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

A Comparison of Component and Factor Patterns: A Monte Carlo Approach.

ERIC Educational Resources Information Center

Velicer, Wayne F.; And Others

1982-01-01

Factor analysis, image analysis, and principal component analysis are compared with respect to the factor patterns they would produce under various conditions. The general conclusion that is reached is that the three methods produce results that are equivalent. (Author/JKS)

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.

An Evaluation of the Effects of Variable Sampling on Component, Image, and Factor Analysis.

ERIC Educational Resources Information Center

Velicer, Wayne F.; Fava, Joseph L.

1987-01-01

Principal component analysis, image component analysis, and maximum likelihood factor analysis were compared to assess the effects of variable sampling. Results with respect to degree of saturation and average number of variables per factor were clear and dramatic. Differential effects on boundary cases and nonconvergence problems were also found.…

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.

Directly reconstructing principal components of heterogeneous particles from cryo-EM images.

PubMed

Tagare, Hemant D; Kucukelbir, Alp; Sigworth, Fred J; Wang, Hongwei; Rao, Murali

2015-08-01

Structural heterogeneity of particles can be investigated by their three-dimensional principal components. This paper addresses the question of whether, and with what algorithm, the three-dimensional principal components can be directly recovered from cryo-EM images. The first part of the paper extends the Fourier slice theorem to covariance functions showing that the three-dimensional covariance, and hence the principal components, of a heterogeneous particle can indeed be recovered from two-dimensional cryo-EM images. The second part of the paper proposes a practical algorithm for reconstructing the principal components directly from cryo-EM images without the intermediate step of calculating covariances. This algorithm is based on maximizing the posterior likelihood using the Expectation-Maximization algorithm. The last part of the paper applies this algorithm to simulated data and to two real cryo-EM data sets: a data set of the 70S ribosome with and without Elongation Factor-G (EF-G), and a data set of the influenza virus RNA dependent RNA Polymerase (RdRP). The first principal component of the 70S ribosome data set reveals the expected conformational changes of the ribosome as the EF-G binds and unbinds. The first principal component of the RdRP data set reveals a conformational change in the two dimers of the RdRP. Copyright © 2015 Elsevier Inc. All rights reserved.

Time series analysis of collective motions in proteins

NASA Astrophysics Data System (ADS)

Alakent, Burak; Doruker, Pemra; ćamurdan, Mehmet C.

2004-01-01

The dynamics of α-amylase inhibitor tendamistat around its native state is investigated using time series analysis of the principal components of the Cα atomic displacements obtained from molecular dynamics trajectories. Collective motion along a principal component is modeled as a homogeneous nonstationary process, which is the result of the damped oscillations in local minima superimposed on a random walk. The motion in local minima is described by a stationary autoregressive moving average model, consisting of the frequency, damping factor, moving average parameters and random shock terms. Frequencies for the first 50 principal components are found to be in the 3-25 cm-1 range, which are well correlated with the principal component indices and also with atomistic normal mode analysis results. Damping factors, though their correlation is less pronounced, decrease as principal component indices increase, indicating that low frequency motions are less affected by friction. The existence of a positive moving average parameter indicates that the stochastic force term is likely to disturb the mode in opposite directions for two successive sampling times, showing the modes tendency to stay close to minimum. All these four parameters affect the mean square fluctuations of a principal mode within a single minimum. The inter-minima transitions are described by a random walk model, which is driven by a random shock term considerably smaller than that for the intra-minimum motion. The principal modes are classified into three subspaces based on their dynamics: essential, semiconstrained, and constrained, at least in partial consistency with previous studies. The Gaussian-type distributions of the intermediate modes, called "semiconstrained" modes, are explained by asserting that this random walk behavior is not completely free but between energy barriers.

The Retention and Attrition of Catholic School Principals

ERIC Educational Resources Information Center

Durow, W. Patrick; Brock, Barbara L.

2004-01-01

This article reports the results of a study of the retention of principals in Catholic elementary and secondary schools in one Midwestern diocese. Findings revealed that personal needs, career advancement, support from employer, and clearly defined role expectations were key factors in principals' retention decisions. A profile of components of…

Genetic algorithm applied to the selection of factors in principal component-artificial neural networks: application to QSAR study of calcium channel antagonist activity of 1,4-dihydropyridines (nifedipine analogous).

PubMed

Hemmateenejad, Bahram; Akhond, Morteza; Miri, Ramin; Shamsipur, Mojtaba

2003-01-01

A QSAR algorithm, principal component-genetic algorithm-artificial neural network (PC-GA-ANN), has been applied to a set of newly synthesized calcium channel blockers, which are of special interest because of their role in cardiac diseases. A data set of 124 1,4-dihydropyridines bearing different ester substituents at the C-3 and C-5 positions of the dihydropyridine ring and nitroimidazolyl, phenylimidazolyl, and methylsulfonylimidazolyl groups at the C-4 position with known Ca(2+) channel binding affinities was employed in this study. Ten different sets of descriptors (837 descriptors) were calculated for each molecule. The principal component analysis was used to compress the descriptor groups into principal components. The most significant descriptors of each set were selected and used as input for the ANN. The genetic algorithm (GA) was used for the selection of the best set of extracted principal components. A feed forward artificial neural network with a back-propagation of error algorithm was used to process the nonlinear relationship between the selected principal components and biological activity of the dihydropyridines. A comparison between PC-GA-ANN and routine PC-ANN shows that the first model yields better prediction ability.

[Assessment of the strength of tobacco control on creating smoke-free hospitals using principal components analysis].

PubMed

Liu, Hui-lin; Wan, Xia; Yang, Gong-huan

2013-02-01

To explore the relationship between the strength of tobacco control and the effectiveness of creating smoke-free hospital, and summarize the main factors that affect the program of creating smoke-free hospitals. A total of 210 hospitals from 7 provinces/municipalities directly under the central government were enrolled in this study using stratified random sampling method. Principle component analysis and regression analysis were conducted to analyze the strength of tobacco control and the effectiveness of creating smoke-free hospitals. Two principal components were extracted in the strength of tobacco control index, which respectively reflected the tobacco control policies and efforts, and the willingness and leadership of hospital managers regarding tobacco control. The regression analysis indicated that only the first principal component was significantly correlated with the progression in creating smoke-free hospital (P<0.001), i.e. hospitals with higher scores on the first principal component had better achievements in smoke-free environment creation. Tobacco control policies and efforts are critical in creating smoke-free hospitals. The principal component analysis provides a comprehensive and objective tool for evaluating the creation of smoke-free hospitals.

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.

The Psychometric Assessment of Children with Learning Disabilities: An Index Derived from a Principal Components Analysis of the WISC-R.

ERIC Educational Resources Information Center

Lawson, J. S.; Inglis, James

1984-01-01

A learning disability index (LDI) for the assessment of intellectual deficits on the Wechsler Intelligence Scale for Children-Revised (WISC-R) is described. The Factor II score coefficients derived from an unrotated principal components analysis of the WISC-R normative data, in combination with the individual's scaled scores, are used for this…

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.

Psychometric evaluation of the Persian version of the Templer's Death Anxiety Scale in cancer patients.

PubMed

Soleimani, Mohammad Ali; Yaghoobzadeh, Ameneh; Bahrami, Nasim; Sharif, Saeed Pahlevan; Sharif Nia, Hamid

2016-10-01

In this study, 398 Iranian cancer patients completed the 15-item Templer's Death Anxiety Scale (TDAS). Tests of internal consistency, principal components analysis, and confirmatory factor analysis were conducted to assess the internal consistency and factorial validity of the Persian TDAS. The construct reliability statistic and average variance extracted were also calculated to measure construct reliability, convergent validity, and discriminant validity. Principal components analysis indicated a 3-component solution, which was generally supported in the confirmatory analysis. However, acceptable cutoffs for construct reliability, convergent validity, and discriminant validity were not fulfilled for the three subscales that were derived from the principal component analysis. This study demonstrated both the advantages and potential limitations of using the TDAS with Persian-speaking cancer patients.

The rate of change in declining steroid hormones: a new parameter of healthy aging in men?

PubMed

Walther, Andreas; Philipp, Michel; Lozza, Niclà; Ehlert, Ulrike

2016-09-20

Research on healthy aging in men has increasingly focused on age-related hormonal changes. Testosterone (T) decline is primarily investigated, while age-related changes in other sex steroids (dehydroepiandrosterone [DHEA], estradiol [E2], progesterone [P]) are mostly neglected. An integrated hormone parameter reflecting aging processes in men has yet to be identified. 271 self-reporting healthy men between 40 and 75 provided both psychometric data and saliva samples for hormone analysis. Correlation analysis between age and sex steroids revealed negative associations for the four sex steroids (T, DHEA, E2, and P). Principal component analysis including ten salivary analytes identified a principal component mainly unifying the variance of the four sex steroid hormones. Subsequent principal component analysis including the four sex steroids extracted the principal component of declining steroid hormones (DSH). Moderation analysis of the association between age and DSH revealed significant moderation effects for psychosocial factors such as depression, chronic stress and perceived general health. In conclusion, these results provide further evidence that sex steroids decline in aging men and that the integrated hormone parameter DSH and its rate of change can be used as biomarkers for healthy aging in men. Furthermore, the negative association of age and DSH is moderated by psychosocial factors.

The rate of change in declining steroid hormones: a new parameter of healthy aging in men?

PubMed Central

Walther, Andreas; Philipp, Michel; Lozza, Niclà; Ehlert, Ulrike

2016-01-01

Research on healthy aging in men has increasingly focused on age-related hormonal changes. Testosterone (T) decline is primarily investigated, while age-related changes in other sex steroids (dehydroepiandrosterone [DHEA], estradiol [E2], progesterone [P]) are mostly neglected. An integrated hormone parameter reflecting aging processes in men has yet to be identified. 271 self-reporting healthy men between 40 and 75 provided both psychometric data and saliva samples for hormone analysis. Correlation analysis between age and sex steroids revealed negative associations for the four sex steroids (T, DHEA, E2, and P). Principal component analysis including ten salivary analytes identified a principal component mainly unifying the variance of the four sex steroid hormones. Subsequent principal component analysis including the four sex steroids extracted the principal component of declining steroid hormones (DSH). Moderation analysis of the association between age and DSH revealed significant moderation effects for psychosocial factors such as depression, chronic stress and perceived general health. In conclusion, these results provide further evidence that sex steroids decline in aging men and that the integrated hormone parameter DSH and its rate of change can be used as biomarkers for healthy aging in men. Furthermore, the negative association of age and DSH is moderated by psychosocial factors. PMID:27589836

Capturing multidimensionality in stroke aphasia: mapping principal behavioural components to neural structures

PubMed Central

Butler, Rebecca A.

