittance peak height (T p ) and area under the transmittance curve [Formula: see text] over the spectral ranges of 2614-4000 and 1465-1853 cm -1 , Gaussian curve fitting algorithm was performed on FTIR data. Principal component and nonlinear regression analyses were utilized for FTIR data analysis. Score plot over the ranges of 2614-4000 and 1465-1853 cm -1 allowed beetroot quality discrimination. Beetroot quality predictive models were developed by employing biphasic dose response function. Validation experiment results confirmed that the accuracy of the beetroot quality predictive model reached 97.5%. This research work proves that FTIR spectroscopy in combination with principal component analysis and beetroot quality predictive models could serve as an effective tool for discriminating moisture content in fresh, half and completely spoiled stages of beetroot samples and for providing status alerts.

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

NASA Astrophysics Data System (ADS)

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

2016-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2015-08-01

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

Polycyclic aromatic hydrocarbons in ambient air, surface soil and wheat grain near a large steel-smelting manufacturer in northern China.

PubMed

Liu, Weijian; Wang, Yilong; Chen, Yuanchen; Tao, Shu; Liu, Wenxin

2017-07-01

The total concentrations and component profiles of polycyclic aromatic hydrocarbons (PAHs) in ambient air, surface soil and wheat grain collected from wheat fields near a large steel-smelting manufacturer in Northern China were determined. Based on the specific isomeric ratios of paired species in ambient air, principle component analysis and multivariate linear regression, the main emission source of local PAHs was identified as a mixture of industrial and domestic coal combustion, biomass burning and traffic exhaust. The total organic carbon (TOC) fraction was considerably correlated with the total and individual PAH concentrations in surface soil. The total concentrations of PAHs in wheat grain were relatively low, with dominant low molecular weight constituents, and the compositional profile was more similar to that in ambient air than in topsoil. Combined with more significant results from partial correlation and linear regression models, the contribution from air PAHs to grain PAHs may be greater than that from soil PAHs. Copyright © 2016. Published by Elsevier B.V.

An index of effluent aquatic toxicity designed by partial least squares regression, using acute and chronic tests and expert judgements.

PubMed

Vindimian, Éric; Garric, Jeanne; Flammarion, Patrick; Thybaud, Éric; Babut, Marc

1999-10-01

The evaluation of the ecotoxicity of effluents requires a battery of biological tests on several species. In order to derive a summary parameter from such a battery, a single endpoint was calculated for all the tests: the EC10, obtained by nonlinear regression, with bootstrap evaluation of the confidence intervals. Principal component analysis was used to characterize and visualize the correlation between the tests. The table of the toxicity of the effluents was then submitted to a panel of experts, who classified the effluents according to the test results. Partial least squares (PLS) regression was used to fit the average value of the experts' judgements to the toxicity data, using a simple equation. Furthermore, PLS regression on partial data sets and other considerations resulted in an optimum battery, with two chronic tests and one acute test. The index is intended to be used for the classification of effluents based on their toxicity to aquatic species. Copyright © 1999 SETAC.

An index of effluent aquatic toxicity designed by partial least squares regression, using acute and chronic tests and expert judgments

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

Vindimian, E.; Garric, J.; Flammarion, P.

1999-10-01

The evaluation of the ecotoxicity of effluents requires a battery of biological tests on several species. In order to derive a summary parameter from such a battery, a single endpoint was calculated for all the tests: the EC10, obtained by nonlinear regression, with bootstrap evaluation of the confidence intervals. Principal component analysis was used to characterize and visualize the correlation between the tests. The table of the toxicity of the effluents was then submitted to a panel of experts, who classified the effluents according to the test results. Partial least squares (PLS) regression was used to fit the average valuemore » of the experts' judgments to the toxicity data, using a simple equation. Furthermore, PLS regression on partial data sets and other considerations resulted in an optimum battery, with two chronic tests and one acute test. The index is intended to be used for the classification of effluents based on their toxicity to aquatic species.« less

Simultaneous Determination of Gallic Acid, Ellagic Acid, and Eugenol in Syzygium aromaticum and Verification of Chemical Antagonistic Effect by the Combination with Curcuma aromatica Using Regression Analysis

PubMed Central

Seo, Chang-Seob; Kim, Seong-Sil; Ha, Hyekyung

2013-01-01

This study was designed to perform simultaneous determination of three reference compounds in Syzygium aromaticum (SA), gallic acid, ellagic acid, and eugenol, and to investigate the chemical antagonistic effect when combining Curcuma aromatica (CA) with SA, based on chromatographic analysis. The values of LODs and LOQs were 0.01–0.11 μg/mL and 0.03–0.36 μg/mL, respectively. The intraday and interday precisions were <3.0 of RSD values, and the recovery was in the range of 92.19–103.24%, with RSD values <3.0%. Repeatability and stability were 0.38–0.73% and 0.49–2.24%, respectively. Compared with the content of reference and relative peaks in SA and SA combined with CA (SAC), the amounts of gallic acid and eugenol were increased, while that of ellagic acid was decreased in SAC (compared with SA), and most of peak areas in SA were reduced in SAC. Regression analysis of the relative peak areas between SA and SAC showed r 2 values >0.87, indicating a linear relationship between SA and SAC. These results demonstrate that the components contained in CA could affect the extraction of components of SA mainly in a decreasing manner. The antagonistic effect of CA on SA was verified by chemical analysis. PMID:23878761

Fast and simultaneously determination of light and heavy rare earth elements in monazite using combination of ultraviolet-visible spectrophotometry and multivariate analysis

NASA Astrophysics Data System (ADS)

Anggraeni, Anni; Arianto, Fernando; Mutalib, Abdul; Pratomo, Uji; Bahti, Husein H.

2017-05-01

Rare Earth Elements (REE) are elements that a lot of function for life, such as metallurgy, optical devices, and manufacture of electronic devices. Sources of REE is present in the mineral, in which each element has similar properties. Currently, to determining the content of REE is used instruments such as ICP-OES, ICP-MS, XRF, and HPLC. But in each instruments, there are still have some weaknesses. Therefore we need an alternative analytical method for the determination of rare earth metal content, one of them is by a combination of UV-Visible spectrophotometry and multivariate analysis, including Principal Component Analysis (PCA), Principal Component Regression (PCR), and Partial Least Square Regression (PLS). The purpose of this experiment is to determine the content of light and medium rare earth elements in the mineral monazite without chemical separation by using a combination of multivariate analysis and UV-Visible spectrophotometric methods. Training set created 22 variations of concentration and absorbance was measured using a UV-Vis spectrophotometer, then the data is processed by PCA, PCR, and PLSR. The results were compared and validated to obtain the mathematical equation with the smallest percent error. From this experiment, mathematical equation used PLS methods was better than PCR after validated, which has RMSE value for La, Ce, Pr, Nd, Gd, Sm, Eu, and Tb respectively 0.095; 0.573; 0.538; 0.440; 3.387; 1.240; 1.870; and 0.639.

A pilot evaluation of a computer-based psychometric test battery designed to detect impairment in patients with cirrhosis.

PubMed

Cook, Nicola A; Kim, Jin Un; Pasha, Yasmin; Crossey, Mary Me; Schembri, Adrian J; Harel, Brian T; Kimhofer, Torben; Taylor-Robinson, Simon D

2017-01-01

Psychometric testing is used to identify patients with cirrhosis who have developed hepatic encephalopathy (HE). Most batteries consist of a series of paper-and-pencil tests, which are cumbersome for most clinicians. A modern, easy-to-use, computer-based battery would be a helpful clinical tool, given that in its minimal form, HE has an impact on both patients' quality of life and the ability to drive and operate machinery (with societal consequences). We compared the Cogstate™ computer battery testing with the Psychometric Hepatic Encephalopathy Score (PHES) tests, with a view to simplify the diagnosis. This was a prospective study of 27 patients with histologically proven cirrhosis. An analysis of psychometric testing was performed using accuracy of task performance and speed of completion as primary variables to create a correlation matrix. A stepwise linear regression analysis was performed with backward elimination, using analysis of variance. Strong correlations were found between the international shopping list, international shopping list delayed recall of Cogstate and the PHES digit symbol test. The Shopping List Tasks were the only tasks that consistently had P values of <0.05 in the linear regression analysis. Subtests of the Cogstate battery correlated very strongly with the digit symbol component of PHES in discriminating severity of HE. These findings would indicate that components of the current PHES battery with the international shopping list tasks of Cogstate would be discriminant and have the potential to be used easily in clinical practice.

Evolution of the Marine Officer Fitness Report: A Multivariate Analysis

DTIC Science & Technology

This thesis explores the evaluation behavior of United States Marine Corps (USMC) Reporting Seniors (RSs) from 2010 to 2017. Using fitness report...RSs evaluate the performance of subordinate active component unrestricted officer MROs over time. I estimate logistic regression models of the...lowest. However, these correlations indicating the effects of race matching on FITREP evaluations narrow in significance when performance-based factors

Habitat suitability index model for brook trout in streams of the Southern Blue Ridge Province: surrogate variables, model evaluation, and suggested improvements

Treesearch

Christoper J. Schmitt; A. Dennis Lemly; Parley V. Winger

1993-01-01

Data from several sources were collated and analyzed by correlation, regression, and principal components analysis to define surrrogate variables for use in the brook trout (Salvelinus fontinalis) habitat suitability index (HSI) model, and to evaluate the applicability of the model for assessing habitat in high elevation streams of the southern Blue Ridge Province (...

Genetic variation and seed transfer guidelines for ponderosa pine in the Ochoco and Malheur National Forests of central Oregon.

Treesearch

Frank C. Sorensen; John C. Weber

1994-01-01

Adaptive genetic variation in seed and seedling traits was evaluated for 280 families from 220 locations. Factor scores from three principal components were related by multiple regression to latitude, longitude, elevation, slope, and aspect of the seed source, and by classification analysis to seed zone and elevation band in seed zone. Location variance was significant...

An Extension of CART's Pruning Algorithm. Program Statistics Research Technical Report No. 91-11.

ERIC Educational Resources Information Center

Kim, Sung-Ho

Among the computer-based methods used for the construction of trees such as AID, THAID, CART, and FACT, the only one that uses an algorithm that first grows a tree and then prunes the tree is CART. The pruning component of CART is analogous in spirit to the backward elimination approach in regression analysis. This idea provides a tool in…

Kernel analysis of partial least squares (PLS) regression models.

PubMed

Shinzawa, Hideyuki; Ritthiruangdej, Pitiporn; Ozaki, Yukihiro

2011-05-01

An analytical technique based on kernel matrix representation is demonstrated to provide further chemically meaningful insight into partial least squares (PLS) regression models. The kernel matrix condenses essential information about scores derived from PLS or principal component analysis (PCA). Thus, it becomes possible to establish the proper interpretation of the scores. A PLS model for the total nitrogen (TN) content in multiple Thai fish sauces is built with a set of near-infrared (NIR) transmittance spectra of the fish sauce samples. The kernel analysis of the scores effectively reveals that the variation of the spectral feature induced by the change in protein content is substantially associated with the total water content and the protein hydration. Kernel analysis is also carried out on a set of time-dependent infrared (IR) spectra representing transient evaporation of ethanol from a binary mixture solution of ethanol and oleic acid. A PLS model to predict the elapsed time is built with the IR spectra and the kernel matrix is derived from the scores. The detailed analysis of the kernel matrix provides penetrating insight into the interaction between the ethanol and the oleic acid.

Risk prediction for myocardial infarction via generalized functional regression models.

PubMed

Ieva, Francesca; Paganoni, Anna M

2016-08-01

In this paper, we propose a generalized functional linear regression model for a binary outcome indicating the presence/absence of a cardiac disease with multivariate functional data among the relevant predictors. In particular, the motivating aim is the analysis of electrocardiographic traces of patients whose pre-hospital electrocardiogram (ECG) has been sent to 118 Dispatch Center of Milan (the Italian free-toll number for emergencies) by life support personnel of the basic rescue units. The statistical analysis starts with a preprocessing of ECGs treated as multivariate functional data. The signals are reconstructed from noisy observations. The biological variability is then removed by a nonlinear registration procedure based on landmarks. Thus, in order to perform a data-driven dimensional reduction, a multivariate functional principal component analysis is carried out on the variance-covariance matrix of the reconstructed and registered ECGs and their first derivatives. We use the scores of the Principal Components decomposition as covariates in a generalized linear model to predict the presence of the disease in a new patient. Hence, a new semi-automatic diagnostic procedure is proposed to estimate the risk of infarction (in the case of interest, the probability of being affected by Left Bundle Brunch Block). The performance of this classification method is evaluated and compared with other methods proposed in literature. Finally, the robustness of the procedure is checked via leave-j-out techniques. © The Author(s) 2013.

Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses

PubMed Central

Nickerson, Lisa D.; Smith, Stephen M.; Öngür, Döst; Beckmann, Christian F.

2017-01-01

Independent Component Analysis (ICA) is one of the most popular techniques for the analysis of resting state FMRI data because it has several advantageous properties when compared with other techniques. Most notably, in contrast to a conventional seed-based correlation analysis, it is model-free and multivariate, thus switching the focus from evaluating the functional connectivity of single brain regions identified a priori to evaluating brain connectivity in terms of all brain resting state networks (RSNs) that simultaneously engage in oscillatory activity. Furthermore, typical seed-based analysis characterizes RSNs in terms of spatially distributed patterns of correlation (typically by means of simple Pearson's coefficients) and thereby confounds together amplitude information of oscillatory activity and noise. ICA and other regression techniques, on the other hand, retain magnitude information and therefore can be sensitive to both changes in the spatially distributed nature of correlations (differences in the spatial pattern or “shape”) as well as the amplitude of the network activity. Furthermore, motion can mimic amplitude effects so it is crucial to use a technique that retains such information to ensure that connectivity differences are accurately localized. In this work, we investigate the dual regression approach that is frequently applied with group ICA to assess group differences in resting state functional connectivity of brain networks. We show how ignoring amplitude effects and how excessive motion corrupts connectivity maps and results in spurious connectivity differences. We also show how to implement the dual regression to retain amplitude information and how to use dual regression outputs to identify potential motion effects. Two key findings are that using a technique that retains magnitude information, e.g., dual regression, and using strict motion criteria are crucial for controlling both network amplitude and motion-related amplitude effects, respectively, in resting state connectivity analyses. We illustrate these concepts using realistic simulated resting state FMRI data and in vivo data acquired in healthy subjects and patients with bipolar disorder and schizophrenia. PMID:28348512

Electrophysiology and optical coherence tomography to evaluate Parkinson disease severity.

PubMed

Garcia-Martin, Elena; Rodriguez-Mena, Diego; Satue, Maria; Almarcegui, Carmen; Dolz, Isabel; Alarcia, Raquel; Seral, Maria; Polo, Vicente; Larrosa, Jose M; Pablo, Luis E

2014-02-04

To evaluate correlations between visual evoked potentials (VEP), pattern electroretinogram (PERG), and macular and retinal nerve fiber layer (RNFL) thickness measured by optical coherence tomography (OCT) and the severity of Parkinson disease (PD). Forty-six PD patients and 33 age and sex-matched healthy controls were enrolled, and underwent VEP, PERG, and OCT measurements of macular and RNFL thicknesses, and evaluation of PD severity using the Hoehn and Yahr scale to measure PD symptom progression, the Schwab and England Activities of Daily Living Scale (SE-ADL) to evaluate patient quality of life (QOL), and disease duration. Logistical regression was performed to analyze which measures, if any, could predict PD symptom progression or effect on QOL. Visual functional parameters (best corrected visual acuity, mean deviation of visual field, PERG positive (P) component at 50 ms -P50- and negative (N) component at 95 ms -N95- component amplitude, and PERG P50 component latency) and structural parameters (OCT measurements of RNFL and retinal thickness) were decreased in PD patients compared with healthy controls. OCT measurements were significantly negatively correlated with the Hoehn and Yahr scale, and significantly positively correlated with the SE-ADL scale. Based on logistical regression analysis, fovea thickness provided by OCT equipment predicted PD severity, and QOL and amplitude of the PERG N95 component predicted a lower SE-ADL score. Patients with greater damage in the RNFL tend to have lower QOL and more severe PD symptoms. Foveal thicknesses and the PERG N95 component provide good biomarkers for predicting QOL and disease severity.

“Smooth” Semiparametric Regression Analysis for Arbitrarily Censored Time-to-Event Data

PubMed Central

Zhang, Min; Davidian, Marie

2008-01-01

Summary A general framework for regression analysis of time-to-event data subject to arbitrary patterns of censoring is proposed. The approach is relevant when the analyst is willing to assume that distributions governing model components that are ordinarily left unspecified in popular semiparametric regression models, such as the baseline hazard function in the proportional hazards model, have densities satisfying mild “smoothness” conditions. Densities are approximated by a truncated series expansion that, for fixed degree of truncation, results in a “parametric” representation, which makes likelihood-based inference coupled with adaptive choice of the degree of truncation, and hence flexibility of the model, computationally and conceptually straightforward with data subject to any pattern of censoring. The formulation allows popular models, such as the proportional hazards, proportional odds, and accelerated failure time models, to be placed in a common framework; provides a principled basis for choosing among them; and renders useful extensions of the models straightforward. The utility and performance of the methods are demonstrated via simulations and by application to data from time-to-event studies. PMID:17970813

Assessing long-term variations in sagebrush habitat: characterization of spatial extents and distribution patterns using multi-temporal satellite remote-sensing data

USGS Publications Warehouse

Xian, George; Homer, Collin G.; Aldridge, Cameron L.

2012-01-01

An approach that can generate sagebrush habitat change estimates for monitoring large-area sagebrush ecosystems has been developed and tested in southwestern Wyoming, USA. This prototype method uses a satellite-based image change detection algorithm and regression models to estimate sub-pixel percentage cover for five sagebrush habitat components: bare ground, herbaceous, litter, sagebrush and shrub. Landsat images from three different months in 1988, 1996 and 2006 were selected to identify potential landscape change during these time periods using change vector (CV) analysis incorporated with an image normalization algorithm. Regression tree (RT) models were used to estimate percentage cover for five components on all change areas identified in 1988 and 1996, using unchanged 2006 baseline data as training for both estimates. Over the entire study area (24 950 km2), a net increase of 98.83 km2, or 0.7%, for bare ground was measured between 1988 and 2006. Over the same period, the other four components had net losses of 20.17 km2, or 0.6%, for herbaceous vegetation; 30.16 km2, or 0.7%, for litter; 32.81 km2, or 1.5%, for sagebrush; and 33.34 km2, or 1.2%, for shrubs. The overall accuracy for shrub vegetation change between 1988 and 2006 was 89.56%. Change patterns within sagebrush habitat components differ spatially and quantitatively from each other, potentially indicating unique responses by these components to disturbances imposed upon them.

SandiaMRCR

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

2012-01-05

SandiaMCR was developed to identify pure components and their concentrations from spectral data. This software efficiently implements the multivariate calibration regression alternating least squares (MCR-ALS), principal component analysis (PCA), and singular value decomposition (SVD). Version 3.37 also includes the PARAFAC-ALS Tucker-1 (for trilinear analysis) algorithms. The alternating least squares methods can be used to determine the composition without or with incomplete prior information on the constituents and their concentrations. It allows the specification of numerous preprocessing, initialization and data selection and compression options for the efficient processing of large data sets. The software includes numerous options including the definition ofmore » equality and non-negativety constraints to realistically restrict the solution set, various normalization or weighting options based on the statistics of the data, several initialization choices and data compression. The software has been designed to provide a practicing spectroscopist the tools required to routinely analysis data in a reasonable time and without requiring expert intervention.« less

Quality of life in multiple sclerosis (MS) and role of fatigue, depression, anxiety, and stress: A bicenter study from north of Iran.

PubMed

Salehpoor, Ghasem; Rezaei, Sajjad; Hosseininezhad, Mozaffar

2014-11-01

Although studies have demonstrated significant negative relationships between quality of life (QOL), fatigue, and the most common psychological symptoms (depression, anxiety, stress), the main ambiguity of previous studies on QOL is in the relative importance of these predictors. Also, there is lack of adequate knowledge about the actual contribution of each of them in the prediction of QOL dimensions. Thus, the main objective of this study is to assess the role of fatigue, depression, anxiety, and stress in relation to QOL of multiple sclerosis (MS) patients. One hundred and sixty-two MS patients completed the questionnaire on demographic variables, and then they were evaluated by the Persian versions of Short-Form Health Survey Questionnaire (SF-36), Fatigue Survey Scale (FSS), and Depression, Anxiety, Stress Scale-21 (DASS-21). Data were analyzed by Pearson correlation coefficient and hierarchical regression. Correlation analysis showed a significant relationship between QOL elements in SF-36 (physical component summary and mental component summary) and depression, fatigue, stress, and anxiety (P < 0.01). Hierarchical regression analysis indicated that among the predictor variables in the final step, fatigue, depression, and anxiety were identified as the physical component summary predictor variables. Anxiety was found to be the most powerful predictor variable amongst all (β = -0.46, P < 0.001). Furthermore, results have shown depression as the only significant mental component summary predictor variable (β = -0.39, P < 0.001). This study has highlighted the role of anxiety, fatigue, and depression in physical dimensions and the role of depression in psychological dimensions of the lives of MS patients. In addition, the findings of this study indirectly suggest that psychological interventions for reducing fatigue, depression, and anxiety can lead to improved QOL of MS patients.

Analysis of Student-Evaluation Data on the Teaching of Fundamental Physics in an Introductory Technology Course, Paying Attention to Students‧ Earnestness to Learn

NASA Astrophysics Data System (ADS)

Nishida, Masahiko

How student evaluations of the teaching of fundamental physics for engineering relate to teaching strategy from academic 2004 to 2006 has been studied, focusing on students‧ earnestness to learn. The teaching emphasized instructing theoretical concepts for 2004 and solving problems for 2005. The instruction during 2006 offered a good balance between the strategy for 2004 and that for 2005. The first and second components produced by principal-component analysis of the evaluation data have indicated the quality of instruction and the scholastic ability of students, respectively, independent of the teaching strategy. While correlation between the second component and the degree of earnestness was positive for 2004 and negative for 2005, the correlation for 2006 has been negligible, as expected. Multiple-regression analysis between the evaluation data and students‧ exam scores has shown little correlation for 2006, in contrast to that for 2004, but similar to that for 2005. Finally, we can say that the teaching strategy for 2006 would lead to educational effects similar to those in 2005 when the exam scores were notably improved.

[Methods of a posteriori identification of food patterns in Brazilian children: a systematic review].

PubMed

Carvalho, Carolina Abreu de; Fonsêca, Poliana Cristina de Almeida; Nobre, Luciana Neri; Priore, Silvia Eloiza; Franceschini, Sylvia do Carmo Castro

2016-01-01

The objective of this study is to provide guidance for identifying dietary patterns using the a posteriori approach, and analyze the methodological aspects of the studies conducted in Brazil that identified the dietary patterns of children. Articles were selected from the Latin American and Caribbean Literature on Health Sciences, Scientific Electronic Library Online and Pubmed databases. The key words were: Dietary pattern; Food pattern; Principal Components Analysis; Factor analysis; Cluster analysis; Reduced rank regression. We included studies that identified dietary patterns of children using the a posteriori approach. Seven studies published between 2007 and 2014 were selected, six of which were cross-sectional and one cohort, Five studies used the food frequency questionnaire for dietary assessment; one used a 24-hour dietary recall and the other a food list. The method of exploratory approach used in most publications was principal components factor analysis, followed by cluster analysis. The sample size of the studies ranged from 232 to 4231, the values of the Kaiser-Meyer-Olkin test from 0.524 to 0.873, and Cronbach's alpha from 0.51 to 0.69. Few Brazilian studies identified dietary patterns of children using the a posteriori approach and principal components factor analysis was the technique most used.

Product competitiveness analysis for e-commerce platform of special agricultural products

NASA Astrophysics Data System (ADS)

Wan, Fucheng; Ma, Ning; Yang, Dongwei; Xiong, Zhangyuan

2017-09-01

On the basis of analyzing the influence factors of the product competitiveness of the e-commerce platform of the special agricultural products and the characteristics of the analytical methods for the competitiveness of the special agricultural products, the price, the sales volume, the postage included service, the store reputation, the popularity, etc. were selected in this paper as the dimensionality for analyzing the competitiveness of the agricultural products, and the principal component factor analysis was taken as the competitiveness analysis method. Specifically, the web crawler was adopted to capture the information of various special agricultural products in the e-commerce platform ---- chi.taobao.com. Then, the original data captured thereby were preprocessed and MYSQL database was adopted to establish the information library for the special agricultural products. Then, the principal component factor analysis method was adopted to establish the analysis model for the competitiveness of the special agricultural products, and SPSS was adopted in the principal component factor analysis process to obtain the competitiveness evaluation factor system (support degree factor, price factor, service factor and evaluation factor) of the special agricultural products. Then, the linear regression method was adopted to establish the competitiveness index equation of the special agricultural products for estimating the competitiveness of the special agricultural products.

Regression Models for Identifying Noise Sources in Magnetic Resonance Images

PubMed Central

Zhu, Hongtu; Li, Yimei; Ibrahim, Joseph G.; Shi, Xiaoyan; An, Hongyu; Chen, Yashen; Gao, Wei; Lin, Weili; Rowe, Daniel B.; Peterson, Bradley S.

2009-01-01

Stochastic noise, susceptibility artifacts, magnetic field and radiofrequency inhomogeneities, and other noise components in magnetic resonance images (MRIs) can introduce serious bias into any measurements made with those images. We formally introduce three regression models including a Rician regression model and two associated normal models to characterize stochastic noise in various magnetic resonance imaging modalities, including diffusion-weighted imaging (DWI) and functional MRI (fMRI). Estimation algorithms are introduced to maximize the likelihood function of the three regression models. We also develop a diagnostic procedure for systematically exploring MR images to identify noise components other than simple stochastic noise, and to detect discrepancies between the fitted regression models and MRI data. The diagnostic procedure includes goodness-of-fit statistics, measures of influence, and tools for graphical display. The goodness-of-fit statistics can assess the key assumptions of the three regression models, whereas measures of influence can isolate outliers caused by certain noise components, including motion artifacts. The tools for graphical display permit graphical visualization of the values for the goodness-of-fit statistic and influence measures. Finally, we conduct simulation studies to evaluate performance of these methods, and we analyze a real dataset to illustrate how our diagnostic procedure localizes subtle image artifacts by detecting intravoxel variability that is not captured by the regression models. PMID:19890478

[Effects of carbon components of fine particulate matter (PM2.5) on atherogenic index of plasma].

PubMed

Fan, Jiao; Qin, Xiaolei; Xue, Xiaodan; Han, Bin; Bai, Zhipeng; Tang, Naijun; Zhang, Liwen

2014-01-01

To evaluate associations between carbon constituents of fine particulate matter (PM2.5) and atherogenic index of plasma (AIP). We collected subjects from two communities by a system sampling, and 112 people aged over 60 years old without cardiovascular disease were recruited. The levels of cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C) of objects, and personal exposure to PM2.5 were measured on December, 2011. Total carbon (TC), organic carbon (OC) and elemental carbon (EC) of PM2.5 were detected and AIP was calculated according to its definition. The value of AIP among the 112 subjects was 0.05 ± 0.26. Personal exposure concentration of PM2.5 and its carbon components (TC,OC and EC) were (164.75 ± 110.67), (53.86 ± 29.65), (44.93 ± 26.37) and (9.49 ± 5.75) µg/m(3), respectively. The Pearson analysis showed the linear relationship between TC,OC,EC and AIP, all significant positive correlations. The correlation coefficients were TC (r = 0.307, P < 0.05),OC (r = 0.287, P < 0.05) and EC (r = 0.252, P < 0.05), respectively. The multiple logistic regression analysis showed that when the AIP risk categories were selected as dependent variable and low risk group as reference group, the regression coefficient of TC,OC and EC was separately 1.03 (95%CI:1.01-1.05), 1.03 (95%CI:1.01-1.05), 1.12 (95%CI:1.02-1.22) in the high risk group; while there was no statistical significance of the regression coefficient and OR in the middle risk group. There was stable associations between the carbon constituents (TC,OC and EC) of fine Particulate Matter (PM2.5) and AIP. The findings suggested that carbon components of PM2.5 should be considered as risk factors of atherogenic.

Technical Efficiency of Automotive Industry Cluster in Chennai

NASA Astrophysics Data System (ADS)

Bhaskaran, E.

2012-07-01

Chennai is also called as Detroit of India due to its automotive industry presence producing over 40 % of the India's vehicle and components. During 2001-2002, diagnostic study was conducted on the Automotive Component Industries (ACI) in Ambattur Industrial Estate, Chennai and in SWOT analysis it was found that it had faced problems on infrastructure, technology, procurement, production and marketing. In the year 2004-2005 under the cluster development approach (CDA), they formed Chennai auto cluster, under public private partnership concept, received grant from Government of India, Government of Tamil Nadu, Ambattur Municipality, bank loans and stake holders. This results development in infrastructure, technology, procurement, production and marketing interrelationships among ACI. The objective is to determine the correlation coefficient, regression equation, technical efficiency, peer weights, slack variables and return to scale of cluster before and after the CDA. The methodology adopted is collection of primary data from ACI and analyzing using data envelopment analysis (DEA) of input oriented Banker-Charnes-Cooper model. There is significant increase in correlation coefficient and the regression analysis reveals that for one percent increase in employment and net worth, the gross output increases significantly after the CDA. The DEA solver gives the technical efficiency of ACI by taking shift, employment, net worth as input data and quality, gross output and export ratio as output data. From the technical score and ranking of ACI, it is found that there is significant increase in technical efficiency of ACI when compared to CDA. The slack variables obtained clearly reveals the excess employment and net worth and no shortage of gross output. To conclude there is increase in technical efficiency of not only Chennai auto cluster in general but also Chennai auto components industries in particular.

Relationship among physical activity, smoking, drinking and clustering of the metabolic syndrome diagnostic components.

PubMed

Katano, Sayuri; Nakamura, Yasuyuki; Nakamura, Aki; Murakami, Yoshitaka; Tanaka, Taichiro; Nakagawa, Hideaki; Takebayashi, Toru; Yamato, Hiroshi; Okayama, Akira; Miura, Katsuyuki; Okamura, Tomonori; Ueshima, Hirotsugu

2010-06-30

To examine the relation between lifestyle and the number of metabolic syndrome (MetS) diagnostic components in a general population, and to find a means of preventing the development of MetS components. We examined baseline data from 3,365 participants (2,714 men and 651 women) aged 19 to 69 years who underwent a physical examination, lifestyle survey, and blood chemical examination. The physical activity of each participant was classified according to the International Physical Activity Questionnaire (IPAQ). We defined four components for MetS in this study as follows: 1) high BP: systolic BP > or = 130 mmHg or diastolic BP > or = 85 mmHg, or the use of antihypertensive drugs; 2) dyslipidemia: high-density lipoprotein-cholesterol concentration < 40 mg/dL, triglycerides concentration > or = 150 mg/dL, or on medication for dyslipidemia; 3) Impaired glucose tolerance: fasting blood sugar level > or = 110 mg/d, or if less than 8 hours after meals > or = 140 mg/dL), or on medication for diabetes mellitus; 4) obesity: body mass index > or = 25 kg/m(2). Those who had 0 to 4 MetS diagnostic components accounted for 1,726, 949, 484, 190, and 16 participants, respectively, in the Poisson distribution. Poisson regression analysis revealed that independent factors contributing to the number of MetS diagnostic components were being male (regression coefficient b=0.600, p < 0.01), age (b=0.027, p < 0.01), IPAQ class (b=-0.272, p= 0.03), and alcohol consumption (b=0.020, p=0.01). The contribution of current smoking was not statistically significant (b=-0.067, p=0.76). Moderate physical activity was inversely associated with the number of MetS diagnostic components, whereas smoking was not associated.

Morningness-eveningness and depressive symptoms: Test on the components level with CES-D in Polish students.

PubMed

Jankowski, Konrad S

2016-05-15

The study aimed to elucidate previously observed associations between morningness-eveningness and depressive symptomatology in university students. Relations between components of depressive symptomatology and morningness-eveningness were analysed. Nine hundred and seventy-four university students completed Polish versions of the Centre for Epidemiological Studies - Depression scale (CES-D; Polish translation appended to this paper) and the Composite Scale of Morningness. Principal component analysis (PCA) was used to test the structure of depressive symptoms. Pearson and partial correlations (with age and sex controlled), along with regression analyses with morning affect (MA) and circadian preference as predictors, were used. PCA revealed three components of depressive symptoms: depressed/somatic affect, positive affect, interpersonal relations. Greater MA was related to less depressive symptoms in three components. Morning circadian preference was related to less depressive symptoms in depressed/somatic and positive affects and unrelated to interpersonal relations. Both morningness-eveningness components exhibited stronger links with depressed/somatic and positive affects than with interpersonal relations. Three CES-D components exhibited stronger links with MA than with circadian preference. In regression analyses only MA was statistically significant for positive affect and better interpersonal relations, whereas more depressed/somatic affect was predicted by lower MA and morning circadian preference (relationship reversed compared to correlations). Self-report assessment. There are three groups of depressive symptoms in Polish university students. Associations of MA with depressed/somatic and positive affects are primarily responsible for the observed links between morningness-eveningness and depressive symptoms in university students. People with evening circadian preference whose MA is not lowered have less depressed/somatic affect. Copyright © 2016 Elsevier B.V. All rights reserved.

A questionnaire-wide association study of personality and mortality: the Vietnam Experience Study.

PubMed

Weiss, Alexander; Gale, Catharine R; Batty, G David; Deary, Ian J

2013-06-01

We examined the association between the Minnesota Multiphasic Personality Inventory (MMPI) and all-cause mortality in 4462 middle-aged Vietnam-era veterans. We split the study population into half-samples. In each half, we used proportional hazards (Cox) regression to test the 550 MMPI items' associations with mortality over 15years. In all participants, we subjected significant (p<.01) items in both halves to principal-components analysis (PCA). We used Cox regression to test whether these components predicted mortality when controlling for other predictors (demographics, cognitive ability, health behaviors, and mental/physical health). Eighty-nine items were associated with mortality in both half-samples. PCA revealed Neuroticism/Negative Affectivity, Somatic Complaints, Psychotic/Paranoia, and Antisocial components, and a higher-order component, Personal Disturbance. Individually, Neuroticism/Negative Affectivity (HR=1.55; 95% CI=1.39, 1.72), Somatic Complaints (HR=1.66; 95% CI=1.52, 1.80), Psychotic/Paranoid (HR=1.44; 95% CI=1.32, 1.57), Antisocial (HR=1.79; 95% CI=1.59, 2.01), and Personal Disturbance (HR=1.74; 95% CI=1.58, 1.91) were associated with risk. Including covariates attenuated these associations (28.4 to 54.5%), though they were still significant. After entering Personal Disturbance into models with each component, Neuroticism/Negative Affectivity and Somatic Complaints were significant, although Neuroticism/Negative Affectivity's were now protective (HR=0.73; 95% CI=0.58, 0.92). When the four components were entered together with or without covariates, Somatic Complaints and Antisocial were significant risk factors. Somatic Complaints and Personal Disturbance are associated with increased mortality risk. Other components' effects varied as a function of variables in the model. Copyright © 2013 Elsevier Inc. All rights reserved.

The effectiveness of formative assessment with understanding by design (UbD) stages in forming habits of mind in prospective teachers

NASA Astrophysics Data System (ADS)

Gloria, R. Y.; Sudarmin, S.; Wiyanto; Indriyanti, D. R.

2018-03-01

Habits of mind are intelligent thinking dispositions that every individual needs to have, and it needs an effort to form them as expected. A behavior can be formed by continuous practice; therefore the student's habits of mind can also be formed and trained. One effort that can be used to encourage the formation of habits of mind is a formative assessment strategy with the stages of UbD (Understanding by Design), and a study needs to be done to prove it. This study aims to determine the contribution of formative assessment to the value of habits of mind owned by prospective teachers. The method used is a quantitative method with a quasi-experimental design. To determine the effectiveness of formative assessment with Ubd stages on the formation of habits of mind, correlation test and regression analysis were conducted in the formative assessment questionnaire consisting of three components, i.e. feed back, peer assessment and self assessment, and habits of mind. The result of the research shows that from the three components of Formative Assessment, only Feedback component does not show correlation to students’ habits of mind (r = 0.323). While peer assessment component (r = 0. 732) and self assessment component (r = 0.625), both indicate correlation. From the regression test the overall component of the formative assessment contributed to the habits of mind at 57.1%. From the result of the research, it can be concluded that the formative assessment with Ubd stages is effective and contributes in forming the student's habits of mind; the formative assessment components that contributed the most are the peer assessment and self assessment. The greatest contribution goes to the Thinking interdependently category.

Variable selection and model choice in geoadditive regression models.

PubMed

Kneib, Thomas; Hothorn, Torsten; Tutz, Gerhard

2009-06-01

Model choice and variable selection are issues of major concern in practical regression analyses, arising in many biometric applications such as habitat suitability analyses, where the aim is to identify the influence of potentially many environmental conditions on certain species. We describe regression models for breeding bird communities that facilitate both model choice and variable selection, by a boosting algorithm that works within a class of geoadditive regression models comprising spatial effects, nonparametric effects of continuous covariates, interaction surfaces, and varying coefficients. The major modeling components are penalized splines and their bivariate tensor product extensions. All smooth model terms are represented as the sum of a parametric component and a smooth component with one degree of freedom to obtain a fair comparison between the model terms. A generic representation of the geoadditive model allows us to devise a general boosting algorithm that automatically performs model choice and variable selection.

A regression-adjusted approach can estimate competing biomass

Treesearch

James H. Miller

1983-01-01

A method is presented for estimating above-ground herbaceous and woody biomass on competition research plots. On a set of destructively-sampled plots, an ocular estimate of biomass by vegetative component is first made, after which vegetation is clipped, dried, and weighed. Linear regressions are then calculated for each component between estimated and actual weights...

The Multivariate Regression Statistics Strategy to Investigate Content-Effect Correlation of Multiple Components in Traditional Chinese Medicine Based on a Partial Least Squares Method.

PubMed

Peng, Ying; Li, Su-Ning; Pei, Xuexue; Hao, Kun

2018-03-01

Amultivariate regression statisticstrategy was developed to clarify multi-components content-effect correlation ofpanaxginseng saponins extract and predict the pharmacological effect by components content. In example 1, firstly, we compared pharmacological effects between panax ginseng saponins extract and individual saponin combinations. Secondly, we examined the anti-platelet aggregation effect in seven different saponin combinations of ginsenoside Rb1, Rg1, Rh, Rd, Ra3 and notoginsenoside R1. Finally, the correlation between anti-platelet aggregation and the content of multiple components was analyzed by a partial least squares algorithm. In example 2, firstly, 18 common peaks were identified in ten different batches of panax ginseng saponins extracts from different origins. Then, we investigated the anti-myocardial ischemia reperfusion injury effects of the ten different panax ginseng saponins extracts. Finally, the correlation between the fingerprints and the cardioprotective effects was analyzed by a partial least squares algorithm. Both in example 1 and 2, the relationship between the components content and pharmacological effect was modeled well by the partial least squares regression equations. Importantly, the predicted effect curve was close to the observed data of dot marked on the partial least squares regression model. This study has given evidences that themulti-component content is a promising information for predicting the pharmacological effects of traditional Chinese medicine.

Experimental variability and data pre-processing as factors affecting the discrimination power of some chemometric approaches (PCA, CA and a new algorithm based on linear regression) applied to (+/-)ESI/MS and RPLC/UV data: Application on green tea extracts.

PubMed

Iorgulescu, E; Voicu, V A; Sârbu, C; Tache, F; Albu, F; Medvedovici, A

2016-08-01

The influence of the experimental variability (instrumental repeatability, instrumental intermediate precision and sample preparation variability) and data pre-processing (normalization, peak alignment, background subtraction) on the discrimination power of multivariate data analysis methods (Principal Component Analysis -PCA- and Cluster Analysis -CA-) as well as a new algorithm based on linear regression was studied. Data used in the study were obtained through positive or negative ion monitoring electrospray mass spectrometry (+/-ESI/MS) and reversed phase liquid chromatography/UV spectrometric detection (RPLC/UV) applied to green tea extracts. Extractions in ethanol and heated water infusion were used as sample preparation procedures. The multivariate methods were directly applied to mass spectra and chromatograms, involving strictly a holistic comparison of shapes, without assignment of any structural identity to compounds. An alternative data interpretation based on linear regression analysis mutually applied to data series is also discussed. Slopes, intercepts and correlation coefficients produced by the linear regression analysis applied on pairs of very large experimental data series successfully retain information resulting from high frequency instrumental acquisition rates, obviously better defining the profiles being compared. Consequently, each type of sample or comparison between samples produces in the Cartesian space an ellipsoidal volume defined by the normal variation intervals of the slope, intercept and correlation coefficient. Distances between volumes graphically illustrates (dis)similarities between compared data. The instrumental intermediate precision had the major effect on the discrimination power of the multivariate data analysis methods. Mass spectra produced through ionization from liquid state in atmospheric pressure conditions of bulk complex mixtures resulting from extracted materials of natural origins provided an excellent data basis for multivariate analysis methods, equivalent to data resulting from chromatographic separations. The alternative evaluation of very large data series based on linear regression analysis produced information equivalent to results obtained through application of PCA an CA. Copyright © 2016 Elsevier B.V. All rights reserved.

Two biased estimation techniques in linear regression: Application to aircraft

NASA Technical Reports Server (NTRS)

Klein, Vladislav

1988-01-01

Several ways for detection and assessment of collinearity in measured data are discussed. Because data collinearity usually results in poor least squares estimates, two estimation techniques which can limit a damaging effect of collinearity are presented. These two techniques, the principal components regression and mixed estimation, belong to a class of biased estimation techniques. Detection and assessment of data collinearity and the two biased estimation techniques are demonstrated in two examples using flight test data from longitudinal maneuvers of an experimental aircraft. The eigensystem analysis and parameter variance decomposition appeared to be a promising tool for collinearity evaluation. The biased estimators had far better accuracy than the results from the ordinary least squares technique.

Evaluation of SLAR and thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

NASA Technical Reports Server (NTRS)

Hoffer, R. M. (Principal Investigator)

1979-01-01

The spatial characteristics of the data were evaluated. A program was developed to reduce the spatial distortions resulting from variable viewing distance, and geometrically adjusted data sets were generated. The potential need for some level of radiometric adjustment was evidenced by an along track band of high reflectance across different cover types in the Varian imagery. A multiple regression analysis was employed to explore the viewing angle effect on measured reflectance. Areas in the data set which appeared to have no across track stratification of cover type were identified. A program was developed which computed the average reflectance by column for each channel, over all of the scan lines in the designated areas. A regression analysis was then run using the first, second, and third degree polynomials, for each channel. An atmospheric effect as a component of the viewing angle source of variance is discussed. Cover type maps were completed and training and test field selection was initiated.

Economics of human performance and systems total ownership cost.

PubMed

Onkham, Wilawan; Karwowski, Waldemar; Ahram, Tareq Z

2012-01-01

Financial costs of investing in people is associated with training, acquisition, recruiting, and resolving human errors have a significant impact on increased total ownership costs. These costs can also affect the exaggerate budgets and delayed schedules. The study of human performance economical assessment in the system acquisition process enhances the visibility of hidden cost drivers which support program management informed decisions. This paper presents the literature review of human total ownership cost (HTOC) and cost impacts on overall system performance. Economic value assessment models such as cost benefit analysis, risk-cost tradeoff analysis, expected value of utility function analysis (EV), growth readiness matrix, multi-attribute utility technique, and multi-regressions model were introduced to reflect the HTOC and human performance-technology tradeoffs in terms of the dollar value. The human total ownership regression model introduces to address the influencing human performance cost component measurement. Results from this study will increase understanding of relevant cost drivers in the system acquisition process over the long term.