2014-01-01

Stroke aphasia is a multidimensional disorder in which patient profiles reflect variation along multiple behavioural continua. We present a novel approach to separating the principal aspects of chronic aphasic performance and isolating their neural bases. Principal components analysis was used to extract core factors underlying performance of 31 participants with chronic stroke aphasia on a large, detailed battery of behavioural assessments. The rotated principle components analysis revealed three key factors, which we labelled as phonology, semantic and executive/cognition on the basis of the common elements in the tests that loaded most strongly on each component. The phonology factor explained the most variance, followed by the semantic factor and then the executive-cognition factor. The use of principle components analysis rendered participants’ scores on these three factors orthogonal and therefore ideal for use as simultaneous continuous predictors in a voxel-based correlational methodology analysis of high resolution structural scans. Phonological processing ability was uniquely related to left posterior perisylvian regions including Heschl’s gyrus, posterior middle and superior temporal gyri and superior temporal sulcus, as well as the white matter underlying the posterior superior temporal gyrus. The semantic factor was uniquely related to left anterior middle temporal gyrus and the underlying temporal stem. The executive-cognition factor was not correlated selectively with the structural integrity of any particular region, as might be expected in light of the widely-distributed and multi-functional nature of the regions that support executive functions. The identified phonological and semantic areas align well with those highlighted by other methodologies such as functional neuroimaging and neurostimulation. The use of principle components analysis allowed us to characterize the neural bases of participants’ behavioural performance more robustly and selectively than the use of raw assessment scores or diagnostic classifications because principle components analysis extracts statistically unique, orthogonal behavioural components of interest. As such, in addition to improving our understanding of lesion–symptom mapping in stroke aphasia, the same approach could be used to clarify brain–behaviour relationships in other neurological disorders. PMID:25348632

[Low-molecular-weight autoregulatory factors in bacteria Thioalkalivibrio versutus and Thioalkalimicrobium aerophilum].

PubMed

Loĭko, N G; Kozlova, A N; Osipov, G A; El'-Registan, G I

2002-01-01

The haloalkaliphilic, lithoautotrophic, sulfur-oxidizing gram-negative bacteria Thioalkalivibrio versutus and Thioalkalimicrobium aerophilum were found to possess a special system for the autoregulation of their growth. The system includes the extracellular autoinducers of anabiosis (the d1 factor) and autolysis (the d2 factor). The principal components of the d1 factor are alkylhydroxybenzenes. The principal components of the d2 factor are free unsaturated fatty acids dominated by oleic acid isomers. Like the respective autoregulators of neutrophilic bacteria, the d1 factor of haloalkaliphilic bacteria presumably controls their growth and transition to a anabiotic state, while the d2 factor controls autolytic processes. Alkylhydroxybenzenes of both microbial and chemical origin were found to influence bacterial respiration. The low-molecular-weight osmoprotectant glycine betaine enhanced the thermostability of trypsin. This suggests that glycine betaine, like the d1 factor, serves as a molecular chaperone.

Factor Analysis and Counseling Research

ERIC Educational Resources Information Center

Weiss, David J.

1970-01-01

Topics discussed include factor analysis versus cluster analysis, analysis of Q correlation matrices, ipsativity and factor analysis, and tests for the significance of a correlation matrix prior to application of factor analytic techniques. Techniques for factor extraction discussed include principal components, canonical factor analysis, alpha…

Adherence to an (n-3) Fatty Acid/Fish Intake Pattern Is Inversely Associated with Metabolic Syndrome among Puerto Rican Adults in the Greater Boston Area123

PubMed Central

Noel, Sabrina E.; Newby, P. K.; Ordovas, Jose M.; Tucker, Katherine L.

2010-01-01

Combinations of fatty acids may affect risk of metabolic syndrome. Puerto Ricans have a disproportionate number of chronic conditions compared with other Hispanic groups. We aimed to characterize fatty acid intake patterns of Puerto Rican adults aged 45–75 y and living in the Greater Boston area (n = 1207) and to examine associations between these patterns and metabolic syndrome. Dietary fatty acids, as a percentage of total fat, were entered into principle components analysis. Spearman correlation coefficients were used to examine associations between fatty acid intake patterns, nutrients, and food groups. Associations with metabolic syndrome were analyzed by using logistic regression and general linear models with quintiles of principal component scores. Four principal components (factors) emerged: factor 1, short- and medium-chain SFA/dairy; factor 2, (n-3) fatty acid/fish; factor 3, very long-chain (VLC) SFA and PUFA/oils; and factor 4, monounsaturated fatty acid/trans fat. The SFA/dairy factor was inversely associated with fasting serum glucose concentrations (P = 0.02) and the VLC SFA/oils factor was negatively related to waist circumference (P = 0.008). However, these associations were no longer significant after additional adjustment for BMI. The (n-3) fatty acid/fish factor was associated with a lower likelihood of metabolic syndrome (Q5 vs. Q1: odds ratio: 0.54, 95% CI: 0.34, 0.86). In summary, principal components analysis of fatty acid intakes revealed 4 dietary fatty acid patterns in this population. Identifying optimal combinations of fatty acids may be beneficial for understanding relationships with health outcomes given their diverse effects on metabolism. PMID:20702744

Research on Air Quality Evaluation based on Principal Component Analysis

NASA Astrophysics Data System (ADS)

Wang, Xing; Wang, Zilin; Guo, Min; Chen, Wei; Zhang, Huan

2018-01-01

Economic growth has led to environmental capacity decline and the deterioration of air quality. Air quality evaluation as a fundamental of environmental monitoring and air pollution control has become increasingly important. Based on the principal component analysis (PCA), this paper evaluates the air quality of a large city in Beijing-Tianjin-Hebei Area in recent 10 years and identifies influencing factors, in order to provide reference to air quality management and air pollution control.

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.

An efficient classification method based on principal component and sparse representation.

PubMed

Zhai, Lin; Fu, Shujun; Zhang, Caiming; Liu, Yunxian; Wang, Lu; Liu, Guohua; Yang, Mingqiang

2016-01-01

As an important application in optical imaging, palmprint recognition is interfered by many unfavorable factors. An effective fusion of blockwise bi-directional two-dimensional principal component analysis and grouping sparse classification is presented. The dimension reduction and normalizing are implemented by the blockwise bi-directional two-dimensional principal component analysis for palmprint images to extract feature matrixes, which are assembled into an overcomplete dictionary in sparse classification. A subspace orthogonal matching pursuit algorithm is designed to solve the grouping sparse representation. Finally, the classification result is gained by comparing the residual between testing and reconstructed images. Experiments are carried out on a palmprint database, and the results show that this method has better robustness against position and illumination changes of palmprint images, and can get higher rate of palmprint recognition.

The Baptist Health Nurse Retention Questionnaire: A Methodological Study, Part 1.

PubMed

Lengerich, Alexander; Bugajski, Andrew; Marchese, Matthew; Hall, Brittany; Yackzan, Susan; Davies, Claire; Brockopp, Dorothy

2017-05-01

The purposes of this study were to develop and test the Baptist Health Nurse Retention Questionnaire (BHNRQ) and examine the importance of nurse retention factors. Multiple factors, including increasing patient acuity levels, have led to concerns regarding nurse retention. An understanding of current factors related to retention is limited. To establish the psychometric properties of the BHNRQ, data were collected from 279 bedside nurses at a 391-bed, Magnet® redesignated community hospital. A principal component analysis was conducted to determine the subscale structure of the BHNRQ. Additional analyses were conducted related to content validity and test-retest reliability. The results of the principal components analysis revealed 3 subscales: nursing practice, management, and staffing. Analyses demonstrate that the BHNRQ is a reliable and valid instrument for measuring nurse retention factors. The BHNRQ was found to be a clinically useful instrument for measuring important factors related to nurse retention.

Short communication: Principal components and factor analytic models for test-day milk yield in Brazilian Holstein cattle.

PubMed

Bignardi, A B; El Faro, L; Rosa, G J M; Cardoso, V L; Machado, P F; Albuquerque, L G

2012-04-01

A total of 46,089 individual monthly test-day (TD) milk yields (10 test-days), from 7,331 complete first lactations of Holstein cattle were analyzed. A standard multivariate analysis (MV), reduced rank analyses fitting the first 2, 3, and 4 genetic principal components (PC2, PC3, PC4), and analyses that fitted a factor analytic structure considering 2, 3, and 4 factors (FAS2, FAS3, FAS4), were carried out. The models included the random animal genetic effect and fixed effects of the contemporary groups (herd-year-month of test-day), age of cow (linear and quadratic effects), and days in milk (linear effect). The residual covariance matrix was assumed to have full rank. Moreover, 2 random regression models were applied. Variance components were estimated by restricted maximum likelihood method. The heritability estimates ranged from 0.11 to 0.24. The genetic correlation estimates between TD obtained with the PC2 model were higher than those obtained with the MV model, especially on adjacent test-days at the end of lactation close to unity. The results indicate that for the data considered in this study, only 2 principal components are required to summarize the bulk of genetic variation among the 10 traits. Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Analysis of environmental variation in a Great Plains reservoir using principal components analysis and geographic information systems

USGS Publications Warehouse

Long, J.M.; Fisher, W.L.

2006-01-01

We present a method for spatial interpretation of environmental variation in a reservoir that integrates principal components analysis (PCA) of environmental data with geographic information systems (GIS). To illustrate our method, we used data from a Great Plains reservoir (Skiatook Lake, Oklahoma) with longitudinal variation in physicochemical conditions. We measured 18 physicochemical features, mapped them using GIS, and then calculated and interpreted four principal components. Principal component 1 (PC1) was readily interpreted as longitudinal variation in water chemistry, but the other principal components (PC2-4) were difficult to interpret. Site scores for PC1-4 were calculated in GIS by summing weighted overlays of the 18 measured environmental variables, with the factor loadings from the PCA as the weights. PC1-4 were then ordered into a landscape hierarchy, an emergent property of this technique, which enabled their interpretation. PC1 was interpreted as a reservoir scale change in water chemistry, PC2 was a microhabitat variable of rip-rap substrate, PC3 identified coves/embayments and PC4 consisted of shoreline microhabitats related to slope. The use of GIS improved our ability to interpret the more obscure principal components (PC2-4), which made the spatial variability of the reservoir environment more apparent. This method is applicable to a variety of aquatic systems, can be accomplished using commercially available software programs, and allows for improved interpretation of the geographic environmental variability of a system compared to using typical PCA plots. ?? Copyright by the North American Lake Management Society 2006.

Research on distributed heterogeneous data PCA algorithm based on cloud platform

NASA Astrophysics Data System (ADS)

Zhang, Jin; Huang, Gang

2018-05-01

Principal component analysis (PCA) of heterogeneous data sets can solve the problem that centralized data scalability is limited. In order to reduce the generation of intermediate data and error components of distributed heterogeneous data sets, a principal component analysis algorithm based on heterogeneous data sets under cloud platform is proposed. The algorithm performs eigenvalue processing by using Householder tridiagonalization and QR factorization to calculate the error component of the heterogeneous database associated with the public key to obtain the intermediate data set and the lost information. Experiments on distributed DBM heterogeneous datasets show that the model method has the feasibility and reliability in terms of execution time and accuracy.

Differential principal component analysis of ChIP-seq.

PubMed

Ji, Hongkai; Li, Xia; Wang, Qian-fei; Ning, Yang

2013-04-23

We propose differential principal component analysis (dPCA) for analyzing multiple ChIP-sequencing datasets to identify differential protein-DNA interactions between two biological conditions. dPCA integrates unsupervised pattern discovery, dimension reduction, and statistical inference into a single framework. It uses a small number of principal components to summarize concisely the major multiprotein synergistic differential patterns between the two conditions. For each pattern, it detects and prioritizes differential genomic loci by comparing the between-condition differences with the within-condition variation among replicate samples. dPCA provides a unique tool for efficiently analyzing large amounts of ChIP-sequencing data to study dynamic changes of gene regulation across different biological conditions. We demonstrate this approach through analyses of differential chromatin patterns at transcription factor binding sites and promoters as well as allele-specific protein-DNA interactions.

A GRAPHICAL DIAGNOSTIC METHOD FOR ASSESSING THE ROTATION IN FACTOR ANALYTICAL MODELS OF ATMOSPHERIC POLLUTION. (R831078)

EPA Science Inventory

Factor analytic tools such as principal component analysis (PCA) and positive matrix factorization (PMF), suffer from rotational ambiguity in the results: different solutions (factors) provide equally good fits to the measured data. The PMF model imposes non-negativity of both...

[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.

Psychometric properties of Connor-Davidson Resilience Scale in a Spanish sample of entrepreneurs.

PubMed

Manzano-García, Guadalupe; Ayala Calvo, Juan Carlos

2013-01-01

The literature regarding entrepreneurship suggests that the resilience of entrepreneurs may help to explain entrepreneurial success, but there is no resilience measure widely accepted by researchers. This study analyzes the psychometric properties of the Connor and Davidson Resilience Scale (CD-RISC) in a sample of Spanish entrepreneurs. A telephone survey research method was used. The participants were entrepreneurs operating in the business services sector. Interviewers telephoned a total of 900 entrepreneurs of whom 783 produced usable questionnaires. The CD-RISC was used as data collection instrument. We used principal component analysis factor and confirmatory factor analysis to determine the factor structure of the CD-RISC. Confirmatory factor analysis failed to verify the original five-factor structure of the CD-RISC, whereas principal component analysis factor yielded a 3-factor structure of resilience (hardiness, resourcefulness and optimism). In this research, 47.48% of the total variance was accounted for by three factors, and the obtained factor structure was verified through confirmatory factor analysis. The CD-RISC has been shown to be a reliable and valid tool for measuring entrepreneurs' resilience.

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.

Geographic variation in the black bear (Ursus americanus) in the eastern United States and Canada

USGS Publications Warehouse

Kennedy, M.L.; Kennedy, P.K.; Bogan, M.A.; Waits, J.L.

2002-01-01

The pattern of geographic variation in morphologic characters of the black bear (Ursus americanus) was assessed at 13 sites in the eastern United States and Canada. Thirty measurements from 206 males and 207 females were recorded to the nearest 0.01 mm using digital calipers and subjected to principal components analysis. A matrix of correlations among skull characters was computed, and the first 3 principal components were extracted. These accounted for 90.5% of the variation in the character set for males and 87.1% for females. Three-dimensional projection of localities onto principal components showed that, for males and females, largest individuals occurred in the more southern localities (e.g., males--Louisiana-Mississippi, eastern Texas; females--Louisiana-eastern Texas) and the smallest animals occurred in the northernmost locality (Quebec). Generally, bears were similar morphologically to those in nearby geographic areas. For males, correlations between morphologic variation and environmental factors indicated a significant relationship between size variation and mean January temperature, mean July temperature, mean annual precipitation, latitude, and actual evapotranspiration; for females, a significant relationship was observed between morphologic variation and mean annual temperature, mean January temperature, mean July temperature, latitude, and actual evapotranspiration. There was no significant correlation for either sex between environmental factors and projections onto components II and III.

A feasibility study on age-related factors of wrist pulse using principal component analysis.

PubMed

Jang-Han Bae; Young Ju Jeon; Sanghun Lee; Jaeuk U Kim

2016-08-01

Various analysis methods for examining wrist pulse characteristics are needed for accurate pulse diagnosis. In this feasibility study, principal component analysis (PCA) was performed to observe age-related factors of wrist pulse from various analysis parameters. Forty subjects in the age group of 20s and 40s were participated, and their wrist pulse signal and respiration signal were acquired with the pulse tonometric device. After pre-processing of the signals, twenty analysis parameters which have been regarded as values reflecting pulse characteristics were calculated and PCA was performed. As a results, we could reduce complex parameters to lower dimension and age-related factors of wrist pulse were observed by combining-new analysis parameter derived from PCA. These results demonstrate that PCA can be useful tool for analyzing wrist pulse signal.

Factors Affecting Turkish Students' Achievement in Mathematics

ERIC Educational Resources Information Center

Demir, Ibrahim; Kilic, Serpil; Depren, Ozer

2009-01-01

Following past researches, student background, learning strategies, self-related cognitions in mathematics and school climate variables were important for achievement. The purpose of this study was to identify a number of factors that represent the relationship among sets of interrelated variables using principal component factor analysis and…

Function Invariant and Parameter Scale-Free Transformation Methods

ERIC Educational Resources Information Center

Bentler, P. M.; Wingard, Joseph A.

1977-01-01

A scale-invariant simple structure function of previously studied function components for principal component analysis and factor analysis is defined. First and second partial derivatives are obtained, and Newton-Raphson iterations are utilized. The resulting solutions are locally optimal and subjectively pleasing. (Author/JKS)

The Factor Structure of Some Piagetian Tasks

ERIC Educational Resources Information Center

Lawson, Anton E.; Nordland, Floyd H.

1976-01-01

Investigated was the hypothesis that conservation tasks are unifactor by administering eight different conservation tasks to 96 seventh-grade science students and performing a principal component analysis on the data. Results indicated that conservation tasks may measure up to three different components of cognitive thought. (SL)

Quantitative descriptive analysis and principal component analysis for sensory characterization of Indian milk product cham-cham.

PubMed

Puri, Ritika; Khamrui, Kaushik; Khetra, Yogesh; Malhotra, Ravinder; Devraja, H C

2016-02-01

Promising development and expansion in the market of cham-cham, a traditional Indian dairy product is expected in the coming future with the organized production of this milk product by some large dairies. The objective of this study was to document the extent of variation in sensory properties of market samples of cham-cham collected from four different locations known for their excellence in cham-cham production and to find out the attributes that govern much of variation in sensory scores of this product using quantitative descriptive analysis (QDA) and principal component analysis (PCA). QDA revealed significant (p < 0.05) difference in sensory attributes of cham-cham among the market samples. PCA identified four significant principal components that accounted for 72.4 % of the variation in the sensory data. Factor scores of each of the four principal components which primarily correspond to sweetness/shape/dryness of interior, surface appearance/surface dryness, rancid and firmness attributes specify the location of each market sample along each of the axes in 3-D graphs. These findings demonstrate the utility of quantitative descriptive analysis for identifying and measuring attributes of cham-cham that contribute most to its sensory acceptability.

Using Structural Equation Modeling To Fit Models Incorporating Principal Components.

ERIC Educational Resources Information Center

Dolan, Conor; Bechger, Timo; Molenaar, Peter

1999-01-01

Considers models incorporating principal components from the perspectives of structural-equation modeling. These models include the following: (1) the principal-component analysis of patterned matrices; (2) multiple analysis of variance based on principal components; and (3) multigroup principal-components analysis. Discusses fitting these models…

Scale for positive aspects of caregiving experience: development, reliability, and factor structure.

PubMed

Kate, N; Grover, S; Kulhara, P; Nehra, R

2012-06-01

OBJECTIVE. To develop an instrument (Scale for Positive Aspects of Caregiving Experience [SPACE]) that evaluates positive caregiving experience and assess its psychometric properties. METHODS. Available scales which assess some aspects of positive caregiving experience were reviewed and a 50-item questionnaire with a 5-point rating was constructed. In all, 203 primary caregivers of patients with severe mental disorders were asked to complete the questionnaire. Internal consistency, test-retest reliability, cross-language reliability, split-half reliability, and face validity were evaluated. Principal component factor analysis was run to assess the factorial validity of the scale. RESULTS. The scale developed as part of the study was found to have good internal consistency, test-retest reliability, cross-language reliability, split-half reliability, and face validity. Principal component factor analysis yielded a 4-factor structure, which also had good test-retest reliability and cross-language reliability. There was a strong correlation between the 4 factors obtained. CONCLUSION. The SPACE developed as part of this study has good psychometric properties.

The Mediating Effects of Generative Cognition on Imagination Stimulation

ERIC Educational Resources Information Center

Hsu, Yuling; Liang, Chaoyun; Chang, Chi-Cheng

2014-01-01

This study, based in Taiwan, aims to explore what psychological factors influence imagination stimulation of education major students, and what the relationship is between these factors and imagination. Both principal component analysis and confirmatory factor analysis were employed to determine the most appropriate structure of the developed…

Authoritarianism Revisited: Evidence for an Aggression Factor.

ERIC Educational Resources Information Center

Raden, David

1981-01-01

Performed a principal components factor analysis on scores of 245 undergraduates to a short version of the F Scale and measures of prejudice, attitude toward welfare, toleration of political deviance, punitiveness toward criminals, and support of the Vietnam War. Analysis produced two factors: authoritarian aggression and attitude toward welfare.…

[Role of school lunch in primary school education: a trial analysis of school teachers' views using an open-ended questionnaire].

PubMed

Inayama, T; Kashiwazaki, H; Sakamoto, M

1998-12-01

We tried to analyze synthetically teachers' view points associated with health education and roles of school lunch in primary education. For this purpose, a survey using an open-ended questionnaire consisting of eight items relating to health education in the school curriculum was carried out in 100 teachers of ten public primary schools. Subjects were asked to describe their view regarding the following eight items: 1) health and physical guidance education, 2) school lunch guidance education, 3) pupils' attitude toward their own health and nutrition, 4) health education, 5) role of school lunch in education, 6) future subjects of health education, 7) class room lesson related to school lunch, 8) guidance in case of pupil with unbalanced dieting and food avoidance. Subjects described their own opinions on an open-ended questionnaire response sheet. Keywords in individual descriptions were selected, rearranged and classified into categories according to their own meanings, and each of the selected keywords were used as the dummy variable. To assess individual opinions synthetically, a principal component analysis was then applied to the variables collected through the teachers' descriptions, and four factors were extracted. The results were as follows. 1) Four factors obtained from the repeated principal component analysis were summarized as; roles of health education and school lunch program (the first principal component), cooperation with nurse-teachers and those in charge of lunch service (the second principal component), time allocation for health education in home-room activity and lunch time (the third principal component) and contents of health education and school lunch guidance and their future plan (the fourth principal component). 2) Teachers regarded the role of school lunch in primary education as providing daily supply of nutrients, teaching of table manners and building up friendships with classmates, health education and food and nutrition education, and developing food preferences through eating lunch together with classmates. 3) Significant positive correlation was observed between "the teachers' opinion about the role of school lunch of providing opportunity to learn good behavior for food preferences through eating lunch together with classmates" and the first principal component "roles of health education and school lunch program" (r = 0.39, p < 0.01). The variable "the role of school lunch is health education and food and nutrition education" showed positive correlation with the principle component "cooperation with nurse-teachers and those in charge of lunch service" (r = 0.27, p < 0.01). Interesting relationships obtained were that teachers with longer educational experience tended to place importance in health education and food and nutrition education as the role of school lunch, and that male teachers regarded the roles of school lunch more importantly for future education in primary education than female teachers did.