Iterative Strain-Gage Balance Calibration Data Analysis for Extended Independent Variable Sets

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert Manfred

2011-01-01

A new method was developed that makes it possible to use an extended set of independent calibration variables for an iterative analysis of wind tunnel strain gage balance calibration data. The new method permits the application of the iterative analysis method whenever the total number of balance loads and other independent calibration variables is greater than the total number of measured strain gage outputs. Iteration equations used by the iterative analysis method have the limitation that the number of independent and dependent variables must match. The new method circumvents this limitation. It simply adds a missing dependent variable to the original data set by using an additional independent variable also as an additional dependent variable. Then, the desired solution of the regression analysis problem can be obtained that fits each gage output as a function of both the original and additional independent calibration variables. The final regression coefficients can be converted to data reduction matrix coefficients because the missing dependent variables were added to the data set without changing the regression analysis result for each gage output. Therefore, the new method still supports the application of the two load iteration equation choices that the iterative method traditionally uses for the prediction of balance loads during a wind tunnel test. An example is discussed in the paper that illustrates the application of the new method to a realistic simulation of temperature dependent calibration data set of a six component balance.

Parametric Analysis to Study the Influence of Aerogel-Based Renders' Components on Thermal and Mechanical Performance.

PubMed

Ximenes, Sofia; Silva, Ana; Soares, António; Flores-Colen, Inês; de Brito, Jorge

2016-05-04

Statistical models using multiple linear regression are some of the most widely used methods to study the influence of independent variables in a given phenomenon. This study's objective is to understand the influence of the various components of aerogel-based renders on their thermal and mechanical performance, namely cement (three types), fly ash, aerial lime, silica sand, expanded clay, type of aerogel, expanded cork granules, expanded perlite, air entrainers, resins (two types), and rheological agent. The statistical analysis was performed using SPSS (Statistical Package for Social Sciences), based on 85 mortar mixes produced in the laboratory and on their values of thermal conductivity and compressive strength obtained using tests in small-scale samples. The results showed that aerial lime assumes the main role in improving the thermal conductivity of the mortars. Aerogel type, fly ash, expanded perlite and air entrainers are also relevant components for a good thermal conductivity. Expanded clay can improve the mechanical behavior and aerogel has the opposite effect.

Parametric Analysis to Study the Influence of Aerogel-Based Renders’ Components on Thermal and Mechanical Performance

PubMed Central

Ximenes, Sofia; Silva, Ana; Soares, António; Flores-Colen, Inês; de Brito, Jorge

2016-01-01

Statistical models using multiple linear regression are some of the most widely used methods to study the influence of independent variables in a given phenomenon. This study’s objective is to understand the influence of the various components of aerogel-based renders on their thermal and mechanical performance, namely cement (three types), fly ash, aerial lime, silica sand, expanded clay, type of aerogel, expanded cork granules, expanded perlite, air entrainers, resins (two types), and rheological agent. The statistical analysis was performed using SPSS (Statistical Package for Social Sciences), based on 85 mortar mixes produced in the laboratory and on their values of thermal conductivity and compressive strength obtained using tests in small-scale samples. The results showed that aerial lime assumes the main role in improving the thermal conductivity of the mortars. Aerogel type, fly ash, expanded perlite and air entrainers are also relevant components for a good thermal conductivity. Expanded clay can improve the mechanical behavior and aerogel has the opposite effect. PMID:28773460

The use of the sexual function questionnaire as a screening tool for women with sexual dysfunction.

PubMed

Quirk, Frances; Haughie, Scott; Symonds, Tara

2005-07-01

To determine if the validated Sexual Function Questionnaire (SFQ), developed to assess efficacy in female sexual dysfunction (FSD) clinical trials, may also have utility in identifying target populations for such studies. Data from five clinical trials and two general population surveys were used to analyze the utility of the SFQ as a tool to discriminate between the presence of specific components of FSD (i.e., hypoactive sexual desire disorder, female sexual arousal disorder, female orgasmic disorder, and dyspareunia). Sensitivity/specificity analysis and logistic regression analysis, using data from all five clinical studies and the general population surveys, confirmed that the SFQ domains have utility in detecting the presence of specific components of FSD and provide scores indicative of the presence of a specific sexual disorder. The SFQ is a valuable new tool for detecting the presence of FSD and identifying the specific components of sexual functions affected (desire, arousal, orgasm, or dyspareunia).

A new technique for spectrophotometric determination of pseudoephedrine and guaifenesin in syrup and synthetic mixture.

PubMed

Riahi, Siavash; Hadiloo, Farshad; Milani, Seyed Mohammad R; Davarkhah, Nazila; Ganjali, Mohammad R; Norouzi, Parviz; Seyfi, Payam

2011-05-01

The accuracy in predicting different chemometric methods was compared when applied on ordinary UV spectra and first order derivative spectra. Principal component regression (PCR) and partial least squares with one dependent variable (PLS1) and two dependent variables (PLS2) were applied on spectral data of pharmaceutical formula containing pseudoephedrine (PDP) and guaifenesin (GFN). The ability to derivative in resolved overlapping spectra chloropheniramine maleate was evaluated when multivariate methods are adopted for analysis of two component mixtures without using any chemical pretreatment. The chemometrics models were tested on an external validation dataset and finally applied to the analysis of pharmaceuticals. Significant advantages were found in analysis of the real samples when the calibration models from derivative spectra were used. It should also be mentioned that the proposed method is a simple and rapid way requiring no preliminary separation steps and can be used easily for the analysis of these compounds, especially in quality control laboratories. Copyright © 2011 John Wiley & Sons, Ltd.

Optimization to the Culture Conditions for Phellinus Production with Regression Analysis and Gene-Set Based Genetic Algorithm

PubMed Central

Li, Zhongwei; Xin, Yuezhen; Wang, Xun; Sun, Beibei; Xia, Shengyu; Li, Hui

2016-01-01

Phellinus is a kind of fungus and is known as one of the elemental components in drugs to avoid cancers. With the purpose of finding optimized culture conditions for Phellinus production in the laboratory, plenty of experiments focusing on single factor were operated and large scale of experimental data were generated. In this work, we use the data collected from experiments for regression analysis, and then a mathematical model of predicting Phellinus production is achieved. Subsequently, a gene-set based genetic algorithm is developed to optimize the values of parameters involved in culture conditions, including inoculum size, PH value, initial liquid volume, temperature, seed age, fermentation time, and rotation speed. These optimized values of the parameters have accordance with biological experimental results, which indicate that our method has a good predictability for culture conditions optimization. PMID:27610365

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

PubMed

Wu, Xue; Sengupta, Kaushik

2018-03-19

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

The physical signs of impending dehydration among elderly people in nursing homes: The association with axillary skin temperature, humidity, intraoral moisture content, and salivary components.

PubMed

Okuyama, Mayumi; Nishida, Masumi

2016-01-01

The aim of the present study was to examine the association between impending dehydration among elderly people in nursing homes and physical signs, including the axillary skin temperature, humidity, intraoral moisture content, and salivary components. The study included 78 elderly individuals who required long-term care in a nursing home (11 men and 67 women; average age, 86.6±7.3 years). The elderly subjects were classified in two groups according to their serum osmolality levels: those with levels between the upper limit reference value (292 mOsm/kg H2O) and the diagnostic reference value of dehydration (300 mOsm/kg H2O) were classified into the boundary zone group and those with levels of <292 mOsm/kg H2O were classified into the normal range group. The following parameters were measured: basic attributes (age, gender and level of care required), body mass index, diet, daily fluid intake per kilogram of body weight, physiological indicators (blood pressure, pulse rate, body temperature, axillary skin temperature, humidity, total body water, body water rate, internal liquid rate, external solution rate, blood components, intraoral water amount, and salivary components), and the indoor environment (room temperature and humidity). We then performed a statistical analysis to compare the boundary zone group with the normal range group. After adjusting for age and the daily fluid intake per kilogram of body weight (<25 ml/≥25 ml), we performed a logistic regression analysis (the boundary zone group was used as an independent variable) for variables that had significance levels of <0.05 (except for blood components). The univariate analysis revealed significant differences in the following parameters: the serum sodium, chloride, and creatinine levels; the blood sugar level; the urea nitrogen/creatinine ratio; the axillary skin temperature; and room humidity. Only the axillary skin temperature showed a significant association in the final model of the logistic regression analysis (odds ratio, 3.664; 95% confidence interval, 1.101-12.197; p = 0.034). As the axillary skin temperature increased by 1°C, there was a 3.67-fold risk of being classified into the boundary zone group instead of the normal range group. Thus, the axillary skin temperature was associated with impending dehydration.

Facility Management as Part of an Integrated Design of Civil Engineering Structures

NASA Astrophysics Data System (ADS)

Hyben, Ivan; Podmanický, Peter

2014-11-01

The present article deals about facility management, as still relatively young component of an integrated planning and design of buildings. Attention is focused on the area of the proposal, which can greatly affect to amount of future operating costs. Operational efficiency has been divided into individual components and satisfaction with the solution of buildings already constructed was assessed by workers, who are actually dedicated facility management in these organizations. The results were then assessed and evaluated through regression analysis. The aim of this paper is to determine to what extent is desired update project documentation of new buildings from the perspective of facility management.

Real time gamma-ray signature identifier

DOEpatents

Rowland, Mark [Alamo, CA; Gosnell, Tom B [Moraga, CA; Ham, Cheryl [Livermore, CA; Perkins, Dwight [Livermore, CA; Wong, James [Dublin, CA

2012-05-15

A real time gamma-ray signature/source identification method and system using principal components analysis (PCA) for transforming and substantially reducing one or more comprehensive spectral libraries of nuclear materials types and configurations into a corresponding concise representation/signature(s) representing and indexing each individual predetermined spectrum in principal component (PC) space, wherein an unknown gamma-ray signature may be compared against the representative signature to find a match or at least characterize the unknown signature from among all the entries in the library with a single regression or simple projection into the PC space, so as to substantially reduce processing time and computing resources and enable real-time characterization and/or identification.

Contact Kinematics Correlates to Tibial Component Migration Following Single Radius Posterior Stabilized Knee Replacement.

PubMed

Teeter, Matthew G; Perry, Kevin I; Yuan, Xunhua; Howard, James L; Lanting, Brent A

2018-03-01

Contact kinematics between total knee arthroplasty components is thought to affect implant migration; however, the interaction between kinematics and tibial component migration has not been thoroughly examined in a modern implant system. A total of 24 knees from 23 patients undergoing total knee arthroplasty with a single radius, posterior stabilized implant were examined. Patients underwent radiostereometric analysis at 2 and 6 weeks, 3 and 6 months, and 1 and 2 years to measure migration of the tibial component in all planes. At 1 year, patients also had standing radiostereometric analysis examinations acquired in 0°, 20°, 40°, and 60° of flexion, and the location of contact and magnitude of any condylar liftoff was measured for each flexion angle. Regression analysis was performed between kinematic variables and migration at 1 year. The average magnitude of maximum total point motion across all patients was 0.671 ± 0.270 mm at 1 year and 0.608 ± 0.359 mm at 2 years (P = .327). Four implants demonstrated continuous migration of >0.2 mm between the first and second year of implantation. There were correlations between the location of contact and tibial component anterior-posterior tilt, varus-valgus tilt, and anterior-posterior translation. The patients with continuous migration demonstrated atypical kinematics and condylar liftoff in some instances. Kinematics can influence tibial component migration, likely through alterations of force transmission. Abnormal kinematics may play a role in long-term implant loosening. Copyright © 2017 Elsevier Inc. All rights reserved.

Improving Global Models of Remotely Sensed Ocean Chlorophyll Content Using Partial Least Squares and Geographically Weighted Regression

NASA Astrophysics Data System (ADS)

Gholizadeh, H.; Robeson, S. M.

2015-12-01

Empirical models have been widely used to estimate global chlorophyll content from remotely sensed data. Here, we focus on the standard NASA empirical models that use blue-green band ratios. These band ratio ocean color (OC) algorithms are in the form of fourth-order polynomials and the parameters of these polynomials (i.e. coefficients) are estimated from the NASA bio-Optical Marine Algorithm Data set (NOMAD). Most of the points in this data set have been sampled from tropical and temperate regions. However, polynomial coefficients obtained from this data set are used to estimate chlorophyll content in all ocean regions with different properties such as sea-surface temperature, salinity, and downwelling/upwelling patterns. Further, the polynomial terms in these models are highly correlated. In sum, the limitations of these empirical models are as follows: 1) the independent variables within the empirical models, in their current form, are correlated (multicollinear), and 2) current algorithms are global approaches and are based on the spatial stationarity assumption, so they are independent of location. Multicollinearity problem is resolved by using partial least squares (PLS). PLS, which transforms the data into a set of independent components, can be considered as a combined form of principal component regression (PCR) and multiple regression. Geographically weighted regression (GWR) is also used to investigate the validity of spatial stationarity assumption. GWR solves a regression model over each sample point by using the observations within its neighbourhood. PLS results show that the empirical method underestimates chlorophyll content in high latitudes, including the Southern Ocean region, when compared to PLS (see Figure 1). Cluster analysis of GWR coefficients also shows that the spatial stationarity assumption in empirical models is not likely a valid assumption.

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

NASA Astrophysics Data System (ADS)

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

2017-01-01

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

Self-criticism interacts with the affective component of pain to predict depressive symptoms in female patients.

PubMed

Lerman, S F; Shahar, G; Rudich, Z

2012-01-01

This longitudinal study examined the role of the trait of self-criticism as a moderator of the relationship between the affective and sensory components of pain, and depression. One hundred and sixty-three chronic pain patients treated at a specialty pain clinic completed self-report questionnaires at two time points assessing affective and sensory components of pain, depression, and self-criticism. Hierarchical linear regression analysis revealed a significant 3-way interaction between self-criticism, affective pain and gender, whereby women with high affective pain and high self-criticism demonstrated elevated levels of depression. Our findings are the first to show within a broad, comprehensive model, that selfcriticism is activated by the affective, but not sensory component of pain in leading to depressive symptoms, and highlight the need to assess patients' personality as part of an effective treatment plan. © 2011 European Federation of International Association for the Study of Pain Chapters.

Using foreground/background analysis to determine leaf and canopy chemistry

NASA Technical Reports Server (NTRS)

Pinzon, J. E.; Ustin, S. L.; Hart, Q. J.; Jacquemoud, S.; Smith, M. O.

1995-01-01

Spectral Mixture Analysis (SMA) has become a well established procedure for analyzing imaging spectrometry data, however, the technique is relatively insensitive to minor sources of spectral variation (e.g., discriminating stressed from unstressed vegetation and variations in canopy chemistry). Other statistical approaches have been tried e.g., stepwise multiple linear regression analysis to predict canopy chemistry. Grossman et al. reported that SMLR is sensitive to measurement error and that the prediction of minor chemical components are not independent of patterns observed in more dominant spectral components like water. Further, they observed that the relationships were strongly dependent on the mode of expressing reflectance (R, -log R) and whether chemistry was expressed on a weight (g/g) or are basis (g/sq m). Thus, alternative multivariate techniques need to be examined. Smith et al. reported a revised SMA that they termed Foreground/Background Analysis (FBA) that permits directing the analysis along any axis of variance by identifying vectors through the n-dimensional spectral volume orthonormal to each other. Here, we report an application of the FBA technique for the detection of canopy chemistry using a modified form of the analysis.

Job characteristics and burnout: The moderating roles of emotional intelligence, motivation and pay among bank employees.

PubMed

Salami, Samuel O; Ajitoni, Sunday O

2016-10-01

This study investigated the prediction of burnout from job characteristics, emotional intelligence, motivation and pay among bank employees. It also examined the interactions of emotional intelligence, motivation, pay and job characteristics in the prediction of burnout. Data obtained from 230 (Males = 127, Females = 103) bank employees were analysed using Pearson's Product Moment Correlation and multiple regression analysis. Results showed that theses variables jointly and separately negatively predicted burnout components. The results further indicated that emotional intelligence, motivation and pay separately interacted with some job characteristic components to negatively predict some burnout components. The findings imply that emotional intelligence, motivation and pay could be considered by counsellors when designing interventions to reduce burnout among bank employees. © 2015 International Union of Psychological Science.

Exploratory factor analysis of borderline personality disorder criteria in hospitalized adolescents.

PubMed

Becker, Daniel F; McGlashan, Thomas H; Grilo, Carlos M

2006-01-01

The authors examined the factor structure of borderline personality disorder (BPD) in hospitalized adolescents and also sought to add to the theoretical and clinical understanding of any homogeneous components by determining whether they may be related to specific forms of Axis I pathology. Subjects were 123 adolescent inpatients, who were reliably assessed with structured diagnostic interviews for Diagnostic and Statistical Manual of Mental Disorders, Revised Third Edition Axes I and II disorders. Exploratory factor analysis identified BPD components, and logistic regression analyses tested whether these components were predictive of specific Axis I disorders. Factor analysis revealed a 4-factor solution that accounted for 67.0% of the variance. Factor 1 ("suicidal threats or gestures" and "emptiness or boredom") predicted depressive disorders and alcohol use disorders. Factor 2 ("affective instability," "uncontrolled anger," and "identity disturbance") predicted anxiety disorders and oppositional defiant disorder. Factor 3 ("unstable relationships" and "abandonment fears") predicted only anxiety disorders. Factor 4 ("impulsiveness" and "identity disturbance") predicted conduct disorder and substance use disorders. Exploratory factor analysis of BPD criteria in adolescent inpatients revealed 4 BPD factors that appear to differ from those reported for similar studies of adults. The factors represent components of self-negation, irritability, poorly modulated relationships, and impulsivity--each of which is associated with characteristic Axis I pathology. These findings shed light on the nature of BPD in adolescents and may also have implications for treatment.

Ripening-dependent metabolic changes in the volatiles of pineapple (Ananas comosus (L.) Merr.) fruit: II. Multivariate statistical profiling of pineapple aroma compounds based on comprehensive two-dimensional gas chromatography-mass spectrometry.

PubMed

Steingass, Christof Björn; Jutzi, Manfred; Müller, Jenny; Carle, Reinhold; Schmarr, Hans-Georg

2015-03-01

Ripening-dependent changes of pineapple volatiles were studied in a nontargeted profiling analysis. Volatiles were isolated via headspace solid phase microextraction and analyzed by comprehensive 2D gas chromatography and mass spectrometry (HS-SPME-GC×GC-qMS). Profile patterns presented in the contour plots were evaluated applying image processing techniques and subsequent multivariate statistical data analysis. Statistical methods comprised unsupervised hierarchical cluster analysis (HCA) and principal component analysis (PCA) to classify the samples. Supervised partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) regression were applied to discriminate different ripening stages and describe the development of volatiles during postharvest storage, respectively. Hereby, substantial chemical markers allowing for class separation were revealed. The workflow permitted the rapid distinction between premature green-ripe pineapples and postharvest-ripened sea-freighted fruits. Volatile profiles of fully ripe air-freighted pineapples were similar to those of green-ripe fruits postharvest ripened for 6 days after simulated sea freight export, after PCA with only two principal components. However, PCA considering also the third principal component allowed differentiation between air-freighted fruits and the four progressing postharvest maturity stages of sea-freighted pineapples.

Factors affecting medication adherence in community-managed patients with hypertension based on the principal component analysis: evidence from Xinjiang, China.

PubMed

Zhang, Yuji; Li, Xiaoju; Mao, Lu; Zhang, Mei; Li, Ke; Zheng, Yinxia; Cui, Wangfei; Yin, Hongpo; He, Yanli; Jing, Mingxia

2018-01-01

The analysis of factors affecting the nonadherence to antihypertensive medications is important in the control of blood pressure among patients with hypertension. The purpose of this study was to assess the relationship between factors and medication adherence in Xinjiang community-managed patients with hypertension based on the principal component analysis. A total of 1,916 community-managed patients with hypertension, selected randomly through a multi-stage sampling, participated in the survey. Self-designed questionnaires were used to classify the participants as either adherent or nonadherent to their medication regimen. A principal component analysis was used in order to eliminate the correlation between factors. Factors related to nonadherence were analyzed by using a χ 2 -test and a binary logistic regression model. This study extracted nine common factors, with a cumulative variance contribution rate of 63.6%. Further analysis revealed that the following variables were significantly related to nonadherence: severity of disease, community management, diabetes, and taking traditional medications. Community management plays an important role in improving the patients' medication-taking behavior. Regular medication regimen instruction and better community management services through community-level have the potential to reduce nonadherence. Mild hypertensive patients should be monitored by community health care providers.

Job Satisfaction and Subjective Well-Being Among Midwives: Analysis of a Multinational Cross-Sectional Survey.

PubMed

Jarosova, Darja; Gurkova, Elena; Ziakova, Katarina; Nedvedova, Daniela; Palese, Alvisa; Godeas, Gloria; Chan, Sally Wai-Chi; Song, Mi Sook; Lee, Jongwon; Cordeiro, Raul; Babiarczyk, Beata; Fras, Malgorzata

2017-03-01

There is a considerable amount of empirical evidence to indicate a positive association between an employee's subjective well-being and workplace performance and job satisfaction. Compared with nursing research, there is a relative lack of consistent scientific evidence concerning midwives' subjective well-being and its determinants related to domains of job satisfaction. The purpose of the study was to examine the association between the domains of job satisfaction and components of subjective well-being in hospital midwives. This cross-sectional descriptive study involved 1190 hospital midwives from 7 countries. Job satisfaction was measured by the McCloskey/Mueller Satisfaction Scale. Subjective well-being was conceptualized in the study by the 2 components (the affective and the cognitive component). The affective component of subjective well-being (ie, emotional well-being) was assessed by the Positive and the Negative Affect Scale. The cognitive component of subjective well-being (ie, life satisfaction) was measured by the Personal Well-Being Index. Pearson correlations and multiple regression analyses were used to determine associations between variables. Findings from correlation and regression analyses indicated an overall weak association between the domains of job satisfaction and components of subjective well-being. Satisfaction with extrinsic rewards, coworkers, and interaction opportunities accounted for only 13% of variance in the cognitive component (life satisfaction). The affective component (emotional well-being) was weakly associated with satisfaction with control and responsibility. The low amount of variance suggests that neither component of subjective well-being is influenced by the domains of job satisfaction. Further studies should focus on identifying other predictors of subjective well-being among midwives. A better understanding of how specific job facets are related to the subjective well-being of midwives might assist employers in the design of counseling and intervention programs for subjective well-being of midwives in the workplace and workplace performance. © 2016 by the American College of Nurse-Midwives.

Fluorescence fingerprint as an instrumental assessment of the sensory quality of tomato juices.

PubMed

Trivittayasil, Vipavee; Tsuta, Mizuki; Imamura, Yoshinori; Sato, Tsuneo; Otagiri, Yuji; Obata, Akio; Otomo, Hiroe; Kokawa, Mito; Sugiyama, Junichi; Fujita, Kaori; Yoshimura, Masatoshi

2016-03-15

Sensory analysis is an important standard for evaluating food products. However, as trained panelists and time are required for the process, the potential of using fluorescence fingerprint as a rapid instrumental method to approximate sensory characteristics was explored in this study. Thirty-five out of 44 descriptive sensory attributes were found to show a significant difference between samples (analysis of variance test). Principal component analysis revealed that principal component 1 could capture 73.84 and 75.28% variance for aroma category and combined flavor and taste category respectively. Fluorescence fingerprints of tomato juices consisted of two visible peaks at excitation/emission wavelengths of 290/350 and 315/425 nm and a long narrow emission peak at 680 nm. The 680 nm peak was only clearly observed in juices obtained from tomatoes cultivated to be eaten raw. The ability to predict overall sensory profiles was investigated by using principal component 1 as a regression target. Fluorescence fingerprint could predict principal component 1 of both aroma and combined flavor and taste with a coefficient of determination above 0.8. The results obtained in this study indicate the potential of using fluorescence fingerprint as an instrumental method for assessing sensory characteristics of tomato juices. © 2015 Society of Chemical Industry.

Reference-Free Removal of EEG-fMRI Ballistocardiogram Artifacts with Harmonic Regression

PubMed Central

Krishnaswamy, Pavitra; Bonmassar, Giorgio; Poulsen, Catherine; Pierce, Eric T; Purdon, Patrick L.; Brown, Emery N.

2016-01-01

Combining electroencephalogram (EEG) recording and functional magnetic resonance imaging (fMRI) offers the potential for imaging brain activity with high spatial and temporal resolution. This potential remains limited by the significant ballistocardiogram (BCG) artifacts induced in the EEG by cardiac pulsation-related head movement within the magnetic field. We model the BCG artifact using a harmonic basis, pose the artifact removal problem as a local harmonic regression analysis, and develop an efficient maximum likelihood algorithm to estimate and remove BCG artifacts. Our analysis paradigm accounts for time-frequency overlap between the BCG artifacts and neurophysiologic EEG signals, and tracks the spatiotemporal variations in both the artifact and the signal. We evaluate performance on: simulated oscillatory and evoked responses constructed with realistic artifacts; actual anesthesia-induced oscillatory recordings; and actual visual evoked potential recordings. In each case, the local harmonic regression analysis effectively removes the BCG artifacts, and recovers the neurophysiologic EEG signals. We further show that our algorithm outperforms commonly used reference-based and component analysis techniques, particularly in low SNR conditions, the presence of significant time-frequency overlap between the artifact and the signal, and/or large spatiotemporal variations in the BCG. Because our algorithm does not require reference signals and has low computational complexity, it offers a practical tool for removing BCG artifacts from EEG data recorded in combination with fMRI. PMID:26151100

Comparison of partial least squares and random forests for evaluating relationship between phenolics and bioactivities of Neptunia oleracea.

PubMed

Lee, Soo Yee; Mediani, Ahmed; Maulidiani, Maulidiani; Khatib, Alfi; Ismail, Intan Safinar; Zawawi, Norhasnida; Abas, Faridah

2018-01-01

Neptunia oleracea is a plant consumed as a vegetable and which has been used as a folk remedy for several diseases. Herein, two regression models (partial least squares, PLS; and random forest, RF) in a metabolomics approach were compared and applied to the evaluation of the relationship between phenolics and bioactivities of N. oleracea. In addition, the effects of different extraction conditions on the phenolic constituents were assessed by pattern recognition analysis. Comparison of the PLS and RF showed that RF exhibited poorer generalization and hence poorer predictive performance. Both the regression coefficient of PLS and the variable importance of RF revealed that quercetin and kaempferol derivatives, caffeic acid and vitexin-2-O-rhamnoside were significant towards the tested bioactivities. Furthermore, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) results showed that sonication and absolute ethanol are the preferable extraction method and ethanol ratio, respectively, to produce N. oleracea extracts with high phenolic levels and therefore high DPPH scavenging and α-glucosidase inhibitory activities. Both PLS and RF are useful regression models in metabolomics studies. This work provides insight into the performance of different multivariate data analysis tools and the effects of different extraction conditions on the extraction of desired phenolics from plants. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Effect of noise in principal component analysis with an application to ozone pollution

NASA Astrophysics Data System (ADS)

Tsakiri, Katerina G.

This thesis analyzes the effect of independent noise in principal components of k normally distributed random variables defined by a covariance matrix. We prove that the principal components as well as the canonical variate pairs determined from joint distribution of original sample affected by noise can be essentially different in comparison with those determined from the original sample. However when the differences between the eigenvalues of the original covariance matrix are sufficiently large compared to the level of the noise, the effect of noise in principal components and canonical variate pairs proved to be negligible. The theoretical results are supported by simulation study and examples. Moreover, we compare our results about the eigenvalues and eigenvectors in the two dimensional case with other models examined before. This theory can be applied in any field for the decomposition of the components in multivariate analysis. One application is the detection and prediction of the main atmospheric factor of ozone concentrations on the example of Albany, New York. Using daily ozone, solar radiation, temperature, wind speed and precipitation data, we determine the main atmospheric factor for the explanation and prediction of ozone concentrations. A methodology is described for the decomposition of the time series of ozone and other atmospheric variables into the global term component which describes the long term trend and the seasonal variations, and the synoptic scale component which describes the short term variations. By using the Canonical Correlation Analysis, we show that solar radiation is the only main factor between the atmospheric variables considered here for the explanation and prediction of the global and synoptic scale component of ozone. The global term components are modeled by a linear regression model, while the synoptic scale components by a vector autoregressive model and the Kalman filter. The coefficient of determination, R2, for the prediction of the synoptic scale ozone component was found to be the highest when we consider the synoptic scale component of the time series for solar radiation and temperature. KEY WORDS: multivariate analysis; principal component; canonical variate pairs; eigenvalue; eigenvector; ozone; solar radiation; spectral decomposition; Kalman filter; time series prediction

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

Biostatistics Series Module 10: Brief Overview of Multivariate Methods.

PubMed

Hazra, Avijit; Gogtay, Nithya

2017-01-01

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

A Questionnaire-Wide Association Study of Personality and Mortality: The Vietnam Experience Study

PubMed Central

Weiss, Alexander; Gale, Catharine R.; Batty, G. David; Deary, Ian J.

2013-01-01

Objective We examined the association between the Minnesota Multiphasic Personality Inventory (MMPI) and all-cause mortality in 4462 middle-aged Vietnam-era veterans. Methods We split the study population into half samples. In each half, we used proportional hazards (Cox) regression to test the 550 MMPI items’ associations with mortality over 15 years. In all participants, we subjected significant (p < .01) items in both halves to principal-components analysis (PCA). We used Cox regression to test whether these components predicted mortality when controlling for other predictors (demographics, cognitive ability, health behaviors, mental/physical health). Results Eighty-nine items were associated with mortality in both half-samples. PCA revealed Neuroticism/Negative Affectivity, Somatic Complaints, Psychotic/Paranoia, and Antisocial components, and a higher-order component, Personal Disturbance. Individually, Neuroticism/Negative Affectivity (HR = 1.55, 95% CI = 1.39,1.72), Somatic Complaints (HR = 1.66; 95% CI = 1.52,1.80), Psychotic/Paranoid (HR = 1.44; 95% CI = 1.32,1.57), Antisocial (HR = 1.79; 95% CI = 1.59,2.01), and Personal Disturbance (HR = 1.74; 95% CI = 1.58,1.91) were associated with risk. Including covariates attenuated these associations (28.4 to 54.5%), though they were still significant. After entering Personal Disturbance into models with each component, Neuroticism/Negative Affectivity and Somatic Complaints were significant, although Neuroticism/Negative Affectivity’s were now protective (HR = 0.73, 95% CI = 0.58,0.92). When the four components were entered together with or without covariates, Somatic Complaints and Antisocial were significant risk factors. Conclusions Somatic Complaints and Personal Disturbance are associated with increased mortality risk. Other components’ effects varied as a function of variables in the model. PMID:23731751

Multi-scale remote sensing sagebrush characterization with regression trees over Wyoming, USA: laying a foundation for monitoring

USGS Publications Warehouse

Homer, Collin G.; Aldridge, Cameron L.; Meyer, Debra K.; Schell, Spencer J.

2012-01-01

agebrush ecosystems in North America have experienced extensive degradation since European settlement. Further degradation continues from exotic invasive plants, altered fire frequency, intensive grazing practices, oil and gas development, and climate change – adding urgency to the need for ecosystem-wide understanding. Remote sensing is often identified as a key information source to facilitate ecosystem-wide characterization, monitoring, and analysis; however, approaches that characterize sagebrush with sufficient and accurate local detail across large enough areas to support this paradigm are unavailable. We describe the development of a new remote sensing sagebrush characterization approach for the state of Wyoming, U.S.A. This approach integrates 2.4 m QuickBird, 30 m Landsat TM, and 56 m AWiFS imagery into the characterization of four primary continuous field components including percent bare ground, percent herbaceous cover, percent litter, and percent shrub, and four secondary components including percent sagebrush (Artemisia spp.), percent big sagebrush (Artemisia tridentata), percent Wyoming sagebrush (Artemisia tridentata Wyomingensis), and shrub height using a regression tree. According to an independent accuracy assessment, primary component root mean square error (RMSE) values ranged from 4.90 to 10.16 for 2.4 m QuickBird, 6.01 to 15.54 for 30 m Landsat, and 6.97 to 16.14 for 56 m AWiFS. Shrub and herbaceous components outperformed the current data standard called LANDFIRE, with a shrub RMSE value of 6.04 versus 12.64 and a herbaceous component RMSE value of 12.89 versus 14.63. This approach offers new advancements in sagebrush characterization from remote sensing and provides a foundation to quantitatively monitor these components into the future.

The effects of components of fine particulate air pollution on mortality in california: results from CALFINE.

PubMed

Ostro, Bart; Feng, Wen-Ying; Broadwin, Rachel; Green, Shelley; Lipsett, Michael

2007-01-01

Several epidemiologic studies provide evidence of an association between daily mortality and particulate matter < 2.5 pm in diameter (PM2.5). Little is known, however, about the relative effects of PM2.5 constituents. We examined associations between 19 PM2.5 components and daily mortality in six California counties. We obtained daily data from 2000 to 2003 on mortality and PM2.5 mass and components, including elemental and organic carbon (EC and OC), nitrates, sulfates, and various metals. We examined associations of PM2.5 and its constituents with daily counts of several mortality categories: all-cause, cardiovascular, respiratory, and mortality age > 65 years. Poisson regressions incorporating natural splines were used to control for time-varying covariates. Effect estimates were determined for each component in each county and then combined using a random-effects model. PM2.5 mass and several constituents were associated with multiple mortality categories, especially cardiovascular deaths. For example, for a 3-day lag, the latter increased by 1.6, 2.1, 1.6, and 1.5% for PM2.5, EC, OC, and nitrates based on interquartile ranges of 14.6, 0.8, 4.6, and 5.5 pg/m(3), respectively. Stronger associations were observed between mortality and additional pollutants, including sulfates and several metals, during the cool season. This multicounty analysis adds to the growing body of evidence linking PM2.5 with mortality and indicates that excess risks may vary among specific PM2.5 components. Therefore, the use of regression coefficients based on PM2.5 mass may underestimate associations with some PM2.5 components. Also, our findings support the hypothesis that combustion-associated pollutants are particularly important in California.

A toolbox to visually explore cerebellar shape changes in cerebellar disease and dysfunction.

PubMed

Abulnaga, S Mazdak; Yang, Zhen; Carass, Aaron; Kansal, Kalyani; Jedynak, Bruno M; Onyike, Chiadi U; Ying, Sarah H; Prince, Jerry L

2016-02-27

The cerebellum plays an important role in motor control and is also involved in cognitive processes. Cerebellar function is specialized by location, although the exact topographic functional relationship is not fully understood. The spinocerebellar ataxias are a group of neurodegenerative diseases that cause regional atrophy in the cerebellum, yielding distinct motor and cognitive problems. The ability to study the region-specific atrophy patterns can provide insight into the problem of relating cerebellar function to location. In an effort to study these structural change patterns, we developed a toolbox in MATLAB to provide researchers a unique way to visually explore the correlation between cerebellar lobule shape changes and function loss, with a rich set of visualization and analysis modules. In this paper, we outline the functions and highlight the utility of the toolbox. The toolbox takes as input landmark shape representations of subjects' cerebellar substructures. A principal component analysis is used for dimension reduction. Following this, a linear discriminant analysis and a regression analysis can be performed to find the discriminant direction associated with a specific disease type, or the regression line of a specific functional measure can be generated. The characteristic structural change pattern of a disease type or of a functional score is visualized by sampling points on the discriminant or regression line. The sampled points are used to reconstruct synthetic cerebellar lobule shapes. We showed a few case studies highlighting the utility of the toolbox and we compare the analysis results with the literature.

A toolbox to visually explore cerebellar shape changes in cerebellar disease and dysfunction

NASA Astrophysics Data System (ADS)

Abulnaga, S. Mazdak; Yang, Zhen; Carass, Aaron; Kansal, Kalyani; Jedynak, Bruno M.; Onyike, Chiadi U.; Ying, Sarah H.; Prince, Jerry L.

2016-03-01

The cerebellum plays an important role in motor control and is also involved in cognitive processes. Cerebellar function is specialized by location, although the exact topographic functional relationship is not fully understood. The spinocerebellar ataxias are a group of neurodegenerative diseases that cause regional atrophy in the cerebellum, yielding distinct motor and cognitive problems. The ability to study the region-specific atrophy patterns can provide insight into the problem of relating cerebellar function to location. In an effort to study these structural change patterns, we developed a toolbox in MATLAB to provide researchers a unique way to visually explore the correlation between cerebellar lobule shape changes and function loss, with a rich set of visualization and analysis modules. In this paper, we outline the functions and highlight the utility of the toolbox. The toolbox takes as input landmark shape representations of subjects' cerebellar substructures. A principal component analysis is used for dimension reduction. Following this, a linear discriminant analysis and a regression analysis can be performed to find the discriminant direction associated with a specific disease type, or the regression line of a specific functional measure can be generated. The characteristic structural change pattern of a disease type or of a functional score is visualized by sampling points on the discriminant or regression line. The sampled points are used to reconstruct synthetic cerebellar lobule shapes. We showed a few case studies highlighting the utility of the toolbox and we compare the analysis results with the literature.

Combining EEG and eye tracking: identification, characterization, and correction of eye movement artifacts in electroencephalographic data

PubMed Central

Plöchl, Michael; Ossandón, José P.; König, Peter

2012-01-01

Eye movements introduce large artifacts to electroencephalographic recordings (EEG) and thus render data analysis difficult or even impossible. Trials contaminated by eye movement and blink artifacts have to be discarded, hence in standard EEG-paradigms subjects are required to fixate on the screen. To overcome this restriction, several correction methods including regression and blind source separation have been proposed. Yet, there is no automated standard procedure established. By simultaneously recording eye movements and 64-channel-EEG during a guided eye movement paradigm, we investigate and review the properties of eye movement artifacts, including corneo-retinal dipole changes, saccadic spike potentials and eyelid artifacts, and study their interrelations during different types of eye- and eyelid movements. In concordance with earlier studies our results confirm that these artifacts arise from different independent sources and that depending on electrode site, gaze direction, and choice of reference these sources contribute differently to the measured signal. We assess the respective implications for artifact correction methods and therefore compare the performance of two prominent approaches, namely linear regression and independent component analysis (ICA). We show and discuss that due to the independence of eye artifact sources, regression-based correction methods inevitably over- or under-correct individual artifact components, while ICA is in principle suited to address such mixtures of different types of artifacts. Finally, we propose an algorithm, which uses eye tracker information to objectively identify eye-artifact related ICA-components (ICs) in an automated manner. In the data presented here, the algorithm performed very similar to human experts when those were given both, the topographies of the ICs and their respective activations in a large amount of trials. Moreover it performed more reliable and almost twice as effective than human experts when those had to base their decision on IC topographies only. Furthermore, a receiver operating characteristic (ROC) analysis demonstrated an optimal balance of false positive and false negative at an area under curve (AUC) of more than 0.99. Removing the automatically detected ICs from the data resulted in removal or substantial suppression of ocular artifacts including microsaccadic spike potentials, while the relevant neural signal remained unaffected. In conclusion the present work aims at a better understanding of individual eye movement artifacts, their interrelations and the respective implications for eye artifact correction. Additionally, the proposed ICA-procedure provides a tool for optimized detection and correction of eye movement-related artifact components. PMID:23087632

A Quantile Regression Approach to Understanding the Relations Among Morphological Awareness, Vocabulary, and Reading Comprehension in Adult Basic Education Students.

PubMed

Tighe, Elizabeth L; Schatschneider, Christopher

2016-07-01

The purpose of this study was to investigate the joint and unique contributions of morphological awareness and vocabulary knowledge at five reading comprehension levels in adult basic education (ABE) students. We introduce the statistical technique of multiple quantile regression, which enabled us to assess the predictive utility of morphological awareness and vocabulary knowledge at multiple points (quantiles) along the continuous distribution of reading comprehension. To demonstrate the efficacy of our multiple quantile regression analysis, we compared and contrasted our results with a traditional multiple regression analytic approach. Our results indicated that morphological awareness and vocabulary knowledge accounted for a large portion of the variance (82%-95%) in reading comprehension skills across all quantiles. Morphological awareness exhibited the greatest unique predictive ability at lower levels of reading comprehension whereas vocabulary knowledge exhibited the greatest unique predictive ability at higher levels of reading comprehension. These results indicate the utility of using multiple quantile regression to assess trajectories of component skills across multiple levels of reading comprehension. The implications of our findings for ABE programs are discussed. © Hammill Institute on Disabilities 2014.

Coping with Multicollinearity: An Example on Application of Principal Components Regression in Dendroecology

Treesearch

B. Desta Fekedulegn; J.J. Colbert; R.R., Jr. Hicks; Michael E. Schuckers

2002-01-01

The theory and application of principal components regression, a method for coping with multicollinearity among independent variables in analyzing ecological data, is exhibited in detail. A concrete example of the complex procedures that must be carried out in developing a diagnostic growth-climate model is provided. We use tree radial increment data taken from breast...

High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics

PubMed Central

Carvalho, Carlos M.; Chang, Jeffrey; Lucas, Joseph E.; Nevins, Joseph R.; Wang, Quanli; West, Mike

2010-01-01

We describe studies in molecular profiling and biological pathway analysis that use sparse latent factor and regression models for microarray gene expression data. We discuss breast cancer applications and key aspects of the modeling and computational methodology. Our case studies aim to investigate and characterize heterogeneity of structure related to specific oncogenic pathways, as well as links between aggregate patterns in gene expression profiles and clinical biomarkers. Based on the metaphor of statistically derived “factors” as representing biological “subpathway” structure, we explore the decomposition of fitted sparse factor models into pathway subcomponents and investigate how these components overlay multiple aspects of known biological activity. Our methodology is based on sparsity modeling of multivariate regression, ANOVA, and latent factor models, as well as a class of models that combines all components. Hierarchical sparsity priors address questions of dimension reduction and multiple comparisons, as well as scalability of the methodology. The models include practically relevant non-Gaussian/nonparametric components for latent structure, underlying often quite complex non-Gaussianity in multivariate expression patterns. Model search and fitting are addressed through stochastic simulation and evolutionary stochastic search methods that are exemplified in the oncogenic pathway studies. Supplementary supporting material provides more details of the applications, as well as examples of the use of freely available software tools for implementing the methodology. PMID:21218139

Decoding and modelling of time series count data using Poisson hidden Markov model and Markov ordinal logistic regression models.

PubMed

Sebastian, Tunny; Jeyaseelan, Visalakshi; Jeyaseelan, Lakshmanan; Anandan, Shalini; George, Sebastian; Bangdiwala, Shrikant I

2018-01-01

Hidden Markov models are stochastic models in which the observations are assumed to follow a mixture distribution, but the parameters of the components are governed by a Markov chain which is unobservable. The issues related to the estimation of Poisson-hidden Markov models in which the observations are coming from mixture of Poisson distributions and the parameters of the component Poisson distributions are governed by an m-state Markov chain with an unknown transition probability matrix are explained here. These methods were applied to the data on Vibrio cholerae counts reported every month for 11-year span at Christian Medical College, Vellore, India. Using Viterbi algorithm, the best estimate of the state sequence was obtained and hence the transition probability matrix. The mean passage time between the states were estimated. The 95% confidence interval for the mean passage time was estimated via Monte Carlo simulation. The three hidden states of the estimated Markov chain are labelled as 'Low', 'Moderate' and 'High' with the mean counts of 1.4, 6.6 and 20.2 and the estimated average duration of stay of 3, 3 and 4 months, respectively. Environmental risk factors were studied using Markov ordinal logistic regression analysis. No significant association was found between disease severity levels and climate components.

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.

Increased prevalence of metabolic syndrome in non-obese asian Indian-an urban-rural comparison.

PubMed

Mahadik, S R; Deo, S S; Mehtalia, S D

2007-06-01

In the present study we evaluated the association of insulin resistance (IR) with different components of Metabolic Syndrome (MS) in an Asian Indian population, and performed a comparative study between urban and rural populations of India. A Total of 267 urban men and women aged 25-70 years participated in this study. RESULTS were compared with rural data from a previously published study. Fasting serum insulin, uric acid, and lipid profile were measured along with fasting and 2 hour plasma glucose. Association of MS and IR was studied by using univariate regression analysis. Prevalence of MS was significantly higher in the urban population compared to that of the rural population (35.2% vs 20.6%, chi(2) = 23.2, p < 0.001). Calculated insulin resistence (HOMA-IR) was common in MS group of both populations. Percentage prevalence of IR was high and almost the same in both population (42%). Percentage prevalence of abdominal obesity and hypertriglyceridemia was significantly higher in the urban population compared to the rural population. Linear regression analysis of IR significantly correlated with different components of MS of both the population. The significant finding of the present study was that the rural population exhibited a high prevalence of MS and IR, though nonobese. IR correlated with components of MS not only in the urban but also in the rural population. To reduce the incidence of Type 2 Diabetes (T2DM) and cardiovascular disease (CVD) in our populations, early identification of populations at risk based on prevalence of MS and IR will become of prime importance.