Pattern classification using an olfactory model with PCA feature selection in electronic noses: study and application.

PubMed

Fu, Jun; Huang, Canqin; Xing, Jianguo; Zheng, Junbao

2012-01-01

Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6~8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3~5 pattern classes considering the trade-off between time consumption and classification rate.

Comparison of factor-analytic and reduced rank models for test-day milk yield in Gyr dairy cattle (Bos indicus).

PubMed

Pereira, R J; Ayres, D R; El Faro, L; Verneque, R S; Vercesi Filho, A E; Albuquerque, L G

2013-09-27

We analyzed 46,161 monthly test-day records of milk production from 7453 first lactations of crossbred dairy Gyr (Bos indicus) x Holstein cows. The following seven models were compared: standard multivariate model (M10), three reduced rank models fitting the first 2, 3, or 4 genetic principal components, and three models considering a 2-, 3-, or 4-factor structure for the genetic covariance matrix. Full rank residual covariance matrices were considered for all models. The model fitting the first two principal components (PC2) was the best according to the model selection criteria. Similar phenotypic, genetic, and residual variances were obtained with models M10 and PC2. The heritability estimates ranged from 0.14 to 0.21 and from 0.13 to 0.21 for models M10 and PC2, respectively. The genetic correlations obtained with model PC2 were slightly higher than those estimated with model M10. PC2 markedly reduced the number of parameters estimated and the time spent to reach convergence. We concluded that two principal components are sufficient to model the structure of genetic covariances between test-day milk yields.

Factors affecting the views and attitudes of Scottish pharmacists to continuing professional development.

PubMed

Power, Ailsa; Grammatiki, Aikaterini; Bates, Ian; Mc Kellar, Susan; Johnson, B Julienne; Diack, H Lesley; Stewart, Derek; Hudson, Steve A

2011-12-01

To explore factors associated with Scottish pharmacists' views and attitudes to continuing professional development (CPD). A retrospective principal component analysis of 552 (22.8%) questionnaires returned from a sample of 2420 Scottish pharmacists randomly selected from the 4300 pharmacists registered with the Royal Pharmaceutical Society of Great Britain and with a Scottish address. Principal component analysis of questionnaire items (n = 19) revealed four factors associated with Scottish pharmacists' views and attitudes to CPD: having positive support in the workplace, having access to resources and meeting learning needs, having confidence in the CPD process and motivation to participate in the CPD process. Community pharmacists were identified as the subgroup of pharmacists that needed most support for CPD regarding all four factors, while pharmacists working in primary care felt that they had most support in the workplace in comparison to other sectors (P < 0.05) and better access to resources and meeting learning needs when compared to community (P < 0.001) and hospital (P = 0.008) colleagues. Pharmacists working in primary care also felt more motivated to participate in the CPD process than those in the community (P < 0.001), and hospital pharmacists reported having more confidence in the CPD process compared to community pharmacists (P < 0.05). Using principal component analysis has identified four factors associated with Scottish pharmacists' views and attitudes to CPD. This may provide an approach to facilitate comparison of CPD views and attitudes with intra and inter professional groupings. Further study may allow identification of good practice and solutions to common CPD issues. © 2011 The Authors. IJPP © 2011 Royal Pharmaceutical Society.

Success-Factors in Transition to University Mathematics

ERIC Educational Resources Information Center

Bengmark, S.; Thunberg, H.; Winberg, T. M.

2017-01-01

This study examines different factors' relative importance for students' performance in the transition to university mathematics. Students' characteristics (motivation, actions and beliefs) were measured when entering the university and at the end of the first year. Principal component analysis revealed four important constructs:…

Impacts of a flash flood on drinking water quality: case study of areas most affected by the 2012 Beijing flood.

PubMed

Sun, Rubao; An, Daizhi; Lu, Wei; Shi, Yun; Wang, Lili; Zhang, Can; Zhang, Ping; Qi, Hongjuan; Wang, Qiang

2016-02-01

In this study, we present a method for identifying sources of water pollution and their relative contributions in pollution disasters. The method uses a combination of principal component analysis and factor analysis. We carried out a case study in three rural villages close to Beijing after torrential rain on July 21, 2012. Nine water samples were analyzed for eight parameters, namely turbidity, total hardness, total dissolved solids, sulfates, chlorides, nitrates, total bacterial count, and total coliform groups. All of the samples showed different degrees of pollution, and most were unsuitable for drinking water as concentrations of various parameters exceeded recommended thresholds. Principal component analysis and factor analysis showed that two factors, the degree of mineralization and agricultural runoff, and flood entrainment, explained 82.50% of the total variance. The case study demonstrates that this method is useful for evaluating and interpreting large, complex water-quality data sets.

Intelligent data analysis to interpret major risk factors for diabetic patients with and without ischemic stroke in a small population

PubMed Central

Gürgen, Fikret; Gürgen, Nurgül

2003-01-01

This study proposes an intelligent data analysis approach to investigate and interpret the distinctive factors of diabetes mellitus patients with and without ischemic (non-embolic type) stroke in a small population. The database consists of a total of 16 features collected from 44 diabetic patients. Features include age, gender, duration of diabetes, cholesterol, high density lipoprotein, triglyceride levels, neuropathy, nephropathy, retinopathy, peripheral vascular disease, myocardial infarction rate, glucose level, medication and blood pressure. Metric and non-metric features are distinguished. First, the mean and covariance of the data are estimated and the correlated components are observed. Second, major components are extracted by principal component analysis. Finally, as common examples of local and global classification approach, a k-nearest neighbor and a high-degree polynomial classifier such as multilayer perceptron are employed for classification with all the components and major components case. Macrovascular changes emerged as the principal distinctive factors of ischemic-stroke in diabetes mellitus. Microvascular changes were generally ineffective discriminators. Recommendations were made according to the rules of evidence-based medicine. Briefly, this case study, based on a small population, supports theories of stroke in diabetes mellitus patients and also concludes that the use of intelligent data analysis improves personalized preventive intervention. PMID:12685939

On the Factor Structure of a Reading Comprehension Test

ERIC Educational Resources Information Center

Salehi, Mohammad

2011-01-01

To investigate the construct validly of a section of a high stakes test, an exploratory factor analysis using principal components analysis was employed. The rotation used was varimax with the suppression level of 0.30. Eleven factors were extracted out of 35 reading comprehension items. The fact that these factors emerged speak to the construct…

The Three Domains of Disgust Scale: Factor Structure, Psychometric Properties, and Conceptual Limitations

ERIC Educational Resources Information Center

Olatunji, Bunmi O.; Adams, Thomas; Ciesielski, Bethany; David, Bieke; Sarawgi, Shivali; Broman-Fulks, Joshua

2012-01-01

This investigation examined the measurement properties of the Three Domains of Disgust Scale (TDDS). Principal components analysis in Study 1 (n = 206) revealed three factors of Pathogen, Sexual, and Moral Disgust that demonstrated excellent reliability, including test-retest over 12 weeks. Confirmatory factor analyses in Study 2 (n = 406)…

Factors Related to the Recruitment and Retention of Professionals from Specialized Disciplines.

ERIC Educational Resources Information Center

Harrington, Deborah; And Others

A survey was developed to identify critical factors in job selection and retention for speech/language pathologists, physical therapists, and occupational therapists. The survey was completed by 455 New Mexico professionals in these disciplines. A principal-components analysis identified six factors that were important in career decisions: (1)…

Optimizing protection efforts for amphibian conservation in Mediterranean landscapes

NASA Astrophysics Data System (ADS)

García-Muñoz, Enrique; Ceacero, Francisco; Carretero, Miguel A.; Pedrajas-Pulido, Luis; Parra, Gema; Guerrero, Francisco

2013-05-01

Amphibians epitomize the modern biodiversity crisis, and attract great attention from the scientific community since a complex puzzle of factors has influence on their disappearance. However, these factors are multiple and spatially variable, and declining in each locality is due to a particular combination of causes. This study shows a suitable statistical procedure to determine threats to amphibian species in medium size administrative areas. For our study case, ten biological and ecological variables feasible to affect the survival of 15 amphibian species were categorized and reduced through Principal Component Analysis. The principal components extracted were related to ecological plasticity, reproductive potential, and specificity of breeding habitats. Finally, the factor scores of species were joined in a presence-absence matrix that gives us information to identify where and why conservation management are requires. In summary, this methodology provides the necessary information to maximize benefits of conservation measures in small areas by identifying which ecological factors need management efforts and where should we focus them on.

Discrimination of a chestnut-oak forest unit for geologic mapping by means of a principal component enhancement of Landsat multispectral scanner data.

USGS Publications Warehouse

Krohn, M.D.; Milton, N.M.; Segal, D.; Enland, A.

1981-01-01

A principal component image enhancement has been effective in applying Landsat data to geologic mapping in a heavily forested area of E Virginia. The image enhancement procedure consists of a principal component transformation, a histogram normalization, and the inverse principal componnet transformation. The enhancement preserves the independence of the principal components, yet produces a more readily interpretable image than does a single principal component transformation. -from Authors

Transforming Graph Data for Statistical Relational Learning

DTIC Science & Technology

2012-10-01

Jordan, 2003), PLSA (Hofmann, 1999), ? Classification via RMN (Taskar et al., 2003) or SVM (Hasan, Chaoji, Salem , & Zaki, 2006) ? Hierarchical...dimensionality reduction methods such as Principal 407 Rossi, McDowell, Aha, & Neville Component Analysis (PCA), Principal Factor Analysis ( PFA ), and...clustering algorithm. Journal of the Royal Statistical Society. Series C, Applied statistics, 28, 100–108. Hasan, M. A., Chaoji, V., Salem , S., & Zaki, M

The risk of misclassifying subjects within principal component based asset index

PubMed Central

2014-01-01

The asset index is often used as a measure of socioeconomic status in empirical research as an explanatory variable or to control confounding. Principal component analysis (PCA) is frequently used to create the asset index. We conducted a simulation study to explore how accurately the principal component based asset index reflects the study subjects’ actual poverty level, when the actual poverty level is generated by a simple factor analytic model. In the simulation study using the PC-based asset index, only 1% to 4% of subjects preserved their real position in a quintile scale of assets; between 44% to 82% of subjects were misclassified into the wrong asset quintile. If the PC-based asset index explained less than 30% of the total variance in the component variables, then we consistently observed more than 50% misclassification across quintiles of the index. The frequency of misclassification suggests that the PC-based asset index may not provide a valid measure of poverty level and should be used cautiously as a measure of socioeconomic status. PMID:24987446

Factor Structure of Psychoeducational and Neuropsychological Measures of Learning-Disabled Children.