Web document ranking via active learning and kernel principal component analysis

NASA Astrophysics Data System (ADS)

Cai, Fei; Chen, Honghui; Shu, Zhen

2015-09-01

Web document ranking arises in many information retrieval (IR) applications, such as the search engine, recommendation system and online advertising. A challenging issue is how to select the representative query-document pairs and informative features as well for better learning and exploring new ranking models to produce an acceptable ranking list of candidate documents of each query. In this study, we propose an active sampling (AS) plus kernel principal component analysis (KPCA) based ranking model, viz. AS-KPCA Regression, to study the document ranking for a retrieval system, i.e. how to choose the representative query-document pairs and features for learning. More precisely, we fill those documents gradually into the training set by AS such that each of which will incur the highest expected DCG loss if unselected. Then, the KPCA is performed via projecting the selected query-document pairs onto p-principal components in the feature space to complete the regression. Hence, we can cut down the computational overhead and depress the impact incurred by noise simultaneously. To the best of our knowledge, we are the first to perform the document ranking via dimension reductions in two dimensions, namely, the number of documents and features simultaneously. Our experiments demonstrate that the performance of our approach is better than that of the baseline methods on the public LETOR 4.0 datasets. Our approach brings an improvement against RankBoost as well as other baselines near 20% in terms of MAP metric and less improvements using P@K and NDCG@K, respectively. Moreover, our approach is particularly suitable for document ranking on the noisy dataset in practice.

A pilot evaluation of a computer-based psychometric test battery designed to detect impairment in patients with cirrhosis

PubMed Central

Cook, Nicola A; Kim, Jin Un; Pasha, Yasmin; Crossey, Mary ME; Schembri, Adrian J; Harel, Brian T; Kimhofer, Torben; Taylor-Robinson, Simon D

2017-01-01

Background Psychometric testing is used to identify patients with cirrhosis who have developed hepatic encephalopathy (HE). Most batteries consist of a series of paper-and-pencil tests, which are cumbersome for most clinicians. A modern, easy-to-use, computer-based battery would be a helpful clinical tool, given that in its minimal form, HE has an impact on both patients’ quality of life and the ability to drive and operate machinery (with societal consequences). Aim We compared the Cogstate™ computer battery testing with the Psychometric Hepatic Encephalopathy Score (PHES) tests, with a view to simplify the diagnosis. Methods This was a prospective study of 27 patients with histologically proven cirrhosis. An analysis of psychometric testing was performed using accuracy of task performance and speed of completion as primary variables to create a correlation matrix. A stepwise linear regression analysis was performed with backward elimination, using analysis of variance. Results Strong correlations were found between the international shopping list, international shopping list delayed recall of Cogstate and the PHES digit symbol test. The Shopping List Tasks were the only tasks that consistently had P values of <0.05 in the linear regression analysis. Conclusion Subtests of the Cogstate battery correlated very strongly with the digit symbol component of PHES in discriminating severity of HE. These findings would indicate that components of the current PHES battery with the international shopping list tasks of Cogstate would be discriminant and have the potential to be used easily in clinical practice. PMID:28919805

Trend Estimation and Regression Analysis in Climatological Time Series: An Application of Structural Time Series Models and the Kalman Filter.

NASA Astrophysics Data System (ADS)

Visser, H.; Molenaar, J.

1995-05-01

The detection of trends in climatological data has become central to the discussion on climate change due to the enhanced greenhouse effect. To prove detection, a method is needed (i) to make inferences on significant rises or declines in trends, (ii) to take into account natural variability in climate series, and (iii) to compare output from GCMs with the trends in observed climate data. To meet these requirements, flexible mathematical tools are needed. A structural time series model is proposed with which a stochastic trend, a deterministic trend, and regression coefficients can be estimated simultaneously. The stochastic trend component is described using the class of ARIMA models. The regression component is assumed to be linear. However, the regression coefficients corresponding with the explanatory variables may be time dependent to validate this assumption. The mathematical technique used to estimate this trend-regression model is the Kaiman filter. The main features of the filter are discussed.Examples of trend estimation are given using annual mean temperatures at a single station in the Netherlands (1706-1990) and annual mean temperatures at Northern Hemisphere land stations (1851-1990). The inclusion of explanatory variables is shown by regressing the latter temperature series on four variables: Southern Oscillation index (SOI), volcanic dust index (VDI), sunspot numbers (SSN), and a simulated temperature signal, induced by increasing greenhouse gases (GHG). In all analyses, the influence of SSN on global temperatures is found to be negligible. The correlations between temperatures and SOI and VDI appear to be negative. For SOI, this correlation is significant, but for VDI it is not, probably because of a lack of volcanic eruptions during the sample period. The relation between temperatures and GHG is positive, which is in agreement with the hypothesis of a warming climate because of increasing levels of greenhouse gases. The prediction performance of the model is rather poor, and possible explanations are discussed.

Estimating the Concentration and Biodegradability of Organic Matter in 22 Wastewater Treatment Plants Using Fluorescence Excitation Emission Matrices and Parallel Factor Analysis

PubMed Central

Yang, Liyang; Shin, Hyun-Sang; Hur, Jin

2014-01-01

This study aimed at monitoring the changes of fluorescent components in wastewater samples from 22 Korean biological wastewater treatment plants and exploring their prediction capabilities for total organic carbon (TOC), dissolved organic carbon (DOC), biochemical oxygen demand (BOD), chemical oxygen demand (COD), and the biodegradability of the wastewater using an optical sensing technique based on fluorescence excitation emission matrices and parallel factor analysis (EEM-PARAFAC). Three fluorescent components were identified from the samples by using EEM-PARAFAC, including protein-like (C1), fulvic-like (C2) and humic-like (C3) components. C1 showed the highest removal efficiencies for all the treatment types investigated here (69% ± 26%–81% ± 8%), followed by C2 (37% ± 27%–65% ± 35%), while humic-like component (i.e., C3) tended to be accumulated during the biological treatment processes. The percentage of C1 in total fluorescence (%C1) decreased from 54% ± 8% in the influents to 28% ± 8% in the effluents, while those of C2 and C3 (%C2 and %C3) increased from 43% ± 6% to 62% ± 9% and from 3% ± 7% to 10% ± 8%, respectively. The concentrations of TOC, DOC, BOD, and COD were the most correlated with the fluorescence intensity (Fmax) of C1 (r = 0.790–0.817), as compared with the other two fluorescent components. The prediction capability of C1 for TOC, BOD, and COD were improved by using multiple regression based on Fmax of C1 and suspended solids (SS) (r = 0.856–0.865), both of which can be easily monitored in situ. The biodegradability of organic matter in BOD/COD were significantly correlated with each PARAFAC component and their combinations (r = −0.598–0.613, p < 0.001), with the highest correlation coefficient shown for %C1. The estimation capability was further enhanced by using multiple regressions based on %C1, %C2 and C3/C2 (r = −0.691). PMID:24448170

Just a Fad? Gamification in Health and Fitness Apps

PubMed Central

2014-01-01

Background Gamification has been a predominant focus of the health app industry in recent years. However, to our knowledge, there has yet to be a review of gamification elements in relation to health behavior constructs, or insight into the true proliferation of gamification in health apps. Objective The objective of this study was to identify the extent to which gamification is used in health apps, and analyze gamification of health and fitness apps as a potential component of influence on a consumer’s health behavior. Methods An analysis of health and fitness apps related to physical activity and diet was conducted among apps in the Apple App Store in the winter of 2014. This analysis reviewed a sample of 132 apps for the 10 effective game elements, the 6 core components of health gamification, and 13 core health behavior constructs. A regression analysis was conducted in order to measure the correlation between health behavior constructs, gamification components, and effective game elements. Results This review of the most popular apps showed widespread use of gamification principles, but low adherence to any professional guidelines or industry standard. Regression analysis showed that game elements were associated with gamification (P<.001). Behavioral theory was associated with gamification (P<.05), but not game elements, and upon further analysis gamification was only associated with composite motivational behavior scores (P<.001), and not capacity or opportunity/trigger. Conclusions This research, to our knowledge, represents the first comprehensive review of gamification use in health and fitness apps, and the potential to impact health behavior. The results show that use of gamification in health and fitness apps has become immensely popular, as evidenced by the number of apps found in the Apple App Store containing at least some components of gamification. This shows a lack of integrating important elements of behavioral theory from the app industry, which can potentially impact the efficacy of gamification apps to change behavior. Apps represent a very promising, burgeoning market and landscape in which to disseminate health behavior change interventions. Initial results show an abundant use of gamification in health and fitness apps, which necessitates the in-depth study and evaluation of the potential of gamification to change health behaviors. PMID:25654660

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.

Impact of Dental Disorders and its Influence on Self Esteem Levels among Adolescents.

PubMed

Kaur, Puneet; Singh, Simarpreet; Mathur, Anmol; Makkar, Diljot Kaur; Aggarwal, Vikram Pal; Batra, Manu; Sharma, Anshika; Goyal, Nikita

2017-04-01

Self esteem is more of a psychological concept therefore, even the common dental disorders like dental trauma, tooth loss and untreated carious lesions may affect the self esteem thus influencing the quality of life. This study aims to assess the impact of dental disorders among the adolescents on their self esteem level. The present cross-sectional study was conducted among 10 to 17 years adolescents. In order to obtain a representative sample, multistage sampling technique was used and sample was selected based on Probability Proportional to Enrolment size (PPE). Oral health assessment was carried out using WHO type III examination and self esteem was estimated using the Rosenberg Self Esteem Scale score (RSES). The descriptive and inferential analysis of the data was done by using IBM SPSS software. Logistic and linear regression analysis was executed to test the individual association of different independent clinical variables with self esteem. Total sample of 1140 adolescents with mean age of 14.95 ±2.08 and RSES of 27.09 ±3.12 were considered. Stepwise multiple linear regression analysis was applied and best predictors in relation to RSES in the descending order were Dental Health Component (DHC), Aesthetic Component (AC), dental decay {(aesthetic zone), (masticatory zone)}, tooth loss {(aesthetic zone), (masticatory zone)} and anterior fracture of tooth. It was found that various dental disorders like malocclusion, anterior traumatic tooth, tooth loss and untreated decay causes a profound impact on aesthetics and psychosocial behaviour of adolescents, thus affecting their self esteem.

Development and Validation of a Practical Instrument for Injury Prevention: The Occupational Safety and Health Monitoring and Assessment Tool (OSH-MAT).

PubMed

Sun, Yi; Arning, Martin; Bochmann, Frank; Börger, Jutta; Heitmann, Thomas

2018-06-01

The Occupational Safety and Health Monitoring and Assessment Tool (OSH-MAT) is a practical instrument that is currently used in the German woodworking and metalworking industries to monitor safety conditions at workplaces. The 12-item scoring system has three subscales rating technical, organizational, and personnel-related conditions in a company. Each item has a rating value ranging from 1 to 9, with higher values indicating higher standard of safety conditions. The reliability of this instrument was evaluated in a cross-sectional survey among 128 companies and its validity among 30,514 companies. The inter-rater reliability of the instrument was examined independently and simultaneously by two well-trained safety engineers. Agreement between the double ratings was quantified by the intraclass correlation coefficient and absolute agreement of the rating values. The content validity of the OSH-MAT was evaluated by quantifying the association between OSH-MAT values and 5-year average injury rates by Poisson regression analysis adjusted for the size of the companies and industrial sectors. The construct validity of OSH-MAT was examined by principle component factor analysis. Our analysis indicated good to very good inter-rater reliability (intraclass correlation coefficient = 0.64-0.74) of OSH-MAT values with an absolute agreement of between 72% and 81%. Factor analysis identified three component subscales that met exactly the structure theory of this instrument. The Poisson regression analysis demonstrated a statistically significant exposure-response relationship between OSH-MAT values and the 5-year average injury rates. These analyses indicate that OSH-MAT is a valid and reliable instrument that can be used effectively to monitor safety conditions at workplaces.

Analysis of Forest Foliage Using a Multivariate Mixture Model

NASA Technical Reports Server (NTRS)

Hlavka, C. A.; Peterson, David L.; Johnson, L. F.; Ganapol, B.

1997-01-01

Data with wet chemical measurements and near infrared spectra of ground leaf samples were analyzed to test a multivariate regression technique for estimating component spectra which is based on a linear mixture model for absorbance. The resulting unmixed spectra for carbohydrates, lignin, and protein resemble the spectra of extracted plant starches, cellulose, lignin, and protein. The unmixed protein spectrum has prominent absorption spectra at wavelengths which have been associated with nitrogen bonds.

A Semiparametric Change-Point Regression Model for Longitudinal Observations.

PubMed

Xing, Haipeng; Ying, Zhiliang

2012-12-01

Many longitudinal studies involve relating an outcome process to a set of possibly time-varying covariates, giving rise to the usual regression models for longitudinal data. When the purpose of the study is to investigate the covariate effects when experimental environment undergoes abrupt changes or to locate the periods with different levels of covariate effects, a simple and easy-to-interpret approach is to introduce change-points in regression coefficients. In this connection, we propose a semiparametric change-point regression model, in which the error process (stochastic component) is nonparametric and the baseline mean function (functional part) is completely unspecified, the observation times are allowed to be subject-specific, and the number, locations and magnitudes of change-points are unknown and need to be estimated. We further develop an estimation procedure which combines the recent advance in semiparametric analysis based on counting process argument and multiple change-points inference, and discuss its large sample properties, including consistency and asymptotic normality, under suitable regularity conditions. Simulation results show that the proposed methods work well under a variety of scenarios. An application to a real data set is also given.

Development of a hybrid proximal sensing method for rapid identification of petroleum contaminated soils.

PubMed

Chakraborty, Somsubhra; Weindorf, David C; Li, Bin; Ali Aldabaa, Abdalsamad Abdalsatar; Ghosh, Rakesh Kumar; Paul, Sathi; Nasim Ali, Md

2015-05-01

Using 108 petroleum contaminated soil samples, this pilot study proposed a new analytical approach of combining visible near-infrared diffuse reflectance spectroscopy (VisNIR DRS) and portable X-ray fluorescence spectrometry (PXRF) for rapid and improved quantification of soil petroleum contamination. Results indicated that an advanced fused model where VisNIR DRS spectra-based penalized spline regression (PSR) was used to predict total petroleum hydrocarbon followed by PXRF elemental data-based random forest regression was used to model the PSR residuals, it outperformed (R(2)=0.78, residual prediction deviation (RPD)=2.19) all other models tested, even producing better generalization than using VisNIR DRS alone (RPD's of 1.64, 1.86, and 1.96 for random forest, penalized spline regression, and partial least squares regression, respectively). Additionally, unsupervised principal component analysis using the PXRF+VisNIR DRS system qualitatively separated contaminated soils from control samples. Fusion of PXRF elemental data and VisNIR derivative spectra produced an optimized model for total petroleum hydrocarbon quantification in soils. Copyright © 2015 Elsevier B.V. All rights reserved.

The impact of young drivers' lifestyle on their road traffic accident risk in greater Athens area.

PubMed

Chliaoutakis, J E; Darviri, C; Demakakos, P T

1999-11-01

Young drivers (18-24) both in Greece and elsewhere appear to have high rates of road traffic accidents. Many factors contribute to the creation of these high road traffic accidents rates. It has been suggested that lifestyle is an important one. The main objective of this study is to find out and clarify the (potential) relationship between young drivers' lifestyle and the road traffic accident risk they face. Moreover, to examine if all the youngsters have the same elevated risk on the road or not. The sample consisted of 241 young Greek drivers of both sexes. The statistical analysis included factor analysis and logistic regression analysis. Through the principal component analysis a ten factor scale was created which included the basic lifestyle traits of young Greek drivers. The logistic regression analysis showed that the young drivers whose dominant lifestyle trait is alcohol consumption or drive without destination have high accident risk, while these whose dominant lifestyle trait is culture, face low accident risk. Furthermore, young drivers who are religious in one way or another seem to have low accident risk. Finally, some preliminary observations on how health promotion should be put into practice are discussed.

Machine learning action parameters in lattice quantum chromodynamics

NASA Astrophysics Data System (ADS)

Shanahan, Phiala E.; Trewartha, Daniel; Detmold, William

2018-05-01

Numerical lattice quantum chromodynamics studies of the strong interaction are important in many aspects of particle and nuclear physics. Such studies require significant computing resources to undertake. A number of proposed methods promise improved efficiency of lattice calculations, and access to regions of parameter space that are currently computationally intractable, via multi-scale action-matching approaches that necessitate parametric regression of generated lattice datasets. The applicability of machine learning to this regression task is investigated, with deep neural networks found to provide an efficient solution even in cases where approaches such as principal component analysis fail. The high information content and complex symmetries inherent in lattice QCD datasets require custom neural network layers to be introduced and present opportunities for further development.

A probability index for surface zonda wind occurrence at Mendoza city through vertical sounding principal components analysis

NASA Astrophysics Data System (ADS)

Otero, Federico; Norte, Federico; Araneo, Diego

2018-01-01

The aim of this work is to obtain an index for predicting the probability of occurrence of zonda event at surface level from sounding data at Mendoza city, Argentine. To accomplish this goal, surface zonda wind events were previously found with an objective classification method (OCM) only considering the surface station values. Once obtained the dates and the onset time of each event, the prior closest sounding for each event was taken to realize a principal component analysis (PCA) that is used to identify the leading patterns of the vertical structure of the atmosphere previously to a zonda wind event. These components were used to construct the index model. For the PCA an entry matrix of temperature ( T) and dew point temperature (Td) anomalies for the standard levels between 850 and 300 hPa was build. The analysis yielded six significant components with a 94 % of the variance explained and the leading patterns of favorable weather conditions for the development of the phenomenon were obtained. A zonda/non-zonda indicator c can be estimated by a logistic multiple regressions depending on the PCA component loadings, determining a zonda probability index \\widehat{c} calculable from T and Td profiles and it depends on the climatological features of the region. The index showed 74.7 % efficiency. The same analysis was performed by adding surface values of T and Td from Mendoza Aero station increasing the index efficiency to 87.8 %. The results revealed four significantly correlated PCs with a major improvement in differentiating zonda cases and a reducing of the uncertainty interval.

Incorporating biological information in sparse principal component analysis with application to genomic data.

PubMed

Li, Ziyi; Safo, Sandra E; Long, Qi

2017-07-11

Sparse principal component analysis (PCA) is a popular tool for dimensionality reduction, pattern recognition, and visualization of high dimensional data. It has been recognized that complex biological mechanisms occur through concerted relationships of multiple genes working in networks that are often represented by graphs. Recent work has shown that incorporating such biological information improves feature selection and prediction performance in regression analysis, but there has been limited work on extending this approach to PCA. In this article, we propose two new sparse PCA methods called Fused and Grouped sparse PCA that enable incorporation of prior biological information in variable selection. Our simulation studies suggest that, compared to existing sparse PCA methods, the proposed methods achieve higher sensitivity and specificity when the graph structure is correctly specified, and are fairly robust to misspecified graph structures. Application to a glioblastoma gene expression dataset identified pathways that are suggested in the literature to be related with glioblastoma. The proposed sparse PCA methods Fused and Grouped sparse PCA can effectively incorporate prior biological information in variable selection, leading to improved feature selection and more interpretable principal component loadings and potentially providing insights on molecular underpinnings of complex diseases.

Rapid and simultaneous analysis of five alkaloids in four parts of Coptidis Rhizoma by near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Jintao, Xue; Yufei, Liu; Liming, Ye; Chunyan, Li; Quanwei, Yang; Weiying, Wang; Yun, Jing; Minxiang, Zhang; Peng, Li

2018-01-01

Near-Infrared Spectroscopy (NIRS) was first used to develop a method for rapid and simultaneous determination of 5 active alkaloids (berberine, coptisine, palmatine, epiberberine and jatrorrhizine) in 4 parts (rhizome, fibrous root, stem and leaf) of Coptidis Rhizoma. A total of 100 samples from 4 main places of origin were collected and studied. With HPLC analysis values as calibration reference, the quantitative analysis of 5 marker components was performed by two different modeling methods, partial least-squares (PLS) regression as linear regression and artificial neural networks (ANN) as non-linear regression. The results indicated that the 2 types of models established were robust, accurate and repeatable for five active alkaloids, and the ANN models was more suitable for the determination of berberine, coptisine and palmatine while the PLS model was more suitable for the analysis of epiberberine and jatrorrhizine. The performance of the optimal models was achieved as follows: the correlation coefficient (R) for berberine, coptisine, palmatine, epiberberine and jatrorrhizine was 0.9958, 0.9956, 0.9959, 0.9963 and 0.9923, respectively; the root mean square error of validation (RMSEP) was 0.5093, 0.0578, 0.0443, 0.0563 and 0.0090, respectively. Furthermore, for the comprehensive exploitation and utilization of plant resource of Coptidis Rhizoma, the established NIR models were used to analysis the content of 5 active alkaloids in 4 parts of Coptidis Rhizoma and 4 main origin of places. This work demonstrated that NIRS may be a promising method as routine screening for off-line fast analysis or on-line quality assessment of traditional Chinese medicine (TCM).

The potential of non-invasive pre- and post-mortem carcass measurements to predict the contribution of carcass components to slaughter yield of guinea pigs.

PubMed

Barba, Lida; Sánchez-Macías, Davinia; Barba, Iván; Rodríguez, Nibaldo

2018-06-01

Guinea pig meat consumption is increasing exponentially worldwide. The evaluation of the contribution of carcass components to carcass quality potentially can allow for the estimation of the value added to food animal origin and make research in guinea pigs more practicable. The aim of this study was to propose a methodology for modelling the contribution of different carcass components to the overall carcass quality of guinea pigs by using non-invasive pre- and post mortem carcass measurements. The selection of predictors was developed through correlation analysis and statistical significance; whereas the prediction models were based on Multiple Linear Regression. The prediction results showed higher accuracy in the prediction of carcass component contribution expressed in grams, compared to when expressed as a percentage of carcass quality components. The proposed prediction models can be useful for the guinea pig meat industry and research institutions by using non-invasive and time- and cost-efficient carcass component measuring techniques. Copyright © 2018 Elsevier Ltd. All rights reserved.

Characterizing Individual Differences in Functional Connectivity Using Dual-Regression and Seed-Based Approaches

PubMed Central

Smith, David V.; Utevsky, Amanda V.; Bland, Amy R.; Clement, Nathan; Clithero, John A.; Harsch, Anne E. W.; Carter, R. McKell; Huettel, Scott A.

2014-01-01

A central challenge for neuroscience lies in relating inter-individual variability to the functional properties of specific brain regions. Yet, considerable variability exists in the connectivity patterns between different brain areas, potentially producing reliable group differences. Using sex differences as a motivating example, we examined two separate resting-state datasets comprising a total of 188 human participants. Both datasets were decomposed into resting-state networks (RSNs) using a probabilistic spatial independent components analysis (ICA). We estimated voxelwise functional connectivity with these networks using a dual-regression analysis, which characterizes the participant-level spatiotemporal dynamics of each network while controlling for (via multiple regression) the influence of other networks and sources of variability. We found that males and females exhibit distinct patterns of connectivity with multiple RSNs, including both visual and auditory networks and the right frontal-parietal network. These results replicated across both datasets and were not explained by differences in head motion, data quality, brain volume, cortisol levels, or testosterone levels. Importantly, we also demonstrate that dual-regression functional connectivity is better at detecting inter-individual variability than traditional seed-based functional connectivity approaches. Our findings characterize robust—yet frequently ignored—neural differences between males and females, pointing to the necessity of controlling for sex in neuroscience studies of individual differences. Moreover, our results highlight the importance of employing network-based models to study variability in functional connectivity. PMID:24662574

Regression to fuzziness method for estimation of remaining useful life in power plant components

NASA Astrophysics Data System (ADS)

Alamaniotis, Miltiadis; Grelle, Austin; Tsoukalas, Lefteri H.

2014-10-01

Mitigation of severe accidents in power plants requires the reliable operation of all systems and the on-time replacement of mechanical components. Therefore, the continuous surveillance of power systems is a crucial concern for the overall safety, cost control, and on-time maintenance of a power plant. In this paper a methodology called regression to fuzziness is presented that estimates the remaining useful life (RUL) of power plant components. The RUL is defined as the difference between the time that a measurement was taken and the estimated failure time of that component. The methodology aims to compensate for a potential lack of historical data by modeling an expert's operational experience and expertise applied to the system. It initially identifies critical degradation parameters and their associated value range. Once completed, the operator's experience is modeled through fuzzy sets which span the entire parameter range. This model is then synergistically used with linear regression and a component's failure point to estimate the RUL. The proposed methodology is tested on estimating the RUL of a turbine (the basic electrical generating component of a power plant) in three different cases. Results demonstrate the benefits of the methodology for components for which operational data is not readily available and emphasize the significance of the selection of fuzzy sets and the effect of knowledge representation on the predicted output. To verify the effectiveness of the methodology, it was benchmarked against the data-based simple linear regression model used for predictions which was shown to perform equal or worse than the presented methodology. Furthermore, methodology comparison highlighted the improvement in estimation offered by the adoption of appropriate of fuzzy sets for parameter representation.

Factors Associated with Clinician Participation in TF-CBT Post-workshop Training Components.

PubMed

Pemberton, Joy R; Conners-Burrow, Nicola A; Sigel, Benjamin A; Sievers, Chad M; Stokes, Lauren D; Kramer, Teresa L

2017-07-01

For proficiency in an evidence-based treatment (EBT), mental health professionals (MHPs) need training activities extending beyond a one-time workshop. Using data from 178 MHPs participating in a statewide TF-CBT dissemination project, we used five variables assessed at the workshop, via multiple and logistic regression, to predict participation in three post-workshop training components. Perceived in-workshop learning and client-treatment mismatch were predictive of consultation call participation and case presentation respectively. Attitudes toward EBTs were predictive of trauma assessment utilization, although only with non-call participants removed from analysis. Productivity requirements and confidence in TF-CBT skills were not associated with participation in post-workshop activities.

Gas chromatography/mass spectrometry based component profiling and quality prediction for Japanese sake.

PubMed

Mimura, Natsuki; Isogai, Atsuko; Iwashita, Kazuhiro; Bamba, Takeshi; Fukusaki, Eiichiro

2014-10-01

Sake is a Japanese traditional alcoholic beverage, which is produced by simultaneous saccharification and alcohol fermentation of polished and steamed rice by Aspergillus oryzae and Saccharomyces cerevisiae. About 300 compounds have been identified in sake, and the contribution of individual components to the sake flavor has been examined at the same time. However, only a few compounds could explain the characteristics alone and most of the attributes still remain unclear. The purpose of this study was to examine the relationship between the component profile and the attributes of sake. Gas chromatography coupled with mass spectrometry (GC/MS)-based non-targeted analysis was employed to obtain the low molecular weight component profile of Japanese sake including both nonvolatile and volatile compounds. Sake attributes and overall quality were assessed by analytical descriptive sensory test and the prediction model of the sensory score from the component profile was constructed by means of orthogonal projections to latent structures (OPLS) regression analysis. Our results showed that 12 sake attributes [ginjo-ka (aroma of premium ginjo sake), grassy/aldehydic odor, sweet aroma/caramel/burnt odor, sulfury odor, sour taste, umami, bitter taste, body, amakara (dryness), aftertaste, pungent/smoothness and appearance] and overall quality were accurately explained by component profiles. In addition, we were able to select statistically significant components according to variable importance on projection (VIP). Our methodology clarified the correlation between sake attribute and 200 low molecular components and presented the importance of each component thus, providing new insights to the flavor study of sake. Copyright © 2014 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Effects of cumulative illness severity on hippocampal gray matter volume in major depression: a voxel-based morphometry study.

PubMed

Zaremba, Dario; Enneking, Verena; Meinert, Susanne; Förster, Katharina; Bürger, Christian; Dohm, Katharina; Grotegerd, Dominik; Redlich, Ronny; Dietsche, Bruno; Krug, Axel; Kircher, Tilo; Kugel, Harald; Heindel, Walter; Baune, Bernhard T; Arolt, Volker; Dannlowski, Udo

2018-02-08

Patients with major depression show reduced hippocampal volume compared to healthy controls. However, the contribution of patients' cumulative illness severity to hippocampal volume has rarely been investigated. It was the aim of our study to find a composite score of cumulative illness severity that is associated with hippocampal volume in depression. We estimated hippocampal gray matter volume using 3-tesla brain magnetic resonance imaging in 213 inpatients with acute major depression according to DSM-IV criteria (employing the SCID interview) and 213 healthy controls. Patients' cumulative illness severity was ascertained by six clinical variables via structured clinical interviews. A principal component analysis was conducted to identify components reflecting cumulative illness severity. Regression analyses and a voxel-based morphometry approach were used to investigate the influence of patients' individual component scores on hippocampal volume. Principal component analysis yielded two main components of cumulative illness severity: Hospitalization and Duration of Illness. While the component Hospitalization incorporated information from the intensity of inpatient treatment, the component Duration of Illness was based on the duration and frequency of illness episodes. We could demonstrate a significant inverse association of patients' Hospitalization component scores with bilateral hippocampal gray matter volume. This relationship was not found for Duration of Illness component scores. Variables associated with patients' history of psychiatric hospitalization seem to be accurate predictors of hippocampal volume in major depression and reliable estimators of patients' cumulative illness severity. Future studies should pay attention to these measures when investigating hippocampal volume changes in major depression.

Analysis of DGGE profiles to explore the relationship between prokaryotic community composition and biogeochemical processes in deep subseafloor sediments from the Peru Margin.

PubMed

Fry, John C; Webster, Gordon; Cragg, Barry A; Weightman, Andrew J; Parkes, R John

2006-10-01

The aim of this work was to relate depth profiles of prokaryotic community composition with geochemical processes in the deep subseafloor biosphere at two shallow-water sites on the Peru Margin in the Pacific Ocean (ODP Leg 201, sites 1228 and 1229). Principal component analysis of denaturing gradient gel electrophoresis banding patterns of deep-sediment Bacteria, Archaea, Euryarchaeota and the novel candidate division JS1, followed by multiple regression, showed strong relationships with prokaryotic activity and geochemistry (R(2)=55-100%). Further correlation analysis, at one site, between the principal components from the community composition profiles for Bacteria and 12 other variables quantitatively confirmed their relationship with activity and geochemistry, which had previously only been implied. Comparison with previously published cell counts enumerated by fluorescent in situ hybridization with rRNA-targeted probes confirmed that these denaturing gradient gel electrophoresis profiles described an active prokaryotic community.

Template based rotation: A method for functional connectivity analysis with a priori templates☆

PubMed Central

Schultz, Aaron P.; Chhatwal, Jasmeer P.; Huijbers, Willem; Hedden, Trey; van Dijk, Koene R.A.; McLaren, Donald G.; Ward, Andrew M.; Wigman, Sarah; Sperling, Reisa A.

2014-01-01

Functional connectivity magnetic resonance imaging (fcMRI) is a powerful tool for understanding the network level organization of the brain in research settings and is increasingly being used to study large-scale neuronal network degeneration in clinical trial settings. Presently, a variety of techniques, including seed-based correlation analysis and group independent components analysis (with either dual regression or back projection) are commonly employed to compute functional connectivity metrics. In the present report, we introduce template based rotation,1 a novel analytic approach optimized for use with a priori network parcellations, which may be particularly useful in clinical trial settings. Template based rotation was designed to leverage the stable spatial patterns of intrinsic connectivity derived from out-of-sample datasets by mapping data from novel sessions onto the previously defined a priori templates. We first demonstrate the feasibility of using previously defined a priori templates in connectivity analyses, and then compare the performance of template based rotation to seed based and dual regression methods by applying these analytic approaches to an fMRI dataset of normal young and elderly subjects. We observed that template based rotation and dual regression are approximately equivalent in detecting fcMRI differences between young and old subjects, demonstrating similar effect sizes for group differences and similar reliability metrics across 12 cortical networks. Both template based rotation and dual-regression demonstrated larger effect sizes and comparable reliabilities as compared to seed based correlation analysis, though all three methods yielded similar patterns of network differences. When performing inter-network and sub-network connectivity analyses, we observed that template based rotation offered greater flexibility, larger group differences, and more stable connectivity estimates as compared to dual regression and seed based analyses. This flexibility owes to the reduced spatial and temporal orthogonality constraints of template based rotation as compared to dual regression. These results suggest that template based rotation can provide a useful alternative to existing fcMRI analytic methods, particularly in clinical trial settings where predefined outcome measures and conserved network descriptions across groups are at a premium. PMID:25150630

HT-FRTC: a fast radiative transfer code using kernel regression

NASA Astrophysics Data System (ADS)

Thelen, Jean-Claude; Havemann, Stephan; Lewis, Warren

2016-09-01

The HT-FRTC is a principal component based fast radiative transfer code that can be used across the electromagnetic spectrum from the microwave through to the ultraviolet to calculate transmittance, radiance and flux spectra. The principal components cover the spectrum at a very high spectral resolution, which allows very fast line-by-line, hyperspectral and broadband simulations for satellite-based, airborne and ground-based sensors. The principal components are derived during a code training phase from line-by-line simulations for a diverse set of atmosphere and surface conditions. The derived principal components are sensor independent, i.e. no extra training is required to include additional sensors. During the training phase we also derive the predictors which are required by the fast radiative transfer code to determine the principal component scores from the monochromatic radiances (or fluxes, transmittances). These predictors are calculated for each training profile at a small number of frequencies, which are selected by a k-means cluster algorithm during the training phase. Until recently the predictors were calculated using a linear regression. However, during a recent rewrite of the code the linear regression was replaced by a Gaussian Process (GP) regression which resulted in a significant increase in accuracy when compared to the linear regression. The HT-FRTC has been trained with a large variety of gases, surface properties and scatterers. Rayleigh scattering as well as scattering by frozen/liquid clouds, hydrometeors and aerosols have all been included. The scattering phase function can be fully accounted for by an integrated line-by-line version of the Edwards-Slingo spherical harmonics radiation code or approximately by a modification to the extinction (Chou scaling).

Managing Complexity in Evidence Analysis: A Worked Example in Pediatric Weight Management.

PubMed

Parrott, James Scott; Henry, Beverly; Thompson, Kyle L; Ziegler, Jane; Handu, Deepa

2018-05-02

Nutrition interventions are often complex and multicomponent. Typical approaches to meta-analyses that focus on individual causal relationships to provide guideline recommendations are not sufficient to capture this complexity. The objective of this study is to describe the method of meta-analysis used for the Pediatric Weight Management (PWM) Guidelines update and provide a worked example that can be applied in other areas of dietetics practice. The effects of PWM interventions were examined for body mass index (BMI), body mass index z-score (BMIZ), and waist circumference at four different time periods. For intervention-level effects, intervention types were identified empirically using multiple correspondence analysis paired with cluster analysis. Pooled effects of identified types were examined using random effects meta-analysis models. Differences in effects among types were examined using meta-regression. Context-level effects are examined using qualitative comparative analysis. Three distinct types (or families) of PWM interventions were identified: medical nutrition, behavioral, and missing components. Medical nutrition and behavioral types showed statistically significant improvements in BMIZ across all time points. Results were less consistent for BMI and waist circumference, although four distinct patterns of weight status change were identified. These varied by intervention type as well as outcome measure. Meta-regression indicated statistically significant differences between the medical nutrition and behavioral types vs the missing component type for both BMIZ and BMI, although the pattern varied by time period and intervention type. Qualitative comparative analysis identified distinct configurations of context characteristics at each time point that were consistent with positive outcomes among the intervention types. Although analysis of individual causal relationships is invaluable, this approach is inadequate to capture the complexity of dietetics practice. An alternative approach that integrates intervention-level with context-level meta-analyses may provide deeper understanding in the development of practice guidelines. Copyright © 2018 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

Accounting for measurement error in log regression models with applications to accelerated testing.

PubMed

Richardson, Robert; Tolley, H Dennis; Evenson, William E; Lunt, Barry M

2018-01-01

In regression settings, parameter estimates will be biased when the explanatory variables are measured with error. This bias can significantly affect modeling goals. In particular, accelerated lifetime testing involves an extrapolation of the fitted model, and a small amount of bias in parameter estimates may result in a significant increase in the bias of the extrapolated predictions. Additionally, bias may arise when the stochastic component of a log regression model is assumed to be multiplicative when the actual underlying stochastic component is additive. To account for these possible sources of bias, a log regression model with measurement error and additive error is approximated by a weighted regression model which can be estimated using Iteratively Re-weighted Least Squares. Using the reduced Eyring equation in an accelerated testing setting, the model is compared to previously accepted approaches to modeling accelerated testing data with both simulations and real data.

Burnout, stress and satisfaction among Australian and New Zealand radiation oncology trainees.

PubMed

Leung, John; Rioseco, Pilar

2017-02-01

To evaluate the incidence of burnout among radiation oncology trainees in Australia and New Zealand and the stress and satisfaction factors related to burnout. A survey of trainees was conducted in mid-2015. There were 42 Likert scale questions on stress, 14 Likert scale questions on satisfaction and the Maslach Burnout Inventory-Human Services Survey assessed burnout. A principal component analysis identified specific stress and satisfaction areas. Categorical variables for the stress and satisfaction factors were computed. Associations between respondent's characteristics and stress and satisfaction subscales were examined by independent sample t-tests and analysis of variance. Effect sizes were calculated using Cohens's d when significant mean differences were observed. This was also done for respondent characteristics and the three burnout subscales. Multiple regression analyses were performed. The response rate was 81.5%. The principal component analysis for stress identified five areas: demands on time, professional development/training, delivery demands, interpersonal demands and administration/organizational issues. There were no significant differences by demographic group or area of interest after P-values were adjusted for the multiple tests conducted. The principal component analysis revealed two satisfaction areas: resources/professional activities and value/delivery of services. There were no significant differences by demographic characteristics or area of interest in the level of satisfaction after P-values were adjusted for the multiple tests conducted. The burnout results revealed 49.5% of respondents scored highly in emotional exhaustion and/or depersonalization and 13.1% had burnout in all three measures. Multiple regression analysis revealed the stress subscales 'demands on time' and 'interpersonal demands' were associated with emotional exhaustion. 'Interpersonal demands' was also associated with depersonalization and correlated negatively with personal accomplishment. The satisfaction of value/delivery of services subscale was associated with higher levels of personal accomplishment. There is a significant level of burnout among radiation oncology trainees in Australia and New Zealand. Further work addressing intervention would be appropriate to reduce levels of burnout. © 2016 The Authors. Journal of Medical Imaging and Radiation Oncology published by John Wiley & Sons Australia, Ltd on behalf of The Royal Australian and New Zealand College of Radiologists.

Hydrodynamic Stability of Multicomponent Droplet Gasification in Reduced Gravity

NASA Technical Reports Server (NTRS)

Aharon, I.; Shaw, B. D.

1995-01-01

This investigation addresses the problem of hydrodynamic stability of a two-component droplet undergoing spherically-symmetrical gasification. The droplet components are assumed to have characteristic liquid species diffusion times that are large relative to characteristic droplet surface regression times. The problem is formulated as a linear stability analysis, with a goal of predicting when spherically-symmetric droplet gasification can be expected to be hydrodynamically unstable from surface-tension gradients acting along the surface of a droplet which result from perturbations. It is found that for the conditions assumed in this paper (quasisteady gas phase, no initial droplet temperature gradients, diffusion-dominated gasification), surface tension gradients do not play a role in the stability characteristics. In addition, all perturbations are predicted to decay such that droplets were hydrodynamically stable. Conditions are identified, however, that deserve more analysis as they may lead to hydrodynamic instabilities driven by capillary effects.

Determination of butter adulteration with margarine using Raman spectroscopy.

PubMed

Uysal, Reyhan Selin; Boyaci, Ismail Hakki; Genis, Hüseyin Efe; Tamer, Ugur

2013-12-15

In this study, adulteration of butter with margarine was analysed using Raman spectroscopy combined with chemometric methods (principal component analysis (PCA), principal component regression (PCR), partial least squares (PLS)) and artificial neural networks (ANNs). Different butter and margarine samples were mixed at various concentrations ranging from 0% to 100% w/w. PCA analysis was applied for the classification of butters, margarines and mixtures. PCR, PLS and ANN were used for the detection of adulteration ratios of butter. Models were created using a calibration data set and developed models were evaluated using a validation data set. The coefficient of determination (R(2)) values between actual and predicted values obtained for PCR, PLS and ANN for the validation data set were 0.968, 0.987 and 0.978, respectively. In conclusion, a combination of Raman spectroscopy with chemometrics and ANN methods can be applied for testing butter adulteration. Copyright © 2013 Elsevier Ltd. All rights reserved.

Characterization of Musa sp. fruits and plantain banana ripening stages according to their physicochemical attributes.

PubMed

Valérie Passo Tsamo, Claudine; Andre, Christelle M; Ritter, Christian; Tomekpe, Kodjo; Ngoh Newilah, Gérard; Rogez, Hervé; Larondelle, Yvan

2014-08-27

This study aimed at understanding the contribution of the fruit physicochemical parameters to Musa sp. diversity and plantain ripening stages. A discriminant analysis was first performed on a collection of 35 Musa sp. cultivars, organized in six groups based on the consumption mode (dessert or cooking banana) and the genomic constitution. A principal component analysis reinforced by a logistic regression on plantain cultivars was proposed as an analytical approach to describe the plantain ripening stages. The results of the discriminant analysis showed that edible fraction, peel pH, pulp water content, and pulp total phenolics were among the most contributing attributes for the discrimination of the cultivar groups. With mean values ranging from 65.4 to 247.3 mg of gallic acid equivalents/100 g of fresh weight, the pulp total phenolics strongly differed between interspecific and monospecific cultivars within dessert and nonplantain cooking bananas. The results of the logistic regression revealed that the best models according to fitting parameters involved more than one physicochemical attribute. Interestingly, pulp and peel total phenolic contents contributed in the building up of these models.

Trace analysis of acids and bases by conductometric titration with multiparametric non-linear regression.

PubMed

Coelho, Lúcia H G; Gutz, Ivano G R

2006-03-15

A chemometric method for analysis of conductometric titration data was introduced to extend its applicability to lower concentrations and more complex acid-base systems. Auxiliary pH measurements were made during the titration to assist the calculation of the distribution of protonable species on base of known or guessed equilibrium constants. Conductivity values of each ionized or ionizable species possibly present in the sample were introduced in a general equation where the only unknown parameters were the total concentrations of (conjugated) bases and of strong electrolytes not involved in acid-base equilibria. All these concentrations were adjusted by a multiparametric nonlinear regression (NLR) method, based on the Levenberg-Marquardt algorithm. This first conductometric titration method with NLR analysis (CT-NLR) was successfully applied to simulated conductometric titration data and to synthetic samples with multiple components at concentrations as low as those found in rainwater (approximately 10 micromol L(-1)). It was possible to resolve and quantify mixtures containing a strong acid, formic acid, acetic acid, ammonium ion, bicarbonate and inert electrolyte with accuracy of 5% or better.

High-Dimensional Heteroscedastic Regression with an Application to eQTL Data Analysis

PubMed Central

Daye, Z. John; Chen, Jinbo; Li, Hongzhe

2011-01-01

Summary We consider the problem of high-dimensional regression under non-constant error variances. Despite being a common phenomenon in biological applications, heteroscedasticity has, so far, been largely ignored in high-dimensional analysis of genomic data sets. We propose a new methodology that allows non-constant error variances for high-dimensional estimation and model selection. Our method incorporates heteroscedasticity by simultaneously modeling both the mean and variance components via a novel doubly regularized approach. Extensive Monte Carlo simulations indicate that our proposed procedure can result in better estimation and variable selection than existing methods when heteroscedasticity arises from the presence of predictors explaining error variances and outliers. Further, we demonstrate the presence of heteroscedasticity in and apply our method to an expression quantitative trait loci (eQTLs) study of 112 yeast segregants. The new procedure can automatically account for heteroscedasticity in identifying the eQTLs that are associated with gene expression variations and lead to smaller prediction errors. These results demonstrate the importance of considering heteroscedasticity in eQTL data analysis. PMID:22547833

Health related quality of life among Iraqi immigrants settled in Malaysia.

PubMed

Daher, Aqil M; Ibrahim, Hisham S; Daher, Thaaer M; Anbori, Ali K

2011-05-30

Migrants everywhere face several demands for health and maintaining good health and quality of life could be challenging. Iraqis are the second largest refugee group that has sought refuge in the recent years, yet little is known about their health related quality of life (HRQOL). The study aims at assessing the HRQOL among Iraqis living in Malaysia. A self-administered Arabic version of Sf-36 questionnaire was distributed among 300 Iraqi migrants in Malaysia. The questionnaire taps eight concepts of physical and mental health to assess the HRQOL. Univariate analysis was performed for group analysis (t test, ANOVA) and Multiple Linear Regression was used to control for confounding effects. Two hundred and fifty three participants ranging in age from 18 to 67 years (Mean = 33.6) returned the completed questionnaire. The majority was males (60.1%) and more than half of the respondents (59.5%) were married. Less than half (45.4%) and about a quarter (25.9%) reported bachelor degree and secondary school education respectively and the remaining 28.7% had either a master or a PhD degree.Univariate analysis showed that the HRQOL scores among male immigrants were found to be higher than those of females in physical function (80.0 vs. 73.5), general health (72.5 vs. 60.7) and bodily pain (87.9 vs. 72.5) subscales. The youngest age group had significantly higher physical function (79.32) and lower mental health scores (57.62).The mean score of physical component summary was higher than the mental component summary mean score (70.22 vs. 63.34).Stepwise multiple linear regression, revealed that gender was significantly associated with physical component summary (β = - 6.06, p = 0.007) and marital status was associated with mental component summary (β = 7.08, p = 0.003). From the data it appears that Iraqi immigrants living in Malaysia have HRQOL scores that might be considered to indicate a relatively moderate HRQOL. The HRQOL is significantly affected by gender and marital status. Further studies are needed to explore determinants of HRQOL consequent to immigration. The findings could be worthy of further exploration.

Health related quality of life among Iraqi immigrants settled in Malaysia

PubMed Central

2011-01-01

Background Migrants everywhere face several demands for health and maintaining good health and quality of life could be challenging. Iraqis are the second largest refugee group that has sought refuge in the recent years, yet little is known about their health related quality of life (HRQOL). The study aims at assessing the HRQOL among Iraqis living in Malaysia. Methods A self-administered Arabic version of Sf-36 questionnaire was distributed among 300 Iraqi migrants in Malaysia. The questionnaire taps eight concepts of physical and mental health to assess the HRQOL. Univariate analysis was performed for group analysis (t test, ANOVA) and Multiple Linear Regression was used to control for confounding effects. Results Two hundred and fifty three participants ranging in age from 18 to 67 years (Mean = 33.6) returned the completed questionnaire. The majority was males (60.1%) and more than half of the respondents (59.5%) were married. Less than half (45.4%) and about a quarter (25.9%) reported bachelor degree and secondary school education respectively and the remaining 28.7% had either a master or a PhD degree. Univariate analysis showed that the HRQOL scores among male immigrants were found to be higher than those of females in physical function (80.0 vs. 73.5), general health (72.5 vs. 60.7) and bodily pain (87.9 vs. 72.5) subscales. The youngest age group had significantly higher physical function (79.32) and lower mental health scores (57.62). The mean score of physical component summary was higher than the mental component summary mean score (70.22 vs. 63.34). Stepwise multiple linear regression, revealed that gender was significantly associated with physical component summary (β = - 6.06, p = 0.007) and marital status was associated with mental component summary (β = 7.08, p = 0.003). Conclusions From the data it appears that Iraqi immigrants living in Malaysia have HRQOL scores that might be considered to indicate a relatively moderate HRQOL. The HRQOL is significantly affected by gender and marital status. Further studies are needed to explore determinants of HRQOL consequent to immigration. The findings could be worthy of further exploration. PMID:21624118

WALLY 1 ...A large, principal components regression program with varimax rotation of the factor weight matrix

Treesearch

James R. Wallis

1965-01-01

Written in Fortran IV and MAP, this computer program can handle up to 120 variables, and retain 40 principal components. It can perform simultaneous regression of up to 40 criterion variables upon the varimax rotated factor weight matrix. The columns and rows of all output matrices are labeled by six-character alphanumeric names. Data input can be from punch cards or...

Reference Models for Structural Technology Assessment and Weight Estimation

NASA Technical Reports Server (NTRS)

Cerro, Jeff; Martinovic, Zoran; Eldred, Lloyd

2005-01-01

Previously the Exploration Concepts Branch of NASA Langley Research Center has developed techniques for automating the preliminary design level of launch vehicle airframe structural analysis for purposes of enhancing historical regression based mass estimating relationships. This past work was useful and greatly reduced design time, however its application area was very narrow in terms of being able to handle a large variety in structural and vehicle general arrangement alternatives. Implementation of the analysis approach presented herein also incorporates some newly developed computer programs. Loft is a program developed to create analysis meshes and simultaneously define structural element design regions. A simple component defining ASCII file is read by Loft to begin the design process. HSLoad is a Visual Basic implementation of the HyperSizer Application Programming Interface, which automates the structural element design process. Details of these two programs and their use are explained in this paper. A feature which falls naturally out of the above analysis paradigm is the concept of "reference models". The flexibility of the FEA based JAVA processing procedures and associated process control classes coupled with the general utility of Loft and HSLoad make it possible to create generic program template files for analysis of components ranging from something as simple as a stiffened flat panel, to curved panels, fuselage and cryogenic tank components, flight control surfaces, wings, through full air and space vehicle general arrangements.

A model for national outcome audit in vascular surgery.

PubMed

Prytherch, D R; Ridler, B M; Beard, J D; Earnshaw, J J

2001-06-01

The aim was to model vascular surgical outcome in a national study using POSSUM scoring. One hundred and twenty-one British and Irish surgeons completed data questionnaires on patients undergoing arterial surgery under their care (mean 12 patients, range 1-49) in May/June 1998. A total of 1480 completed data records were available for logistic regression analysis using P-POSSUM methodology. Information collected included all POSSUM data items plus other factors thought to have a significant bearing on patient outcome: "extra items". The main outcome measures were death and major postoperative complications. The data were checked and inconsistent records were excluded. The remaining 1313 were divided into two sets for analysis. The first "training" set was used to obtain logistic regression models that were applied prospectively to the second "test" dataset. using POSSUM data items alone, it was possible to predict both mortality and morbidity after vascular reconstruction using P-POSSUM analysis. The addition of the "extra items" found significant in regression analysis did not significantly improve the accuracy of prediction. It was possible to predict both mortality and morbidity derived from the preoperative physiology components of the POSSUM data items alone. this study has shown that P-POSSUM methodology can be used to predict outcome after arterial surgery across a range of surgeons in different hospitals and could form the basis of a national outcome audit. It was also possible to obtain accurate models for both mortality and major morbidity from the POSSUM physiology scores alone. Copyright 2001 Harcourt Publishers Limited.

Advanced glycation end products and antioxidant status in type 2 diabetic patients with and without peripheral artery disease.

PubMed

Lapolla, Annunziata; Piarulli, Francesco; Sartore, Giovanni; Ceriello, Antonio; Ragazzi, Eugenio; Reitano, Rachele; Baccarin, Lorenzo; Laverda, Barbara; Fedele, Domenico

2007-03-01

Advanced glycation end products (AGEs), pentosidine and malondialdehyde (MDA), are elevated in type 2 diabetic subjects with coronary and carotid angiopathy. We investigated the relationship of AGEs, MDA, total reactive antioxidant potentials (TRAPs), and vitamin E in type 2 diabetic patients with and without peripheral artery disease (PAD). AGEs, pentosidine, MDA, TRAP, vitamin E, and ankle-brachial index (ABI) were measured in 99 consecutive type 2 diabetic subjects and 20 control subjects. AGEs, pentosidine, and MDA were higher and vitamin E and TRAP were lower in patients with PAD (ABI <0.9) than in patients without PAD (ABI >0.9) (P < 0.001). After multiple regression analysis, a correlation between AGEs and pentosidine, as independent variables, and ABI, as the dependent variable, was found in both patients with and without PAD (r = 0.9198, P < 0.001 and r = 0.5764, P < 0.001, respectively) but not in control subjects. When individual regression coefficients were evaluated, only that due to pentosidine was confirmed as significant. For patients with PAD, considering TRAP, vitamin E, and MDA as independent variables and ABI as the dependent variable produced an overall significant regression (r = 0.6913, P < 0.001). The regression coefficients for TRAP and vitamin E were not significant, indicating that the model is best explained by a single linear regression between MDA and ABI. These findings were also confirmed by principal component analysis. Results show that pentosidine and MDA are strongly associated with PAD in type 2 diabetic patients.

Separation mechanism of nortriptyline and amytriptyline in RPLC

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

Gritti, Fabrice; Guiochon, Georges A

2005-08-01

The single and the competitive equilibrium isotherms of nortriptyline and amytriptyline were acquired by frontal analysis (FA) on the C{sub 18}-bonded discovery column, using a 28/72 (v/v) mixture of acetonitrile and water buffered with phosphate (20 mM, pH 2.70). The adsorption energy distributions (AED) of each compound were calculated from the raw adsorption data. Both the fitting of the adsorption data using multi-linear regression analysis and the AEDs are consistent with a trimodal isotherm model. The single-component isotherm data fit well to the tri-Langmuir isotherm model. The extension to a competitive two-component tri-Langmuir isotherm model based on the best parametersmore » of the single-component isotherms does not account well for the breakthrough curves nor for the overloaded band profiles measured for mixtures of nortriptyline and amytriptyline. However, it was possible to derive adjusted parameters of a competitive tri-Langmuir model based on the fitting of the adsorption data obtained for these mixtures. A very good agreement was then found between the calculated and the experimental overloaded band profiles of all the mixtures injected.« less

Drivers of wetland conversion: a global meta-analysis.

PubMed

van Asselen, Sanneke; Verburg, Peter H; Vermaat, Jan E; Janse, Jan H

2013-01-01

Meta-analysis of case studies has become an important tool for synthesizing case study findings in land change. Meta-analyses of deforestation, urbanization, desertification and change in shifting cultivation systems have been published. This present study adds to this literature, with an analysis of the proximate causes and underlying forces of wetland conversion at a global scale using two complementary approaches of systematic review. Firstly, a meta-analysis of 105 case-study papers describing wetland conversion was performed, showing that different combinations of multiple-factor proximate causes, and underlying forces, drive wetland conversion. Agricultural development has been the main proximate cause of wetland conversion, and economic growth and population density are the most frequently identified underlying forces. Secondly, to add a more quantitative component to the study, a logistic meta-regression analysis was performed to estimate the likelihood of wetland conversion worldwide, using globally-consistent biophysical and socioeconomic location factor maps. Significant factors explaining wetland conversion, in order of importance, are market influence, total wetland area (lower conversion probability), mean annual temperature and cropland or built-up area. The regression analyses results support the outcomes of the meta-analysis of the processes of conversion mentioned in the individual case studies. In other meta-analyses of land change, similar factors (e.g., agricultural development, population growth, market/economic factors) are also identified as important causes of various types of land change (e.g., deforestation, desertification). Meta-analysis helps to identify commonalities across the various local case studies and identify which variables may lead to individual cases to behave differently. The meta-regression provides maps indicating the likelihood of wetland conversion worldwide based on the location factors that have determined historic conversions.

Drivers of Wetland Conversion: a Global Meta-Analysis

PubMed Central

van Asselen, Sanneke; Verburg, Peter H.; Vermaat, Jan E.; Janse, Jan H.

2013-01-01

Meta-analysis of case studies has become an important tool for synthesizing case study findings in land change. Meta-analyses of deforestation, urbanization, desertification and change in shifting cultivation systems have been published. This present study adds to this literature, with an analysis of the proximate causes and underlying forces of wetland conversion at a global scale using two complementary approaches of systematic review. Firstly, a meta-analysis of 105 case-study papers describing wetland conversion was performed, showing that different combinations of multiple-factor proximate causes, and underlying forces, drive wetland conversion. Agricultural development has been the main proximate cause of wetland conversion, and economic growth and population density are the most frequently identified underlying forces. Secondly, to add a more quantitative component to the study, a logistic meta-regression analysis was performed to estimate the likelihood of wetland conversion worldwide, using globally-consistent biophysical and socioeconomic location factor maps. Significant factors explaining wetland conversion, in order of importance, are market influence, total wetland area (lower conversion probability), mean annual temperature and cropland or built-up area. The regression analyses results support the outcomes of the meta-analysis of the processes of conversion mentioned in the individual case studies. In other meta-analyses of land change, similar factors (e.g., agricultural development, population growth, market/economic factors) are also identified as important causes of various types of land change (e.g., deforestation, desertification). Meta-analysis helps to identify commonalities across the various local case studies and identify which variables may lead to individual cases to behave differently. The meta-regression provides maps indicating the likelihood of wetland conversion worldwide based on the location factors that have determined historic conversions. PMID:24282580

Predictors of burnout among correctional mental health professionals.

PubMed

Gallavan, Deanna B; Newman, Jody L

2013-02-01

This study focused on the experience of burnout among a sample of correctional mental health professionals. We examined the relationship of a linear combination of optimism, work family conflict, and attitudes toward prisoners with two dimensions derived from the Maslach Burnout Inventory and the Professional Quality of Life Scale. Initially, three subscales from the Maslach Burnout Inventory and two subscales from the Professional Quality of Life Scale were subjected to principal components analysis with oblimin rotation in order to identify underlying dimensions among the subscales. This procedure resulted in two components accounting for approximately 75% of the variance (r = -.27). The first component was labeled Negative Experience of Work because it seemed to tap the experience of being emotionally spent, detached, and socially avoidant. The second component was labeled Positive Experience of Work and seemed to tap a sense of competence, success, and satisfaction in one's work. Two multiple regression analyses were subsequently conducted, in which Negative Experience of Work and Positive Experience of Work, respectively, were predicted from a linear combination of optimism, work family conflict, and attitudes toward prisoners. In the first analysis, 44% of the variance in Negative Experience of Work was accounted for, with work family conflict and optimism accounting for the most variance. In the second analysis, 24% of the variance in Positive Experience of Work was accounted for, with optimism and attitudes toward prisoners accounting for the most variance.

Self-other rating agreement and leader-member exchange (LMX): a quasi-replication.

PubMed

Barbuto, John E; Wilmot, Michael P; Singh, Matthew; Story, Joana S P

2012-04-01

Data from a sample of 83 elected community leaders and 391 direct-report staff (resulting in 333 useable leader-member dyads) were reanalyzed to test relations between self-other rating agreement of servant leadership and member-reported leader-member exchange (LMX). Polynomial regression analysis indicated that the self-other rating agreement model was not statistically significant. Instead, all of the variance in member-reported LMX was accounted for by the others' ratings component alone.

Fertilizer nitrogen, soil chemical properties, and their determinacy on rice yield: Evidence from 92 paddy fields of a large-scale farm in the Kanto Region of Japan

NASA Astrophysics Data System (ADS)

Li, D.; Nanseki, T.; Chomei, Y.; Yokota, S.

2017-07-01

Rice, a staple crop in Japan, is at risk of decreasing production and its yield highly depends on soil fertility. This study aimed to investigate determinants of rice yield, from the perspectives of fertilizer nitrogen and soil chemical properties. The data were sampled in 2014 and 2015 from 92 peat soil paddy fields on a large-scale farm located in the Kanto Region of Japan. The rice variety used was the most widely planted Koshihikari in Japan. Regression analysis indicated that fertilizer nitrogen significantly affected the yield, with a significant sustained effect to the subsequent year. Twelve soil chemical properties, including pH, cation exchange capacity, content of pyridine base elements, phosphoric acid, and silicic acid, were estimated. In addition to silicic acid, magnesia, in forms of its exchangeable content, saturation, and ratios to potassium and lime, positively affected the yield, while phosphoric acid negatively affected the yield. We assessed the soil chemical properties by soil quality index and principal component analysis. Positive effects were identified for both approaches, with the former performing better in explaining the rice yield. For soil quality index, the individual standardized soil properties and margins for improvement were indicated for each paddy field. Finally, multivariate regression on the principal components identified the most significant properties.

Safety climate and mindful safety practices in the oil and gas industry.

PubMed

Dahl, Øyvind; Kongsvik, Trond

2018-02-01

The existence of a positive association between safety climate and the safety behavior of sharp-end workers in high-risk organizations is supported by a considerable body of research. Previous research has primarily analyzed two components of safety behavior, namely safety compliance and safety participation. The present study extends previous research by looking into the relationship between safety climate and another component of safety behavior, namely mindful safety practices. Mindful safety practices are defined as the ability to be aware of critical factors in the environment and to act appropriately when dangers arise. Regression analysis was used to examine whether mindful safety practices are, like compliance and participation, promoted by a positive safety climate, in a questionnaire-based study of 5712 sharp-end workers in the oil and gas industry. The analysis revealed that a positive safety climate promotes mindful safety practices. The regression model accounted for roughly 31% of the variance in mindful safety practices. The most important safety climate factor was safety leadership. The findings clearly demonstrate that mindful safety practices are highly context-dependent, hence, manageable and susceptible to change. In order to improve safety climate in a direction which is favorable for mindful safety practices, the results demonstrate that it is important to give the fundamental features of safety climate high priority and in particular that of safety leadership. Copyright © 2017 National Safety Council and Elsevier Ltd. All rights reserved.

Digital histologic analysis reveals morphometric patterns of age-related involution in breast epithelium and stroma.

PubMed

Sandhu, Rupninder; Chollet-Hinton, Lynn; Kirk, Erin L; Midkiff, Bentley; Troester, Melissa A

2016-02-01

Complete age-related regression of mammary epithelium, often termed postmenopausal involution, is associated with decreased breast cancer risk. However, most studies have qualitatively assessed involution. We quantitatively analyzed epithelium, stroma, and adipose tissue from histologically normal breast tissue of 454 patients in the Normal Breast Study. High-resolution digital images of normal breast hematoxylin and eosin-stained slides were partitioned into epithelium, adipose tissue, and nonfatty stroma. Percentage area and nuclei per unit area (nuclear density) were calculated for each component. Quantitative data were evaluated in association with age using linear regression and cubic spline models. Stromal area decreased (P = 0.0002), and adipose tissue area increased (P < 0.0001), with an approximate 0.7% change in area for each component, until age 55 years when these area measures reached a steady state. Although epithelial area did not show linear changes with age, epithelial nuclear density decreased linearly beginning in the third decade of life. No significant age-related trends were observed for stromal or adipose nuclear density. Digital image analysis offers a high-throughput method for quantitatively measuring tissue morphometry and for objectively assessing age-related changes in adipose tissue, stroma, and epithelium. Epithelial nuclear density is a quantitative measure of age-related breast involution that begins to decline in the early premenopausal period. Copyright © 2015 Elsevier Inc. All rights reserved.

Multivariate methods for indoor PM10 and PM2.5 modelling in naturally ventilated schools buildings

NASA Astrophysics Data System (ADS)

Elbayoumi, Maher; Ramli, Nor Azam; Md Yusof, Noor Faizah Fitri; Yahaya, Ahmad Shukri Bin; Al Madhoun, Wesam; Ul-Saufie, Ahmed Zia

2014-09-01

In this study the concentrations of PM10, PM2.5, CO and CO2 concentrations and meteorological variables (wind speed, air temperature, and relative humidity) were employed to predict the annual and seasonal indoor concentration of PM10 and PM2.5 using multivariate statistical methods. The data have been collected in twelve naturally ventilated schools in Gaza Strip (Palestine) from October 2011 to May 2012 (academic year). The bivariate correlation analysis showed that the indoor PM10 and PM2.5 were highly positive correlated with outdoor concentration of PM10 and PM2.5. Further, Multiple linear regression (MLR) was used for modelling and R2 values for indoor PM10 were determined as 0.62 and 0.84 for PM10 and PM2.5 respectively. The Performance indicators of MLR models indicated that the prediction for PM10 and PM2.5 annual models were better than seasonal models. In order to reduce the number of input variables, principal component analysis (PCA) and principal component regression (PCR) were applied by using annual data. The predicted R2 were 0.40 and 0.73 for PM10 and PM2.5, respectively. PM10 models (MLR and PCR) show the tendency to underestimate indoor PM10 concentrations as it does not take into account the occupant's activities which highly affect the indoor concentrations during the class hours.

The effects of a household conditional cash transfer programme on coverage and quality of antenatal care: a secondary analysis of Indonesia's pilot programme.

PubMed

Triyana, Margaret; Shankar, Anuraj H

2017-10-22

To analyse the effectiveness of a household conditional cash transfer programme (CCT) on antenatal care (ANC) coverage reported by women and ANC quality reported by midwives. The CCT was piloted as a cluster randomised control trial in 2007. Intent-to-treat parameters were estimated using linear regression and logistic regression. Secondary analysis of the longitudinal CCT impact evaluation survey, conducted in 2007 and 2009. This included 6869 pregnancies and 1407 midwives in 180 control subdistricts and 180 treated subdistricts in Indonesia. ANC component coverage index, a composite measure of each ANC service component as self-reported by women, and ANC provider quality index, a composite measure of ANC service provided as self-reported by midwives. Each index was created by principal component analysis (PCA). Specific ANC component items were also assessed. The CCT was associated with improved ANC component coverage index by 0.07 SD (95% CI 0.002 to 0.141). Women were more likely to receive the following assessments: weight (OR 1.56 (95% CI 1.25 to 1.95)), height (OR 1.41 (95% CI 1.247 to 1.947)), blood pressure (OR 1.36 (95% CI 1.045 to 1.761)), fundal height measurements (OR 1.65 (95% CI 1.372 to 1.992)), fetal heart beat monitoring (OR 1.29 (95% CI 1.006 to 1.653)), external pelvic examination (OR 1.28 (95% CI 1.086 to 1.505)), iron-folic acid pills (OR 1.42 (95% CI 1.081 to 1.859)) and information on pregnancy complications (OR 2.09 (95% CI 1.724 to 2.551)). On the supply side, the CCT had no significant effect on the ANC provider quality index based on reports from midwives. The CCT programme improved ANC coverage for women, but midwives did not improve ANC quality. The results suggest that enhanced ANC utilisation may not be sufficient to improve health outcomes, and steps to improve ANC quality are essential for programme impact. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Sequential Processing and the Matching-Stimulus Interval Effect in ERP Components: An Exploration of the Mechanism Using Multiple Regression

PubMed Central

Steiner, Genevieve Z.; Barry, Robert J.; Gonsalvez, Craig J.

2016-01-01

In oddball tasks, increasing the time between stimuli within a particular condition (target-to-target interval, TTI; nontarget-to-nontarget interval, NNI) systematically enhances N1, P2, and P300 event-related potential (ERP) component amplitudes. This study examined the mechanism underpinning these effects in ERP components recorded from 28 adults who completed a conventional three-tone oddball task. Bivariate correlations, partial correlations and multiple regression explored component changes due to preceding ERP component amplitudes and intervals found within the stimulus series, rather than constraining the task with experimentally constructed intervals, which has been adequately explored in prior studies. Multiple regression showed that for targets, N1 and TTI predicted N2, TTI predicted P3a and P3b, and Processing Negativity (PN), P3b, and TTI predicted reaction time. For rare nontargets, P1 predicted N1, NNI predicted N2, and N1 predicted Slow Wave (SW). Findings show that the mechanism is operating on separate stages of stimulus-processing, suggestive of either increased activation within a number of stimulus-specific pathways, or very long component generator recovery cycles. These results demonstrate the extent to which matching-stimulus intervals influence ERP component amplitudes and behavior in a three-tone oddball task, and should be taken into account when designing similar studies. PMID:27445774

Sequential Processing and the Matching-Stimulus Interval Effect in ERP Components: An Exploration of the Mechanism Using Multiple Regression.

PubMed

Steiner, Genevieve Z; Barry, Robert J; Gonsalvez, Craig J

2016-01-01

In oddball tasks, increasing the time between stimuli within a particular condition (target-to-target interval, TTI; nontarget-to-nontarget interval, NNI) systematically enhances N1, P2, and P300 event-related potential (ERP) component amplitudes. This study examined the mechanism underpinning these effects in ERP components recorded from 28 adults who completed a conventional three-tone oddball task. Bivariate correlations, partial correlations and multiple regression explored component changes due to preceding ERP component amplitudes and intervals found within the stimulus series, rather than constraining the task with experimentally constructed intervals, which has been adequately explored in prior studies. Multiple regression showed that for targets, N1 and TTI predicted N2, TTI predicted P3a and P3b, and Processing Negativity (PN), P3b, and TTI predicted reaction time. For rare nontargets, P1 predicted N1, NNI predicted N2, and N1 predicted Slow Wave (SW). Findings show that the mechanism is operating on separate stages of stimulus-processing, suggestive of either increased activation within a number of stimulus-specific pathways, or very long component generator recovery cycles. These results demonstrate the extent to which matching-stimulus intervals influence ERP component amplitudes and behavior in a three-tone oddball task, and should be taken into account when designing similar studies.

77 FR 3121 - Program Integrity: Gainful Employment-Debt Measures; Correction

Federal Register 2010, 2011, 2012, 2013, 2014

2012-01-23

...On June 13, 2011, the Secretary of Education (Secretary) published a notice of final regulations in the Federal Register for Program Integrity: Gainful Employment--Debt Measures (Gainful Employment--Debt Measures) (76 FR 34386). In the preamble of the final regulations, we used the wrong data to calculate the percent of total variance in institutions' repayment rates that may be explained by race/ethnicity. Our intent was to use the data that included all minority students per institution. However, we mistakenly used the data for a subset of minority students per institution. We have now recalculated the total variance using the data that includes all minority students. Through this document, we correct, in the preamble of the Gainful Employment--Debt Measures final regulations, the errors resulting from this misapplication. We do not change the regression analysis model itself; we are using the same model with the appropriate data. Through this notice we also correct, in the preamble of the Gainful Employment--Debt Measures final regulations, our description of one component of the regression analysis. The preamble referred to use of an institutional variable measuring acceptance rates. This description was incorrect; in fact we used an institutional variable measuring retention rates. Correcting this language does not change the regression analysis model itself or the variance explained by the model. The text of the final regulations remains unchanged.

Using Spatial Multiple Regression to Identify Intrinsic Connectivity Networks Involved in Working Memory Performance

PubMed Central

Gordon, Evan M.; Stollstorff, Melanie; Vaidya, Chandan J.

2012-01-01

Many researchers have noted that the functional architecture of the human brain is relatively invariant during task performance and the resting state. Indeed, intrinsic connectivity networks (ICNs) revealed by resting-state functional connectivity analyses are spatially similar to regions activated during cognitive tasks. This suggests that patterns of task-related activation in individual subjects may result from the engagement of one or more of these ICNs; however, this has not been tested. We used a novel analysis, spatial multiple regression, to test whether the patterns of activation during an N-back working memory task could be well described by a linear combination of ICNs delineated using Independent Components Analysis at rest. We found that across subjects, the cingulo-opercular Set Maintenance ICN, as well as right and left Frontoparietal Control ICNs, were reliably activated during working memory, while Default Mode and Visual ICNs were reliably deactivated. Further, involvement of Set Maintenance, Frontoparietal Control, and Dorsal Attention ICNs was sensitive to varying working memory load. Finally, the degree of left Frontoparietal Control network activation predicted response speed, while activation in both left Frontoparietal Control and Dorsal Attention networks predicted task accuracy. These results suggest that a close relationship between resting-state networks and task-evoked activation is functionally relevant for behavior, and that spatial multiple regression analysis is a suitable method for revealing that relationship. PMID:21761505

Loneliness Literacy Scale: Development and Evaluation of an Early Indicator for Loneliness Prevention.

PubMed

Honigh-de Vlaming, Rianne; Haveman-Nies, Annemien; Bos-Oude Groeniger, Inge; Hooft van Huysduynen, Eveline J C; de Groot, Lisette C P G M; Van't Veer, Pieter

2014-01-01

To develop and evaluate the Loneliness Literacy Scale for the assessment of short-term outcomes of a loneliness prevention programme among Dutch elderly persons. Scale development was based on evidence from literature and experiences from local stakeholders and representatives of the target group. The scale was pre-tested among 303 elderly persons aged 65 years and over. Principal component analysis and internal consistency analysis were used to affirm the scale structure, reduce the number of items and assess the reliability of the constructs. Linear regression analysis was conducted to evaluate the association between the literacy constructs and loneliness. The four constructs "motivation", "self-efficacy", "perceived social support" and "subjective norm" derived from principal component analysis captured 56 % of the original variance. Cronbach's coefficient α was above 0.7 for each construct. The constructs "self-efficacy" and "perceived social support" were positively and "subjective norm" was negatively associated with loneliness. To our knowledge this is the first study developing a short-term indicator for loneliness prevention. The indicator contributes to the need of evaluating public health interventions more close to the intervention activities.

Nonlinear multivariate and time series analysis by neural network methods

NASA Astrophysics Data System (ADS)

Hsieh, William W.

2004-03-01

Methods in multivariate statistical analysis are essential for working with large amounts of geophysical data, data from observational arrays, from satellites, or from numerical model output. In classical multivariate statistical analysis, there is a hierarchy of methods, starting with linear regression at the base, followed by principal component analysis (PCA) and finally canonical correlation analysis (CCA). A multivariate time series method, the singular spectrum analysis (SSA), has been a fruitful extension of the PCA technique. The common drawback of these classical methods is that only linear structures can be correctly extracted from the data. Since the late 1980s, neural network methods have become popular for performing nonlinear regression and classification. More recently, neural network methods have been extended to perform nonlinear PCA (NLPCA), nonlinear CCA (NLCCA), and nonlinear SSA (NLSSA). This paper presents a unified view of the NLPCA, NLCCA, and NLSSA techniques and their applications to various data sets of the atmosphere and the ocean (especially for the El Niño-Southern Oscillation and the stratospheric quasi-biennial oscillation). These data sets reveal that the linear methods are often too simplistic to describe real-world systems, with a tendency to scatter a single oscillatory phenomenon into numerous unphysical modes or higher harmonics, which can be largely alleviated in the new nonlinear paradigm.

Selective Weighted Least Squares Method for Fourier Transform Infrared Quantitative Analysis.

PubMed

Wang, Xin; Li, Yan; Wei, Haoyun; Chen, Xia

2017-06-01

Classical least squares (CLS) regression is a popular multivariate statistical method used frequently for quantitative analysis using Fourier transform infrared (FT-IR) spectrometry. Classical least squares provides the best unbiased estimator for uncorrelated residual errors with zero mean and equal variance. However, the noise in FT-IR spectra, which accounts for a large portion of the residual errors, is heteroscedastic. Thus, if this noise with zero mean dominates in the residual errors, the weighted least squares (WLS) regression method described in this paper is a better estimator than CLS. However, if bias errors, such as the residual baseline error, are significant, WLS may perform worse than CLS. In this paper, we compare the effect of noise and bias error in using CLS and WLS in quantitative analysis. Results indicated that for wavenumbers with low absorbance, the bias error significantly affected the error, such that the performance of CLS is better than that of WLS. However, for wavenumbers with high absorbance, the noise significantly affected the error, and WLS proves to be better than CLS. Thus, we propose a selective weighted least squares (SWLS) regression that processes data with different wavenumbers using either CLS or WLS based on a selection criterion, i.e., lower or higher than an absorbance threshold. The effects of various factors on the optimal threshold value (OTV) for SWLS have been studied through numerical simulations. These studies reported that: (1) the concentration and the analyte type had minimal effect on OTV; and (2) the major factor that influences OTV is the ratio between the bias error and the standard deviation of the noise. The last part of this paper is dedicated to quantitative analysis of methane gas spectra, and methane/toluene mixtures gas spectra as measured using FT-IR spectrometry and CLS, WLS, and SWLS. The standard error of prediction (SEP), bias of prediction (bias), and the residual sum of squares of the errors (RSS) from the three quantitative analyses were compared. In methane gas analysis, SWLS yielded the lowest SEP and RSS among the three methods. In methane/toluene mixture gas analysis, a modification of the SWLS has been presented to tackle the bias error from other components. The SWLS without modification presents the lowest SEP in all cases but not bias and RSS. The modification of SWLS reduced the bias, which showed a lower RSS than CLS, especially for small components.

Global and system-specific resting-state fMRI fluctuations are uncorrelated: principal component analysis reveals anti-correlated networks.

PubMed

Carbonell, Felix; Bellec, Pierre; Shmuel, Amir

2011-01-01

The influence of the global average signal (GAS) on functional-magnetic resonance imaging (fMRI)-based resting-state functional connectivity is a matter of ongoing debate. The global average fluctuations increase the correlation between functional systems beyond the correlation that reflects their specific functional connectivity. Hence, removal of the GAS is a common practice for facilitating the observation of network-specific functional connectivity. This strategy relies on the implicit assumption of a linear-additive model according to which global fluctuations, irrespective of their origin, and network-specific fluctuations are super-positioned. However, removal of the GAS introduces spurious negative correlations between functional systems, bringing into question the validity of previous findings of negative correlations between fluctuations in the default-mode and the task-positive networks. Here we present an alternative method for estimating global fluctuations, immune to the complications associated with the GAS. Principal components analysis was applied to resting-state fMRI time-series. A global-signal effect estimator was defined as the principal component (PC) that correlated best with the GAS. The mean correlation coefficient between our proposed PC-based global effect estimator and the GAS was 0.97±0.05, demonstrating that our estimator successfully approximated the GAS. In 66 out of 68 runs, the PC that showed the highest correlation with the GAS was the first PC. Since PCs are orthogonal, our method provides an estimator of the global fluctuations, which is uncorrelated to the remaining, network-specific fluctuations. Moreover, unlike the regression of the GAS, the regression of the PC-based global effect estimator does not introduce spurious anti-correlations beyond the decrease in seed-based correlation values allowed by the assumed additive model. After regressing this PC-based estimator out of the original time-series, we observed robust anti-correlations between resting-state fluctuations in the default-mode and the task-positive networks. We conclude that resting-state global fluctuations and network-specific fluctuations are uncorrelated, supporting a Resting-State Linear-Additive Model. In addition, we conclude that the network-specific resting-state fluctuations of the default-mode and task-positive networks show artifact-free anti-correlations.

Quality of life in multiple sclerosis (MS) and role of fatigue, depression, anxiety, and stress: A bicenter study from north of Iran

PubMed Central

Salehpoor, Ghasem; Rezaei, Sajjad; Hosseininezhad, Mozaffar

2014-01-01

Background: Although studies have demonstrated significant negative relationships between quality of life (QOL), fatigue, and the most common psychological symptoms (depression, anxiety, stress), the main ambiguity of previous studies on QOL is in the relative importance of these predictors. Also, there is lack of adequate knowledge about the actual contribution of each of them in the prediction of QOL dimensions. Thus, the main objective of this study is to assess the role of fatigue, depression, anxiety, and stress in relation to QOL of multiple sclerosis (MS) patients. Materials and Methods: One hundred and sixty-two MS patients completed the questionnaire on demographic variables, and then they were evaluated by the Persian versions of Short-Form Health Survey Questionnaire (SF-36), Fatigue Survey Scale (FSS), and Depression, Anxiety, Stress Scale-21 (DASS-21). Data were analyzed by Pearson correlation coefficient and hierarchical regression. Results: Correlation analysis showed a significant relationship between QOL elements in SF-36 (physical component summary and mental component summary) and depression, fatigue, stress, and anxiety (P < 0.01). Hierarchical regression analysis indicated that among the predictor variables in the final step, fatigue, depression, and anxiety were identified as the physical component summary predictor variables. Anxiety was found to be the most powerful predictor variable amongst all (β = −0.46, P < 0.001). Furthermore, results have shown depression as the only significant mental component summary predictor variable (β = −0.39, P < 0.001). Conclusions: This study has highlighted the role of anxiety, fatigue, and depression in physical dimensions and the role of depression in psychological dimensions of the lives of MS patients. In addition, the findings of this study indirectly suggest that psychological interventions for reducing fatigue, depression, and anxiety can lead to improved QOL of MS patients. PMID:25558256

The relationship between health-related fitness and quality of life in postmenopausal women from Southern Taiwan

PubMed Central

Hsu, Wei-Hsiu; Chen, Chi-lung; Kuo, Liang Tseng; Fan, Chun-Hao; Lee, Mel S; Hsu, Robert Wen-Wei

2014-01-01

Background Health-related fitness has been reported to be associated with improved quality of life (QoL) in the elderly. Health-related fitness is comprised of several dimensions that could be enhanced by specific training regimens. It has remained unclear how various dimensions of health-related fitness interact with QoL in postmenopausal women. Objective The purpose of the current study was to investigate the relationship between the dimensions of health-related fitness and QoL in elderly women. Methods A cohort of 408 postmenopausal women in a rural area of Taiwan was prospectively collected. Dimensions of health-related fitness, consisting of muscular strength, balance, cardiorespiratory endurance, flexibility, muscle endurance, and agility, were assessed. QoL was determined using the Short Form Health Survey (SF-36). Differences between age groups (stratified by decades) were calculated using a one-way analysis of variance (ANOVA) and multiple comparisons using a Scheffé test. A Spearman’s correlation analysis was performed to examine differences between QoL and each dimension of fitness. Multiple linear regression with forced-entry procedure was performed to evaluate the effects of health-related fitness. A P-value of <0.05 was considered statistically significant. Results Age-related decreases in health-related fitness were shown for sit-ups, back strength, grip strength, side steps, trunk extension, and agility (P<0.05). An age-related decrease in QoL, specifically in physical functioning, role limitation due to physical problems, and physical component score, was also demonstrated (P<0.05). Multiple linear regression analyses demonstrated that back strength significantly contributed to the physical component of QoL (adjusted beta of 0.268 [P<0.05]). Conclusion Back strength was positively correlated with the physical component of QoL among the examined dimensions of health-related fitness. Health-related fitness, as well as the physical component of QoL, declined with increasing age. PMID:25258526

Dirichlet Component Regression and its Applications to Psychiatric Data.

PubMed

Gueorguieva, Ralitza; Rosenheck, Robert; Zelterman, Daniel

2008-08-15

We describe a Dirichlet multivariable regression method useful for modeling data representing components as a percentage of a total. This model is motivated by the unmet need in psychiatry and other areas to simultaneously assess the effects of covariates on the relative contributions of different components of a measure. The model is illustrated using the Positive and Negative Syndrome Scale (PANSS) for assessment of schizophrenia symptoms which, like many other metrics in psychiatry, is composed of a sum of scores on several components, each in turn, made up of sums of evaluations on several questions. We simultaneously examine the effects of baseline socio-demographic and co-morbid correlates on all of the components of the total PANSS score of patients from a schizophrenia clinical trial and identify variables associated with increasing or decreasing relative contributions of each component. Several definitions of residuals are provided. Diagnostics include measures of overdispersion, Cook's distance, and a local jackknife influence metric.

Real-Time Analysis of Isoprene in Breath by Using Ultraviolet-Absorption Spectroscopy with a Hollow Optical Fiber Gas Cell

PubMed Central

Iwata, Takuro; Katagiri, Takashi; Matsuura, Yuji

2016-01-01

A breath analysis system based on ultraviolet-absorption spectroscopy was developed by using a hollow optical fiber as a gas cell for real-time monitoring of isoprene in breath. The hollow optical fiber functions as an ultra-small-volume gas cell with a long path. The measurement sensitivity of the system was evaluated by using nitric-oxide gas as a gas sample. The evaluation result showed that the developed system, using a laser-driven, high-intensity light source and a 3-m-long, aluminum-coated hollow optical fiber, could successfully measure nitric-oxide gas with a 50 ppb concentration. An absorption spectrum of a breath sample in the wavelength region of around 200–300 nm was measured, and the measured spectrum revealed the main absorbing components in breath as water vapor, isoprene, and ozone converted from oxygen by radiation of ultraviolet light. The concentration of isoprene in breath was estimated by multiple linear regression. The regression analysis results showed that the proposed analysis system enables real-time monitoring of isoprene during the exhaling of breath. Accordingly, it is suitable for measuring the circadian variation of isoprene. PMID:27929387

Real-Time Analysis of Isoprene in Breath by Using Ultraviolet-Absorption Spectroscopy with a Hollow Optical Fiber Gas Cell.

PubMed

Iwata, Takuro; Katagiri, Takashi; Matsuura, Yuji

2016-12-05

A breath analysis system based on ultraviolet-absorption spectroscopy was developed by using a hollow optical fiber as a gas cell for real-time monitoring of isoprene in breath. The hollow optical fiber functions as an ultra-small-volume gas cell with a long path. The measurement sensitivity of the system was evaluated by using nitric-oxide gas as a gas sample. The evaluation result showed that the developed system, using a laser-driven, high-intensity light source and a 3-m-long, aluminum-coated hollow optical fiber, could successfully measure nitric-oxide gas with a 50 ppb concentration. An absorption spectrum of a breath sample in the wavelength region of around 200-300 nm was measured, and the measured spectrum revealed the main absorbing components in breath as water vapor, isoprene, and ozone converted from oxygen by radiation of ultraviolet light. The concentration of isoprene in breath was estimated by multiple linear regression. The regression analysis results showed that the proposed analysis system enables real-time monitoring of isoprene during the exhaling of breath. Accordingly, it is suitable for measuring the circadian variation of isoprene.

Do MCAT scores predict USMLE scores? An analysis on 5 years of medical student data.

PubMed

Gauer, Jacqueline L; Wolff, Josephine M; Jackson, J Brooks

2016-01-01

The purpose of this study was to determine the associations and predictive values of Medical College Admission Test (MCAT) component and composite scores prior to 2015 with U.S. Medical Licensure Exam (USMLE) Step 1 and Step 2 Clinical Knowledge (CK) scores, with a focus on whether students scoring low on the MCAT were particularly likely to continue to score low on the USMLE exams. Multiple linear regression, correlation, and chi-square analyses were performed to determine the relationship between MCAT component and composite scores and USMLE Step 1 and Step 2 CK scores from five graduating classes (2011-2015) at the University of Minnesota Medical School ( N =1,065). The multiple linear regression analyses were both significant ( p <0.001). The three MCAT component scores together explained 17.7% of the variance in Step 1 scores ( p< 0.001) and 12.0% of the variance in Step 2 CK scores ( p <0.001). In the chi-square analyses, significant, albeit weak associations were observed between almost all MCAT component scores and USMLE scores (Cramer's V ranged from 0.05 to 0.24). Each of the MCAT component scores was significantly associated with USMLE Step 1 and Step 2 CK scores, although the effect size was small. Being in the top or bottom scoring range of the MCAT exam was predictive of being in the top or bottom scoring range of the USMLE exams, although the strengths of the associations were weak to moderate. These results indicate that MCAT scores are predictive of student performance on the USMLE exams, but, given the small effect sizes, should be considered as part of the holistic view of the student.