ERIC Educational Resources Information Center

Chittooran, Mary Mathai; And Others

1993-01-01

Administered Wechsler Intelligence Scale for Children-Revised, Halstead-Reitan Neuropsychological Battery, Peabody Picture Vocabulary Test, and Wide Range Achievement Test to 934 learning-disabled students between ages 8 and 16. Principal-components analysis with varimax rotation indicated existence of seven factors: Verbal Reasoning, Academic…

Does the Assessment of Recovery Capital scale reflect a single or multiple domains?

PubMed

Arndt, Stephan; Sahker, Ethan; Hedden, Suzy

2017-01-01

The goal of this study was to determine whether the 50-item Assessment of Recovery Capital scale represents a single general measure or whether multiple domains might be psychometrically useful for research or clinical applications. Data are from a cross-sectional de-identified existing program evaluation information data set with 1,138 clients entering substance use disorder treatment. Principal components and iterated factor analysis were used on the domain scores. Multiple group factor analysis provided a quasi-confirmatory factor analysis. The solution accounted for 75.24% of the total variance, suggesting that 10 factors provide a reasonably good fit. However, Tucker's congruence coefficients between the factor structure and defining weights (0.41-0.52) suggested a poor fit to the hypothesized 10-domain structure. Principal components of the 10-domain scores yielded one factor whose eigenvalue was greater than one (5.93), accounting for 75.8% of the common variance. A few domains had perceptible but small unique variance components suggesting that a few of the domains may warrant enrichment. Our findings suggest that there is one general factor, with a caveat. Using the 10 measures inflates the chance for Type I errors. Using one general measure avoids this issue, is simple to interpret, and could reduce the number of items. However, those seeking to maximally predict later recovery success may need to use the full instrument and all 10 domains.

Components of Spatial Thinking: Evidence from a Spatial Thinking Ability Test

ERIC Educational Resources Information Center

Lee, Jongwon; Bednarz, Robert

2012-01-01

This article introduces the development and validation of the spatial thinking ability test (STAT). The STAT consists of sixteen multiple-choice questions of eight types. The STAT was validated by administering it to a sample of 532 junior high, high school, and university students. Factor analysis using principal components extraction was applied…

Investigating Predictors of Listening Comprehension in Third-, Seventh-, and Tenth-Grade Students: A Dominance Analysis Approach

ERIC Educational Resources Information Center

Tighe, Elizabeth L.; Spencer, Mercedes; Schatschneider, Christopher

2015-01-01

This study rank ordered the contributive importance of several predictors of listening comprehension for third, seventh, and tenth graders. Principal components analyses revealed that a three-factor solution with fluency, reasoning, and working memory components provided the best fit across grade levels. Dominance analyses indicated that fluency…

A multifactor approach to forecasting Romanian gross domestic product (GDP) in the short run.

PubMed

Armeanu, Daniel; Andrei, Jean Vasile; Lache, Leonard; Panait, Mirela

2017-01-01

The purpose of this paper is to investigate the application of a generalized dynamic factor model (GDFM) based on dynamic principal components analysis to forecasting short-term economic growth in Romania. We have used a generalized principal components approach to estimate a dynamic model based on a dataset comprising 86 economic and non-economic variables that are linked to economic output. The model exploits the dynamic correlations between these variables and uses three common components that account for roughly 72% of the information contained in the original space. We show that it is possible to generate reliable forecasts of quarterly real gross domestic product (GDP) using just the common components while also assessing the contribution of the individual variables to the dynamics of real GDP. In order to assess the relative performance of the GDFM to standard models based on principal components analysis, we have also estimated two Stock-Watson (SW) models that were used to perform the same out-of-sample forecasts as the GDFM. The results indicate significantly better performance of the GDFM compared with the competing SW models, which empirically confirms our expectations that the GDFM produces more accurate forecasts when dealing with large datasets.

A multifactor approach to forecasting Romanian gross domestic product (GDP) in the short run

PubMed Central

Armeanu, Daniel; Lache, Leonard; Panait, Mirela

2017-01-01

The purpose of this paper is to investigate the application of a generalized dynamic factor model (GDFM) based on dynamic principal components analysis to forecasting short-term economic growth in Romania. We have used a generalized principal components approach to estimate a dynamic model based on a dataset comprising 86 economic and non-economic variables that are linked to economic output. The model exploits the dynamic correlations between these variables and uses three common components that account for roughly 72% of the information contained in the original space. We show that it is possible to generate reliable forecasts of quarterly real gross domestic product (GDP) using just the common components while also assessing the contribution of the individual variables to the dynamics of real GDP. In order to assess the relative performance of the GDFM to standard models based on principal components analysis, we have also estimated two Stock-Watson (SW) models that were used to perform the same out-of-sample forecasts as the GDFM. The results indicate significantly better performance of the GDFM compared with the competing SW models, which empirically confirms our expectations that the GDFM produces more accurate forecasts when dealing with large datasets. PMID:28742100

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

The conservative behavior of dissolved organic carbon in surface waters of the southern Chukchi Sea, Arctic Ocean, during early summer

PubMed Central

Tanaka, Kazuki; Takesue, Nobuyuki; Nishioka, Jun; Kondo, Yoshiko; Ooki, Atsushi; Kuma, Kenshi; Hirawake, Toru; Yamashita, Youhei

2016-01-01

The spatial distribution of dissolved organic carbon (DOC) concentrations and the optical properties of dissolved organic matter (DOM) determined by ultraviolet-visible absorbance and fluorescence spectroscopy were measured in surface waters of the southern Chukchi Sea, western Arctic Ocean, during the early summer of 2013. Neither the DOC concentration nor the optical parameters of the DOM correlated with salinity. Principal component analysis using the DOM optical parameters clearly separated the DOM sources. A significant linear relationship was evident between the DOC and the principal component score for specific water masses, indicating that a high DOC level was related to a terrigenous source, whereas a low DOC level was related to a marine source. Relationships between the DOC and the principal component scores of the surface waters of the southern Chukchi Sea implied that the major factor controlling the distribution of DOC concentrations was the mixing of plural water masses rather than local production and degradation. PMID:27658444

Study on nondestructive discrimination of genuine and counterfeit wild ginsengs using NIRS

NASA Astrophysics Data System (ADS)

Lu, Q.; Fan, Y.; Peng, Z.; Ding, H.; Gao, H.

2012-07-01

A new approach for the nondestructive discrimination between genuine wild ginsengs and the counterfeit ones by near infrared spectroscopy (NIRS) was developed. Both discriminant analysis and back propagation artificial neural network (BP-ANN) were applied to the model establishment for discrimination. Optimal modeling wavelengths were determined based on the anomalous spectral information of counterfeit samples. Through principal component analysis (PCA) of various wild ginseng samples, genuine and counterfeit, the cumulative percentages of variance of the principal components were obtained, serving as a reference for principal component (PC) factor determination. Discriminant analysis achieved an identification ratio of 88.46%. With sample' truth values as its outputs, a three-layer BP-ANN model was built, which yielded a higher discrimination accuracy of 100%. The overall results sufficiently demonstrate that NIRS combined with BP-ANN classification algorithm performs better on ginseng discrimination than discriminant analysis, and can be used as a rapid and nondestructive method for the detection of counterfeit wild ginsengs in food and pharmaceutical industry.

A Fast and Sensitive New Satellite SO2 Retrieval Algorithm based on Principal Component Analysis: Application to the Ozone Monitoring Instrument

NASA Technical Reports Server (NTRS)

Li, Can; Joiner, Joanna; Krotkov, A.; Bhartia, Pawan K.

2013-01-01

We describe a new algorithm to retrieve SO2 from satellite-measured hyperspectral radiances. We employ the principal component analysis technique in regions with no significant SO2 to capture radiance variability caused by both physical processes (e.g., Rayleigh and Raman scattering and ozone absorption) and measurement artifacts. We use the resulting principal components and SO2 Jacobians calculated with a radiative transfer model to directly estimate SO2 vertical column density in one step. Application to the Ozone Monitoring Instrument (OMI) radiance spectra in 310.5-340 nm demonstrates that this approach can greatly reduce biases in the operational OMI product and decrease the noise by a factor of 2, providing greater sensitivity to anthropogenic emissions. The new algorithm is fast, eliminates the need for instrument-specific radiance correction schemes, and can be easily adapted to other sensors. These attributes make it a promising technique for producing longterm, consistent SO2 records for air quality and climate research.

Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application

PubMed Central

Fu, Jun; Huang, Canqin; Xing, Jianguo; Zheng, Junbao

2012-01-01

Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6∼8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3∼5 pattern classes considering the trade-off between time consumption and classification rate. PMID:22736979

Assessing School Work Culture: A Higher-Order Analysis and Strategy.

ERIC Educational Resources Information Center

Johnson, William L.; Johnson, Annabel M.; Zimmerman, Kurt J.

This paper reviews a work culture productivity model and reports the development of a work culture instrument based on the culture productivity model. Higher order principal components analysis was used to assess work culture, and a third-order factor analysis shows how the first-order factors group into higher-order factors. The school work…

Factor Structure and Psychometric Properties of the Injection Phobia Scale-Anxiety

ERIC Educational Resources Information Center

Olatunji, Bunmi O.; Sawchuk, Craig N.; Moretz, Melanie W.; David, Bieke; Armstrong, Thomas; Ciesielski, Bethany G.

2010-01-01

The present investigation examined the factor structure and psychometric properties of the Injection Phobia Scale-Anxiety (IPS-Anx). Principal components analysis of IPS-Anx items in Study 1 (n = 498) revealed a 2-factor structure consisting of Distal Fear and Contact Fear. However, CFA results in Study 2 (n = 567) suggest that a 1-factor…

Factor structure of DSM-IV criteria for obsessive compulsive personality disorder in patients with binge eating disorder.

PubMed

Grilo, C M

2004-01-01

To examine the factor structure of DSM-IV criteria for obsessive compulsive personality disorder (OCPD) in patients with binge eating disorder (BED). Two hundred and eleven consecutive out-patients with axis I diagnoses of BED were reliably assessed with semi-structured diagnostic interviews. The eight criteria for the OCPD diagnosis were examined with reliability and correlational analyses. Exploratory factor analysis was performed to identify potential components. Cronbach's coefficient alpha for the OCPD criteria was 0.77. Principal components factor analysis with varimax rotation revealed a three-factor solution (rigidity, perfectionism, and miserliness), which accounted for 65% of variance. The DSM-IV criteria for OCPD showed good internal consistency. Exploratory factor analysis, however, revealed three components that may reflect distinct interpersonal, intrapersonal (cognitive), and behavioral features.

A Comprehensive Needs Assessment To Facilitate Prevention of School Drop Out and Violence.

ERIC Educational Resources Information Center

Hunt, Mary Ellen; Meyers, Joel; Davies, Gwen; Meyers, Barbara; Grogg, Kathryn Rogers; Neel, John

2002-01-01

Study addresses school violence and dropout and proposes that the underlying factor of school connectedness/school climate should guide preventive and intervention efforts. Principal components analysis revealed five distinct factors: school connectedness/positive school climate, causes of violence, causes of school dropout, interventions for drop…

Assessing Underreporting Response Bias on the MMPI-2

ERIC Educational Resources Information Center

Bagby, R. Michael; Marshall, Margarita B.

2004-01-01

The authors assess the replicability of the two-factor model of underreporting response style. They then examine the relative performance of scales measuring these styles in analog (ARD) and differential prevalence group (DPG) designs. Principal components analysis produced a two-factor structure corresponding to self-deceptive (SD) and impression…

Principal variance component analysis of crop composition data: a case study on herbicide-tolerant cotton.

PubMed

Harrison, Jay M; Howard, Delia; Malven, Marianne; Halls, Steven C; Culler, Angela H; Harrigan, George G; Wolfinger, Russell D

2013-07-03

Compositional studies on genetically modified (GM) and non-GM crops have consistently demonstrated that their respective levels of key nutrients and antinutrients are remarkably similar and that other factors such as germplasm and environment contribute more to compositional variability than transgenic breeding. We propose that graphical and statistical approaches that can provide meaningful evaluations of the relative impact of different factors to compositional variability may offer advantages over traditional frequentist testing. A case study on the novel application of principal variance component analysis (PVCA) in a compositional assessment of herbicide-tolerant GM cotton is presented. Results of the traditional analysis of variance approach confirmed the compositional equivalence of the GM and non-GM cotton. The multivariate approach of PVCA provided further information on the impact of location and germplasm on compositional variability relative to GM.

[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.

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

Genetic diversity analysis of fruit characteristics of hawthorn germplasm.

PubMed

Su, K; Guo, Y S; Wang, G; Zhao, Y H; Dong, W X

2015-12-07

One hundred and six accessions of hawthorn intraspecific resources, from the National Germplasm Repository at Shenyang, were subjected to genetic diversity and principal component analysis based on evaluation data of 15 fruit traits. Results showed that the genetic diversity of hawthorn fruit traits varied. Among the 15 traits, the fruit shape variable coefficient had the most obvious evaluation, followed by fruit surface state, dot color, taste, weight of single fruit, sepal posture, peduncle form, and metula traits. These are the primary traits by which hawthorn could be classified in the future. The principal component demonstrated that these traits are the most influential factors of hawthorn fruit characteristics.

Psychometric Evaluation of a Triage Decision Making Inventory

DTIC Science & Technology

2011-06-27

the correlation matrix and inter-item correlations were reviewed. The Bartlett’s test of sphericity and the Kaiser - Meyer Olkin (KMO) were examined to...nursing experience. Principal component factor analysis with Varimax rotation was conducted using SPSS version 16. The Kaiser - Meyer - Olkin Measure of...Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 7 iterations

The principal components model: a model for advancing spirituality and spiritual care within nursing and health care practice.

PubMed

McSherry, Wilfred

2006-07-01

The aim of this study was to generate a deeper understanding of the factors and forces that may inhibit or advance the concepts of spirituality and spiritual care within both nursing and health care. This manuscript presents a model that emerged from a qualitative study using grounded theory. Implementation and use of this model may assist all health care practitioners and organizations to advance the concepts of spirituality and spiritual care within their own sphere of practice. The model has been termed the principal components model because participants identified six components as being crucial to the advancement of spiritual health care. Grounded theory was used meaning that there was concurrent data collection and analysis. Theoretical sampling was used to develop the emerging theory. These processes, along with data analysis, open, axial and theoretical coding led to the identification of a core category and the construction of the principal components model. Fifty-three participants (24 men and 29 women) were recruited and all consented to be interviewed. The sample included nurses (n=24), chaplains (n=7), a social worker (n=1), an occupational therapist (n=1), physiotherapists (n=2), patients (n=14) and the public (n=4). The investigation was conducted in three phases to substantiate the emerging theory and the development of the model. The principal components model contained six components: individuality, inclusivity, integrated, inter/intra-disciplinary, innate and institution. A great deal has been written on the concepts of spirituality and spiritual care. However, rhetoric alone will not remove some of the intrinsic and extrinsic barriers that are inhibiting the advancement of the spiritual dimension in terms of theory and practice. An awareness of and adherence to the principal components model may assist nurses and health care professionals to engage with and overcome some of the structural, organizational, political and social variables that are impacting upon spiritual care.

Development of a scale to measure adherence to self-monitoring of blood glucose with latent variable measurement.

PubMed

Wagner, J A; Schnoll, R A; Gipson, M T

1998-07-01

Adherence to self-monitoring of blood glucose (SMBG) is problematic for many people with diabetes. Self-reports of adherence have been found to be unreliable, and existing paper-and-pencil measures have limitations. This study developed a brief measure of SMBG adherence with good psychometric properties and a useful factor structure that can be used in research and in practice. A total of 216 adults with diabetes responded to 30 items rated on a 9-point Likert scale that asked about blood monitoring habits. In part I of the study, items were evaluated and retained based on their psychometric properties. The sample was divided into exploratory and confirmatory halves. Using the exploratory half, items with acceptable psychometric properties were subjected to a principal components analysis. In part II of the study, structural equation modeling was used to confirm the component solution with the entire sample. Structural modeling was also used to test the relationship between these components. It was hypothesized that the scale would produce four correlated factors. Principal components analysis suggested a two-component solution, and confirmatory factor analysis confirmed this solution. The first factor measures the degree to which patients rely on others to help them test and thus was named "social influence." The second component measures the degree to which patients use physical symptoms of blood glucose levels to help them test and thus was named "physical influence." Results of the structural model show that the components are correlated and make up the higher-order latent variable adherence. The resulting 15-item scale provides a short, reliable way to assess patient adherence to SMBG. Despite the existence of several aspects of adherence, this study indicates that the construct consists of only two components. This scale is an improvement on previous measures of adherence because of its good psychometric properties, its interpretable factor structure, and its rigorous empirical development.

Spectral decomposition of asteroid Itokawa based on principal component analysis

NASA Astrophysics Data System (ADS)

Koga, Sumire C.; Sugita, Seiji; Kamata, Shunichi; Ishiguro, Masateru; Hiroi, Takahiro; Tatsumi, Eri; Sasaki, Sho

2018-01-01

The heliocentric stratification of asteroid spectral types may hold important information on the early evolution of the Solar System. Asteroid spectral taxonomy is based largely on principal component analysis. However, how the surface properties of asteroids, such as the composition and age, are projected in the principal-component (PC) space is not understood well. We decompose multi-band disk-resolved visible spectra of the Itokawa surface with principal component analysis (PCA) in comparison with main-belt asteroids. The obtained distribution of Itokawa spectra projected in the PC space of main-belt asteroids follows a linear trend linking the Q-type and S-type regions and is consistent with the results of space-weathering experiments on ordinary chondrites and olivine, suggesting that this trend may be a space-weathering-induced spectral evolution track for S-type asteroids. Comparison with space-weathering experiments also yield a short average surface age (< a few million years) for Itokawa, consistent with the cosmic-ray-exposure time of returned samples from Itokawa. The Itokawa PC score distribution exhibits asymmetry along the evolution track, strongly suggesting that space weathering has begun saturated on this young asteroid. The freshest spectrum found on Itokawa exhibits a clear sign for space weathering, indicating again that space weathering occurs very rapidly on this body. We also conducted PCA on Itokawa spectra alone and compared the results with space-weathering experiments. The obtained results indicate that the first principal component of Itokawa surface spectra is consistent with spectral change due to space weathering and that the spatial variation in the degree of space weathering is very large (a factor of three in surface age), which would strongly suggest the presence of strong regional/local resurfacing process(es) on this small asteroid.

Source apportionment of exposures to volatile organic compounds. I. Evaluation of receptor models using simulated exposure data

NASA Astrophysics Data System (ADS)

Miller, Shelly L.; Anderson, Melissa J.; Daly, Eileen P.; Milford, Jana B.

Four receptor-oriented source apportionment models were evaluated by applying them to simulated personal exposure data for select volatile organic compounds (VOCs) that were generated by Monte Carlo sampling from known source contributions and profiles. The exposure sources modeled are environmental tobacco smoke, paint emissions, cleaning and/or pesticide products, gasoline vapors, automobile exhaust, and wastewater treatment plant emissions. The receptor models analyzed are chemical mass balance, principal component analysis/absolute principal component scores, positive matrix factorization (PMF), and graphical ratio analysis for composition estimates/source apportionment by factors with explicit restriction, incorporated in the UNMIX model. All models identified only the major contributors to total exposure concentrations. PMF extracted factor profiles that most closely represented the major sources used to generate the simulated data. None of the models were able to distinguish between sources with similar chemical profiles. Sources that contributed <5% to the average total VOC exposure were not identified.

Design of experiments and principal component analysis as approaches for enhancing performance of gas-diffusional air-breathing bilirubin oxidase cathode

NASA Astrophysics Data System (ADS)

Babanova, Sofia; Artyushkova, Kateryna; Ulyanova, Yevgenia; Singhal, Sameer; Atanassov, Plamen

2014-01-01

Two statistical methods, design of experiments (DOE) and principal component analysis (PCA) are employed to investigate and improve performance of air-breathing gas-diffusional enzymatic electrodes. DOE is utilized as a tool for systematic organization and evaluation of various factors affecting the performance of the composite system. Based on the results from the DOE, an improved cathode is constructed. The current density generated utilizing the improved cathode (755 ± 39 μA cm-2 at 0.3 V vs. Ag/AgCl) is 2-5 times higher than the highest current density previously achieved. Three major factors contributing to the cathode performance are identified: the amount of enzyme, the volume of phosphate buffer used to immobilize the enzyme, and the thickness of the gas-diffusion layer (GDL). PCA is applied as an independent confirmation tool to support conclusions made by DOE and to visualize the contribution of factors in individual cathode configurations.

Principal component reconstruction (PCR) for cine CBCT with motion learning from 2D fluoroscopy.