Do MCAT scores predict USMLE scores? An analysis on 5 years of medical student data

PubMed Central

Gauer, Jacqueline L.; Wolff, Josephine M.; Jackson, J. Brooks

2016-01-01

Introduction The purpose of this study was to determine the associations and predictive values of Medical College Admission Test (MCAT) component and composite scores prior to 2015 with U.S. Medical Licensure Exam (USMLE) Step 1 and Step 2 Clinical Knowledge (CK) scores, with a focus on whether students scoring low on the MCAT were particularly likely to continue to score low on the USMLE exams. Method Multiple linear regression, correlation, and chi-square analyses were performed to determine the relationship between MCAT component and composite scores and USMLE Step 1 and Step 2 CK scores from five graduating classes (2011–2015) at the University of Minnesota Medical School (N=1,065). Results The multiple linear regression analyses were both significant (p<0.001). The three MCAT component scores together explained 17.7% of the variance in Step 1 scores (p<0.001) and 12.0% of the variance in Step 2 CK scores (p<0.001). In the chi-square analyses, significant, albeit weak associations were observed between almost all MCAT component scores and USMLE scores (Cramer's V ranged from 0.05 to 0.24). Discussion Each of the MCAT component scores was significantly associated with USMLE Step 1 and Step 2 CK scores, although the effect size was small. Being in the top or bottom scoring range of the MCAT exam was predictive of being in the top or bottom scoring range of the USMLE exams, although the strengths of the associations were weak to moderate. These results indicate that MCAT scores are predictive of student performance on the USMLE exams, but, given the small effect sizes, should be considered as part of the holistic view of the student. PMID:27702431

Real Time Monitoring of Dissolved Organic Carbon Concentration and Disinfection By-Product Formation Potential in a Surface Water Treatment Plant with Simulaneous UV-VIS Absorbance and Fluorescence Excitation-Emission Mapping

NASA Astrophysics Data System (ADS)

Gilmore, A. M.

2015-12-01

This study describes a method based on simultaneous absorbance and fluorescence excitation-emission mapping for rapidly and accurately monitoring dissolved organic carbon concentration and disinfection by-product formation potential for surface water sourced drinking water treatment. The method enables real-time monitoring of the Dissolved Organic Carbon (DOC), absorbance at 254 nm (UVA), the Specific UV Absorbance (SUVA) as well as the Simulated Distribution System Trihalomethane (THM) Formation Potential (SDS-THMFP) for the source and treated water among other component parameters. The method primarily involves Parallel Factor Analysis (PARAFAC) decomposition of the high and lower molecular weight humic and fulvic organic component concentrations. The DOC calibration method involves calculating a single slope factor (with the intercept fixed at 0 mg/l) by linear regression for the UVA divided by the ratio of the high and low molecular weight component concentrations. This method thus corrects for the changes in the molecular weight component composition as a function of the source water composition and coagulation treatment effects. The SDS-THMFP calibration involves a multiple linear regression of the DOC, organic component ratio, chlorine residual, pH and alkalinity. Both the DOC and SDS-THMFP correlations over a period of 18 months exhibited adjusted correlation coefficients with r2 > 0.969. The parameters can be reported as a function of compliance rules associated with required % removals of DOC (as a function of alkalinity) and predicted maximum contaminant levels (MCL) of THMs. The single instrument method, which is compatible with continuous flow monitoring or grab sampling, provides a rapid (2-3 minute) and precise indicator of drinking water disinfectant treatability without the need for separate UV photometric and DOC meter measurements or independent THM determinations.

Assessing the Liquidity of Firms: Robust Neural Network Regression as an Alternative to the Current Ratio

NASA Astrophysics Data System (ADS)

de Andrés, Javier; Landajo, Manuel; Lorca, Pedro; Labra, Jose; Ordóñez, Patricia

Artificial neural networks have proven to be useful tools for solving financial analysis problems such as financial distress prediction and audit risk assessment. In this paper we focus on the performance of robust (least absolute deviation-based) neural networks on measuring liquidity of firms. The problem of learning the bivariate relationship between the components (namely, current liabilities and current assets) of the so-called current ratio is analyzed, and the predictive performance of several modelling paradigms (namely, linear and log-linear regressions, classical ratios and neural networks) is compared. An empirical analysis is conducted on a representative data base from the Spanish economy. Results indicate that classical ratio models are largely inadequate as a realistic description of the studied relationship, especially when used for predictive purposes. In a number of cases, especially when the analyzed firms are microenterprises, the linear specification is improved by considering the flexible non-linear structures provided by neural networks.

[Childbirth pain, perinatal dissociation and perinatal distress as predictors of posttraumatic stress symptoms].

PubMed

Boudou, M; Séjourné, N; Chabrol, H

2007-11-01

This prospective, longitudinal study investigated the contributive role of childbirth pain, perinatal distress and perinatal dissociation to the development of PTSD symptoms following childbirth. One hundred and seventeen women participated at the study. The first day after delivery they completed a questionnaire to evaluate pain, the peritraumatic distress inventory (PDI) and the peritraumatic dissociative experience questionnaire (PDEQ). Six weeks after birth, they completed the impact of event scale-revised (IES-R) to measure posttraumatic stress symptoms and the Edinburgh Postnatal Depression Scale (EPDS) to assess maternal depression. A multiple regression analysis revealed that only both components of perinatal distress, life-threat perception and dysphoric emotions were significant predictors of posttraumatic stress symptoms. In another multiple regression analysis predicting dysphoric emotions, affective dimension of pain was the only significant predictor. Perinatal distress was the best predictor of posttraumatic stress symptoms. Dysphoric emotions were associated with affective dimension of pain, suggesting that women distressed by the childbirth pain would have higher risk to develop posttraumatic stress symptoms.

Atmospheric concentrations, sources and gas-particle partitioning of PAHs in Beijing after the 29th Olympic Games.

PubMed

Ma, Wan-Li; Sun, De-Zhi; Shen, Wei-Guo; Yang, Meng; Qi, Hong; Liu, Li-Yan; Shen, Ji-Min; Li, Yi-Fan

2011-07-01

A comprehensive sampling campaign was carried out to study atmospheric concentration of polycyclic aromatic hydrocarbons (PAHs) in Beijing and to evaluate the effectiveness of source control strategies in reducing PAHs pollution after the 29th Olympic Games. The sub-cooled liquid vapor pressure (logP(L)(o))-based model and octanol-air partition coefficient (K(oa))-based model were applied based on each seasonal dateset. Regression analysis among log K(P), logP(L)(o) and log K(oa) exhibited high significant correlations for four seasons. Source factors were identified by principle component analysis and contributions were further estimated by multiple linear regression. Pyrogenic sources and coke oven emission were identified as major sources for both the non-heating and heating seasons. As compared with literatures, the mean PAH concentrations before and after the 29th Olympic Games were reduced by more than 60%, indicating that the source control measures were effective for reducing PAHs pollution in Beijing. Copyright © 2011 Elsevier Ltd. All rights reserved.

Patient-Reported Allergies Predict Worse Outcomes After Hip and Knee Arthroplasty: Results From a Prospective Cohort Study.

PubMed

Otero, Jesse E; Graves, Christopher M; Gao, Yubo; Olson, Tyler S; Dickinson, Christopher C; Chalus, Rhonda J; Vittetoe, David A; Goetz, Devon D; Callaghan, John J

2016-12-01

Retrospective analyses have demonstrated correlation between patient-reported allergies and negative outcomes after total joint arthroplasty. We sought to validate these observations in a prospective cohort. One hundred forty-four patients undergoing total hip arthroplasty and 302 patients undergoing total knee arthroplasty were prospectively enrolled. Preoperatively, patients listed their allergies and completed the Medical Outcomes Study Short Form 36 (SF-36) and the Charlson Comorbidity Index (CCI) Questionnaire. At a mean of 17 months (range 12-25 months) postoperatively, SF-36, CCI, and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) were obtained by telephone survey. Regression analysis was used to determine the strength of correlation between patient age, comorbidity burden, and number of allergies and outcome measurements. In 446 patients, 273 reported at least 1 allergy. The number of allergies reported ranged from 0 to 33. Penicillin or its derivative was the most frequently reported allergy followed by sulfa, environmental allergen, and narcotic pain medication. Patients reporting at least 1 allergy had a significantly lower postoperative SF-36 Physical Component Score compared to those reporting no allergies (51.3 vs 49.4, P = .01). The SF-36 postoperative Mental Component Score was no different between groups. Multivariate regression analysis showed that age and patient reported allergies, but not comorbidities, were independently associated with worse postoperative SF-36 Physical Component Summary (PCS) and WOMAC score. Patients with allergies experienced the same improvement in SF-36 PCS as those without an allergy. Comorbidities did not correlate with patient-reported function postoperatively. Patients who report allergies have lower postoperative outcome scores but may experience the same increment in improvement after total joint arthroplasty. Copyright © 2016 Elsevier Inc. All rights reserved.

Structural Time Series Model for El Niño Prediction

NASA Astrophysics Data System (ADS)

Petrova, Desislava; Koopman, Siem Jan; Ballester, Joan; Rodo, Xavier

2015-04-01

ENSO is a dominant feature of climate variability on inter-annual time scales destabilizing weather patterns throughout the globe, and having far-reaching socio-economic consequences. It does not only lead to extensive rainfall and flooding in some regions of the world, and anomalous droughts in others, thus ruining local agriculture, but also substantially affects the marine ecosystems and the sustained exploitation of marine resources in particular coastal zones, especially the Pacific South American coast. As a result, forecasting of ENSO and especially of the warm phase of the oscillation (El Niño/EN) has long been a subject of intense research and improvement. Thus, the present study explores a novel method for the prediction of the Niño 3.4 index. In the state-of-the-art the advantageous statistical modeling approach of Structural Time Series Analysis has not been applied. Therefore, we have developed such a model using a State Space approach for the unobserved components of the time series. Its distinguishing feature is that observations consist of various components - level, seasonality, cycle, disturbance, and regression variables incorporated as explanatory covariates. These components are aimed at capturing the various modes of variability of the N3.4 time series. They are modeled separately, then combined in a single model for analysis and forecasting. Customary statistical ENSO prediction models essentially use SST, SLP and wind stress in the equatorial Pacific. We introduce new regression variables - subsurface ocean temperature in the western equatorial Pacific, motivated by recent (Ramesh and Murtugudde, 2012) and classical research (Jin, 1997), (Wyrtki, 1985), showing that subsurface processes and heat accumulation there are fundamental for initiation of an El Niño event; and a southern Pacific temperature-difference tracer, the Rossbell dipole, leading EN by about nine months (Ballester, 2011).

Revised Healthy Lifestyle-Diet Index and associations with obesity and iron deficiency in schoolchildren: The Healthy Growth Study.

PubMed

Manios, Y; Moschonis, G; Papandreou, C; Politidou, E; Naoumi, A; Peppas, D; Mavrogianni, C; Lionis, C; Chrousos, G P

2015-02-01

The Healthy Lifestyle-Diet Index (HLD-index), previously developed to assess the degree of adherence to dietary and lifestyle guidelines for primary schoolchildren, was revised according to updated recommendations. Τhe association of the revised HLD-index (R-HLD-index) with obesity and iron deficiency (ID) was also examined. A representative sample of 2660 primary schoolchildren from Greece (9-13 years old) participating in the 'Healthy Growth Study' was examined. Twelve components related to dietary and lifestyle patterns were used to develop the R-HLD-index. Scores from 0 up to 4 were assigned to each one of these components, giving a total score ranging from 0 to 48. The associations between the R-HLD-index, obesity and ID were examined via logistic regression analysis. The total score of the R-HLD-index calculated for each one of the study participants was found to range between 2 and 32 units, with higher scores being indicative of a healthier lifestyle and better diet quality. After adjusting for potential confounders, logistic regression analysis showed that an increase in the R-HLD-index score by one unit was associated with 6% lower odds for obesity. However, no significant association was observed between the R-HLD-index score and ID. The R-HLD-index may be a useful tool for public health policy makers and healthcare professionals when assessing diet quality and lifestyle patterns of primary schoolchildren. Identification of children with lower scores in the R-HLD-index and its individual components could guide tailored made interventions targeting specific children and behaviors. © 2013 The British Dietetic Association Ltd.

A church-based diet and physical activity intervention for rural, lower Mississippi Delta African American adults: Delta Body and Soul effectiveness study, 2010-2011.

PubMed

Tussing-Humphreys, Lisa; Thomson, Jessica L; Mayo, Tanyatta; Edmond, Emanuel

2013-06-06

Obesity, diabetes, and hypertension have reached epidemic levels in the largely rural Lower Mississippi Delta (LMD) region. We assessed the effectiveness of a 6-month, church-based diet and physical activity intervention, conducted during 2010 through 2011, for improving diet quality (measured by the Healthy Eating Index-2005) and increasing physical activity of African American adults in the LMD region. We used a quasi-experimental design in which 8 self-selected eligible churches were assigned to intervention or control. Assessments included dietary, physical activity, anthropometric, and clinical measures. Statistical tests for group comparisons included χ(2), Fisher's exact, and McNemar's tests for categorical variables, and mixed-model regression analysis for continuous variables and modeling intervention effects. Retention rates were 85% (176 of 208) for control and 84% (163 of 195) for intervention churches. Diet quality components, including total fruit, total vegetables, and total quality improved significantly in both control (mean [standard deviation], 0.3 [1.8], 0.2 [1.1], and 3.4 [9.6], respectively) and intervention (0.6 [1.7], 0.3 [1.2], and 3.2 [9.7], respectively) groups, while significant increases in aerobic (22%) and strength/flexibility (24%) physical activity indicators were apparent in the intervention group only. Regression analysis indicated that intervention participation level and vehicle ownership were significant positive predictors of change for several diet quality components. This church-based diet and physical activity intervention may be effective in improving diet quality and increasing physical activity of LMD African American adults. Components key to the success of such programs are participant engagement in educational sessions and vehicle access.

Audiometric analyses confirm a cochlear component, disproportional to age, in stapedial otosclerosis.

PubMed

Topsakal, Vedat; Fransen, Erik; Schmerber, Sébastien; Declau, Frank; Yung, Matthew; Gordts, Frans; Van Camp, Guy; Van de Heyning, Paul

2006-09-01

To report the preoperative audiometric profile of surgically confirmed otosclerosis. Retrospective, multicenter study. Four tertiary referral centers. One thousand sixty-four surgically confirmed patients with otosclerosis. Therapeutic ear surgery for hearing improvement. Preoperative audiometric air conduction (AC) and bone conduction (BC) hearing thresholds were obtained retrospectively for 1064 patients with otosclerosis. A cross-sectional multiple linear regression analysis was performed on audiometric data of affected ears. Influences of age and sex were analyzed and age-related typical audiograms were created. Bone conduction thresholds were corrected for Carhart effect and presbyacusis; in addition, we tested to see if separate cochlear otosclerosis component existed. Corrected thresholds were than analyzed separately for progression of cochlear otosclerosis. The study population consisted of 35% men and 65% women (mean age, 44 yr). The mean pure-tone average at 0.5, 1, and 2 kHz was 57 dB hearing level. Multiple linear regression analysis showed significant progression for all measured AC and BC thresholds. The average annual threshold deterioration for AC was 0.45 dB/yr and the annual threshold deterioration for BC was 0.37 dB/yr. The average annual gap expansion was 0.08 dB/year. The corrected BC thresholds for Carhart effect and presbyacusis remained significantly different from zero, but only showed progression at 2 kHz. The preoperative audiological profile of otosclerosis is described. There is a significant sensorineural component in patients with otosclerosis planned for stapedotomy, which is worse than age-related hearing loss by itself. Deterioration rates of AC and BC thresholds have been reported, which can be helpful in clinical practice and might also guide the characterization of allegedly different phenotypes for familial and sporadic otosclerosis.

Haplotype-Based Association Analysis via Variance-Components Score Test

PubMed Central

Tzeng, Jung-Ying ; Zhang, Daowen 

2007-01-01

Haplotypes provide a more informative format of polymorphisms for genetic association analysis than do individual single-nucleotide polymorphisms. However, the practical efficacy of haplotype-based association analysis is challenged by a trade-off between the benefits of modeling abundant variation and the cost of the extra degrees of freedom. To reduce the degrees of freedom, several strategies have been considered in the literature. They include (1) clustering evolutionarily close haplotypes, (2) modeling the level of haplotype sharing, and (3) smoothing haplotype effects by introducing a correlation structure for haplotype effects and studying the variance components (VC) for association. Although the first two strategies enjoy a fair extent of power gain, empirical evidence showed that VC methods may exhibit only similar or less power than the standard haplotype regression method, even in cases of many haplotypes. In this study, we report possible reasons that cause the underpowered phenomenon and show how the power of the VC strategy can be improved. We construct a score test based on the restricted maximum likelihood or the marginal likelihood function of the VC and identify its nontypical limiting distribution. Through simulation, we demonstrate the validity of the test and investigate the power performance of the VC approach and that of the standard haplotype regression approach. With suitable choices for the correlation structure, the proposed method can be directly applied to unphased genotypic data. Our method is applicable to a wide-ranging class of models and is computationally efficient and easy to implement. The broad coverage and the fast and easy implementation of this method make the VC strategy an effective tool for haplotype analysis, even in modern genomewide association studies. PMID:17924336

Assessment of temporal and spatial water quality in international Gomishan Lagoon, Iran, using multivariate analysis.

PubMed

Basatnia, Nabee; Hossein, Seyed Abbas; Rodrigo-Comino, Jesús; Khaledian, Yones; Brevik, Eric C; Aitkenhead-Peterson, Jacqueline; Natesan, Usha

2018-04-29

Coastal lagoon ecosystems are vulnerable to eutrophication, which leads to the accumulation of nutrients from the surrounding watershed over the long term. However, there is a lack of information about methods that could accurate quantify this problem in rapidly developed countries. Therefore, various statistical methods such as cluster analysis (CA), principal component analysis (PCA), partial least square (PLS), principal component regression (PCR), and ordinary least squares regression (OLS) were used in this study to estimate total organic matter content in sediments (TOM) using other parameters such as temperature, dissolved oxygen (DO), pH, electrical conductivity (EC), nitrite (NO 2 ), nitrate (NO 3 ), biological oxygen demand (BOD), phosphate (PO 4 ), total phosphorus (TP), salinity, and water depth along a 3-km transect in the Gomishan Lagoon (Iran). Results indicated that nutrient concentration and the dissolved oxygen gradient were the most significant parameters in the lagoon water quality heterogeneity. Additionally, anoxia at the bottom of the lagoon in sediments and re-suspension of the sediments were the main factors affecting internal nutrient loading. To validate the models, R 2 , RMSECV, and RPDCV were used. The PLS model was stronger than the other models. Also, classification analysis of the Gomishan Lagoon identified two hydrological zones: (i) a North Zone characterized by higher water exchange, higher dissolved oxygen and lower salinity and nutrients, and (ii) a Central and South Zone with high residence time, higher nutrient concentrations, lower dissolved oxygen, and higher salinity. A recommendation for the management of coastal lagoons, specifically the Gomishan Lagoon, to decrease or eliminate nutrient loadings is discussed and should be transferred to policy makers, the scientific community, and local inhabitants.

Rationale for hedging initiatives: Empirical evidence from the energy industry

NASA Astrophysics Data System (ADS)

Dhanarajata, Srirajata

Theory offers different rationales for hedging including (i) financial distress and bankruptcy cost, (ii) capacity to capture attractive investment opportunities, (iii) information asymmetry, (iv) economy of scale, (v) substitution for hedging, (vi) managerial risk aversion, and (vii) convexity of tax schedule. The purpose of this dissertation is to empirically test the explanatory power of the first five theoretical rationales on hedging done by oil and gas exploration and production (E&P) companies. The level of hedging is measured by the percentage of production effectively hedged, calculated based on the concept of delta and delta-gamma hedging. I employ Tobit regression, principal components, and panel data analysis on dependent and raw independent variables. Tobit regression is applied due to the fact that the dependent variable used in the analysis is non-negative. Principal component analysis helps to reduce the dimension of explanatory variables while panel data analysis combines/pools the data that is a combination of time-series and cross-sectional. Based on the empirical results, leverage level is consistently found to be a significant factor on hedging activities, either due to an attempt to avoid financial distress by the firm, or an attempt to control agency cost by debtholders, or both. The effect of capital expenditures and discretionary cash flows are both indeterminable due possibly to a potential mismatch in timing of realized cash flow items and hedging decision. Firm size is found to be positively related to hedging supporting economy of scale hypothesis, which is introduced in past literature, as well as the argument that large firm usually are more sophisticated and should be more willing and more comfortable to use hedge instruments than smaller firms.

Lipidomics study of plasma phospholipid metabolism in early type 2 diabetes rats with ancient prescription Huang-Qi-San intervention by UPLC/Q-TOF-MS and correlation coefficient.

PubMed

Wu, Xia; Zhu, Jian-Cheng; Zhang, Yu; Li, Wei-Min; Rong, Xiang-Lu; Feng, Yi-Fan

2016-08-25

Potential impact of lipid research has been increasingly realized both in disease treatment and prevention. An effective metabolomics approach based on ultra-performance liquid chromatography/quadrupole-time-of-flight mass spectrometry (UPLC/Q-TOF-MS) along with multivariate statistic analysis has been applied for investigating the dynamic change of plasma phospholipids compositions in early type 2 diabetic rats after the treatment of an ancient prescription of Chinese Medicine Huang-Qi-San. The exported UPLC/Q-TOF-MS data of plasma samples were subjected to SIMCA-P and processed by bioMark, mixOmics, Rcomdr packages with R software. A clear score plots of plasma sample groups, including normal control group (NC), model group (MC), positive medicine control group (Flu) and Huang-Qi-San group (HQS), were achieved by principal-components analysis (PCA), partial least-squares discriminant analysis (PLS-DA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). Biomarkers were screened out using student T test, principal component regression (PCR), partial least-squares regression (PLS) and important variable method (variable influence on projection, VIP). Structures of metabolites were identified and metabolic pathways were deduced by correlation coefficient. The relationship between compounds was explained by the correlation coefficient diagram, and the metabolic differences between similar compounds were illustrated. Based on KEGG database, the biological significances of identified biomarkers were described. The correlation coefficient was firstly applied to identify the structure and deduce the metabolic pathways of phospholipids metabolites, and the study provided a new methodological cue for further understanding the molecular mechanisms of metabolites in the process of regulating Huang-Qi-San for treating early type 2 diabetes. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Efficacy of a numerical value of a fixed-effect estimator in stochastic frontier analysis as an indicator of hospital production structure.

PubMed

Kawaguchi, Hiroyuki; Hashimoto, Hideki; Matsuda, Shinya

2012-09-22

The casemix-based payment system has been adopted in many countries, although it often needs complementary adjustment taking account of each hospital's unique production structure such as teaching and research duties, and non-profit motives. It has been challenging to numerically evaluate the impact of such structural heterogeneity on production, separately of production inefficiency. The current study adopted stochastic frontier analysis and proposed a method to assess unique components of hospital production structures using a fixed-effect variable. There were two stages of analyses in this study. In the first stage, we estimated the efficiency score from the hospital production function using a true fixed-effect model (TFEM) in stochastic frontier analysis. The use of a TFEM allowed us to differentiate the unobserved heterogeneity of individual hospitals as hospital-specific fixed effects. In the second stage, we regressed the obtained fixed-effect variable for structural components of hospitals to test whether the variable was explicitly related to the characteristics and local disadvantages of the hospitals. In the first analysis, the estimated efficiency score was approximately 0.6. The mean value of the fixed-effect estimator was 0.784, the standard deviation was 0.137, the range was between 0.437 and 1.212. The second-stage regression confirmed that the value of the fixed effect was significantly correlated with advanced technology and local conditions of the sample hospitals. The obtained fixed-effect estimator may reflect hospitals' unique structures of production, considering production inefficiency. The values of fixed-effect estimators can be used as evaluation tools to improve fairness in the reimbursement system for various functions of hospitals based on casemix classification.

The influence of climate variables on dengue in Singapore.

PubMed

Pinto, Edna; Coelho, Micheline; Oliver, Leuda; Massad, Eduardo

2011-12-01

In this work we correlated dengue cases with climatic variables for the city of Singapore. This was done through a Poisson Regression Model (PRM) that considers dengue cases as the dependent variable and the climatic variables (rainfall, maximum and minimum temperature and relative humidity) as independent variables. We also used Principal Components Analysis (PCA) to choose the variables that influence in the increase of the number of dengue cases in Singapore, where PC₁ (Principal component 1) is represented by temperature and rainfall and PC₂ (Principal component 2) is represented by relative humidity. We calculated the probability of occurrence of new cases of dengue and the relative risk of occurrence of dengue cases influenced by climatic variable. The months from July to September showed the highest probabilities of the occurrence of new cases of the disease throughout the year. This was based on an analysis of time series of maximum and minimum temperature. An interesting result was that for every 2-10°C of variation of the maximum temperature, there was an average increase of 22.2-184.6% in the number of dengue cases. For the minimum temperature, we observed that for the same variation, there was an average increase of 26.1-230.3% in the number of the dengue cases from April to August. The precipitation and the relative humidity, after analysis of correlation, were discarded in the use of Poisson Regression Model because they did not present good correlation with the dengue cases. Additionally, the relative risk of the occurrence of the cases of the disease under the influence of the variation of temperature was from 1.2-2.8 for maximum temperature and increased from 1.3-3.3 for minimum temperature. Therefore, the variable temperature (maximum and minimum) was the best predictor for the increased number of dengue cases in Singapore.

Relationship between the Decomposition Process of Coarse Woody Debris and Fungal Community Structure as Detected by High-Throughput Sequencing in a Deciduous Broad-Leaved Forest in Japan

PubMed Central

Yamashita, Satoshi; Masuya, Hayato; Abe, Shin; Masaki, Takashi; Okabe, Kimiko

2015-01-01

We examined the relationship between the community structure of wood-decaying fungi, detected by high-throughput sequencing, and the decomposition rate using 13 years of data from a forest dynamics plot. For molecular analysis and wood density measurements, drill dust samples were collected from logs and stumps of Fagus and Quercus in the plot. Regression using a negative exponential model between wood density and time since death revealed that the decomposition rate of Fagus was greater than that of Quercus. The residual between the expected value obtained from the regression curve and the observed wood density was used as a decomposition rate index. Principal component analysis showed that the fungal community compositions of both Fagus and Quercus changed with time since death. Principal component analysis axis scores were used as an index of fungal community composition. A structural equation model for each wood genus was used to assess the effect of fungal community structure traits on the decomposition rate and how the fungal community structure was determined by the traits of coarse woody debris. Results of the structural equation model suggested that the decomposition rate of Fagus was affected by two fungal community composition components: one that was affected by time since death and another that was not affected by the traits of coarse woody debris. In contrast, the decomposition rate of Quercus was not affected by coarse woody debris traits or fungal community structure. These findings suggest that, in the case of Fagus coarse woody debris, the fungal community structure is related to the decomposition process of its host substrate. Because fungal community structure is affected partly by the decay stage and wood density of its substrate, these factors influence each other. Further research on interactive effects is needed to improve our understanding of the relationship between fungal community structure and the woody debris decomposition process. PMID:26110605

Quasi-experimental evidence on tobacco tax regressivity.

PubMed

Koch, Steven F

2018-01-01

Tobacco taxes are known to reduce tobacco consumption and to be regressive, such that tobacco control policy may have the perverse effect of further harming the poor. However, if tobacco consumption falls faster amongst the poor than the rich, tobacco control policy can actually be progressive. We take advantage of persistent and committed tobacco control activities in South Africa to examine the household tobacco expenditure burden. For the analysis, we make use of two South African Income and Expenditure Surveys (2005/06 and 2010/11) that span a series of such tax increases and have been matched across the years, yielding 7806 matched pairs of tobacco consuming households and 4909 matched pairs of cigarette consuming households. By matching households across the surveys, we are able to examine both the regressivity of the household tobacco burden, and any change in that regressivity, and since tobacco taxes have been a consistent component of tobacco prices, our results also relate to the regressivity of tobacco taxes. Like previous research into cigarette and tobacco expenditures, we find that the tobacco burden is regressive; thus, so are tobacco taxes. However, we find that over the five-year period considered, the tobacco burden has decreased, and, most importantly, falls less heavily on the poor. Thus, the tobacco burden and the tobacco tax is less regressive in 2010/11 than in 2005/06. Thus, increased tobacco taxes can, in at least some circumstances, reduce the financial burden that tobacco places on households. Copyright © 2017 Elsevier Ltd. All rights reserved.

Machine learning action parameters in lattice quantum chromodynamics

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

Shanahan, Phiala; Trewartha, Daneil; Detmold, William

Numerical lattice quantum chromodynamics studies of the strong interaction underpin theoretical understanding of many aspects of particle and nuclear physics. Such studies require significant computing resources to undertake. A number of proposed methods promise improved efficiency of lattice calculations, and access to regions of parameter space that are currently computationally intractable, via multi-scale action-matching approaches that necessitate parametric regression of generated lattice datasets. The applicability of machine learning to this regression task is investigated, with deep neural networks found to provide an efficient solution even in cases where approaches such as principal component analysis fail. Finally, the high information contentmore » and complex symmetries inherent in lattice QCD datasets require custom neural network layers to be introduced and present opportunities for further development.« less

Machine learning action parameters in lattice quantum chromodynamics

DOE PAGES

Shanahan, Phiala; Trewartha, Daneil; Detmold, William

2018-05-16

Numerical lattice quantum chromodynamics studies of the strong interaction underpin theoretical understanding of many aspects of particle and nuclear physics. Such studies require significant computing resources to undertake. A number of proposed methods promise improved efficiency of lattice calculations, and access to regions of parameter space that are currently computationally intractable, via multi-scale action-matching approaches that necessitate parametric regression of generated lattice datasets. The applicability of machine learning to this regression task is investigated, with deep neural networks found to provide an efficient solution even in cases where approaches such as principal component analysis fail. Finally, the high information contentmore » and complex symmetries inherent in lattice QCD datasets require custom neural network layers to be introduced and present opportunities for further development.« less

Assessment of mechanical properties of isolated bovine intervertebral discs from multi-parametric magnetic resonance imaging.

PubMed

Recuerda, Maximilien; Périé, Delphine; Gilbert, Guillaume; Beaudoin, Gilles

2012-10-12

The treatment planning of spine pathologies requires information on the rigidity and permeability of the intervertebral discs (IVDs). Magnetic resonance imaging (MRI) offers great potential as a sensitive and non-invasive technique for describing the mechanical properties of IVDs. However, the literature reported small correlation coefficients between mechanical properties and MRI parameters. Our hypothesis is that the compressive modulus and the permeability of the IVD can be predicted by a linear combination of MRI parameters. Sixty IVDs were harvested from bovine tails, and randomly separated in four groups (in-situ, digested-6h, digested-18h, digested-24h). Multi-parametric MRI acquisitions were used to quantify the relaxation times T1 and T2, the magnetization transfer ratio MTR, the apparent diffusion coefficient ADC and the fractional anisotropy FA. Unconfined compression, confined compression and direct permeability measurements were performed to quantify the compressive moduli and the hydraulic permeabilities. Differences between groups were evaluated from a one way ANOVA. Multi linear regressions were performed between dependent mechanical properties and independent MRI parameters to verify our hypothesis. A principal component analysis was used to convert the set of possibly correlated variables into a set of linearly uncorrelated variables. Agglomerative Hierarchical Clustering was performed on the 3 principal components. Multilinear regressions showed that 45 to 80% of the Young's modulus E, the aggregate modulus in absence of deformation HA0, the radial permeability kr and the axial permeability in absence of deformation k0 can be explained by the MRI parameters within both the nucleus pulposus and the annulus pulposus. The principal component analysis reduced our variables to two principal components with a cumulative variability of 52-65%, which increased to 70-82% when considering the third principal component. The dendograms showed a natural division into four clusters for the nucleus pulposus and into three or four clusters for the annulus fibrosus. The compressive moduli and the permeabilities of isolated IVDs can be assessed mostly by MT and diffusion sequences. However, the relationships have to be improved with the inclusion of MRI parameters more sensitive to IVD degeneration. Before the use of this technique to quantify the mechanical properties of IVDs in vivo on patients suffering from various diseases, the relationships have to be defined for each degeneration state of the tissue that mimics the pathology. Our MRI protocol associated to principal component analysis and agglomerative hierarchical clustering are promising tools to classify the degenerated intervertebral discs and further find biomarkers and predictive factors of the evolution of the pathologies.

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.

Factor analysis of metabolic syndrome components and predicting type 2 diabetes: Results of 10-year follow-up in a Middle Eastern population.

PubMed

Ayubi, Erfan; Khalili, Davood; Delpisheh, Ali; Hadaegh, Farzad; Azizi, Fereidoun

2015-11-01

The relationship among components of metabolic syndrome (MetS) and their association with diabetes is unclear in West Asia. The aim of the present study was to conduct factor analysis of MetS components and the effect these factors have on the incidence of type 2 diabetes (T2D) in a population-based cohort study of the Tehran Lipid and Glucose Study (TLGS). The present study enrolled 1861 men and 2706 women (20-60 years of age), from Tehran (Iran) who were free of diabetes at baseline and followed them for 10 years. A principal component analysis was performed to extract standardized factors from MetS components. Logistic regression was used to detect associations between the extracted factors and the incidence of diabetes. A propensity score was used to correct differential selection bias resulting from loss to follow-up. Factor analysis identified three factors (blood pressure, lipids and glycemia). Waist circumference was shared in three all factors. Blood pressure, lipids and glycemia were related to the incidence of diabetes with odds ratios (95% confidence intervals) of 2.23 (1.31-3.78), 1.89 (1.27-3.67), and 7.54 (4.09-13.91), respectively, in men and 2.13 (1.34-3.40), 2.06 (1.35-3.15), and 13.91 (7.29-26.51), respectively, in women for the third versus the first tertile of these standardized factors. Central adiposity may have a pivotal role in MetS linking other risk factors together. Glycemia had a high impact on the incidence of diabetes, whereas blood pressure and lipid had a similar moderate effect on the incidence of diabetes. © 2014 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and Wiley Publishing Asia Pty Ltd.

Construction and analysis of a modular model of caspase activation in apoptosis

PubMed Central

Harrington, Heather A; Ho, Kenneth L; Ghosh, Samik; Tung, KC

2008-01-01

Background A key physiological mechanism employed by multicellular organisms is apoptosis, or programmed cell death. Apoptosis is triggered by the activation of caspases in response to both extracellular (extrinsic) and intracellular (intrinsic) signals. The extrinsic and intrinsic pathways are characterized by the formation of the death-inducing signaling complex (DISC) and the apoptosome, respectively; both the DISC and the apoptosome are oligomers with complex formation dynamics. Additionally, the extrinsic and intrinsic pathways are coupled through the mitochondrial apoptosis-induced channel via the Bcl-2 family of proteins. Results A model of caspase activation is constructed and analyzed. The apoptosis signaling network is simplified through modularization methodologies and equilibrium abstractions for three functional modules. The mathematical model is composed of a system of ordinary differential equations which is numerically solved. Multiple linear regression analysis investigates the role of each module and reduced models are constructed to identify key contributions of the extrinsic and intrinsic pathways in triggering apoptosis for different cell lines. Conclusion Through linear regression techniques, we identified the feedbacks, dissociation of complexes, and negative regulators as the key components in apoptosis. The analysis and reduced models for our model formulation reveal that the chosen cell lines predominately exhibit strong extrinsic caspase, typical of type I cell, behavior. Furthermore, under the simplified model framework, the selected cells lines exhibit different modes by which caspase activation may occur. Finally the proposed modularized model of apoptosis may generalize behavior for additional cells and tissues, specifically identifying and predicting components responsible for the transition from type I to type II cell behavior. PMID:19077196

Speech and gait in Parkinson's disease: When rhythm matters.

PubMed

Ricciardi, Lucia; Ebreo, Michela; Graziosi, Adriana; Barbuto, Marianna; Sorbera, Chiara; Morgante, Letterio; Morgante, Francesca

2016-11-01

Speech disturbances in Parkinson's disease (PD) are heterogeneous, ranging from hypokinetic to hyperkinetic types. Repetitive speech disorder has been demonstrated in more advanced disease stages and has been considered the speech equivalent of freezing of gait (FOG). We aimed to verify a possible relationship between speech and FOG in patients with PD. Forty-three consecutive PD patients and 20 healthy control subjects underwent standardized speech evaluation using the Italian version of the Dysarthria Profile (DP), for its motor component, and subsets of the Battery for the Analysis of the Aphasic Deficit (BADA), for its procedural component. DP is a scale composed of 7 sub-sections assessing different features of speech; the rate/prosody section of DP includes items investigating the presence of repetitive speech disorder. Severity of FOG was evaluated with the new freezing of gait questionnaire (NFGQ). PD patients performed worse at DP and BADA compared to healthy controls; patients with FOG or with Hoehn-Yahr >2 reported lower scores in the articulation, intellibility, rate/prosody sections of DP and in the semantic verbal fluency test. Logistic regression analysis showed that only age and rate/prosody scores were significantly associated to FOG in PD. Multiple regression analysis showed that only the severity of FOG was associated to rate/prosody score. Our data demonstrate that repetitive speech disorder is related to FOG and is associated to advanced disease stages and independent of disease duration. Speech dysfluency represents a disorder of motor speech control, possibly sharing pathophysiological mechanisms with FOG. Copyright © 2016 Elsevier Ltd. All rights reserved.

Chemical composition and sensory profile of pomelo (Citrus grandis (L.) Osbeck) juice.

PubMed

Cheong, Mun Wai; Liu, Shao Quan; Zhou, Weibiao; Curran, Philip; Yu, Bin

2012-12-15

Two cultivars (Citrus grandis (L.) Osbeck PO 51 and PO 52) of Malaysian pomelo juices were studied by examining their physicochemical properties (i.e. pH, °Brix and titratable acidity), volatile and non-volatile components (sugars and organic acids). Using solvent extraction and headspace solid-phase microextraction, 49 and 65 volatile compounds were identified by gas chromatography-mass spectrometer/flame ionisation detector, respectively. Compared to pink pomelo juice (cultivar PO 52), white pomelo juice (cultivar PO 51) contained lower amount of total volatiles but higher terpenoids. Descriptive sensory evaluation indicated that white pomelo juice was milder in taste especially acidity. Furthermore, principal component analysis and partial least square regression revealed a strong correlation in pomelo juices between their chemical components and some flavour attributes (i.e. acidic, fresh, peely and sweet). Hence, this research enabled a deeper insight into the flavour of this unique citrus fruit. Copyright © 2012 Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

Multiresponse semiparametric regression for modelling the effect of regional socio-economic variables on the use of information technology

NASA Astrophysics Data System (ADS)

Wibowo, Wahyu; Wene, Chatrien; Budiantara, I. Nyoman; Permatasari, Erma Oktania

2017-03-01

Multiresponse semiparametric regression is simultaneous equation regression model and fusion of parametric and nonparametric model. The regression model comprise several models and each model has two components, parametric and nonparametric. The used model has linear function as parametric and polynomial truncated spline as nonparametric component. The model can handle both linearity and nonlinearity relationship between response and the sets of predictor variables. The aim of this paper is to demonstrate the application of the regression model for modeling of effect of regional socio-economic on use of information technology. More specific, the response variables are percentage of households has access to internet and percentage of households has personal computer. Then, predictor variables are percentage of literacy people, percentage of electrification and percentage of economic growth. Based on identification of the relationship between response and predictor variable, economic growth is treated as nonparametric predictor and the others are parametric predictors. The result shows that the multiresponse semiparametric regression can be applied well as indicate by the high coefficient determination, 90 percent.

Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure.

PubMed

Allegrini, Franco; Braga, Jez W B; Moreira, Alessandro C O; Olivieri, Alejandro C

2018-06-29

A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS). Copyright © 2018 Elsevier B.V. All rights reserved.

A Two-Step Method to Select Major Surge-Producing Extratropical Cyclones from a 10,000-Year Stochastic Catalog

NASA Astrophysics Data System (ADS)

Keshtpoor, M.; Carnacina, I.; Yablonsky, R. M.

2016-12-01

Extratropical cyclones (ETCs) are the primary driver of storm surge events along the UK and northwest mainland Europe coastlines. In an effort to evaluate the storm surge risk in coastal communities in this region, a stochastic catalog is developed by perturbing the historical storm seeds of European ETCs to account for 10,000 years of possible ETCs. Numerical simulation of the storm surge generated by the full 10,000-year stochastic catalog, however, is computationally expensive and may take several months to complete with available computational resources. A new statistical regression model is developed to select the major surge-generating events from the stochastic ETC catalog. This regression model is based on the maximum storm surge, obtained via numerical simulations using a calibrated version of the Delft3D-FM hydrodynamic model with a relatively coarse mesh, of 1750 historical ETC events that occurred over the past 38 years in Europe. These numerically-simulated surge values were regressed to the local sea level pressure and the U and V components of the wind field at the location of 196 tide gauge stations near the UK and northwest mainland Europe coastal areas. The regression model suggests that storm surge values in the area of interest are highly correlated to the U- and V-component of wind speed, as well as the sea level pressure. Based on these correlations, the regression model was then used to select surge-generating storms from the 10,000-year stochastic catalog. Results suggest that roughly 105,000 events out of 480,000 stochastic storms are surge-generating events and need to be considered for numerical simulation using a hydrodynamic model. The selected stochastic storms were then simulated in Delft3D-FM, and the final refinement of the storm population was performed based on return period analysis of the 1750 historical event simulations at each of the 196 tide gauges in preparation for Delft3D-FM fine mesh simulations.

Linear models for calculating digestibile energy for sheep diets.

PubMed

Fonnesbeck, P V; Christiansen, M L; Harris, L E

1981-05-01

Equations for estimating the digestible energy (DE) content of sheep diets were generated from the chemical contents and a factorial description of diets fed to lambs in digestion trials. The diet factors were two forages (alfalfa and grass hay), harvested at three stages of maturity (late vegetative, early bloom and full bloom), fed in two ingredient combinations (all hay or a 50:50 hay and corn grain mixture) and prepared by two forage texture processes (coarsely chopped or finely chopped and pelleted). The 2 x 3 x 2 x 2 factorial arrangement produced 24 diet treatments. These were replicated twice, for a total of 48 lamb digestion trials. In model 1 regression equations, DE was calculated directly from chemical composition of the diet. In model 2, regression equations predicted the percentage of digested nutrient from the chemical contents of the diet and then DE of the diet was calculated as the sum of the gross energy of the digested organic components. Expanded forms of model 1 and model 2 were also developed that included diet factors as qualitative indicator variables to adjust the regression constant and regression coefficients for the diet description. The expanded forms of the equations accounted for significantly more variation in DE than did the simple models and more accurately estimated DE of the diet. Information provided by the diet description proved as useful as chemical analyses for the prediction of digestibility of nutrients. The statistics indicate that, with model 1, neutral detergent fiber and plant cell wall analyses provided as much information for the estimation of DE as did model 2 with the combined information from crude protein, available carbohydrate, total lipid, cellulose and hemicellulose. Regression equations are presented for estimating DE with the most currently analyzed organic components, including linear and curvilinear variables and diet factors that significantly reduce the standard error of the estimate. To estimate De of a diet, the user utilizes the equation that uses the chemical analysis information and diet description most effectively.

Demonstrated Potential of Ion Mobility Spectrometry for Detection of Adulterated Perfumes and Plant Speciation

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

Clark, Jared Matthew; Daum, Keith Alvin; Kalival, J. H.

2003-01-01

This initial study evaluates the use of ion mobility spectrometry (IMS) as a rapid test procedure for potential detection of adulterated perfumes and speciation of plant life. Sample types measured consist of five genuine perfumes, two species of sagebrush, and four species of flowers. Each sample type is treated as a separate classification problem. It is shown that discrimination using principal component analysis with K-nearest neighbors can distinguish one class from another. Discriminatory models generated using principal component regressions are not as effective. Results from this examination are encouraging and represent an initial phase demonstrating that perfumes and plants possessmore » characteristic chemical signatures that can be used for reliable identification.« less

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.

Healthcare Coverage and Disability Evaluation for Reserve Component Personnel: Research for the 11th Quadrennial Review of Military Compensation

DTIC Science & Technology

2012-01-01

personnel (E-5–E-9), and all warrant and commis- sioned officers. There is some variation across the services, especially for junior enlisted...the PTSD cases in 2007 and 2008. After the policy memo directing a minimum tempo - rary rating of 50 percent, the ratings for cases involving PTSD...dles the medical evaluation (these results are given in the Appendix). The regression analysis controls for this variation , so the RC-AC difference

Vestibular schwannomas: Accuracy of tumor volume estimated by ice cream cone formula using thin-sliced MR images.

PubMed

Ho, Hsing-Hao; Li, Ya-Hui; Lee, Jih-Chin; Wang, Chih-Wei; Yu, Yi-Lin; Hueng, Dueng-Yuan; Ma, Hsin-I; Hsu, Hsian-He; Juan, Chun-Jung

2018-01-01

We estimated the volume of vestibular schwannomas by an ice cream cone formula using thin-sliced magnetic resonance images (MRI) and compared the estimation accuracy among different estimating formulas and between different models. The study was approved by a local institutional review board. A total of 100 patients with vestibular schwannomas examined by MRI between January 2011 and November 2015 were enrolled retrospectively. Informed consent was waived. Volumes of vestibular schwannomas were estimated by cuboidal, ellipsoidal, and spherical formulas based on a one-component model, and cuboidal, ellipsoidal, Linskey's, and ice cream cone formulas based on a two-component model. The estimated volumes were compared to the volumes measured by planimetry. Intraobserver reproducibility and interobserver agreement was tested. Estimation error, including absolute percentage error (APE) and percentage error (PE), was calculated. Statistical analysis included intraclass correlation coefficient (ICC), linear regression analysis, one-way analysis of variance, and paired t-tests with P < 0.05 considered statistically significant. Overall tumor size was 4.80 ± 6.8 mL (mean ±standard deviation). All ICCs were no less than 0.992, suggestive of high intraobserver reproducibility and high interobserver agreement. Cuboidal formulas significantly overestimated the tumor volume by a factor of 1.9 to 2.4 (P ≤ 0.001). The one-component ellipsoidal and spherical formulas overestimated the tumor volume with an APE of 20.3% and 29.2%, respectively. The two-component ice cream cone method, and ellipsoidal and Linskey's formulas significantly reduced the APE to 11.0%, 10.1%, and 12.5%, respectively (all P < 0.001). The ice cream cone method and other two-component formulas including the ellipsoidal and Linskey's formulas allow for estimation of vestibular schwannoma volume more accurately than all one-component formulas.

Uric acid association with pulsatile and steady components of central and peripheral blood pressures.

PubMed

Lepeytre, Fanny; Lavoie, Pierre-Luc; Troyanov, Stéphan; Madore, François; Agharazii, Mohsen; Goupil, Rémi

2018-03-01

Whether the cardiovascular risk attributed to elevated uric acid levels may be explained by changes in central and peripheral pulsatile and/or steady blood pressure (BP) components remains controversial. In a cross-sectional analysis of normotensive and untreated hypertensive participants of the CARTaGENE populational cohort, we examined the relationship between uric acid, and both pulsatile and steady components of peripheral and central BP, using sex-stratified linear regressions. Of the 20 004 participants, 10 161 individuals without antihypertensive or uric acid-lowering drugs had valid pulse wave analysis and serum uric acid levels. In multivariate analysis, pulsatile components of BP were not associated with uric acid levels, whereas steady components [mean BP (MBP), peripheral and central DBP] were all associated with higher levels of uric acid levels in women and men (all P < 0.001). Furthermore, there was a gradual increase of central SBP (cSBP), DBP and MBP from the lowest to the highest quintiles of uric acid levels but not for MBP-adjusted cSBP. Peripheral and cSBP, which are aggregate measures of pulsatile and steady BP, were also associated with uric acid levels in women (β = 0.063 and 0.072, respectively, both P < 0.001) and men (β = 0.043 and 0.051, both P ≤ 0.003). After further adjustments for MBP to account for the concomitant increase in steady component of BP, SBPs were no longer associated with uric acid levels. Serum uric acid levels appear to be associated with both central and peripheral steady but not pulsatile BP, regardless of sex.

Multivariate analysis of cytokine profiles in pregnancy complications.

PubMed

Azizieh, Fawaz; Dingle, Kamaludin; Raghupathy, Raj; Johnson, Kjell; VanderPlas, Jacob; Ansari, Ali

2018-03-01

The immunoregulation to tolerate the semiallogeneic fetus during pregnancy includes a harmonious dynamic balance between anti- and pro-inflammatory cytokines. Several earlier studies reported significantly different levels and/or ratios of several cytokines in complicated pregnancy as compared to normal pregnancy. However, as cytokines operate in networks with potentially complex interactions, it is also interesting to compare groups with multi-cytokine data sets, with multivariate analysis. Such analysis will further examine how great the differences are, and which cytokines are more different than others. Various multivariate statistical tools, such as Cramer test, classification and regression trees, partial least squares regression figures, 2-dimensional Kolmogorov-Smirmov test, principal component analysis and gap statistic, were used to compare cytokine data of normal vs anomalous groups of different pregnancy complications. Multivariate analysis assisted in examining if the groups were different, how strongly they differed, in what ways they differed and further reported evidence for subgroups in 1 group (pregnancy-induced hypertension), possibly indicating multiple causes for the complication. This work contributes to a better understanding of cytokines interaction and may have important implications on targeting cytokine balance modulation or design of future medications or interventions that best direct management or prevention from an immunological approach. © 2018 The Authors. American Journal of Reproductive Immunology Published by John Wiley & Sons Ltd.

Characterizing individual differences in functional connectivity using dual-regression and seed-based approaches.

PubMed

Smith, David V; Utevsky, Amanda V; Bland, Amy R; Clement, Nathan; Clithero, John A; Harsch, Anne E W; McKell Carter, R; Huettel, Scott A

2014-07-15

A central challenge for neuroscience lies in relating inter-individual variability to the functional properties of specific brain regions. Yet, considerable variability exists in the connectivity patterns between different brain areas, potentially producing reliable group differences. Using sex differences as a motivating example, we examined two separate resting-state datasets comprising a total of 188 human participants. Both datasets were decomposed into resting-state networks (RSNs) using a probabilistic spatial independent component analysis (ICA). We estimated voxel-wise functional connectivity with these networks using a dual-regression analysis, which characterizes the participant-level spatiotemporal dynamics of each network while controlling for (via multiple regression) the influence of other networks and sources of variability. We found that males and females exhibit distinct patterns of connectivity with multiple RSNs, including both visual and auditory networks and the right frontal-parietal network. These results replicated across both datasets and were not explained by differences in head motion, data quality, brain volume, cortisol levels, or testosterone levels. Importantly, we also demonstrate that dual-regression functional connectivity is better at detecting inter-individual variability than traditional seed-based functional connectivity approaches. Our findings characterize robust-yet frequently ignored-neural differences between males and females, pointing to the necessity of controlling for sex in neuroscience studies of individual differences. Moreover, our results highlight the importance of employing network-based models to study variability in functional connectivity. Copyright © 2014 Elsevier Inc. All rights reserved.

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.

Metabolomics study on primary dysmenorrhea patients during the luteal regression stage based on ultra performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry

PubMed Central

Fang, Ling; Gu, Caiyun; Liu, Xinyu; Xie, Jiabin; Hou, Zhiguo; Tian, Meng; Yin, Jia; Li, Aizhu; Li, Yubo

2017-01-01

Primary dysmenorrhea (PD) is a common gynecological disorder which, while not life-threatening, severely affects the quality of life of women. Most patients with PD suffer ovarian hormone imbalances caused by uterine contraction, which results in dysmenorrhea. PD patients may also suffer from increases in estrogen levels caused by increased levels of prostaglandin synthesis and release during luteal regression and early menstruation. Although PD pathogenesis has been previously reported on, these studies only examined the menstrual period and neglected the importance of the luteal regression stage. Therefore, the present study used urine metabolomics to examine changes in endogenous substances and detect urine biomarkers for PD during luteal regression. Ultra performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry was used to create metabolomic profiles for 36 patients with PD and 27 healthy controls. Principal component analysis and partial least squares discriminate analysis were used to investigate the metabolic alterations associated with PD. Ten biomarkers for PD were identified, including ornithine, dihydrocortisol, histidine, citrulline, sphinganine, phytosphingosine, progesterone, 17-hydroxyprogesterone, androstenedione, and 15-keto-prostaglandin F2α. The specificity and sensitivity of these biomarkers was assessed based on the area under the curve of receiver operator characteristic curves, which can be used to distinguish patients with PD from healthy controls. These results provide novel targets for the treatment of PD. PMID:28098892

Improved Regression Analysis of Temperature-Dependent Strain-Gage Balance Calibration Data

NASA Technical Reports Server (NTRS)

Ulbrich, N.

2015-01-01

An improved approach is discussed that may be used to directly include first and second order temperature effects in the load prediction algorithm of a wind tunnel strain-gage balance. The improved approach was designed for the Iterative Method that fits strain-gage outputs as a function of calibration loads and uses a load iteration scheme during the wind tunnel test to predict loads from measured gage outputs. The improved approach assumes that the strain-gage balance is at a constant uniform temperature when it is calibrated and used. First, the method introduces a new independent variable for the regression analysis of the balance calibration data. The new variable is designed as the difference between the uniform temperature of the balance and a global reference temperature. This reference temperature should be the primary calibration temperature of the balance so that, if needed, a tare load iteration can be performed. Then, two temperature{dependent terms are included in the regression models of the gage outputs. They are the temperature difference itself and the square of the temperature difference. Simulated temperature{dependent data obtained from Triumph Aerospace's 2013 calibration of NASA's ARC-30K five component semi{span balance is used to illustrate the application of the improved approach.

Lumbar subcutaneous edema and degenerative spinal disease in patients with low back pain: a retrospective MRI study.

PubMed

Quattrocchi, C C; Giona, A; Di Martino, A; Gaudino, F; Mallio, C A; Errante, Y; Occhicone, F; Vitali, M A; Zobel, B B; Denaro, V

2015-08-01

This study was designed to determine the association between LSE, spondylolisthesis, facet arthropathy, lumbar canal stenosis, BMI, radiculopathy and bone marrow edema at conventional lumbar spine MR imaging. This is a retrospective radiological study; 441 consecutive patients with low back pain (224 men and 217 women; mean age 57.3 years; mean BMI 26) underwent conventional lumbar MRI using a 1.5-T magnet (Avanto, Siemens). Lumbar MR images were reviewed by consensus for the presence of LSE, spondylolisthesis, facet arthropathy, lumbar canal stenosis, radiculopathy and bone marrow edema. Descriptive statistics and association studies were conducted using STATA software 11.0. Association studies have been performed using linear univariate regression analysis and multivariate regression analysis, considering LSE as response variable. The overall prevalence of LSE was 40%; spondylolisthesis (p = 0.01), facet arthropathy (p < 0.001), BMI (p = 0.008) and lumbar canal stenosis (p < 0.001) were included in the multivariate regression model, whereas bone marrow edema, radiculopathy and age were not. LSE is highly associated with spondylolisthesis, facet arthropathy and BMI, suggesting underestimation of its clinical impact as an integral component in chronic lumbar back pain. Longitudinal simultaneous X-ray/MRI studies should be conducted to test the relationship of LSE with lumbar spinal instability and low back pain.

Social support and support groups among people with HIV/AIDS in Ghana.

PubMed

Abrefa-Gyan, Tina; Wu, Liyun; Lewis, Marilyn W

2016-01-01

HIV/AIDS, a chronic burden in Ghana, poses social and health outcome concerns to those infected. Examining the Medical Outcome Study Social Support Survey (MOS-SSS) instrument among 300 Ghanaians from a cross-sectional design, Principal Component Analysis yielded four factors (positive interaction, trust building, information giving, and essential support), which accounted for 85.73% of the total variance in the MOS-SSS. A logistic regression analysis showed that essential support was the strongest predictor of the length of time an individual stayed in the support group, whereas positive interaction indicated negative association. The study's implications for policy, research, and practice were discussed.

Pulse gas chromatographic study of adsorption of substituted aromatics and heterocyclic molecules on MIL-47 at zero coverage.

PubMed

Duerinck, Tim; Couck, Sarah; Vermoortele, Frederik; De Vos, Dirk E; Baron, Gino V; Denayer, Joeri F M

2012-10-02

The low coverage adsorptive properties of the MIL-47 metal organic framework toward aromatic and heterocyclic molecules are reported in this paper. The effect of molecular functionality and size on Henry adsorption constants and adsorption enthalpies of alkyl and heteroatom functionalized benzene derivates and heterocyclic molecules was studied using pulse gas chromatography. By means of statistical analysis, experimental data was analyzed and modeled using principal component analysis and partial least-squares regression. Structure-property relationships were established, revealing and confirming several trends. Among the molecular properties governing the adsorption process, vapor pressure, mean polarizability, and dipole moment play a determining role.

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.

The Use of Principal Component Analysis for Source Identification of PM2.5 from Selected Urban and Regional Background Sites in Poland

NASA Astrophysics Data System (ADS)

Błaszczak, Barbara

2018-01-01

The paper reports the results of the measurements of water-soluble ions and carbonaceous matter content in the fine particulate matter (PM2.5), as well as the contributions of major sources in PM2.5. Daily PM2.5 samples were collected during heating and non-heating season of the year 2013 in three different locations in Poland: Szczecin (urban background), Trzebinia (urban background) and Złoty Potok (regional background). The concentrations of PM2.5, and its related components, exhibited clear spatiotemporal variability with higher levels during the heating period. The share of the total carbon (TC) in PM2.5 exceeded 40% and was primarily determined by fluctuations in the share of OC. Sulfates (SO42-), nitrates (NO3-) and ammonium (NH4+) dominated in the ionic composition of PM2.5 and accounted together 34% (Szczecin), 30% (Trzebinia) and 18% (Złoty Potok) of PM2.5 mass. Source apportionment analysis, performed by PCA-MLRA model (Principal Component Analysis - Multilinear Regression Analysis), revealed that secondary aerosol, whose presence is related to oxidation of gaseous precursors emitted from fuel combustion and biomass burning, had the largest contribution in observed PM2.5 concentrations. In addition, the contribution of traffic sources together with road dust resuspension, was observed. The share of natural sources (sea spray, crustal dust) was generally lower.

Quality of life in adults with congenital adrenal hyperplasia relates to glucocorticoid treatment, adiposity and insulin resistance: United Kingdom Congenital adrenal Hyperplasia Adult Study Executive (CaHASE)

PubMed Central

Han, Thang S; Krone, Nils; Willis, Debbie S; Conway, Gerard S; Hahner, Stefanie; Rees, D Aled; Stimson, Roland H; Walker, Brian R; Arlt, Wiebke; Ross, Richard J

2013-01-01

Context Quality of life (QoL) has been variously reported as normal or impaired in adults with congenital adrenal hyperplasia (CAH). To explore the reasons for this discrepancy we investigated the relationship between QoL, glucocorticoid treatment and other health outcomes in CAH adults. Methods Cross-sectional analysis of 151 adults with 21-hydroxylase deficiency aged 18–69 years in whom QoL (assessed using the Short Form Health Survey), glucocorticoid regimen, anthropometric and metabolic measures were recorded. Relationships were examined between QoL, type of glucocorticoid (hydrocortisone, prednisolone and dexamethasone) and dose of glucocorticoid expressed as prednisolone dose equivalent (PreDEq). QoL was expressed as z-scores calculated from matched controls (14 430 subjects from UK population). Principal components analysis (PCA) was undertaken to identify clusters of associated clinical and biochemical features and the principal component (PC) scores used in regression analysis as predictor of QoL. Results QoL scores were associated with type of glucocorticoid treatment for vitality (P=0.002) and mental health (P=0.011), with higher z-scores indicating better QoL in patients on hydrocortisone monotherapy (P<0.05). QoL did not relate to PreDEq or mutation severity. PCA identified three PCs (PC1, disease control; PC2, adiposity and insulin resistance and PC3, blood pressure and mutations) that explained 61% of the variance in observed variables. Stepwise multiple regression analysis demonstrated that PC2, reflecting adiposity and insulin resistance (waist circumference, serum triglycerides, homeostasis model assessment of insulin resistance and HDL-cholesterol), related to QoL scores, specifically impaired physical functioning, bodily pain, general health, Physical Component Summary Score (P<0.001) and vitality (P=0.002). Conclusions Increased adiposity, insulin resistance and use of prednisolone or dexamethasone are associated with impaired QoL in adults with CAH. Intervention trials are required to establish whether choice of glucocorticoid treatment and/or weight loss can improve QoL in CAH adults. PMID:23520247

Study on Hyperspectral Estimation Model of Total Nitrogen Content in Soil of Shaanxi Province

NASA Astrophysics Data System (ADS)

Liu, Jinbao; Dong, Zhenyu; Chen, Xi

2018-01-01

The development of hyperspectral remote sensing technology has been widely used in soil nutrient prediction. The soil is the representative soil type in Shaanxi Province. In this study, the soil total nitrogen content in Shaanxi soil was used as the research target, and the soil samples were measured by reflectance spectroscopy using ASD method. Pre-treatment, the first order differential, second order differential and reflectance logarithmic transformation of the reflected spectrum after pre-treatment, and the hyperspectral estimation model is established by using the least squares regression method and the principal component regression method. The results show that the correlation between the reflectance spectrum and the total nitrogen content of the soil is significantly improved. The correlation coefficient between the original reflectance and soil total nitrogen content is in the range of 350 ~ 2500nm. The correlation coefficient of soil total nitrogen content and first deviation of reflectance is more than 0.5 at 142nm, 1963nm, 2204nm and 2307nm, the second deviation has a significant positive correlation at 1114nm, 1470nm, 1967nm, 2372nm and 2402nm, respectively. After the reciprocal logarithmic transformation of the reflectance with the total nitrogen content of the correlation analysis found that the effect is not obvious. Rc2 = 0.7102, RMSEC = 0.0788; Rv2 = 0.8480, RMSEP = 0.0663, which can achieve the rapid prediction of the total nitrogen content in the region. The results show that the principal component regression model is the best.

Structured penalties for functional linear models-partially empirical eigenvectors for regression.

PubMed

Randolph, Timothy W; Harezlak, Jaroslaw; Feng, Ziding

2012-01-01

One of the challenges with functional data is incorporating geometric structure, or local correlation, into the analysis. This structure is inherent in the output from an increasing number of biomedical technologies, and a functional linear model is often used to estimate the relationship between the predictor functions and scalar responses. Common approaches to the problem of estimating a coefficient function typically involve two stages: regularization and estimation. Regularization is usually done via dimension reduction, projecting onto a predefined span of basis functions or a reduced set of eigenvectors (principal components). In contrast, we present a unified approach that directly incorporates geometric structure into the estimation process by exploiting the joint eigenproperties of the predictors and a linear penalty operator. In this sense, the components in the regression are 'partially empirical' and the framework is provided by the generalized singular value decomposition (GSVD). The form of the penalized estimation is not new, but the GSVD clarifies the process and informs the choice of penalty by making explicit the joint influence of the penalty and predictors on the bias, variance and performance of the estimated coefficient function. Laboratory spectroscopy data and simulations are used to illustrate the concepts.

The analysis of milk components and pathogenic bacteria isolated from bovine raw milk in Korea.

PubMed

Park, Y K; Koo, H C; Kim, S H; Hwang, S Y; Jung, W K; Kim, J M; Shin, S; Kim, R T; Park, Y H

2007-12-01

Bovine mastitis can be diagnosed by abnormalities in milk components and somatic cell count (SCC), as well as by clinical signs. We examined raw milk in Korea by analyzing SCC, milk urea nitrogen (MUN), and the percentages of milk components (milk fat, protein, and lactose). The associations between SCC or MUN and other milk components were investigated, as well as the relationships between the bacterial species isolated from milk. Somatic cell counts, MUN, and the percentages of milk fat, protein, and lactose were analyzed in 30,019 raw milk samples collected from 2003 to 2006. The regression coefficients of natural logarithmic-transformed SCC (SCCt) on milk fat (-0.0149), lactose (-0.8910), and MUN (-0.0096), and those of MUN on milk fat (-0.3125), protein (-0.8012), and SCCt (-0.0671) were negative, whereas the regression coefficient of SCCt on protein was positive (0.3023). When the data were categorized by the presence or absence of bacterial infection in raw milk, SCCt was negatively associated with milk fat (-0.0172), protein (-0.2693), and lactose (-0.4108). The SCCt values were significantly affected by bacterial species. In particular, 104 milk samples infected with Staphylococcus aureus had the highest SCCt (1.67) compared with milk containing other mastitis-causing bacteria: coagulase-negative staphylococci (n = 755, 1.50), coagulase-positive staphylococci (except Staphylococcus aureus; n = 77, 1.59), Streptococcus spp. (Streptococcus dysgalactiae, n = 37; Streptococcus uberis, n = 12, 0.83), Enterococcus spp. (n = 46, 1.04), Escherichia coli (n = 705, 1.56), Pseudomonas spp. (n = 456, 1.59), and yeast (n = 189, 1.52). These results show that high SCC and MUN negatively affect milk components and that a statistical approach associating SCC, MUN, and milk components by bacterial infection can explain the patterns among them. Bacterial species present in raw milk are an important influence on SCC in Korea.

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

DOE PAGES

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

2015-12-04

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

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

PubMed

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

2017-07-01

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

Changes in the Milk Metabolome of the Giant Panda (Ailuropoda melanoleuca) with Time after Birth--Three Phases in Early Lactation and Progressive Individual Differences.

PubMed

Zhang, Tong; Zhang, Rong; Zhang, Liang; Zhang, Zhihe; Hou, Rong; Wang, Hairui; Loeffler, I Kati; Watson, David G; Kennedy, Malcolm W

2015-01-01

Ursids (bears) in general, and giant pandas in particular, are highly altricial at birth. The components of bear milks and their changes with time may be uniquely adapted to nourish relatively immature neonates, protect them from pathogens, and support the maturation of neonatal digestive physiology. Serial milk samples collected from three giant pandas in early lactation were subjected to untargeted metabolite profiling and multivariate analysis. Changes in milk metabolites with time after birth were analysed by Principal Component Analysis, Hierarchical Cluster Analysis and further supported by Orthogonal Partial Least Square-Discriminant Analysis, revealing three phases of milk maturation: days 1-6 (Phase 1), days 7-20 (Phase 2), and beyond day 20 (Phase 3). While the compositions of Phase 1 milks were essentially indistinguishable among individuals, divergences emerged during the second week of lactation. OPLS regression analysis positioned against the growth rate of one cub tentatively inferred a correlation with changes in the abundance of a trisaccharide, isoglobotriose, previously observed to be a major oligosaccharide in ursid milks. Three artificial milk formulae used to feed giant panda cubs were also analysed, and were found to differ markedly in component content from natural panda milk. These findings have implications for the dependence of the ontogeny of all species of bears, and potentially other members of the Carnivora and beyond, on the complexity and sequential changes in maternal provision of micrometabolites in the immediate period after birth.

Effect of the statin therapy on biochemical laboratory tests--a chemometrics study.

PubMed

Durceková, Tatiana; Mocák, Ján; Boronová, Katarína; Balla, Ján

2011-01-05

Statins are the first-line choice for lowering total and LDL cholesterol levels and very important medicaments for reducing the risk of coronary artery disease. The aim of this study is therefore assessment of the results of biochemical tests characterizing the condition of 172 patients before and after administration of statins. For this purpose, several chemometric tools, namely principal component analysis, cluster analysis, discriminant analysis, logistic regression, KNN classification, ROC analysis, descriptive statistics and ANOVA were used. Mutual relations of 11 biochemical laboratory tests, the patient's age and gender were investigated in detail. Achieved results enable to evaluate the extent of the statin treatment in each individual case. They may also help in monitoring the dynamic progression of the disease. Copyright © 2010 Elsevier B.V. All rights reserved.

Dirichlet Component Regression and its Applications to Psychiatric Data

PubMed Central

Gueorguieva, Ralitza; Rosenheck, Robert; Zelterman, Daniel

2011-01-01

Summary We describe a Dirichlet multivariable regression method useful for modeling data representing components as a percentage of a total. This model is motivated by the unmet need in psychiatry and other areas to simultaneously assess the effects of covariates on the relative contributions of different components of a measure. The model is illustrated using the Positive and Negative Syndrome Scale (PANSS) for assessment of schizophrenia symptoms which, like many other metrics in psychiatry, is composed of a sum of scores on several components, each in turn, made up of sums of evaluations on several questions. We simultaneously examine the effects of baseline socio-demographic and co-morbid correlates on all of the components of the total PANSS score of patients from a schizophrenia clinical trial and identify variables associated with increasing or decreasing relative contributions of each component. Several definitions of residuals are provided. Diagnostics include measures of overdispersion, Cook’s distance, and a local jackknife influence metric. PMID:22058582

Chemometric expertise of the quality of groundwater sources for domestic use.

PubMed

Spanos, Thomas; Ene, Antoaneta; Simeonova, Pavlina

2015-01-01

In the present study 49 representative sites have been selected for the collection of water samples from central water supplies with different geographical locations in the region of Kavala, Northern Greece. Ten physicochemical parameters (pH, electric conductivity, nitrate, chloride, sodium, potassium, total alkalinity, total hardness, bicarbonate and calcium) were analyzed monthly, in the period from January 2010 to December 2010. Chemometric methods were used for monitoring data mining and interpretation (cluster analysis, principal components analysis and source apportioning by principal components regression). The clustering of the chemical indicators delivers two major clusters related to the water hardness and the mineral components (impacted by sea, bedrock and acidity factors). The sampling locations are separated into three major clusters corresponding to the spatial distribution of the sites - coastal, lowland and semi-mountainous. The principal components analysis reveals two latent factors responsible for the data structures, which are also an indication for the sources determining the groundwater quality of the region (conditionally named "mineral" factor and "water hardness" factor). By the apportionment approach it is shown what the contribution is of each of the identified sources to the formation of the total concentration of each one of the chemical parameters. The mean values of the studied physicochemical parameters were found to be within the limits given in the 98/83/EC Directive. The water samples are appropriate for human consumption. The results of this study provide an overview of the hydrogeological profile of water supply system for the studied area.

Cumulative trauma, hyperarousal, and suicidality in the general population: a path analysis.

PubMed

Briere, John; Godbout, Natacha; Dias, Colin

2015-01-01

Although trauma exposure and posttraumatic stress disorder (PTSD) both have been linked to suicidal thoughts and behavior, the underlying basis for this relationship is not clear. In a sample of 357 trauma-exposed individuals from the general population, younger participant age, cumulative trauma exposure, and all three Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, PTSD clusters (reexperiencing, avoidance, and hyperarousal) were correlated with clinical levels of suicidality. However, logistic regression analysis indicated that when all PTSD clusters were considered simultaneously, only hyperarousal continued to be predictive. A path analysis confirmed that posttraumatic hyperarousal (but not other components of PTSD) fully mediated the relationship between extent of trauma exposure and degree of suicidal thoughts and behaviors.

Modelling of Hydrophilic Interaction Liquid Chromatography Stationary Phases Using Chemometric Approaches

PubMed Central

Ortiz-Villanueva, Elena; Tauler, Romà

2017-01-01

Metabolomics is a powerful and widely used approach that aims to screen endogenous small molecules (metabolites) of different families present in biological samples. The large variety of compounds to be determined and their wide diversity of physical and chemical properties have promoted the development of different types of hydrophilic interaction liquid chromatography (HILIC) stationary phases. However, the selection of the most suitable HILIC stationary phase is not straightforward. In this work, four different HILIC stationary phases have been compared to evaluate their potential application for the analysis of a complex mixture of metabolites, a situation similar to that found in non-targeted metabolomics studies. The obtained chromatographic data were analyzed by different chemometric methods to explore the behavior of the considered stationary phases. ANOVA-simultaneous component analysis (ASCA), principal component analysis (PCA) and partial least squares regression (PLS) were used to explore the experimental factors affecting the stationary phase performance, the main similarities and differences among chromatographic conditions used (stationary phase and pH) and the molecular descriptors most useful to understand the behavior of each stationary phase. PMID:29064436

Principal Component Analysis for Enhancement of Infrared Spectra Monitoring

NASA Astrophysics Data System (ADS)

Haney, Ricky Lance

The issue of air quality within the aircraft cabin is receiving increasing attention from both pilot and flight attendant unions. This is due to exposure events caused by poor air quality that in some cases may have contained toxic oil components due to bleed air that flows from outside the aircraft and then through the engines into the aircraft cabin. Significant short and long-term medical issues for aircraft crew have been attributed to exposure. The need for air quality monitoring is especially evident in the fact that currently within an aircraft there are no sensors to monitor the air quality and potentially harmful gas levels (detect-to-warn sensors), much less systems to monitor and purify the air (detect-to-treat sensors) within the aircraft cabin. The specific purpose of this research is to utilize a mathematical technique called principal component analysis (PCA) in conjunction with principal component regression (PCR) and proportionality constant calculations (PCC) to simplify complex, multi-component infrared (IR) spectra data sets into a reduced data set used for determination of the concentrations of the individual components. Use of PCA can significantly simplify data analysis as well as improve the ability to determine concentrations of individual target species in gas mixtures where significant band overlap occurs in the IR spectrum region. Application of this analytical numerical technique to IR spectrum analysis is important in improving performance of commercial sensors that airlines and aircraft manufacturers could potentially use in an aircraft cabin environment for multi-gas component monitoring. The approach of this research is two-fold, consisting of a PCA application to compare simulation and experimental results with the corresponding PCR and PCC to determine quantitatively the component concentrations within a mixture. The experimental data sets consist of both two and three component systems that could potentially be present as air contaminants in an aircraft cabin. In addition, experimental data sets are analyzed for a hydrogen peroxide (H2O2) aqueous solution mixture to determine H2O2 concentrations at various levels that could be produced during use of a vapor phase hydrogen peroxide (VPHP) decontamination system. After the PCA application to two and three component systems, the analysis technique is further expanded to include the monitoring of potential bleed air contaminants from engine oil combustion. Simulation data sets created from database spectra were utilized to predict gas components and concentrations in unknown engine oil samples at high temperatures as well as time-evolved gases from the heating of engine oils.

Improving the long-lead predictability of El Niño using a novel forecasting scheme based on a dynamic components model

NASA Astrophysics Data System (ADS)

Petrova, Desislava; Koopman, Siem Jan; Ballester, Joan; Rodó, Xavier

2017-02-01

El Niño (EN) is a dominant feature of climate variability on inter-annual time scales driving changes in the climate throughout the globe, and having wide-spread natural and socio-economic consequences. In this sense, its forecast is an important task, and predictions are issued on a regular basis by a wide array of prediction schemes and climate centres around the world. This study explores a novel method for EN forecasting. In the state-of-the-art the advantageous statistical technique of unobserved components time series modeling, also known as structural time series modeling, has not been applied. Therefore, we have developed such a model where the statistical analysis, including parameter estimation and forecasting, is based on state space methods, and includes the celebrated Kalman filter. The distinguishing feature of this dynamic model is the decomposition of a time series into a range of stochastically time-varying components such as level (or trend), seasonal, cycles of different frequencies, irregular, and regression effects incorporated as explanatory covariates. These components are modeled separately and ultimately combined in a single forecasting scheme. Customary statistical models for EN prediction essentially use SST and wind stress in the equatorial Pacific. In addition to these, we introduce a new domain of regression variables accounting for the state of the subsurface ocean temperature in the western and central equatorial Pacific, motivated by our analysis, as well as by recent and classical research, showing that subsurface processes and heat accumulation there are fundamental for the genesis of EN. An important feature of the scheme is that different regression predictors are used at different lead months, thus capturing the dynamical evolution of the system and rendering more efficient forecasts. The new model has been tested with the prediction of all warm events that occurred in the period 1996-2015. Retrospective forecasts of these events were made for long lead times of at least two and a half years. Hence, the present study demonstrates that the theoretical limit of ENSO prediction should be sought much longer than the commonly accepted "Spring Barrier". The high correspondence between the forecasts and observations indicates that the proposed model outperforms all current operational statistical models, and behaves comparably to the best dynamical models used for EN prediction. Thus, the novel way in which the modeling scheme has been structured could also be used for improving other statistical and dynamical modeling systems.

The Neck Disability Index (NDI) and its correlation with quality of life and mental health measures among patients with single-level cervical disc disease scheduled for surgery.

PubMed

Sundseth, J; Kolstad, F; Johnsen, L G; Pripp, A H; Nygaard, O P; Andresen, H; Fredriksli, O A; Myrseth, E; Züchner, M; Zwart, J A

2015-10-01

The Neck Disability Index (NDI) is widely used as a self-rated disability score in patients with cervical radiculopathy. The purpose of this study was to evaluate whether the NDI score correlated with other assessments of quality of life and mental health in a specific group of patients with single-level cervical disc disease and corresponding radiculopathy. One hundred thirty-six patients were included in a prospective, randomized controlled clinical multicenter study on one-level anterior cervical discectomy with arthroplasty (ACDA) versus one-level anterior cervical discectomy with fusion (ACDF). The preoperative data were obtained at hospital admission 1 to 3 days prior to surgery. The NDI score was used as the dependent variable and correlation as well as regression analyses were conducted to assess the relationship with the short form-36, EuroQol-5Dimension-3 level and Hospital Anxiety and Depression Scale. The mean age at inclusion was 44.1 years (SD ±7.0, range 26-59 years), of which 46.3 % were male. Mean NDI score was 48.6 (SD = 12.3, minimum 30 and maximum 88). Simple linear regression analysis demonstrated a significant correlation between NDI and the EuroQol-5Dimension-3 level [R = -0.64, 95 % confidence interval (CI) -30.1- -19.8, p < 0.001] and to a lesser extent between NDI and the short form-36 physical component summary [R = -0.49, 95 % CI (-1.10- -0.58), p < 0.001] and the short form-36 mental component summary [R = -0.25, 95 % CI (-0.47- -0-09), p = 0.004]. Regarding NDI and the Hospital Anxiety and Depression Scale, a significant correlation for depression was found [R = 0.26, 95 % CI (0.21-1.73), p = 0.01]. Multiple linear regression analysis showed a statistically significant and the strongest correlation between NDI and the independent variables in the following order: EuroQol-5Dimension-3 level [R = -0.64, 95 % CI (-23.5- -7.9), p <0.001], short form-36 physical component summary [R = -0.41, 95 % CI (-0.93- -0.23), p = 0.001] and short form-36 mental component summary [R = -0.36, 95 % CI (-0.53- -0.15), p = 0.001]. The results from the present study show that the NDI correlated significantly with a different quality of life and mental health measures among patients with single-level cervical disc disease and corresponding radiculopathy.

The Digital Shoreline Analysis System (DSAS) Version 4.0 - An ArcGIS extension for calculating shoreline change

USGS Publications Warehouse

Thieler, E. Robert; Himmelstoss, Emily A.; Zichichi, Jessica L.; Ergul, Ayhan

2009-01-01

The Digital Shoreline Analysis System (DSAS) version 4.0 is a software extension to ESRI ArcGIS v.9.2 and above that enables a user to calculate shoreline rate-of-change statistics from multiple historic shoreline positions. A user-friendly interface of simple buttons and menus guides the user through the major steps of shoreline change analysis. Components of the extension and user guide include (1) instruction on the proper way to define a reference baseline for measurements, (2) automated and manual generation of measurement transects and metadata based on user-specified parameters, and (3) output of calculated rates of shoreline change and other statistical information. DSAS computes shoreline rates of change using four different methods: (1) endpoint rate, (2) simple linear regression, (3) weighted linear regression, and (4) least median of squares. The standard error, correlation coefficient, and confidence interval are also computed for the simple and weighted linear-regression methods. The results of all rate calculations are output to a table that can be linked to the transect file by a common attribute field. DSAS is intended to facilitate the shoreline change-calculation process and to provide rate-of-change information and the statistical data necessary to establish the reliability of the calculated results. The software is also suitable for any generic application that calculates positional change over time, such as assessing rates of change of glacier limits in sequential aerial photos, river edge boundaries, land-cover changes, and so on.

Professional Burnout and Concurrent Health Complaints in Neonatal Nursing.

PubMed

Skorobogatova, Natalija; Žemaitienė, Nida; Šmigelskas, Kastytis; Tamelienė, Rasa

2017-01-01

The aim of this study was to analyze nurses' professional burnout and health complaints and the relationship between the two components. The anonymous survey included 94 neonatal intensive care nurses from two centers of perinatology. The Maslach Burnout Inventory-Human Services Survey (MBI-HSS) was used to evaluate professional burnout; it consisted of 3 components, Emotional Exhaustion, Depersonalization, and Personal Accomplishments, with 22 items in total. Health complaints were evaluated by 21 items, where nurses were asked to report the occurrence of symptoms within the last year. Scale means were presented with standard deviations (SD). Inferential analysis was conducted with multivariate logistic regression, adjusting for age, residence, and work experience. The mean score of professional burnout on the Emotional Exhaustion subscale was 14.4 (SD=7.91), Depersonalization 3.8 (SD=4.75), and Personal Accomplishment 29.1 (SD=10.12). The health assessment revealed that sleeplessness, lack of rest, nervousness, and tiredness were the most common complaints. The regression analysis revealed that tiredness was independently associated with significantly increased odds of professional burnout (OR=4.1). In our study, more than half of the nurses in neonatal intensive care had moderate or high levels of emotional exhaustion, while levels of depersonalization were significantly lower. In contrast, the level of personal accomplishment was low in more than half of the nurses. The most common health complaints were sleep disturbances, nervousness, and tiredness. Tiredness was most strongly associated with professional burnout.

The Parameters Affecting the Success of Irrigation and Debridement with Component Retention in the Treatment of Acutely Infected Total Knee Arthroplasty

PubMed Central

Kim, Jae Gyoon; Bae, Ji Hoon; Lee, Seung Yup; Cho, Won Tae

2015-01-01

Background The aims of our study were to evaluate the success rate of irrigation and debridement with component retention (IDCR) for acutely infected total knee arthroplasty (TKA) (< 4 weeks of symptom duration) and to analyze the factors affecting prognosis of IDCR. Methods We retrospectively reviewed 28 knees treated by IDCR for acutely infected TKA from 2003 to 2012. We evaluated the success rate of IDCR. All variables were compared between the success and failure groups. Multivariable logistic regression analysis was also used to examine the relative contribution of these parameters to the success of IDCR. Results Seventeen knees (60.7%) were successfully treated. Between the success and failure groups, there were significant differences in the time from primary TKA to IDCR (p = 0.021), the preoperative erythrocyte sedimentation rate (ESR; p = 0.021), microorganism (p = 0.006), and polyethylene liner exchange (p = 0.017). Multivariable logistic regression analysis of parameters affecting the success of IDCR demonstrated that preoperative ESR (odds ratio [OR], 1.02; p = 0.041), microorganism (OR, 12.4; p = 0.006), and polyethylene liner exchange (OR, 0.07; p = 0.021) were significant parameters. Conclusions The results show that 60.7% of the cases were successfully treated by IDCR for acutely infected TKA. The preoperative ESR, microorganism, and polyethylene liner exchange were factors that affected the success of IDCR in acutely infected TKA. PMID:25729521

Sufficient Forecasting Using Factor Models

PubMed Central

Fan, Jianqing; Xue, Lingzhou; Yao, Jiawei

2017-01-01

We consider forecasting a single time series when there is a large number of predictors and a possible nonlinear effect. The dimensionality was first reduced via a high-dimensional (approximate) factor model implemented by the principal component analysis. Using the extracted factors, we develop a novel forecasting method called the sufficient forecasting, which provides a set of sufficient predictive indices, inferred from high-dimensional predictors, to deliver additional predictive power. The projected principal component analysis will be employed to enhance the accuracy of inferred factors when a semi-parametric (approximate) factor model is assumed. Our method is also applicable to cross-sectional sufficient regression using extracted factors. The connection between the sufficient forecasting and the deep learning architecture is explicitly stated. The sufficient forecasting correctly estimates projection indices of the underlying factors even in the presence of a nonparametric forecasting function. The proposed method extends the sufficient dimension reduction to high-dimensional regimes by condensing the cross-sectional information through factor models. We derive asymptotic properties for the estimate of the central subspace spanned by these projection directions as well as the estimates of the sufficient predictive indices. We further show that the natural method of running multiple regression of target on estimated factors yields a linear estimate that actually falls into this central subspace. Our method and theory allow the number of predictors to be larger than the number of observations. We finally demonstrate that the sufficient forecasting improves upon the linear forecasting in both simulation studies and an empirical study of forecasting macroeconomic variables. PMID:29731537

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.

[A competency model of rural general practitioners: theory construction and empirical study].

PubMed

Yang, Xiu-Mu; Qi, Yu-Long; Shne, Zheng-Fu; Han, Bu-Xin; Meng, Bei

2015-04-01

To perform theory construction and empirical study of the competency model of rural general practitioners. Through literature study, job analysis, interviews, and expert team discussion, the questionnaire of rural general practitioners competency was constructed. A total of 1458 rural general practitioners were surveyed by the questionnaire in 6 central provinces. The common factors were constructed using the principal component method of exploratory factor analysis and confirmatory factor analysis. The influence of the competency characteristics on the working performance was analyzed using regression equation analysis. The Cronbach 's alpha coefficient of the questionnaire was 0.974. The model consisted of 9 dimensions and 59 items. The 9 competency dimensions included basic public health service ability, basic clinical skills, system analysis capability, information management capability, communication and cooperation ability, occupational moral ability, non-medical professional knowledge, personal traits and psychological adaptability. The rate of explained cumulative total variance was 76.855%. The model fitting index were Χ(2)/df 1.88, GFI=0.94, NFI=0.96, NNFI=0.98, PNFI=0.91, RMSEA=0.068, CFI=0.97, IFI=0.97, RFI=0.96, suggesting good model fitting. Regression analysis showed that the competency characteristics had a significant effect on job performance. The rural general practitioners competency model provides reference for rural doctor training, rural order directional cultivation of medical students, and competency performance management of the rural general practitioners.

Development of the upper-body dressing scale for a buttoned shirt: a preliminary correlational study.

PubMed

Suzuki, Makoto; Yamada, Sumio; Omori, Mikayo; Hatakeyama, Mayumi; Sugimura, Yuko; Matsushita, Kazuhiko; Tagawa, Yoshikatsu

2008-09-01

A patient with poststroke hemiparesis learns to use the nonparetic arm to compensate for the weakness of the paretic arm to achieve independence in dressing. This is the learning process of new component actions on dressing. The purpose of this study was to develop the Upper-Body Dressing Scale (UBDS) for buttoned shirt dressing, which evaluates the component actions of upper-body dressing, and to provide preliminary data on internal consistency of the UBDS, as well as its reproducibility, validity, and sensitivity to clinical change. Correlational study of concurrent validity and reliability in which 63 consecutive stroke patients were enrolled in the study and were assessed repeatedly by the UBDS and the dressing item of Functional Independent Measure (FIM). Fifty-one patients completed the 3-wk study. The Cronbach's coefficient alpha of UBDS was 0.88. The principal component analysis extracted two components, which explained 62.3% of total variance. All items of the scale had high loading on the first component (0.65-0.83). Actions on the paralytic side were the positive loadings and actions on the healthy side were the negative loadings on the second component. Intraclass correlation coefficient was 0.87. The level of correlation between UBDS score and FIM dressing item scores was -0.72. Logistic regression analysis showed that only the score of UBDS on the first day of evaluation was a significant independent predictor of dressing ability (odds ratio, 0.82; 95% confidence interval, 0.71-0.95). The UBDS scores for paralytic hand passed into the sleeve, sleeve pulled up beyond the elbow joint, and sleeve pulled up beyond the shoulder joint were worse than the score for the other components of the task. These component actions had positive loading on the second component, which was identified by the principal component analysis. The UBDS has good internal consistency, reproducibility, validity, and sensitivity to clinical changes of patients with poststroke hemiparesis. This detailed UBDS assessment enables us to document the most difficult stages in dressing and to assess motor and process skills for independence of dressing.

Preference mapping of dulce de leche commercialized in Brazilian markets.