PubMed

Gao, Hao; Zhang, Yawei; Ren, Lei; Yin, Fang-Fang

2018-01-01

This work aims to generate cine CT images (i.e., 4D images with high-temporal resolution) based on a novel principal component reconstruction (PCR) technique with motion learning from 2D fluoroscopic training images. In the proposed PCR method, the matrix factorization is utilized as an explicit low-rank regularization of 4D images that are represented as a product of spatial principal components and temporal motion coefficients. The key hypothesis of PCR is that temporal coefficients from 4D images can be reasonably approximated by temporal coefficients learned from 2D fluoroscopic training projections. For this purpose, we can acquire fluoroscopic training projections for a few breathing periods at fixed gantry angles that are free from geometric distortion due to gantry rotation, that is, fluoroscopy-based motion learning. Such training projections can provide an effective characterization of the breathing motion. The temporal coefficients can be extracted from these training projections and used as priors for PCR, even though principal components from training projections are certainly not the same for these 4D images to be reconstructed. For this purpose, training data are synchronized with reconstruction data using identical real-time breathing position intervals for projection binning. In terms of image reconstruction, with a priori temporal coefficients, the data fidelity for PCR changes from nonlinear to linear, and consequently, the PCR method is robust and can be solved efficiently. PCR is formulated as a convex optimization problem with the sum of linear data fidelity with respect to spatial principal components and spatiotemporal total variation regularization imposed on 4D image phases. The solution algorithm of PCR is developed based on alternating direction method of multipliers. The implementation is fully parallelized on GPU with NVIDIA CUDA toolbox and each reconstruction takes about a few minutes. The proposed PCR method is validated and compared with a state-of-art method, that is, PICCS, using both simulation and experimental data with the on-board cone-beam CT setting. The results demonstrated the feasibility of PCR for cine CBCT and significantly improved reconstruction quality of PCR from PICCS for cine CBCT. With a priori estimated temporal motion coefficients using fluoroscopic training projections, the PCR method can accurately reconstruct spatial principal components, and then generate cine CT images as a product of temporal motion coefficients and spatial principal components. © 2017 American Association of Physicists in Medicine.

Exploratory factor analysis of the 12-item Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being Scale in people newly diagnosed with advanced cancer.

PubMed

Bai, Mei; Dixon, Jane K

2014-01-01

The purpose of this study was to reexamine the factor pattern of the 12-item Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being Scale (FACIT-Sp-12) using exploratory factor analysis in people newly diagnosed with advanced cancer. Principal components analysis (PCA) and 3 common factor analysis methods were used to explore the factor pattern of the FACIT-Sp-12. Factorial validity was assessed in association with quality of life (QOL). Principal factor analysis (PFA), iterative PFA, and maximum likelihood suggested retrieving 3 factors: Peace, Meaning, and Faith. Both Peace and Meaning positively related to QOL, whereas only Peace uniquely contributed to QOL. This study supported the 3-factor model of the FACIT-Sp-12. Suggestions for revision of items and further validation of the identified factor pattern were provided.

A stable systemic risk ranking in China's banking sector: Based on principal component analysis

NASA Astrophysics Data System (ADS)

Fang, Libing; Xiao, Binqing; Yu, Honghai; You, Qixing

2018-02-01

In this paper, we compare five popular systemic risk rankings, and apply principal component analysis (PCA) model to provide a stable systemic risk ranking for the Chinese banking sector. Our empirical results indicate that five methods suggest vastly different systemic risk rankings for the same bank, while the combined systemic risk measure based on PCA provides a reliable ranking. Furthermore, according to factor loadings of the first component, PCA combined ranking is mainly based on fundamentals instead of market price data. We clearly find that price-based rankings are not as practical a method as fundamentals-based ones. This PCA combined ranking directly shows systemic risk contributions of each bank for banking supervision purpose and reminds banks to prevent and cope with the financial crisis in advance.

Use of Cusp Catastrophe for Risk Analysis of Navigational Environment: A Case Study of Three Gorges Reservoir Area

PubMed Central

Hao, Guozhu

2016-01-01

A water traffic system is a huge, nonlinear, complex system, and its stability is affected by various factors. Water traffic accidents can be considered to be a kind of mutation of a water traffic system caused by the coupling of multiple navigational environment factors. In this study, the catastrophe theory, principal component analysis (PCA), and multivariate statistics are integrated to establish a situation recognition model for a navigational environment with the aim of performing a quantitative analysis of the situation of this environment via the extraction and classification of its key influencing factors; in this model, the natural environment and traffic environment are considered to be two control variables. The Three Gorges Reservoir area of the Yangtze River is considered as an example, and six critical factors, i.e., the visibility, wind, current velocity, route intersection, channel dimension, and traffic flow, are classified into two principal components: the natural environment and traffic environment. These two components are assumed to have the greatest influence on the navigation risk. Then, the cusp catastrophe model is employed to identify the safety situation of the regional navigational environment in the Three Gorges Reservoir area. The simulation results indicate that the situation of the navigational environment of this area is gradually worsening from downstream to upstream. PMID:27391057

Use of Cusp Catastrophe for Risk Analysis of Navigational Environment: A Case Study of Three Gorges Reservoir Area.

PubMed

Jiang, Dan; Hao, Guozhu; Huang, Liwen; Zhang, Dan

2016-01-01

A water traffic system is a huge, nonlinear, complex system, and its stability is affected by various factors. Water traffic accidents can be considered to be a kind of mutation of a water traffic system caused by the coupling of multiple navigational environment factors. In this study, the catastrophe theory, principal component analysis (PCA), and multivariate statistics are integrated to establish a situation recognition model for a navigational environment with the aim of performing a quantitative analysis of the situation of this environment via the extraction and classification of its key influencing factors; in this model, the natural environment and traffic environment are considered to be two control variables. The Three Gorges Reservoir area of the Yangtze River is considered as an example, and six critical factors, i.e., the visibility, wind, current velocity, route intersection, channel dimension, and traffic flow, are classified into two principal components: the natural environment and traffic environment. These two components are assumed to have the greatest influence on the navigation risk. Then, the cusp catastrophe model is employed to identify the safety situation of the regional navigational environment in the Three Gorges Reservoir area. The simulation results indicate that the situation of the navigational environment of this area is gradually worsening from downstream to upstream.

Differential use of fresh water environments by wintering waterfowl of coastal Texas

USGS Publications Warehouse

White, D.H.; James, D.

1978-01-01

A comparative study of the environmental relationships among 14 species of wintering waterfowl was conducted at the Welder Wildlife Foundation, San Patricia County, near Sinton, Texas during the fall and early winter of 1973. Measurements of 20 environmental factors (social, vegetational, physical, and chemical) were subjected to multivariate statistical methods to determine certain niche characteristics and environmental relationships of waterfowl wintering in the aquatic community.....Each waterfowl species occupied a unique realized niche by responding to distinct combinations of environmental factors identified by principal component analysis. One percent confidence ellipses circumscribing the mean scores plotted for the first and second principal components gave an indication of relative niche width for each species. The waterfowl environments were significantly different interspecifically and water depth at feeding site and % emergent vegetation were most important in the separation. This was shown by subjecting the transformed data to multivariate analysis of variance with an associated step-down procedure. The species were distributed along a community cline extending from shallow water with abundant emergent vegetation to open deep water with little emergent vegetation of any kind. Four waterfowl subgroups were significantly separated along the cline, as indicated by one-way analysis of variance with Duncan?s multiple range test. Clumping of the bird species toward the middle of the available habitat hyperspace was shown in a plot of the principal component scores for the random samples and individual species.....Naturally occurring relationships among waterfowl were clarified using principal comcomponent analysis and related multivariate procedures. These techniques may prove useful in wetland management for particular groups of waterfowl based on habitat preferences.

Factor Structure and Reliability of the 2008 and 2009 SERU/UCUES Questionnaire Core. SERU Project Technical Report

ERIC Educational Resources Information Center

Chatman, Steve

2009-01-01

This technical report summarizes the third independent factor analysis of the SERU/UCUES questionnaire responses of students with majors. The 2009 solution employed the same quantitative analysis used in the prior solutions--varimax orthogonal rotation to determine principal components followed by promax oblique rotation to identify…

Career Interests of Students in Psychology Specialties Degrees: Psychometric Evidence and Correlations with the RIASEC Dimensions

ERIC Educational Resources Information Center

Ferreira, Aristides I.; Rodrigues, Rosa I.; da Costa Ferreira, Paula

2016-01-01

In this study, we present the development of a vocational interest scale for university students studying psychology. Three dimensions were extracted through principal component analysis, namely, organizational, educational, and clinical psychology. A second study with confirmatory factor analysis replicated the same three factors obtained in the…

On the Fallibility of Principal Components in Research

ERIC Educational Resources Information Center

Raykov, Tenko; Marcoulides, George A.; Li, Tenglong

2017-01-01

The measurement error in principal components extracted from a set of fallible measures is discussed and evaluated. It is shown that as long as one or more measures in a given set of observed variables contains error of measurement, so also does any principal component obtained from the set. The error variance in any principal component is shown…

Large Covariance Estimation by Thresholding Principal Orthogonal Complements

PubMed Central

Fan, Jianqing; Liao, Yuan; Mincheva, Martina

2012-01-01

This paper deals with the estimation of a high-dimensional covariance with a conditional sparsity structure and fast-diverging eigenvalues. By assuming sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-sectional correlation even after taking out common but unobservable factors. We introduce the Principal Orthogonal complEment Thresholding (POET) method to explore such an approximate factor structure with sparsity. The POET estimator includes the sample covariance matrix, the factor-based covariance matrix (Fan, Fan, and Lv, 2008), the thresholding estimator (Bickel and Levina, 2008) and the adaptive thresholding estimator (Cai and Liu, 2011) as specific examples. We provide mathematical insights when the factor analysis is approximately the same as the principal component analysis for high-dimensional data. The rates of convergence of the sparse residual covariance matrix and the conditional sparse covariance matrix are studied under various norms. It is shown that the impact of estimating the unknown factors vanishes as the dimensionality increases. The uniform rates of convergence for the unobserved factors and their factor loadings are derived. The asymptotic results are also verified by extensive simulation studies. Finally, a real data application on portfolio allocation is presented. PMID:24348088

Large Covariance Estimation by Thresholding Principal Orthogonal Complements.

PubMed

Fan, Jianqing; Liao, Yuan; Mincheva, Martina

2013-09-01

This paper deals with the estimation of a high-dimensional covariance with a conditional sparsity structure and fast-diverging eigenvalues. By assuming sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-sectional correlation even after taking out common but unobservable factors. We introduce the Principal Orthogonal complEment Thresholding (POET) method to explore such an approximate factor structure with sparsity. The POET estimator includes the sample covariance matrix, the factor-based covariance matrix (Fan, Fan, and Lv, 2008), the thresholding estimator (Bickel and Levina, 2008) and the adaptive thresholding estimator (Cai and Liu, 2011) as specific examples. We provide mathematical insights when the factor analysis is approximately the same as the principal component analysis for high-dimensional data. The rates of convergence of the sparse residual covariance matrix and the conditional sparse covariance matrix are studied under various norms. It is shown that the impact of estimating the unknown factors vanishes as the dimensionality increases. The uniform rates of convergence for the unobserved factors and their factor loadings are derived. The asymptotic results are also verified by extensive simulation studies. Finally, a real data application on portfolio allocation is presented.

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.