PubMed

Gaze, L V; Oliveira, B R; Ferrao, L L; Granato, D; Cavalcanti, R N; Conte Júnior, C A; Cruz, A G; Freitas, M Q

2015-03-01

Dulce de leche samples available in the Brazilian market were submitted to sensory profiling by quantitative descriptive analysis and acceptance test, as well sensory evaluation using the just-about-right scale and purchase intent. External preference mapping and the ideal sensory characteristics of dulce de leche were determined. The results were also evaluated by principal component analysis, hierarchical cluster analysis, partial least squares regression, artificial neural networks, and logistic regression. Overall, significant product acceptance was related to intermediate scores of the sensory attributes in the descriptive test, and this trend was observed even after consumer segmentation. The results obtained by sensometric techniques showed that optimizing an ideal dulce de leche from the sensory standpoint is a multidimensional process, with necessary adjustments on the appearance, aroma, taste, and texture attributes of the product for better consumer acceptance and purchase. The optimum dulce de leche was characterized by high scores for the attributes sweet taste, caramel taste, brightness, color, and caramel aroma in accordance with the preference mapping findings. In industrial terms, this means changing the parameters used in the thermal treatment and quantitative changes in the ingredients used in formulations. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Technical Assistance and Changes in Nutrition and Physical Activity Practices in the National Early Care and Education Learning Collaboratives Project, 2015-2016.

PubMed

Chiappone, Alethea; Smith, Teresa M; Estabrooks, Paul A; Rasmussen, Cristy Geno; Blaser, Casey; Yaroch, Amy L

2018-04-26

The National Early Care and Education Learning Collaboratives Project (ECELC) aims to improve best practices in early care and education (ECE) programs in topic areas of the Nutrition and Physical Activity Self-Assessment in Child Care (NAP SACC). Technical assistance is a component of the ECELC, yet its effect on outcomes is unclear. Beyond dose and duration of technical assistance, limited research exists on characteristics of technical assistance that contribute to outcomes. The objective of this study was to identify and describe technical assistance characteristics and explore associations with NAP SACC outcomes. We collected data from 10 collaboratives comprising 84 ECE programs in 2 states in 2015-2016. The objective of technical assistance was to support programs in improving best practices. Technical assistance was provided to programs via on-site, telephone, or email and was tailored to program needs. We used a mixed-methods design to examine associations between technical assistance and NAP SACC outcomes. We used multiple regression analysis to assess quantitative data and qualitative comparative analysis to determine necessary and sufficient technical assistance conditions supporting NAP SACC outcomes. We also conducted a document review to describe technical assistance that referred conditions identified by the qualitative comparative analysis. Regression analyses detected an inverse relationship between changes in NAP SACC scores and hours of technical assistance. No clear pattern emerged in the qualitative comparative analysis, leaving no necessary and sufficient conditions. However, the qualitative comparative analysis identified feedback as a potentially important component of technical assistance, whereas resource sharing and frequent email were characteristics that seemed to reduce the likelihood of improved outcomes. Email and resource sharing were considered primarily general information rather than tailored technical assistance. Technical assistance may be used in programs and made adaptable to program needs. The inclusion and evaluation of technical assistance, especially tailored approaches, is warranted for environmental interventions, including ECE settings.

Effectiveness of Knee Injury and Anterior Cruciate Ligament Tear Prevention Programs: A Meta-Analysis

PubMed Central

Collins, Jamie E.; Yang, Heidi Y.; Goczalk, Melissa G.; Katz, Jeffrey N.; Losina, Elena

2015-01-01

Objective Individuals frequently involved in jumping, pivoting or cutting are at increased risk of knee injury, including anterior cruciate ligament (ACL) tears. We sought to use meta-analytic techniques to establish whether neuromuscular and proprioceptive training is efficacious in preventing knee and ACL injury and to identify factors related to greater efficacy of such programs. Methods We performed a systematic literature search of studies published in English between 1996 and 2014. Intervention efficacy was ascertained from incidence rate ratios (IRRs) weighted by their precision (1/variance) using a random effects model. Separate analyses were performed for knee and ACL injury. We examined whether year of publication, study quality, or specific components of the intervention were associated with efficacy of the intervention in a meta-regression analysis. Results Twenty-four studies met the inclusion criteria and were used in the meta-analysis. The mean study sample was 1,093 subjects. Twenty studies reported data on knee injury in general terms and 16 on ACL injury. Maximum Jadad score was 3 (on a 0–5 scale). The summary incidence rate ratio was estimated at 0.731 (95% CI: 0.614, 0.871) for knee injury and 0.493 (95% CI: 0.285, 0.854) for ACL injury, indicating a protective effect of intervention. Meta-regression analysis did not identify specific intervention components associated with greater efficacy but established that later year of publication was associated with more conservative estimates of intervention efficacy. Conclusion The current meta-analysis provides evidence that neuromuscular and proprioceptive training reduces knee injury in general and ACL injury in particular. Later publication date was associated with higher quality studies and more conservative efficacy estimates. As study quality was generally low, these data suggest that higher quality studies should be implemented to confirm the preventive efficacy of such programs. PMID:26637173

Chemical characteristics combined with bioactivity for comprehensive evaluation of Panax ginseng C.A. Meyer in different ages and seasons based on HPLC-DAD and chemometric methods.

PubMed

Shan, Si-Ming; Luo, Jian-Guang; Huang, Fang; Kong, Ling-Yi

2014-02-01

Panax ginseng C.A. Meyer has been known as a valuable traditional Chinese medicines for thousands years of history. Ginsenosides, the main active constituents, exhibit prominent immunoregulation effect. The present study first describes a holistic method based on chemical characteristic and lymphocyte proliferative capacity to evaluate systematically the quality of P. ginseng in thirty samples from different seasons during 2-6 years. The HPLC fingerprints were evaluated using principle component analysis (PCA) and hierarchical clustering analysis (HCA). The spectrum-efficacy model between HPLC fingerprints and T-lymphocyte proliferative activities was investigated by principal component regression (PCR) and partial least squares (PLS). The results indicated that the growth of the ginsenosides could be grouped into three periods and from August of the fifth year, P. ginseng appeared significant lymphocyte proliferative capacity. Close correlation existed between the spectrum-efficacy relationship and ginsenosides Rb1, Ro, Rc, Rb2 and Re were the main contributive components to the lymphocyte proliferative capacity. This comprehensive strategy, providing reliable and adequate scientific evidence, could be applied to other TCMs to ameliorate their quality control. Copyright © 2013 Elsevier B.V. All rights reserved.

Bioactive components on immuno-enhancement effects in the traditional Chinese medicine Shenqi Fuzheng Injection based on relevance analysis between chemical HPLC fingerprints and in vivo biological effects.

PubMed

Wang, Jinxu; Tong, Xin; Li, Peibo; Liu, Menghua; Peng, Wei; Cao, Hui; Su, Weiwei

2014-08-08

Shenqi Fuzheng Injection (SFI) is an injectable traditional Chinese herbal formula comprised of two Chinese herbs, Radix codonopsis and Radix astragali, which were commonly used to improve immune functions against chronic diseases in an integrative and holistic way in China and other East Asian countries for thousands of years. This present study was designed to explore the bioactive components on immuno-enhancement effects in SFI using the relevance analysis between chemical fingerprints and biological effects in vivo. According to a four-factor, nine-level uniform design, SFI samples were prepared with different proportions of the four portions separated from SFI via high speed counter current chromatography (HSCCC). SFI samples were assessed with high performance liquid chromatography (HPLC) for 23 identified components. For the immunosuppressed murine experiments, biological effects in vivo were evaluated on spleen index (E1), peripheral white blood cell counts (E2), bone marrow cell counts (E3), splenic lymphocyte proliferation (E4), splenic natural killer cell activity (E5), peritoneal macrophage phagocytosis (E6) and the amount of interleukin-2 (E7). Based on the hypothesis that biological effects in vivo varied with differences in components, multivariate relevance analysis, including gray relational analysis (GRA), multi-linear regression analysis (MLRA) and principal component analysis (PCA), were performed to evaluate the contribution of each identified component. The results indicated that the bioactive components of SFI on immuno-enhancement activities were calycosin-7-O-β-d-glucopyranoside (P9), isomucronulatol-7,2'-di-O-glucoside (P11), biochanin-7-glucoside (P12), 9,10-dimethoxypterocarpan-3-O-xylosylglucoside (P15) and astragaloside IV (P20), which might have positive effects on spleen index (E1), splenic lymphocyte proliferation (E4), splenic natural killer cell activity (E5), peritoneal macrophage phagocytosis (E6) and the amount of interleukin-2 (E7), while 5-hydroxymethyl-furaldehyde (P5) and lobetyolin (P13) might have negative effects on E1, E4, E5, E6 and E7. Finally, the bioactive HPLC fingerprint of SFI based on its bioactive components on immuno-enhancement effects was established for quality control of SFI. In summary, this study provided a perspective to explore the bioactive components in a traditional Chinese herbal formula with a series of HPLC and animal experiments, which would be helpful to improve quality control and inspire further clinical studies of traditional Chinese medicines. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

An empirical comparative study on biological age estimation algorithms with an application of Work Ability Index (WAI).

PubMed

Cho, Il Haeng; Park, Kyung S; Lim, Chang Joo

2010-02-01

In this study, we described the characteristics of five different biological age (BA) estimation algorithms, including (i) multiple linear regression, (ii) principal component analysis, and somewhat unique methods developed by (iii) Hochschild, (iv) Klemera and Doubal, and (v) a variant of Klemera and Doubal's method. The objective of this study is to find the most appropriate method of BA estimation by examining the association between Work Ability Index (WAI) and the differences of each algorithm's estimates from chronological age (CA). The WAI was found to be a measure that reflects an individual's current health status rather than the deterioration caused by a serious dependency with the age. Experiments were conducted on 200 Korean male participants using a BA estimation system developed principally under the concept of non-invasive, simple to operate and human function-based. Using the empirical data, BA estimation as well as various analyses including correlation analysis and discriminant function analysis was performed. As a result, it had been confirmed by the empirical data that Klemera and Doubal's method with uncorrelated variables from principal component analysis produces relatively reliable and acceptable BA estimates. 2009 Elsevier Ireland Ltd. All rights reserved.

Motivation and Self-Management Behavior of the Individuals With Chronic Low Back Pain.

PubMed

Jung, Mi Jung; Jeong, Younhee

2016-01-01

Self-management behavior is an important component for successful pain management in individuals with chronic low back pain. Motivation has been considered as an effective way to change behavior. Because there are other physical, social, and psychological factors affecting individuals with pain, it is necessary to identify the main effect of motivation on self-management behavior without the influence of those factors. The purpose of this study was to investigate the effect of motivation on self-management in controlling pain, depression, and social support. We used a nonexperimental, cross-sectional, descriptive design with mediation analysis and included 120 participants' data in the final analysis. We also used hierarchical multiple regression to test the effect of motivation, and multiple regression analysis and Sobel test were used to examine the mediating effect. Motivation itself accounted for 23.4% of the variance in self-management, F(1, 118) = 35.003, p < .001. After controlling covariates, motivation was also a significant factor for self-management. In the mediation analysis, motivation completely mediated the relationship between education and self-management, z = 2.292, p = .021. Motivation is an important part of self-management, and self-management education is not effective without motivation. The results of our study suggest that nurses incorporate motivation in nursing intervention, rather than only giving information.

Response of dissolved trace metals to land use/land cover and their source apportionment using a receptor model in a subtropic river, China.

PubMed

Li, Siyue; Zhang, Quanfa

2011-06-15

Water samples were collected for determination of dissolved trace metals in 56 sampling sites throughout the upper Han River, China. Multivariate statistical analyses including correlation analysis, stepwise multiple linear regression models, and principal component and factor analysis (PCA/FA) were employed to examine the land use influences on trace metals, and a receptor model of factor analysis-multiple linear regression (FA-MLR) was used for source identification/apportionment of anthropogenic heavy metals in the surface water of the River. Our results revealed that land use was an important factor in water metals in the snow melt flow period and land use in the riparian zone was not a better predictor of metals than land use away from the river. Urbanization in a watershed and vegetation along river networks could better explain metals, and agriculture, regardless of its relative location, however slightly explained metal variables in the upper Han River. FA-MLR analysis identified five source types of metals, and mining, fossil fuel combustion, and vehicle exhaust were the dominant pollutions in the surface waters. The results demonstrated great impacts of human activities on metal concentrations in the subtropical river of China. Copyright © 2011 Elsevier B.V. All rights reserved.

[The trial of business data analysis at the Department of Radiology by constructing the auto-regressive integrated moving-average (ARIMA) model].

PubMed

Tani, Yuji; Ogasawara, Katsuhiko

2012-01-01

This study aimed to contribute to the management of a healthcare organization by providing management information using time-series analysis of business data accumulated in the hospital information system, which has not been utilized thus far. In this study, we examined the performance of the prediction method using the auto-regressive integrated moving-average (ARIMA) model, using the business data obtained at the Radiology Department. We made the model using the data used for analysis, which was the number of radiological examinations in the past 9 years, and we predicted the number of radiological examinations in the last 1 year. Then, we compared the actual value with the forecast value. We were able to establish that the performance prediction method was simple and cost-effective by using free software. In addition, we were able to build the simple model by pre-processing the removal of trend components using the data. The difference between predicted values and actual values was 10%; however, it was more important to understand the chronological change rather than the individual time-series values. Furthermore, our method was highly versatile and adaptable compared to the general time-series data. Therefore, different healthcare organizations can use our method for the analysis and forecasting of their business data.

Analysis of Wind Tunnel Lateral Oscillatory Data of the F-16XL Aircraft

NASA Technical Reports Server (NTRS)

Klein, Vladislav; Murphy, Patrick C.; Szyba, Nathan M.

2004-01-01

Static and dynamic wind tunnel tests were performed on an 18% scale model of the F-16XL aircraft. These tests were performed over a wide range of angles of attack and sideslip with oscillation amplitudes from 5 deg. to 30 deg. and reduced frequencies from 0.073 to 0.269. Harmonic analysis was used to estimate Fourier coefficients and in-phase and out-of-phase components. For frequency dependent data from rolling oscillations, a two-step regression method was used to obtain unsteady models (indicial functions), and derivatives due to sideslip angle, roll rate and yaw rate from in-phase and out-of-phase components. Frequency dependence was found for angles of attack between 20 deg. and 50 deg. Reduced values of coefficient of determination and increased values of fit error were found for angles of attack between 35 deg. and 45 deg. An attempt to estimate model parameters from yaw oscillations failed, probably due to the low number of test cases at different frequencies.

Simultaneous kinetic spectrometric determination of three flavonoid antioxidants in fruit with the aid of chemometrics

NASA Astrophysics Data System (ADS)

Sun, Ruiling; Wang, Yong; Ni, Yongnian; Kokot, Serge

2014-03-01

A simple, inexpensive and sensitive kinetic spectrophotometric method was developed for the simultaneous determination of three anti-carcinogenic flavonoids: catechin, quercetin and naringenin, in fruit samples. A yellow chelate product was produced in the presence neocuproine and Cu(I) - a reduction product of the reaction between the flavonoids with Cu(II), and this enabled the quantitative measurements with UV-vis spectrophotometry. The overlapping spectra obtained, were resolved with chemometrics calibration models, and the best performing method was the fast independent component analysis (fast-ICA/PCR (Principal component regression)); the limits of detection were 0.075, 0.057 and 0.063 mg L-1 for catechin, quercetin and naringenin, respectively. The novel method was found to outperform significantly the common HPLC procedure.

Pyridostigmine bromide and the long-term subjective health status of a sample of female reserve component Gulf War veterans: a brief report.

PubMed

Schumm, W R; Reppert, E J; Jurich, A P; Bollman, S R; Castelo, C; Sanders, D; Webb, F J

2001-02-01

The role of pyridostigmine bromide (PB) pills in explaining the long-term subjective health status of a sample of over 100 female Reserve Component Gulf War veterans was examined through regression analysis. Results fell just short of significance (p < .06) for the prediction of subjective health approximately six years after the war and were clearly not significant for the prediction of subjective health at previous times. Results parallel Golomb's 1999 RAND report, which found suggestive but not conclusive evidence for the possible adverse effects of Gulf War veterans' consumption of pyridostigmine bromide pills. Our data suggest that use of more than 10 pills may have been especially risky with respect to long-term subjective health.

[Establishment of the Mathematical Model for PMI Estimation Using FTIR Spectroscopy and Data Mining Method].

PubMed

Wang, L; Qin, X C; Lin, H C; Deng, K F; Luo, Y W; Sun, Q R; Du, Q X; Wang, Z Y; Tuo, Y; Sun, J H

2018-02-01

To analyse the relationship between Fourier transform infrared （FTIR） spectrum of rat's spleen tissue and postmortem interval （PMI） for PMI estimation using FTIR spectroscopy combined with data mining method. Rats were sacrificed by cervical dislocation, and the cadavers were placed at 20 ℃. The FTIR spectrum data of rats' spleen tissues were taken and measured at different time points. After pretreatment, the data was analysed by data mining method. The absorption peak intensity of rat's spleen tissue spectrum changed with the PMI, while the absorption peak position was unchanged. The results of principal component analysis （PCA） showed that the cumulative contribution rate of the first three principal components was 96%. There was an obvious clustering tendency for the spectrum sample at each time point. The methods of partial least squares discriminant analysis （PLS-DA） and support vector machine classification （SVMC） effectively divided the spectrum samples with different PMI into four categories （0-24 h, 48-72 h, 96-120 h and 144-168 h）. The determination coefficient （ R ²） of the PMI estimation model established by PLS regression analysis was 0.96, and the root mean square error of calibration （RMSEC） and root mean square error of cross validation （RMSECV） were 9.90 h and 11.39 h respectively. In prediction set, the R ² was 0.97, and the root mean square error of prediction （RMSEP） was 10.49 h. The FTIR spectrum of the rat's spleen tissue can be effectively analyzed qualitatively and quantitatively by the combination of FTIR spectroscopy and data mining method, and the classification and PLS regression models can be established for PMI estimation. Copyright© by the Editorial Department of Journal of Forensic Medicine.

Reexamining the Validity and Dimensionality of the Moorong Self-Efficacy Scale: Improving Its Clinical Utility.

PubMed

Middleton, James W; Tran, Yvonne; Lo, Charles; Craig, Ashley

2016-12-01

To improve the clinical utility of the Moorong Self-Efficacy Scale (MSES) by reexamining its factor structure and comparing its performance against a measure of general self-efficacy in persons with spinal cord injury (SCI). Cross-sectional survey design. Community. Adults with SCI (N=161; 118 men and 43 women) recruited from Australia (n=82) and the United States (n=79), including 86 with paraplegia and 75 with tetraplegia. None. Confirmatory factor analysis deriving fit indices on reported 1-, 2-, and 3-factor structures for the MSES. Exploratory factor analysis of MSES using principal component analysis with promax oblique rotation and structure validation, with correlations and multiple regression using cross-sectional data from the Sherer General Self-Efficacy Scale and Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36). The MSES was confirmed to have a 3-factor structure, explaining 61% of variance. Two of the factors, labeled social function self-efficacy and personal function self-efficacy, were SCI condition-specific, whereas the other factor (accounting for 9.7% of variance) represented general self-efficacy, correlating most strongly with the Sherer General Self-Efficacy Scale. Correlations and multiple regression analyses between MSES factors, Sherer General Self-Efficacy Scale total score, SF-36 Physical and Mental Component Summary scores, and SF-36 domain scores support validity of this MSES factor structure. No significant cross-cultural differences existed between Australia and the United States in total MSES or factor scores. The findings support a 3-factor structure encompassing general and SCI domain-specific self-efficacy beliefs and better position the MSES to assist SCI rehabilitation assessment, planning, and research. Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Differences in cannabis-related experiences between patients with a first episode of psychosis and controls.

PubMed

Bianconi, F; Bonomo, M; Marconi, A; Kolliakou, A; Stilo, S A; Iyegbe, C; Gurillo Muñoz, P; Homayoun, S; Mondelli, V; Luzi, S; Dazzan, P; Prata, D; La Cascia, C; O'Connor, J; David, A; Morgan, C; Murray, R M; Lynskey, M; Di Forti, M

2016-04-01

Many studies have reported that cannabis use increases the risk of a first episode of psychosis (FEP). However, only a few studies have investigated the nature of cannabis-related experiences in FEP patients, and none has examined whether these experiences are similar in FEP and general populations. The aim of this study was to explore differences in self-reported cannabis experiences between FEP and non-psychotic populations. A total of 252 subjects, who met International Classification of Diseases (ICD)-10 criteria for FEP, and 217 controls who reported cannabis use were selected from the Genetics and Psychosis (GAP) study. The Medical Research Council Social Schedule and the Cannabis Experience Questionnaire were used to collect sociodemographic data and cannabis use information, respectively. Both 'bad' and 'enjoyable' experiences were more commonly reported by FEP subjects than controls. Principal components factor analysis identified four components which explained 62.3% of the variance. Linear regression analysis on the whole sample showed that the type of cannabis used and beliefs about the effect of cannabis on health all contributed to determining the intensity and frequency of experiences. Linear regression analysis on FEP subjects showed that the duration of cannabis use and amount of money spent on cannabis were strongly related to the intensity and frequency of enjoyable experiences in this population. These results suggest a higher sensitivity to cannabis effects among people who have suffered their first psychotic episode; this hypersensitivity results in them reporting both more 'bad' and 'enjoyable' experiences. The greater enjoyment experienced may provide an explanation of why FEP patients are more likely to use cannabis and to continue to use it despite experiencing an exacerbation of their psychotic symptoms.

Self-care agency in systemic lupus erythematosus and its associated factors: a cross-sectional study.

PubMed

Yang, Hui; Xie, Xia; Song, Yuqing; Nie, Anliu; Chen, Hong

2018-01-01

The aim of this study was to estimate the level of self-care agency and explore its associated factors in patients with systemic lupus erythematosus (SLE). In this cross-sectional study, all patients were from a tertiary general hospital between July and October 2016 in Southwest China. The self-care agency was assessed using the Exercise of Self-care Agency Scale. Other variables were measured by the Visual Analog Scale, Systemic Lupus Erythematosus Disease Activity Index 2000, the physical component summary, and mental component summary of the 36-item Short Form Health Survey. Multivariate regression analysis was performed to explore the associated factors of self-care agency. A total of 123 patients were recruited. The mean score of Exercise of Self-care Agency Scale was 86.29. In univariate analysis, self-care agency of patients differed in regard to gender, work status, educational level, household income monthly per capita, and disease activity ( P <0.05). Additionally, higher body mass index, higher level of fatigue, and worse mental health were found in patients with lower self-care agency ( P <0.05). The stepwise multivariate regression analysis showed that male gender ( P =0.001), lower educational level ( P =0.003), lower household income monthly per capita ( P <0.001), and worse mental health ( P <0.001) could predict lower self-care agency. Patients with SLE had a middle level of self-care agency, suggesting that there is still much scope for improvement. The lower level of self-care agency was associated with male gender, lower educational level, lower household income monthly per capita, and worse mental health. Therefore, health care providers should develop targeted and comprehensive interventions to enhance self-care agency in patients with SLE.

Quantitative analysis of multi-component gas mixture based on AOTF-NIR spectroscopy

NASA Astrophysics Data System (ADS)

Hao, Huimin; Zhang, Yong; Liu, Junhua

2007-12-01

Near Infrared (NIR) spectroscopy analysis technology has attracted many eyes and has wide application in many domains in recent years because of its remarkable advantages. But the NIR spectrometer can only be used for liquid and solid analysis by now. In this paper, a new quantitative analysis method of gas mixture by using new generation NIR spectrometer is explored. To collect the NIR spectra of gas mixtures, a vacuumable gas cell was designed and assembled to Luminar 5030-731 Acousto-Optic Tunable Filter (AOTF)-NIR spectrometer. Standard gas samples of methane (CH 4), ethane (C IIH 6) and propane (C 3H 8) are diluted with super pure nitrogen via precision volumetric gas flow controllers to obtain gas mixture samples of different concentrations dynamically. The gas mixtures were injected into the gas cell and the spectra of wavelength between 1100nm-2300nm were collected. The feature components extracted from gas mixture spectra by using Partial Least Squares (PLS) were used as the inputs of the Support Vector Regress Machine (SVR) to establish the quantitative analysis model. The effectiveness of the model is tested by the samples of predicting set. The prediction Root Mean Square Error (RMSE) of CH 4, C IIH 6 and C 3H 8 is respectively 1.27%, 0.89%, and 1.20% when the concentrations of component gas are over 0.5%. It shows that the AOTF-NIR spectrometer with gas cell can be used for gas mixture analysis. PLS combining with SVR has a good performance in NIR spectroscopy analysis. This paper provides the bases for extending the application of NIR spectroscopy analysis to gas detection.

[Interrelations between plant communities and environmental factors of wetlands and surrounding lands in mid- and lower reaches of Tarim River].

PubMed

Zhao, Ruifeng; Zhou, Huarong; Qian, Yibing; Zhang, Jianjun

2006-06-01

A total of 16 quadrants of wetlands and surrounding lands in the mid- and lower reaches of Tarim River were surveyed, and the data about the characteristics of plant communities and environmental factors were collected and counted. By using PCA (principal component analysis) ordination and regression procedure, the distribution patterns of plant communities and the relationships between the characteristics of plant community structure and environmental factors were analyzed. The results showed that the distribution of the plant communities was closely related to soil moisture, salt, and nutrient contents. The accumulative contribution rate of soil moisture and salt contents in the first principal component accounted for 35.70%, and that of soil nutrient content in the second principal component reached 25.97%. There were 4 types of habitats for the plant community distribution, i. e., fenny--light salt--medium nutrient, moist--medium salt--medium nutrient, mesophytic--medium salt--low nutrient, and medium xerophytic-heavy salt--low nutrient. Along these habitats, swamp vegetation, meadow vegetation, riparian sparse forest, halophytic desert, and salinized shrub were distributed. In the wetlands and surrounding lands of mid- and lower reaches of Tarim River, the ecological dominance of the plant communities was markedly and unitary-linearly correlated with the compound gradient of soil moisture and salt contents. The relationships between species diversity, ecological dominance, and compound gradient of soil moisture and salt contents were significantly accorded to binary-linear regression model.

Parents' Perception of Satisfaction With Pediatric Nurse Practitioners' Care And Parental Intent to Adhere To Recommended Health Care Regimen.

PubMed

Kinder, Frances DiAnna

2016-01-01

The purposes of this study were to explore parents' perceptions of satisfaction with care from primary care pediatric nurse practitioners (PNPs) and to explore the relationships of the four components of parental satisfaction with parents' intent to adhere to recommended health care regimen. The study used a descriptive correlational research design. A convenience sample of 91 participants was recruited from practices in southeastern Pennsylvania. The 28-item, Parents' Perceptions of Satisfaction with Care from Pediatric Nurse Practitioners (PPSC-PNP) tool was developed to measure four components of satisfaction and overall satisfaction of parents with PNP care after the health visit. A 100 mm visual analog (VAS) scale measured parental intent to adhere to the care regimen recommended by the PNP. Parents' perceptions of overall satisfaction with care from PNPs and satisfaction with each of the four components (communication, clinical competence, caring behavior, and decisional control) were high as measured by the PPSC-PNP. Multiple regression analysis revealed that clinical competence had the strongest positive relationship with parental intent to adhere to PNP recommended health regimen and was the only variable to enter the regression equation. The findings of this study have implications for nursing practice. The PPSC-PNP instrument may be used with a variety of pediatric populations and settings as a benchmark for quality care. Clinical competence is important for the role of the PNP. Other variables of parental intent to adhere to the health regimen should be explored in future studies.

Design and testing of digitally manufactured paraffin Acrylonitrile-butadiene-styrene hybrid rocket motors

NASA Astrophysics Data System (ADS)

McCulley, Jonathan M.

This research investigates the application of additive manufacturing techniques for fabricating hybrid rocket fuel grains composed of porous Acrylonitrile-butadiene-styrene impregnated with paraffin wax. The digitally manufactured ABS substrate provides mechanical support for the paraffin fuel material and serves as an additional fuel component. The embedded paraffin provides an enhanced fuel regression rate while having no detrimental effect on the thermodynamic burn properties of the fuel grain. Multiple fuel grains with various ABS-to-Paraffin mass ratios were fabricated and burned with nitrous oxide. Analytical predictions for end-to-end motor performance and fuel regression are compared against static test results. Baseline fuel grain regression calculations use an enthalpy balance energy analysis with the material and thermodynamic properties based on the mean paraffin/ABS mass fractions within the fuel grain. In support of these analytical comparisons, a novel method for propagating the fuel port burn surface was developed. In this modeling approach the fuel cross section grid is modeled as an image with white pixels representing the fuel and black pixels representing empty or burned grid cells.

New machine-learning algorithms for prediction of Parkinson's disease

NASA Astrophysics Data System (ADS)

Mandal, Indrajit; Sairam, N.

2014-03-01

This article presents an enhanced prediction accuracy of diagnosis of Parkinson's disease (PD) to prevent the delay and misdiagnosis of patients using the proposed robust inference system. New machine-learning methods are proposed and performance comparisons are based on specificity, sensitivity, accuracy and other measurable parameters. The robust methods of treating Parkinson's disease (PD) includes sparse multinomial logistic regression, rotation forest ensemble with support vector machines and principal components analysis, artificial neural networks, boosting methods. A new ensemble method comprising of the Bayesian network optimised by Tabu search algorithm as classifier and Haar wavelets as projection filter is used for relevant feature selection and ranking. The highest accuracy obtained by linear logistic regression and sparse multinomial logistic regression is 100% and sensitivity, specificity of 0.983 and 0.996, respectively. All the experiments are conducted over 95% and 99% confidence levels and establish the results with corrected t-tests. This work shows a high degree of advancement in software reliability and quality of the computer-aided diagnosis system and experimentally shows best results with supportive statistical inference.

Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition

EPA Science Inventory

Boosted regression tree (BRT) models were developed to quantify the nonlinear relationships between landscape variables and nutrient concentrations in a mesoscale mixed land cover watershed during base-flow conditions. Factors that affect instream biological components, based on ...

Domain adaptation via transfer component analysis.

PubMed

Pan, Sinno Jialin; Tsang, Ivor W; Kwok, James T; Yang, Qiang

2011-02-01

Domain adaptation allows knowledge from a source domain to be transferred to a different but related target domain. Intuitively, discovering a good feature representation across domains is crucial. In this paper, we first propose to find such a representation through a new learning method, transfer component analysis (TCA), for domain adaptation. TCA tries to learn some transfer components across domains in a reproducing kernel Hilbert space using maximum mean miscrepancy. In the subspace spanned by these transfer components, data properties are preserved and data distributions in different domains are close to each other. As a result, with the new representations in this subspace, we can apply standard machine learning methods to train classifiers or regression models in the source domain for use in the target domain. Furthermore, in order to uncover the knowledge hidden in the relations between the data labels from the source and target domains, we extend TCA in a semisupervised learning setting, which encodes label information into transfer components learning. We call this extension semisupervised TCA. The main contribution of our work is that we propose a novel dimensionality reduction framework for reducing the distance between domains in a latent space for domain adaptation. We propose both unsupervised and semisupervised feature extraction approaches, which can dramatically reduce the distance between domain distributions by projecting data onto the learned transfer components. Finally, our approach can handle large datasets and naturally lead to out-of-sample generalization. The effectiveness and efficiency of our approach are verified by experiments on five toy datasets and two real-world applications: cross-domain indoor WiFi localization and cross-domain text classification.

Evaluating filterability of different types of sludge by statistical analysis: The role of key organic compounds in extracellular polymeric substances.

PubMed

Xiao, Keke; Chen, Yun; Jiang, Xie; Zhou, Yan

2017-03-01

An investigation was conducted for 20 different types of sludge in order to identify the key organic compounds in extracellular polymeric substances (EPS) that are important in assessing variations of sludge filterability. The different types of sludge varied in initial total solids (TS) content, organic composition and pre-treatment methods. For instance, some of the sludges were pre-treated by acid, ultrasonic, thermal, alkaline, or advanced oxidation technique. The Pearson's correlation results showed significant correlations between sludge filterability and zeta potential, pH, dissolved organic carbon, protein and polysaccharide in soluble EPS (SB EPS), loosely bound EPS (LB EPS) and tightly bound EPS (TB EPS). The principal component analysis (PCA) method was used to further explore correlations between variables and similarities among EPS fractions of different types of sludge. Two principal components were extracted: principal component 1 accounted for 59.24% of total EPS variations, while principal component 2 accounted for 25.46% of total EPS variations. Dissolved organic carbon, protein and polysaccharide in LB EPS showed higher eigenvector projection values than the corresponding compounds in SB EPS and TB EPS in principal component 1. Further characterization of fractionized key organic compounds in LB EPS was conducted with size-exclusion chromatography-organic carbon detection-organic nitrogen detection (LC-OCD-OND). A numerical multiple linear regression model was established to describe relationship between organic compounds in LB EPS and sludge filterability. Copyright © 2016 Elsevier Ltd. All rights reserved.

Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials

NASA Astrophysics Data System (ADS)

Thompson, A. P.; Swiler, L. P.; Trott, C. R.; Foiles, S. M.; Tucker, G. J.

2015-03-01

We present a new interatomic potential for solids and liquids called Spectral Neighbor Analysis Potential (SNAP). The SNAP potential has a very general form and uses machine-learning techniques to reproduce the energies, forces, and stress tensors of a large set of small configurations of atoms, which are obtained using high-accuracy quantum electronic structure (QM) calculations. The local environment of each atom is characterized by a set of bispectrum components of the local neighbor density projected onto a basis of hyperspherical harmonics in four dimensions. The bispectrum components are the same bond-orientational order parameters employed by the GAP potential [1]. The SNAP potential, unlike GAP, assumes a linear relationship between atom energy and bispectrum components. The linear SNAP coefficients are determined using weighted least-squares linear regression against the full QM training set. This allows the SNAP potential to be fit in a robust, automated manner to large QM data sets using many bispectrum components. The calculation of the bispectrum components and the SNAP potential are implemented in the LAMMPS parallel molecular dynamics code. We demonstrate that a previously unnoticed symmetry property can be exploited to reduce the computational cost of the force calculations by more than one order of magnitude. We present results for a SNAP potential for tantalum, showing that it accurately reproduces a range of commonly calculated properties of both the crystalline solid and the liquid phases. In addition, unlike simpler existing potentials, SNAP correctly predicts the energy barrier for screw dislocation migration in BCC tantalum.

Reconstruction of spatio-temporal temperature from sparse historical records using robust probabilistic principal component regression

USGS Publications Warehouse

Tipton, John; Hooten, Mevin B.; Goring, Simon

2017-01-01

Scientific records of temperature and precipitation have been kept for several hundred years, but for many areas, only a shorter record exists. To understand climate change, there is a need for rigorous statistical reconstructions of the paleoclimate using proxy data. Paleoclimate proxy data are often sparse, noisy, indirect measurements of the climate process of interest, making each proxy uniquely challenging to model statistically. We reconstruct spatially explicit temperature surfaces from sparse and noisy measurements recorded at historical United States military forts and other observer stations from 1820 to 1894. One common method for reconstructing the paleoclimate from proxy data is principal component regression (PCR). With PCR, one learns a statistical relationship between the paleoclimate proxy data and a set of climate observations that are used as patterns for potential reconstruction scenarios. We explore PCR in a Bayesian hierarchical framework, extending classical PCR in a variety of ways. First, we model the latent principal components probabilistically, accounting for measurement error in the observational data. Next, we extend our method to better accommodate outliers that occur in the proxy data. Finally, we explore alternatives to the truncation of lower-order principal components using different regularization techniques. One fundamental challenge in paleoclimate reconstruction efforts is the lack of out-of-sample data for predictive validation. Cross-validation is of potential value, but is computationally expensive and potentially sensitive to outliers in sparse data scenarios. To overcome the limitations that a lack of out-of-sample records presents, we test our methods using a simulation study, applying proper scoring rules including a computationally efficient approximation to leave-one-out cross-validation using the log score to validate model performance. The result of our analysis is a spatially explicit reconstruction of spatio-temporal temperature from a very sparse historical record.

Relationships of maternal and paternal anthropometry with neonatal body size, proportions and adiposity in an Australian cohort.

PubMed

Pomeroy, Emma; Wells, Jonathan C K; Cole, Tim J; O'Callaghan, Michael; Stock, Jay T

2015-04-01

The patterns of association between maternal or paternal and neonatal phenotype may offer insight into how neonatal characteristics are shaped by evolutionary processes, such as conflicting parental interests in fetal investment and obstetric constraints. Paternal interests are theoretically served by maximizing fetal growth, and maternal interests by managing investment in current and future offspring, but whether paternal and maternal influences act on different components of overall size is unknown. We tested whether parents' prepregnancy height and body mass index (BMI) were related to neonatal anthropometry (birthweight, head circumference, absolute and proportional limb segment and trunk lengths, subcutaneous fat) among 1,041 Australian neonates using stepwise linear regression. Maternal and paternal height and maternal BMI were associated with birthweight. Paternal height related to offspring forearm and lower leg lengths, maternal height and BMI to neonatal head circumference, and maternal BMI to offspring adiposity. Principal components analysis identified three components of variability reflecting neonatal "head and trunk skeletal size," "adiposity," and "limb lengths." Regression analyses of the component scores supported the associations of head and trunk size or adiposity with maternal anthropometry, and limb lengths with paternal anthropometry. Our results suggest that while neonatal fatness reflects environmental conditions (maternal physiology), head circumference and limb and trunk lengths show differing associations with parental anthropometry. These patterns may reflect genetics, parental imprinting and environmental influences in a manner consistent with parental conflicts of interest. Paternal height may relate to neonatal limb length as a means of increasing fetal growth without exacerbating the risk of obstetric complications. © 2014 The Authors American Journal of Physical Anthropology Published by Wiley Periodicals, Inc.

[Effects of different concentrations of nitrogen and phosphorus on growth and active components of Salvia miltiorrhiza].

PubMed

Xia, Gui-Hui; Wang, Qiu-Ling; Wang, Wen-Quan; Hou, Jun-Ling; Song, Qing-Yan; Luo, Lin; Zhang, Dou-Dou; Yang, Xiang

2016-11-01

With annual Salvia miltiorrhiza seedlings as experimental material, using "3414" optimal regression design recommended by the Ministry of Agriculture and regularly watered with nutrient solution, through the dynamic sampling of S. miltiorrhiza in different growing stages, and the growth index, dry weight of plant root and content of active components were measured. The potted experiments were applied to study the effects of different nitrogen and phosphorus ratios on the growth, dry matter accumulation and accumulation of active components of S. miltiorrhiza, in order to explore a compatible fertilization method of nitrogen and phosphorus ratio that are suitable for production and quality of S. miltiorrhiza. The results reported as follows：①High concentrations of nitrogen fertilizer was beneficial to dry matter accumulation of S. miltiorrhiza aerial parts, and low concentration of nitrogen fertilizer transferred the dry matter accumulation to underground, and N1P1 could make the transfer ahead of time;②Regression analysis showed that in the early growth stage (before early July), we could use the nitrogen and phosphorus as basic fertilizer at a concentration of 1.521,0.355 g•L⁻¹ respectively to promote the growth of S. miltiorrhiza and at a concentration of 2.281,0.710 g•L⁻¹ respectively to promote the dry matter accumulation of root (after mid-August);③Five kinds of active components of S. miltiorrhiza decreased with the increase of nitrogen concentration, and increased with the increase of the concentration of phosphate fertilizer. Nitrogenous fertilizer, phosphate fertilizer in N-P=2∶3 ratio was more suitable for the accumulation of salvianolic acids, in N-P=1∶2 ratio was more suitable for the accumulation of tanshinone. Copyright© by the Chinese Pharmaceutical Association.

Relationships of maternal and paternal anthropometry with neonatal body size, proportions and adiposity in an Australian cohort

PubMed Central

Pomeroy, Emma; Wells, Jonathan CK; Cole, Tim J; O'Callaghan, Michael; Stock, Jay T

2015-01-01

The patterns of association between maternal or paternal and neonatal phenotype may offer insight into how neonatal characteristics are shaped by evolutionary processes, such as conflicting parental interests in fetal investment and obstetric constraints. Paternal interests are theoretically served by maximizing fetal growth, and maternal interests by managing investment in current and future offspring, but whether paternal and maternal influences act on different components of overall size is unknown. We tested whether parents' prepregnancy height and body mass index (BMI) were related to neonatal anthropometry (birthweight, head circumference, absolute and proportional limb segment and trunk lengths, subcutaneous fat) among 1,041 Australian neonates using stepwise linear regression. Maternal and paternal height and maternal BMI were associated with birthweight. Paternal height related to offspring forearm and lower leg lengths, maternal height and BMI to neonatal head circumference, and maternal BMI to offspring adiposity. Principal components analysis identified three components of variability reflecting neonatal “head and trunk skeletal size,” “adiposity,” and “limb lengths.” Regression analyses of the component scores supported the associations of head and trunk size or adiposity with maternal anthropometry, and limb lengths with paternal anthropometry. Our results suggest that while neonatal fatness reflects environmental conditions (maternal physiology), head circumference and limb and trunk lengths show differing associations with parental anthropometry. These patterns may reflect genetics, parental imprinting and environmental influences in a manner consistent with parental conflicts of interest. Paternal height may relate to neonatal limb length as a means of increasing fetal growth without exacerbating the risk of obstetric complications. Am J Phys Anthropol 156:625–636, 2015. PMID:25502164

Individual differences in the recognition of facial expressions: an event-related potentials study.

PubMed

Tamamiya, Yoshiyuki; Hiraki, Kazuo

2013-01-01

Previous studies have shown that early posterior components of event-related potentials (ERPs) are modulated by facial expressions. The goal of the current study was to investigate individual differences in the recognition of facial expressions by examining the relationship between ERP components and the discrimination of facial expressions. Pictures of 3 facial expressions (angry, happy, and neutral) were presented to 36 young adults during ERP recording. Participants were asked to respond with a button press as soon as they recognized the expression depicted. A multiple regression analysis, where ERP components were set as predictor variables, assessed hits and reaction times in response to the facial expressions as dependent variables. The N170 amplitudes significantly predicted for accuracy of angry and happy expressions, and the N170 latencies were predictive for accuracy of neutral expressions. The P2 amplitudes significantly predicted reaction time. The P2 latencies significantly predicted reaction times only for neutral faces. These results suggest that individual differences in the recognition of facial expressions emerge from early components in visual processing.

Diversity of soil yeasts isolated from South Victoria Land, Antarctica

USGS Publications Warehouse

Connell, L.; Redman, R.; Craig, S.; Scorzetti, G.; Iszard, M.; Rodriguez, R.

2008-01-01

Unicellular fungi, commonly referred to as yeasts, were found to be components of the culturable soil fungal population in Taylor Valley, Mt. Discovery, Wright Valley, and two mountain peaks of South Victoria Land, Antarctica. Samples were taken from sites spanning a diversity of soil habitats that were not directly associated with vertebrate activity. A large proportion of yeasts isolated in this study were basidiomycetous species (89%), of which 43% may represent undescribed species, demonstrating that culturable yeasts remain incompletely described in these polar desert soils. Cryptococcus species represented the most often isolated genus (33%) followed by Leucosporidium (22%). Principle component analysis and multiple linear regression using stepwise selection was used to model the relation between abiotic variables (principle component 1 and principle component 2 scores) and yeast biodiversity (the number of species present at a given site). These analyses identified soil pH and electrical conductivity as significant predictors of yeast biodiversity. Species-specific PCR primers were designed to rapidly discriminate among the Dioszegia and Leucosporidium species collected in this study. ?? 2008 Springer Science+Business Media, LLC.

Extraction of pure components from overlapped signals in gas chromatography-mass spectrometry (GC-MS)

PubMed Central

Likić, Vladimir A

2009-01-01

Gas chromatography-mass spectrometry (GC-MS) is a widely used analytical technique for the identification and quantification of trace chemicals in complex mixtures. When complex samples are analyzed by GC-MS it is common to observe co-elution of two or more components, resulting in an overlap of signal peaks observed in the total ion chromatogram. In such situations manual signal analysis is often the most reliable means for the extraction of pure component signals; however, a systematic manual analysis over a number of samples is both tedious and prone to error. In the past 30 years a number of computational approaches were proposed to assist in the process of the extraction of pure signals from co-eluting GC-MS components. This includes empirical methods, comparison with library spectra, eigenvalue analysis, regression and others. However, to date no approach has been recognized as best, nor accepted as standard. This situation hampers general GC-MS capabilities, and in particular has implications for the development of robust, high-throughput GC-MS analytical protocols required in metabolic profiling and biomarker discovery. Here we first discuss the nature of GC-MS data, and then review some of the approaches proposed for the extraction of pure signals from co-eluting components. We summarize and classify different approaches to this problem, and examine why so many approaches proposed in the past have failed to live up to their full promise. Finally, we give some thoughts on the future developments in this field, and suggest that the progress in general computing capabilities attained in the past two decades has opened new horizons for tackling this important problem. PMID:19818154

Modeling pollen time series using seasonal-trend decomposition procedure based on LOESS smoothing

NASA Astrophysics Data System (ADS)

Rojo, Jesús; Rivero, Rosario; Romero-Morte, Jorge; Fernández-González, Federico; Pérez-Badia, Rosa

2017-02-01

Analysis of airborne pollen concentrations provides valuable information on plant phenology and is thus a useful tool in agriculture—for predicting harvests in crops such as the olive and for deciding when to apply phytosanitary treatments—as well as in medicine and the environmental sciences. Variations in airborne pollen concentrations, moreover, are indicators of changing plant life cycles. By modeling pollen time series, we can not only identify the variables influencing pollen levels but also predict future pollen concentrations. In this study, airborne pollen time series were modeled using a seasonal-trend decomposition procedure based on LOcally wEighted Scatterplot Smoothing (LOESS) smoothing (STL). The data series—daily Poaceae pollen concentrations over the period 2006-2014—was broken up into seasonal and residual (stochastic) components. The seasonal component was compared with data on Poaceae flowering phenology obtained by field sampling. Residuals were fitted to a model generated from daily temperature and rainfall values, and daily pollen concentrations, using partial least squares regression (PLSR). This method was then applied to predict daily pollen concentrations for 2014 (independent validation data) using results for the seasonal component of the time series and estimates of the residual component for the period 2006-2013. Correlation between predicted and observed values was r = 0.79 (correlation coefficient) for the pre-peak period (i.e., the period prior to the peak pollen concentration) and r = 0.63 for the post-peak period. Separate analysis of each of the components of the pollen data series enables the sources of variability to be identified more accurately than by analysis of the original non-decomposed data series, and for this reason, this procedure has proved to be a suitable technique for analyzing the main environmental factors influencing airborne pollen concentrations.

Modeling pollen time series using seasonal-trend decomposition procedure based on LOESS smoothing.

PubMed

Rojo, Jesús; Rivero, Rosario; Romero-Morte, Jorge; Fernández-González, Federico; Pérez-Badia, Rosa

2017-02-01

Analysis of airborne pollen concentrations provides valuable information on plant phenology and is thus a useful tool in agriculture-for predicting harvests in crops such as the olive and for deciding when to apply phytosanitary treatments-as well as in medicine and the environmental sciences. Variations in airborne pollen concentrations, moreover, are indicators of changing plant life cycles. By modeling pollen time series, we can not only identify the variables influencing pollen levels but also predict future pollen concentrations. In this study, airborne pollen time series were modeled using a seasonal-trend decomposition procedure based on LOcally wEighted Scatterplot Smoothing (LOESS) smoothing (STL). The data series-daily Poaceae pollen concentrations over the period 2006-2014-was broken up into seasonal and residual (stochastic) components. The seasonal component was compared with data on Poaceae flowering phenology obtained by field sampling. Residuals were fitted to a model generated from daily temperature and rainfall values, and daily pollen concentrations, using partial least squares regression (PLSR). This method was then applied to predict daily pollen concentrations for 2014 (independent validation data) using results for the seasonal component of the time series and estimates of the residual component for the period 2006-2013. Correlation between predicted and observed values was r = 0.79 (correlation coefficient) for the pre-peak period (i.e., the period prior to the peak pollen concentration) and r = 0.63 for the post-peak period. Separate analysis of each of the components of the pollen data series enables the sources of variability to be identified more accurately than by analysis of the original non-decomposed data series, and for this reason, this procedure has proved to be a suitable technique for analyzing the main environmental factors influencing airborne pollen concentrations.

Comparative study of outcome measures and analysis methods for traumatic brain injury trials.

PubMed

Alali, Aziz S; Vavrek, Darcy; Barber, Jason; Dikmen, Sureyya; Nathens, Avery B; Temkin, Nancy R

2015-04-15

Batteries of functional and cognitive measures have been proposed as alternatives to the Extended Glasgow Outcome Scale (GOSE) as the primary outcome for traumatic brain injury (TBI) trials. We evaluated several approaches to analyzing GOSE and a battery of four functional and cognitive measures. Using data from a randomized trial, we created a "super" dataset of 16,550 subjects from patients with complete data (n=331) and then simulated multiple treatment effects across multiple outcome measures. Patients were sampled with replacement (bootstrapping) to generate 10,000 samples for each treatment effect (n=400 patients/group). The percentage of samples where the null hypothesis was rejected estimates the power. All analytic techniques had appropriate rates of type I error (≤5%). Accounting for baseline prognosis either by using sliding dichotomy for GOSE or using regression-based methods substantially increased the power over the corresponding analysis without accounting for prognosis. Analyzing GOSE using multivariate proportional odds regression or analyzing the four-outcome battery with regression-based adjustments had the highest power, assuming equal treatment effect across all components. Analyzing GOSE using a fixed dichotomy provided the lowest power for both unadjusted and regression-adjusted analyses. We assumed an equal treatment effect for all measures. This may not be true in an actual clinical trial. Accounting for baseline prognosis is critical to attaining high power in Phase III TBI trials. The choice of primary outcome for future trials should be guided by power, the domain of brain function that an intervention is likely to impact, and the feasibility of collecting outcome data.

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

Comparative Study of Outcome Measures and Analysis Methods for Traumatic Brain Injury Trials

PubMed Central

Alali, Aziz S.; Vavrek, Darcy; Barber, Jason; Dikmen, Sureyya; Nathens, Avery B.

2015-01-01

Abstract Batteries of functional and cognitive measures have been proposed as alternatives to the Extended Glasgow Outcome Scale (GOSE) as the primary outcome for traumatic brain injury (TBI) trials. We evaluated several approaches to analyzing GOSE and a battery of four functional and cognitive measures. Using data from a randomized trial, we created a “super” dataset of 16,550 subjects from patients with complete data (n=331) and then simulated multiple treatment effects across multiple outcome measures. Patients were sampled with replacement (bootstrapping) to generate 10,000 samples for each treatment effect (n=400 patients/group). The percentage of samples where the null hypothesis was rejected estimates the power. All analytic techniques had appropriate rates of type I error (≤5%). Accounting for baseline prognosis either by using sliding dichotomy for GOSE or using regression-based methods substantially increased the power over the corresponding analysis without accounting for prognosis. Analyzing GOSE using multivariate proportional odds regression or analyzing the four-outcome battery with regression-based adjustments had the highest power, assuming equal treatment effect across all components. Analyzing GOSE using a fixed dichotomy provided the lowest power for both unadjusted and regression-adjusted analyses. We assumed an equal treatment effect for all measures. This may not be true in an actual clinical trial. Accounting for baseline prognosis is critical to attaining high power in Phase III TBI trials. The choice of primary outcome for future trials should be guided by power, the domain of brain function that an intervention is likely to impact, and the feasibility of collecting outcome data. PMID:25317951

Effects of eye artifact removal methods on single trial P300 detection, a comparative study.

PubMed

Ghaderi, Foad; Kim, Su Kyoung; Kirchner, Elsa Andrea

2014-01-15

Electroencephalographic signals are commonly contaminated by eye artifacts, even if recorded under controlled conditions. The objective of this work was to quantitatively compare standard artifact removal methods (regression, filtered regression, Infomax, and second order blind identification (SOBI)) and two artifact identification approaches for independent component analysis (ICA) methods, i.e. ADJUST and correlation. To this end, eye artifacts were removed and the cleaned datasets were used for single trial classification of P300 (a type of event related potentials elicited using the oddball paradigm). Statistical analysis of the results confirms that the combination of Infomax and ADJUST provides a relatively better performance (0.6% improvement on average of all subject) while the combination of SOBI and correlation performs the worst. Low-pass filtering the data at lower cutoffs (here 4 Hz) can also improve the classification accuracy. Without requiring any artifact reference channel, the combination of Infomax and ADJUST improves the classification performance more than the other methods for both examined filtering cutoffs, i.e., 4 Hz and 25 Hz. Copyright © 2013 Elsevier B.V. All rights reserved.

Dough performance, quality and shelf life of flat bread supplemented with fractions of germinated date seed.

PubMed

Hejri-Zarifi, Sudiyeh; Ahmadian-Kouchaksaraei, Zahra; Pourfarzad, Amir; Khodaparast, Mohammad Hossein Haddad

2014-12-01

Germinated palm date seeds were milled into two fractions: germ and residue. Dough rheological characteristics, baking (specific volume and sensory evaluation), and textural properties (at first day and during storage for 5 days) were determined in Barbari flat bread. Germ and residue fractions were incorporated at various levels ranged in 0.5-3 g/100 g of wheat flour. Water absorption, arrival time and gelatination temperature were decreased by germ fraction but accompanied by an increasing effect on the mixing tolerance index and degree of softening in most levels. Although improvement in dough stability was monitored but specific volume of bread was not affected by both fractions. Texture analysis of bread samples during 5 days of storage indicated that both fractions of germinated date seeds were able to diminish bread staling. Avrami non-linear regression equation was chosen as useful mathematical model to properly study bread hardening kinetics. In addition, principal component analysis (PCA) allowed discriminating among dough and bread specialties. Partial least squares regression (PLSR) models were applied to determine the relationships between sensory and instrumental data.

Detection of Butter Adulteration with Lard by Employing (1)H-NMR Spectroscopy and Multivariate Data Analysis.

PubMed

Fadzillah, Nurrulhidayah Ahmad; Man, Yaakob bin Che; Rohman, Abdul; Rosman, Arieff Salleh; Ismail, Amin; Mustafa, Shuhaimi; Khatib, Alfi

2015-01-01

The authentication of food products from the presence of non-allowed components for certain religion like lard is very important. In this study, we used proton Nuclear Magnetic Resonance ((1)H-NMR) spectroscopy for the analysis of butter adulterated with lard by simultaneously quantification of all proton bearing compounds, and consequently all relevant sample classes. Since the spectra obtained were too complex to be analyzed visually by the naked eyes, the classification of spectra was carried out.The multivariate calibration of partial least square (PLS) regression was used for modelling the relationship between actual value of lard and predicted value. The model yielded a highest regression coefficient (R(2)) of 0.998 and the lowest root mean square error calibration (RMSEC) of 0.0091% and root mean square error prediction (RMSEP) of 0.0090, respectively. Cross validation testing evaluates the predictive power of the model. PLS model was shown as good models as the intercept of R(2)Y and Q(2)Y were 0.0853 and -0.309, respectively.

Relationship between Metabolic Syndrome Components and Periodontal Disease in a Japanese General Population: the Suita Study.

PubMed

Kikui, Miki; Kokubo, Yoshihiro; Ono, Takahiro; Kida, Momoyo; Kosaka, Takayuki; Yamamoto, Masaaki; Watanabe, Makoto; Maeda, Yoshinobu; Miyamoto, Yoshihiro

2017-05-01

A positive association between metabolic syndrome (MetS) and periodontal status has recently been noted. However, no study has evaluated the relationship by sex and in a general urban population using the uniform definition proposed in the 2009 Joint Interim Statement. The aim of this study was to clarify the relationship between MetS and periodontal status using the uniform definition in a general urban Japanese population. A total of 1,856 Japanese men and women (mean age: 66.4 years) were studied using data from the Suita study. Periodontal status was evaluated by the Community Periodontal Index (CPI). MetS was defined using the 2009 Joint Interim Statement. The associations of the MetS and its components with periodontal disease were investigated using multiple logistic regression analysis adjusting for age, drinking, and smoking. Among the components of the MetS, low HDL cholesterol level was significantly associated with periodontal disease in men and women [odds ratios (OR)=2.39 and 1.53; 95% confidence intervals=1.36-4.19 and 1.06-2.19]. Furthermore, the risk of periodontal disease showed 1.43-, 1.42-, and 1.89-fold increases in those with 2, 3, and ≥4 components, respectively, compared with those having no components (P trend ＜0.001). For the analysis by sex, the risk of periodontal disease was increased 2.27- and 1.76-fold in those with ≥4 components in men and women, respectively (both P trend =0.001). These findings suggest that MetS and lower HDL cholesterol are associated with periodontal disease. Subjects with two or more MetS components had a significantly higher prevalence of periodontal disease.

Relationship between Metabolic Syndrome Components and Periodontal Disease in a Japanese General Population: the Suita Study

PubMed Central

Kikui, Miki; Kokubo, Yoshihiro; Kida, Momoyo; Kosaka, Takayuki; Yamamoto, Masaaki; Watanabe, Makoto; Maeda, Yoshinobu; Miyamoto, Yoshihiro

2017-01-01

Aim: A positive association between metabolic syndrome (MetS) and periodontal status has recently been noted. However, no study has evaluated the relationship by sex and in a general urban population using the uniform definition proposed in the 2009 Joint Interim Statement. The aim of this study was to clarify the relationship between MetS and periodontal status using the uniform definition in a general urban Japanese population. Methods: A total of 1,856 Japanese men and women (mean age: 66.4 years) were studied using data from the Suita study. Periodontal status was evaluated by the Community Periodontal Index (CPI). MetS was defined using the 2009 Joint Interim Statement. The associations of the MetS and its components with periodontal disease were investigated using multiple logistic regression analysis adjusting for age, drinking, and smoking. Results: Among the components of the MetS, low HDL cholesterol level was significantly associated with periodontal disease in men and women [odds ratios (OR) = 2.39 and 1.53; 95% confidence intervals = 1.36–4.19 and 1.06–2.19]. Furthermore, the risk of periodontal disease showed 1.43-, 1.42-, and 1.89-fold increases in those with 2, 3, and ≥ 4 components, respectively, compared with those having no components (Ptrend < 0.001). For the analysis by sex, the risk of periodontal disease was increased 2.27- and 1.76-fold in those with ≥ 4 components in men and women, respectively (both Ptrend = 0.001). Conclusion: These findings suggest that MetS and lower HDL cholesterol are associated with periodontal disease. Subjects with two or more MetS components had a significantly higher prevalence of periodontal disease. PMID:27725400

Calculation and Analysis of Magnetic Gradient Tensor Components of Global Magnetic Models

NASA Astrophysics Data System (ADS)

Schiffler, M.; Queitsch, M.; Schneider, M.; Goepel, A.; Stolz, R.; Krech, W.; Meyer, H. G.; Kukowski, N.

2014-12-01

Global Earth's magnetic field models like the International Geomagnetic Reference Field (IGRF), the World Magnetic Model (WMM) or the High Definition Geomagnetic Model (HDGM) are harmonic analysis regressions to available magnetic observations stored as spherical harmonic coefficients. Input data combine recordings from magnetic observatories, airborne magnetic surveys and satellite data. The advance of recent magnetic satellite missions like SWARM and its predecessors like CHAMP offer high resolution measurements while providing a full global coverage. This deserves expansion of the theoretical framework of harmonic synthesis to magnetic gradient tensor components. Measurement setups for Full Tensor Magnetic Gradiometry equipped with high sensitive gradiometers like the JeSSY STAR system can directly measure the gradient tensor components, which requires precise knowledge about the background regional gradients which can be calculated with this extension. In this study we develop the theoretical framework for calculation of the magnetic gradient tensor components from the harmonic series expansion and apply our approach to the IGRF and HDGM. The gradient tensor component maps for entire Earth's surface produced for the IGRF show low gradients reflecting the variation from the dipolar character, whereas maps for the HDGM (up to degree N=729) reveal new information about crustal structure, especially across the oceans, and deeply situated ore bodies. From the gradient tensor components, the rotational invariants, the Eigenvalues, and the normalized source strength (NSS) are calculated. The NSS focuses on shallower and stronger anomalies. Euler deconvolution using either the tensor components or the NSS applied to the HDGM reveals an estimate of the average source depth for the entire magnetic crust as well as individual plutons and ore bodies. The NSS reveals the boundaries between the anomalies of major continental provinces like southern Africa or the Eastern European Craton.

Greater impairment of postprandial triacylglycerol than glucose response in metabolic syndrome subjects with fasting hyperglycaemia.

PubMed

Jackson, Kim G; Walden, Charlotte M; Murray, Peter; Smith, Adrian M; Minihane, Anne M; Lovegrove, Julie A; Williams, Christine M

2013-08-01

Studies have started to question whether a specific component or combinations of metabolic syndrome (MetS) components may be more important in relation to cardiovascular disease risk. Our aim was to examine the impact of the presence of raised fasting glucose as a MetS component on postprandial lipaemia. Men classified with the MetS underwent a sequential test meal investigation, in which blood samples were taken at regular intervals after a test breakfast (t=0 min) and lunch (t=330 min). Lipids, glucose and insulin were measured in the fasting and postprandial samples. MetS subjects with 3 or 4 components were subdivided into those without (n=34) and with (n=23) fasting hyperglycaemia (≥5.6 mmol/l), irrespective of the combination of components. Fasting lipids and insulin were similar in the two groups, with glucose significantly higher in the men with glucose as a MetS component (P<0.001). Following the test meals, there were higher maximum concentration (maxC), area under the curve (AUC) and incremental AUC (P ≤0.016) for the postprandial triacylglycerol (TAG) response in men with fasting hyperglycaemia. Greater glucose AUC (P<0.001) and insulin maxC (P=0.010) were also observed in these individuals after the test meals. Multiple regression analysis revealed fasting glucose to be an important predictor of the postprandial TAG and glucose response. Our data analysis has revealed a greater impairment of postprandial TAG than glucose response in MetS subjects with raised fasting glucose. The worsening of postprandial lipaemic control may contribute to the greater CVD risk reported in individuals with MetS component combinations which include hyperglycaemia. Copyright © 2013 Elsevier Inc. All rights reserved.

Motivations and self-presentation strategies on Korean-based "Cyworld" weblog format personal homepages.

PubMed

Jung, Taejin; Youn, Hyunsook; McClung, Steven

2007-02-01

The main purposes of this study are to find out individuals' motives and interpersonal self-presentation strategies on constructing Korean weblog format personal homepage (e.g., "Cyworld mini-homepage"). The study also attempts to find predictor motives that lead to the activities of posting and maintaining a homepage and compare the self-presentation strategies used on the Web with those commonly used in interpersonal situations. By using a principal component factor analysis, four salient self-presentation strategy factors and five interpretable mini-homepage hosting motive factors were identified. Accompanying multiple regression analysis shows that entertainment and personal income factors are major predictors in explaining homepage maintenance expenditures and frequencies of updating.

Selenium uptake and assessment of the biochemical changes in Arthrospira (Spirulina) platensis biomass during the synthesis of selenium nanoparticles.

PubMed

Zinicovscaia, I; Chiriac, T; Cepoi, L; Rudi, L; Culicov, O; Frontasyeva, M; Rudic, V

2017-01-01

The process of selenium uptake by biomass of the cyanobacterium Arthrospira (Spirulina) platensis was investigated by neutron activation analysis at different selenium concentrations in solution and at different contact times. Experimental data showed good fit with the Freundlich adsorption isotherm model, with a regression coefficient value of 0.99. In terms of absorption dependence on time, the maximal selenium content was adsorbed in the first 5 min of interaction without significant further changes. It was also found that A. platensis biomass forms spherical selenium nanoparticles. Biochemical analysis was used to assess the changes in the main components of spirulina biomass (proteins, lipids, carbohydrates, and phycobilin) during nanoparticle formation.

Indriect Measurement Of Nitrogen In A Mult-Component Natural Gas By Heating The Gas

DOEpatents

Morrow, Thomas B.; Behring, II, Kendricks A.

2004-06-22

Methods of indirectly measuring the nitrogen concentration in a natural gas by heating the gas. In two embodiments, the heating energy is correlated to the speed of sound in the gas, the diluent concentrations in the gas, and constant values, resulting in a model equation. Regression analysis is used to calculate the constant values, which can then be substituted into the model equation. If the diluent concentrations other than nitrogen (typically carbon dioxide) are known, the model equation can be solved for the nitrogen concentration.

Global and System-Specific Resting-State fMRI Fluctuations Are Uncorrelated: Principal Component Analysis Reveals Anti-Correlated Networks

PubMed Central

Carbonell, Felix; Bellec, Pierre

2011-01-01

Abstract The influence of the global average signal (GAS) on functional-magnetic resonance imaging (fMRI)–based resting-state functional connectivity is a matter of ongoing debate. The global average fluctuations increase the correlation between functional systems beyond the correlation that reflects their specific functional connectivity. Hence, removal of the GAS is a common practice for facilitating the observation of network-specific functional connectivity. This strategy relies on the implicit assumption of a linear-additive model according to which global fluctuations, irrespective of their origin, and network-specific fluctuations are super-positioned. However, removal of the GAS introduces spurious negative correlations between functional systems, bringing into question the validity of previous findings of negative correlations between fluctuations in the default-mode and the task-positive networks. Here we present an alternative method for estimating global fluctuations, immune to the complications associated with the GAS. Principal components analysis was applied to resting-state fMRI time-series. A global-signal effect estimator was defined as the principal component (PC) that correlated best with the GAS. The mean correlation coefficient between our proposed PC-based global effect estimator and the GAS was 0.97±0.05, demonstrating that our estimator successfully approximated the GAS. In 66 out of 68 runs, the PC that showed the highest correlation with the GAS was the first PC. Since PCs are orthogonal, our method provides an estimator of the global fluctuations, which is uncorrelated to the remaining, network-specific fluctuations. Moreover, unlike the regression of the GAS, the regression of the PC-based global effect estimator does not introduce spurious anti-correlations beyond the decrease in seed-based correlation values allowed by the assumed additive model. After regressing this PC-based estimator out of the original time-series, we observed robust anti-correlations between resting-state fluctuations in the default-mode and the task-positive networks. We conclude that resting-state global fluctuations and network-specific fluctuations are uncorrelated, supporting a Resting-State Linear-Additive Model. In addition, we conclude that the network-specific resting-state fluctuations of the default-mode and task-positive networks show artifact-free anti-correlations. PMID:22444074

Plant, soil, and shadow reflectance components of row crops

NASA Technical Reports Server (NTRS)

Richardson, A. J.; Wiegand, C. L.; Gausman, H. W.; Cuellar, J. A.; Gerbermann, A. H.

1975-01-01

Data from the first Earth Resource Technology Satellite (LANDSAT-1) multispectral scanner (MSS) were used to develop three plant canopy models (Kubelka-Munk (K-M), regression, and combined K-M and regression models) for extracting plant, soil, and shadow reflectance components of cropped fields. The combined model gave the best correlation between MSS data and ground truth, by accounting for essentially all of the reflectance of plants, soil, and shadow between crop rows. The principles presented can be used to better forecast crop yield and to estimate acreage.

[Application of chemometrics in composition-activity relationship research of traditional Chinese medicine].

PubMed

Han, Sheng-Nan

2014-07-01

Chemometrics is a new branch of chemistry which is widely applied to various fields of analytical chemistry. Chemometrics can use theories and methods of mathematics, statistics, computer science and other related disciplines to optimize the chemical measurement process and maximize access to acquire chemical information and other information on material systems by analyzing chemical measurement data. In recent years, traditional Chinese medicine has attracted widespread attention. In the research of traditional Chinese medicine, it has been a key problem that how to interpret the relationship between various chemical components and its efficacy, which seriously restricts the modernization of Chinese medicine. As chemometrics brings the multivariate analysis methods into the chemical research, it has been applied as an effective research tool in the composition-activity relationship research of Chinese medicine. This article reviews the applications of chemometrics methods in the composition-activity relationship research in recent years. The applications of multivariate statistical analysis methods (such as regression analysis, correlation analysis, principal component analysis, etc. ) and artificial neural network (such as back propagation artificial neural network, radical basis function neural network, support vector machine, etc. ) are summarized, including the brief fundamental principles, the research contents and the advantages and disadvantages. Finally, the existing main problems and prospects of its future researches are proposed.

Application of Sensory Evaluation, HS-SPME GC-MS, E-Nose, and E-Tongue for Quality Detection in Citrus Fruits.

PubMed

Qiu, Shanshan; Wang, Jun

2015-10-01

In this study, electronic tongue (E-tongue), headspace solid-phase microextraction gas chromatography-mass spectrometer (GC-MS), electronic nose (E-nose), and quantitative describe analysis (QDA) were applied to describe the 2 types of citrus fruits (Satsuma mandarins [Citrus unshiu Marc.] and sweet oranges [Citrus sinensis {L.} Osbeck]) and their mixing juices systematically and comprehensively. As some aroma components or some flavor molecules interacted with the whole juice matrix, the changes of most components in the fruit juice were not in proportion to the mixing ratio of the 2 citrus fruits. The potential correlations among the signals of E-tongue and E-nose, volatile components, and sensory attributes were analyzed by using analysis of variance partial least squares regression. The result showed that the variables from the sensor signals (E-tongue system and E-nose system) had significant and positive (or negative) correlations to the most variables of volatile components (GC-MS) and sensory attributes (QDA). The simultaneous utilization of E-tongue and E-nose obtained a perfect classification result with 100% accuracy rate based on linear discriminant analysis and also attained a satisfying prediction with high coefficient association for the sensory attributes (R(2) > 0.994 for training sets and R(2) > 0.983 for testing sets) and for the volatile components (R(2) > 0.992 for training sets and R(2) > 0.990 for testing sets) based on random forest. Being easy-to-use, cost-effective, robust, and capable of providing a fast analysis procedure, E-nose and E-tongue could be used as an alternative detection system to traditional analysis methods, such as GC-MS and sensory evaluation by human panel in the fruit industry. Being easy-to-use, cost-effective, robust, and capable of providing a fast analysis procedure, E-nose and E-tongue could be used as an alternative detection system to traditional analysis methods for characterizing food flavors. Based on those results, one can draw a conclusion that the fusion system composed of E-tongue and E-nose could guarantee a satisfying result in the prediction of sensory attributes and volatile components for fruit quality profile. © 2015 Institute of Food Technologists®

Statistical downscaling modeling with quantile regression using lasso to estimate extreme rainfall

NASA Astrophysics Data System (ADS)

Santri, Dewi; Wigena, Aji Hamim; Djuraidah, Anik

2016-02-01

Rainfall is one of the climatic elements with high diversity and has many negative impacts especially extreme rainfall. Therefore, there are several methods that required to minimize the damage that may occur. So far, Global circulation models (GCM) are the best method to forecast global climate changes include extreme rainfall. Statistical downscaling (SD) is a technique to develop the relationship between GCM output as a global-scale independent variables and rainfall as a local- scale response variable. Using GCM method will have many difficulties when assessed against observations because GCM has high dimension and multicollinearity between the variables. The common method that used to handle this problem is principal components analysis (PCA) and partial least squares regression. The new method that can be used is lasso. Lasso has advantages in simultaneuosly controlling the variance of the fitted coefficients and performing automatic variable selection. Quantile regression is a method that can be used to detect extreme rainfall in dry and wet extreme. Objective of this study is modeling SD using quantile regression with lasso to predict extreme rainfall in Indramayu. The results showed that the estimation of extreme rainfall (extreme wet in January, February and December) in Indramayu could be predicted properly by the model at quantile 90th.

Predicting the level of job satisfaction based on hardiness and its components among nurses with tension headache.

PubMed

Mahdavi, A; Nikmanesh, E; AghaeI, M; Kamran, F; Zahra Tavakoli, Z; Khaki Seddigh, F

2015-01-01

Nurses are the most significant part of human resources in a sanitary and health system. Job satisfaction results in the enhancement of organizational productivity, employee commitment to the organization and ensuring his/ her physical and mental health. The present research was conducted with the aim of predicting the level of job satisfaction based on hardiness and its components among the nurses with tension headache. The research method was correlational. The population consisted of all the nurses with tension headache who referred to the relevant specialists in Tehran. The sample size consisted of 50 individuals who were chosen by using the convenience sampling method and were measured and investigated by using the research tools of "Job Satisfaction Test" of Davis, Lofkvist and Weiss and "Personal Views Survey" of Kobasa. The data analysis was carried out by using the Pearson Correlation Coefficient and the Regression Analysis. The research findings demonstrated that the correlation coefficient obtained for "hardiness", "job satisfaction" was 0.506, and this coefficient was significant at the 0.01 level. Moreover, it was specified that the sense of commitment and challenge were stronger predictors for job satisfaction of nurses with tension headache among the components of hardiness, and, about 16% of the variance of "job satisfaction" could be explained by the two components (sense of commitment and challenge).

Multivariate and geo-spatial approach for seawater quality of Chidiyatappu Bay, south Andaman Islands, India.

PubMed

Jha, Dilip Kumar; Vinithkumar, Nambali Valsalan; Sahu, Biraja Kumar; Dheenan, Palaiya Sukumaran; Das, Apurba Kumar; Begum, Mehmuna; Devi, Marimuthu Prashanthi; Kirubagaran, Ramalingam

2015-07-15

Chidiyatappu Bay is one of the least disturbed marine environments of Andaman & Nicobar Islands, the union territory of India. Oceanic flushing from southeast and northwest direction is prevalent in this bay. Further, anthropogenic activity is minimal in the adjoining environment. Considering the pristine nature of this bay, seawater samples collected from 12 sampling stations covering three seasons were analyzed. Principal Component Analysis (PCA) revealed 69.9% of total variance and exhibited strong factor loading for nitrite, chlorophyll a and phaeophytin. In addition, analysis of variance (ANOVA-one way), regression analysis, box-whisker plots and Geographical Information System based hot spot analysis further simplified and supported multivariate results. The results obtained are important to establish reference conditions for comparative study with other similar ecosystems in the region. Copyright © 2015 Elsevier Ltd. All rights reserved.

Regression Analysis by Example. 5th Edition

ERIC Educational Resources Information Center

Chatterjee, Samprit; Hadi, Ali S.

2012-01-01

Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. "Regression Analysis by Example, Fifth Edition" has been expanded and thoroughly…

An improved geographically weighted regression model for PM2.5 concentration estimation in large areas

NASA Astrophysics Data System (ADS)

Zhai, Liang; Li, Shuang; Zou, Bin; Sang, Huiyong; Fang, Xin; Xu, Shan

2018-05-01

Considering the spatial non-stationary contributions of environment variables to PM2.5 variations, the geographically weighted regression (GWR) modeling method has been using to estimate PM2.5 concentrations widely. However, most of the GWR models in reported studies so far were established based on the screened predictors through pretreatment correlation analysis, and this process might cause the omissions of factors really driving PM2.5 variations. This study therefore developed a best subsets regression (BSR) enhanced principal component analysis-GWR (PCA-GWR) modeling approach to estimate PM2.5 concentration by fully considering all the potential variables' contributions simultaneously. The performance comparison experiment between PCA-GWR and regular GWR was conducted in the Beijing-Tianjin-Hebei (BTH) region over a one-year-period. Results indicated that the PCA-GWR modeling outperforms the regular GWR modeling with obvious higher model fitting- and cross-validation based adjusted R2 and lower RMSE. Meanwhile, the distribution map of PM2.5 concentration from PCA-GWR modeling also clearly depicts more spatial variation details in contrast to the one from regular GWR modeling. It can be concluded that the BSR enhanced PCA-GWR modeling could be a reliable way for effective air pollution concentration estimation in the coming future by involving all the potential predictor variables' contributions to PM2.5 variations.

Spectroscopic characterization of enzymatic flax retting: Factor analysis of FT-IR and FT-Raman data

NASA Astrophysics Data System (ADS)

Archibald, D. D.; Henrikssen, G.; Akin, D. E.; Barton, F. E.

1998-06-01

Flax retting is a chemical, microbial or enzymatic process which releases the bast fibers from the stem matrix so they can be suitable for mechanical processing before spinning into linen yarn. This study aims to determine the vibrational spectral features and sampling methods which can be used to evaluate the retting process. Flax stems were retted on a small scale using an enzyme mixture known to yield good retted flax. Processed stems were harvested at various time points in the process and the retting was evaluated by conventional methods including weight loss, color difference and Fried's test, a visual ranking of how the stems disintegrate in hot water. Spectroscopic measurements were performed on either whole stems or powders of the fibers that were mechanically extracted from the stems. Selected regions of spectra were baseline and amplitude corrected using a variant of the multiplicative signal correction method. Principal component regression and partial least-squares regression with full cross-validation were used to determine the spectral features and rate of spectral transformation by regressing the spectra against the retting time in hours. FT-Raman of fiber powders and FT-IR reflectance of whole stems were the simplest and most precise methods for monitoring the retting transformation. Raman tracks the retting by measuring the decrease in aromatic signal and subtle changes in the C-H stretching vibrations. The IR method uses complex spectral features in the fingerprint and carbonyl region, many of which are due to polysaccharide components. Both spectral techniques monitor the retting process with greater precision than the reference method.

Relationship between Alcohol Consumption and Components of the Metabolic Syndrome in Adult Population from Maracaibo City, Venezuela

PubMed Central

Bermúdez, Valmore; Martínez, María Sofía; Chávez-Castillo, Mervin; Olivar, Luis Carlos; Morillo, Jessenia; Mejías, José Carlos; Rojas, Milagros; Salazar, Juan; Rojas, Joselyn; Añez, Roberto; Cabrera, Mayela

2015-01-01

Introduction. Although the relationships between alcohol and disorders such as cancer and liver disease have been thoroughly researched, its effects on cardiometabolic health remain controversial. Therefore, the objective of this study was to assess the association between alcohol consumption, the Metabolic Syndrome (MS), and its components in our locality. Materials and Methods. Descriptive, cross-sectional study with randomized, multistaged sampling, which included 2,230 subjects of both genders. Two previously determined population-specific alcohol consumption pattern classifications were utilized in each gender: daily intake quartiles and conglomerates yielded by cluster analysis. MS was defined according to the 2009 consensus criteria. Association was evaluated through various multiple logistic regression models. Results. In univariate analysis (daily intake quartiles), only hypertriacylglyceridemia was associated with alcohol consumption in both genders. In multivariate analysis, daily alcohol intake ≤3.8 g/day was associated with lower risk of hypertriacylglyceridemia in females (OR = 0.29, CI 95%: 0.09–0.86; p = 0.03). Among men, subjects consuming 28.41–47.33 g/day had significantly increased risk of MS, hyperglycemia, high blood pressure, hypertriacylglyceridemia, and elevated waist circumference. Conclusions. The relationship between drinking, MS, and its components is complex and not directly proportional. Categorization by daily alcohol intake quartiles appears to be the most efficient method for quantitative assessment of alcohol consumption in our region. PMID:26779349

[Studies on the brand traceability of milk powder based on NIR spectroscopy technology].

PubMed

Guan, Xiao; Gu, Fang-Qing; Liu, Jing; Yang, Yong-Jian

2013-10-01

Brand traceability of several different kinds of milk powder was studied by combining near infrared spectroscopy diffuse reflectance mode with soft independent modeling of class analogy (SIMCA) in the present paper. The near infrared spectrum of 138 samples, including 54 Guangming milk powder samples, 43 Netherlands samples, and 33 Nestle samples and 8 Yili samples, were collected. After pretreatment of full spectrum data variables in training set, principal component analysis was performed, and the contribution rate of the cumulative variance of the first three principal components was about 99.07%. Milk powder principal component regression model based on SIMCA was established, and used to classify the milk powder samples in prediction sets. The results showed that the recognition rate of Guangming milk powder, Netherlands milk powder and Nestle milk powder was 78%, 75% and 100%, the rejection rate was 100%, 87%, and 88%, respectively. Therefore, the near infrared spectroscopy combined with SIMCA model can classify milk powder with high accuracy, and is a promising identification method of milk powder variety.

Characterization of shrubland ecosystem components as continuous fields in the northwest United States

USGS Publications Warehouse

Xian, George Z.; Homer, Collin G.; Rigge, Matthew B.; Shi, Hua; Meyer, Debbie

2015-01-01

Accurate and consistent estimates of shrubland ecosystem components are crucial to a better understanding of ecosystem conditions in arid and semiarid lands. An innovative approach was developed by integrating multiple sources of information to quantify shrubland components as continuous field products within the National Land Cover Database (NLCD). The approach consists of several procedures including field sample collections, high-resolution mapping of shrubland components using WorldView-2 imagery and regression tree models, Landsat 8 radiometric balancing and phenological mosaicking, medium resolution estimates of shrubland components following different climate zones using Landsat 8 phenological mosaics and regression tree models, and product validation. Fractional covers of nine shrubland components were estimated: annual herbaceous, bare ground, big sagebrush, herbaceous, litter, sagebrush, shrub, sagebrush height, and shrub height. Our study area included the footprint of six Landsat 8 scenes in the northwestern United States. Results show that most components have relatively significant correlations with validation data, have small normalized root mean square errors, and correspond well with expected ecological gradients. While some uncertainties remain with height estimates, the model formulated in this study provides a cross-validated, unbiased, and cost effective approach to quantify shrubland components at a regional scale and advances knowledge of horizontal and vertical variability of these components.

Methods for trend analysis: Examples with problem/failure data

NASA Technical Reports Server (NTRS)

Church, Curtis K.

1989-01-01

Statistics are emphasized as an important role in quality control and reliability. Consequently, Trend Analysis Techniques recommended a variety of statistical methodologies that could be applied to time series data. The major goal of the working handbook, using data from the MSFC Problem Assessment System, is to illustrate some of the techniques in the NASA standard, some different techniques, and to notice patterns of data. Techniques for trend estimation used are: regression (exponential, power, reciprocal, straight line) and Kendall's rank correlation coefficient. The important details of a statistical strategy for estimating a trend component are covered in the examples. However, careful analysis and interpretation is necessary because of small samples and frequent zero problem reports in a given time period. Further investigations to deal with these issues are being conducted.

The impact of gun control (Bill C-51) on suicide in Canada.

PubMed

Leenaars, Antoon A; Moksony, Ferenc; Lester, David; Wenckstern, Susanne

2003-01-01

Suicide is a multiply determined behavior, calling for diverse prevention efforts. Gun control has been proposed as an important component of society's response, and an opportunity for studying the effects of legislative gun control laws on suicide rates was provided by Canada's Criminal Law Amendment Act of 1977 (Bill C-51). This article reviews previous studies of the impact of this act on the total population of Canada and subpopulations by age and gender and, in addition, presents the results of 2 new studies: a different method of analysis, an interrupted time-series analysis, and the results of a multiple regression analysis that controls for some social variables. It appears that Bill C-51 may have had an impact on suicide rates, even after controls for social variables.

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

PubMed

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

2010-05-15

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

Valuing the visual impact of wind farms: A calculus method for synthesizing choice experiments studies.

PubMed

Wen, Cheng; Dallimer, Martin; Carver, Steve; Ziv, Guy

2018-05-06

Despite the great potential of mitigating carbon emission, development of wind farms is often opposed by local communities due to the visual impact on landscape. A growing number of studies have applied nonmarket valuation methods like Choice Experiments (CE) to value the visual impact by eliciting respondents' willingness to pay (WTP) or willingness to accept (WTA) for hypothetical wind farms through survey questions. Several meta-analyses have been found in the literature to synthesize results from different valuation studies, but they have various limitations related to the use of the prevailing multivariate meta-regression analysis. In this paper, we propose a new meta-analysis method to establish general functions for the relationships between the estimated WTP or WTA and three wind farm attributes, namely the distance to residential/coastal areas, the number of turbines and turbine height. This method involves establishing WTA or WTP functions for individual studies, fitting the average derivative functions and deriving the general integral functions of WTP or WTA against wind farm attributes. Results indicate that respondents in different studies consistently showed increasing WTP for moving wind farms to greater distances, which can be fitted by non-linear (natural logarithm) functions. However, divergent preferences for the number of turbines and turbine height were found in different studies. We argue that the new analysis method proposed in this paper is an alternative to the mainstream multivariate meta-regression analysis for synthesizing CE studies and the general integral functions of WTP or WTA against wind farm attributes are useful for future spatial modelling and benefit transfer studies. We also suggest that future multivariate meta-analyses should include non-linear components in the regression functions. Copyright © 2018. Published by Elsevier B.V.

NiftyNet: a deep-learning platform for medical imaging.

PubMed

Gibson, Eli; Li, Wenqi; Sudre, Carole; Fidon, Lucas; Shakir, Dzhoshkun I; Wang, Guotai; Eaton-Rosen, Zach; Gray, Robert; Doel, Tom; Hu, Yipeng; Whyntie, Tom; Nachev, Parashkev; Modat, Marc; Barratt, Dean C; Ourselin, Sébastien; Cardoso, M Jorge; Vercauteren, Tom

2018-05-01

Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this domain of application requires substantial implementation effort. Consequently, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. The NiftyNet infrastructure provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on the TensorFlow framework and supports features such as TensorBoard visualization of 2D and 3D images and computational graphs by default. We present three illustrative medical image analysis applications built using NiftyNet infrastructure: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. The NiftyNet infrastructure enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation

PubMed Central

Jiang, Xiaoqian; Aziz, Md Momin Al; Wang, Shuang; Mohammed, Noman

2018-01-01

Background Machine learning is an effective data-driven tool that is being widely used to extract valuable patterns and insights from data. Specifically, predictive machine learning models are very important in health care for clinical data analysis. The machine learning algorithms that generate predictive models often require pooling data from different sources to discover statistical patterns or correlations among different attributes of the input data. The primary challenge is to fulfill one major objective: preserving the privacy of individuals while discovering knowledge from data. Objective Our objective was to develop a hybrid cryptographic framework for performing regression analysis over distributed data in a secure and efficient way. Methods Existing secure computation schemes are not suitable for processing the large-scale data that are used in cutting-edge machine learning applications. We designed, developed, and evaluated a hybrid cryptographic framework, which can securely perform regression analysis, a fundamental machine learning algorithm using somewhat homomorphic encryption and a newly introduced secure hardware component of Intel Software Guard Extensions (Intel SGX) to ensure both privacy and efficiency at the same time. Results Experimental results demonstrate that our proposed method provides a better trade-off in terms of security and efficiency than solely secure hardware-based methods. Besides, there is no approximation error. Computed model parameters are exactly similar to plaintext results. Conclusions To the best of our knowledge, this kind of secure computation model using a hybrid cryptographic framework, which leverages both somewhat homomorphic encryption and Intel SGX, is not proposed or evaluated to this date. Our proposed framework ensures data security and computational efficiency at the same time. PMID:29506966

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

PubMed Central

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

2010-01-01

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

Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation.

PubMed

Sadat, Md Nazmus; Jiang, Xiaoqian; Aziz, Md Momin Al; Wang, Shuang; Mohammed, Noman

2018-03-05

Machine learning is an effective data-driven tool that is being widely used to extract valuable patterns and insights from data. Specifically, predictive machine learning models are very important in health care for clinical data analysis. The machine learning algorithms that generate predictive models often require pooling data from different sources to discover statistical patterns or correlations among different attributes of the input data. The primary challenge is to fulfill one major objective: preserving the privacy of individuals while discovering knowledge from data. Our objective was to develop a hybrid cryptographic framework for performing regression analysis over distributed data in a secure and efficient way. Existing secure computation schemes are not suitable for processing the large-scale data that are used in cutting-edge machine learning applications. We designed, developed, and evaluated a hybrid cryptographic framework, which can securely perform regression analysis, a fundamental machine learning algorithm using somewhat homomorphic encryption and a newly introduced secure hardware component of Intel Software Guard Extensions (Intel SGX) to ensure both privacy and efficiency at the same time. Experimental results demonstrate that our proposed method provides a better trade-off in terms of security and efficiency than solely secure hardware-based methods. Besides, there is no approximation error. Computed model parameters are exactly similar to plaintext results. To the best of our knowledge, this kind of secure computation model using a hybrid cryptographic framework, which leverages both somewhat homomorphic encryption and Intel SGX, is not proposed or evaluated to this date. Our proposed framework ensures data security and computational efficiency at the same time. ©Md Nazmus Sadat, Xiaoqian Jiang, Md Momin Al Aziz, Shuang Wang, Noman Mohammed. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 05.03.2018.