A Multivariate Analysis of the Early Dropout Process

ERIC Educational Resources Information Center

Fiester, Alan R.; Rudestam, Kjell E.

1975-01-01

Principal-component factor analyses were performed on patient input (demographic and pretherapy expectations), therapist input (demographic), and patient perspective therapy process variables that significantly differentiated early dropout from nondropout outpatients at two community mental health centers. (Author)

Source Evaluation and Trace Metal Contamination in Benthic Sediments from Equatorial Ecosystems Using Multivariate Statistical Techniques

PubMed Central

Benson, Nsikak U.; Asuquo, Francis E.; Williams, Akan B.; Essien, Joseph P.; Ekong, Cyril I.; Akpabio, Otobong; Olajire, Abaas A.

2016-01-01

Trace metals (Cd, Cr, Cu, Ni and Pb) concentrations in benthic sediments were analyzed through multi-step fractionation scheme to assess the levels and sources of contamination in estuarine, riverine and freshwater ecosystems in Niger Delta (Nigeria). The degree of contamination was assessed using the individual contamination factors (ICF) and global contamination factor (GCF). Multivariate statistical approaches including principal component analysis (PCA), cluster analysis and correlation test were employed to evaluate the interrelationships and associated sources of contamination. The spatial distribution of metal concentrations followed the pattern Pb>Cu>Cr>Cd>Ni. Ecological risk index by ICF showed significant potential mobility and bioavailability for Cu, Cu and Ni. The ICF contamination trend in the benthic sediments at all studied sites was Cu>Cr>Ni>Cd>Pb. The principal component and agglomerative clustering analyses indicate that trace metals contamination in the ecosystems was influenced by multiple pollution sources. PMID:27257934

Climate drivers on malaria transmission in Arunachal Pradesh, India.

PubMed

Upadhyayula, Suryanaryana Murty; Mutheneni, Srinivasa Rao; Chenna, Sumana; Parasaram, Vaideesh; Kadiri, Madhusudhan Rao

2015-01-01

The present study was conducted during the years 2006 to 2012 and provides information on prevalence of malaria and its regulation with effect to various climatic factors in East Siang district of Arunachal Pradesh, India. Correlation analysis, Principal Component Analysis and Hotelling's T² statistics models are adopted to understand the effect of weather variables on malaria transmission. The epidemiological study shows that the prevalence of malaria is mostly caused by the parasite Plasmodium vivax followed by Plasmodium falciparum. It is noted that, the intensity of malaria cases declined gradually from the year 2006 to 2012. The transmission of malaria observed was more during the rainy season, as compared to summer and winter seasons. Further, the data analysis study with Principal Component Analysis and Hotelling's T² statistic has revealed that the climatic variables such as temperature and rainfall are the most influencing factors for the high rate of malaria transmission in East Siang district of Arunachal Pradesh.

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.

Inequalities in the spiritual health of young Canadians: a national, cross-sectional study.

PubMed

Michaelson, Valerie; Freeman, John; King, Nathan; Ascough, Hannah; Davison, Colleen; Trothen, Tracy; Phillips, Sian; Pickett, William

2016-11-28

Spiritual health, along with physical, emotional, and social aspects, is one of four domains of health. Assessment in this field of research is challenging methodologically. No contemporary population-based studies have profiled the spiritual health of adolescent Canadians with a focus on health inequalities. In a 2014 nationally representative sample of Canadians aged 11-15 years we therefore: (1) psychometrically evaluated a series of items used to assess the perceived importance of spiritual health and its four potential sub-domains (connections with: self, others, nature and the natural environment, and the transcendent) to adolescents; (2) described potential inequalities in spiritual health within adolescent populations, overall and by spiritual health sub-domain, by key socio-demographic factors. Cross-sectional analysis of survey reports from the 2014 (Cycle 7) of the Canadian Health Behaviour in School-aged Children study (weighted n = 25,036). Principal components analysis followed by confirmatory factor analysis were used to explore the psychometric properties of the spiritual health items and the associated composite scale describing perceived importance of spiritual health. Associations among this composite scale, its individual sub-domains, and key socio-demographic factors were then explored. The principal components analysis best supported a four-factor structure where the eight scale items loaded highly according to the original four domains. This was also supported in confirmatory factor analyses. We then combined the eight items into composite spiritual health score as supported by theory, principal components analysis findings, and acceptable tests of reliability. Further confirmatory factor analysis suggested the need for additional refinements to this scale. Based upon exploratory cross-sectional analyses, strong socio-demographic inequalities were observed in the spiritual health measures by age, gender, relative material wealth, immigration status, and province/territory. Study findings highlight potential inequalities in the spiritual health of young Canadians, as well as opportunities for methodological advances in the assessment of adolescent spiritual health in our population.

Asymptotics of empirical eigenstructure for high dimensional spiked covariance.

PubMed

Wang, Weichen; Fan, Jianqing

2017-06-01

We derive the asymptotic distributions of the spiked eigenvalues and eigenvectors under a generalized and unified asymptotic regime, which takes into account the magnitude of spiked eigenvalues, sample size, and dimensionality. This regime allows high dimensionality and diverging eigenvalues and provides new insights into the roles that the leading eigenvalues, sample size, and dimensionality play in principal component analysis. Our results are a natural extension of those in Paul (2007) to a more general setting and solve the rates of convergence problems in Shen et al. (2013). They also reveal the biases of estimating leading eigenvalues and eigenvectors by using principal component analysis, and lead to a new covariance estimator for the approximate factor model, called shrinkage principal orthogonal complement thresholding (S-POET), that corrects the biases. Our results are successfully applied to outstanding problems in estimation of risks of large portfolios and false discovery proportions for dependent test statistics and are illustrated by simulation studies.

Asymptotics of empirical eigenstructure for high dimensional spiked covariance

PubMed Central

Wang, Weichen

2017-01-01

We derive the asymptotic distributions of the spiked eigenvalues and eigenvectors under a generalized and unified asymptotic regime, which takes into account the magnitude of spiked eigenvalues, sample size, and dimensionality. This regime allows high dimensionality and diverging eigenvalues and provides new insights into the roles that the leading eigenvalues, sample size, and dimensionality play in principal component analysis. Our results are a natural extension of those in Paul (2007) to a more general setting and solve the rates of convergence problems in Shen et al. (2013). They also reveal the biases of estimating leading eigenvalues and eigenvectors by using principal component analysis, and lead to a new covariance estimator for the approximate factor model, called shrinkage principal orthogonal complement thresholding (S-POET), that corrects the biases. Our results are successfully applied to outstanding problems in estimation of risks of large portfolios and false discovery proportions for dependent test statistics and are illustrated by simulation studies. PMID:28835726

Recovery of a spectrum based on a compressive-sensing algorithm with weighted principal component analysis

NASA Astrophysics Data System (ADS)

Dafu, Shen; Leihong, Zhang; Dong, Liang; Bei, Li; Yi, Kang

2017-07-01

The purpose of this study is to improve the reconstruction precision and better copy the color of spectral image surfaces. A new spectral reflectance reconstruction algorithm based on an iterative threshold combined with weighted principal component space is presented in this paper, and the principal component with weighted visual features is the sparse basis. Different numbers of color cards are selected as the training samples, a multispectral image is the testing sample, and the color differences in the reconstructions are compared. The channel response value is obtained by a Mega Vision high-accuracy, multi-channel imaging system. The results show that spectral reconstruction based on weighted principal component space is superior in performance to that based on traditional principal component space. Therefore, the color difference obtained using the compressive-sensing algorithm with weighted principal component analysis is less than that obtained using the algorithm with traditional principal component analysis, and better reconstructed color consistency with human eye vision is achieved.

Comparison of three-dimensional fluorescence analysis methods for predicting formation of trihalomethanes and haloacetic acids.

PubMed

Peleato, Nicolás M; Andrews, Robert C

2015-01-01

This work investigated the application of several fluorescence excitation-emission matrix analysis methods as natural organic matter (NOM) indicators for use in predicting the formation of trihalomethanes (THMs) and haloacetic acids (HAAs). Waters from four different sources (two rivers and two lakes) were subjected to jar testing followed by 24hr disinfection by-product formation tests using chlorine. NOM was quantified using three common measures: dissolved organic carbon, ultraviolet absorbance at 254 nm, and specific ultraviolet absorbance as well as by principal component analysis, peak picking, and parallel factor analysis of fluorescence spectra. Based on multi-linear modeling of THMs and HAAs, principle component (PC) scores resulted in the lowest mean squared prediction error of cross-folded test sets (THMs: 43.7 (μg/L)(2), HAAs: 233.3 (μg/L)(2)). Inclusion of principle components representative of protein-like material significantly decreased prediction error for both THMs and HAAs. Parallel factor analysis did not identify a protein-like component and resulted in prediction errors similar to traditional NOM surrogates as well as fluorescence peak picking. These results support the value of fluorescence excitation-emission matrix-principal component analysis as a suitable NOM indicator in predicting the formation of THMs and HAAs for the water sources studied. Copyright © 2014. Published by Elsevier B.V.

Comparison of gray matter volume and thickness for analysis of cortical changes in Alzheimer's disease

NASA Astrophysics Data System (ADS)

Liu, Jiachao; Li, Ziyi; Chen, Kewei; Yao, Li; Wang, Zhiqun; Li, Kunchen; Guo, Xiaojuan

2011-03-01

Gray matter volume and cortical thickness are two indices of concern in brain structure magnetic resonance imaging research. Gray matter volume reflects mixed-measurement information of cerebral cortex, while cortical thickness reflects only the information of distance between inner surface and outer surface of cerebral cortex. Using Scaled Subprofile Modeling based on Principal Component Analysis (SSM_PCA) and Pearson's Correlation Analysis, this study further provided quantitative comparisons and depicted both global relevance and local relevance to comprehensively investigate morphometrical abnormalities in cerebral cortex in Alzheimer's disease (AD). Thirteen patients with AD and thirteen age- and gender-matched healthy controls were included in this study. Results showed that factor scores from the first 8 principal components accounted for ~53.38% of the total variance for gray matter volume, and ~50.18% for cortical thickness. Factor scores from the fifth principal component showed significant correlation. In addition, gray matter voxel-based volume was closely related to cortical thickness alterations in most cortical cortex, especially, in some typical abnormal brain regions such as insula and the parahippocampal gyrus in AD. These findings suggest that these two measurements are effective indices for understanding the neuropathology in AD. Studies using both gray matter volume and cortical thickness can separate the causes of the discrepancy, provide complementary information and carry out a comprehensive description of the morphological changes of brain structure.

[Study of building quantitative analysis model for chlorophyll in winter wheat with reflective spectrum using MSC-ANN algorithm].

PubMed

Liang, Xue; Ji, Hai-yan; Wang, Peng-xin; Rao, Zhen-hong; Shen, Bing-hui

2010-01-01

Preprocess method of multiplicative scatter correction (MSC) was used to reject noises in the original spectra produced by the environmental physical factor effectively, then the principal components of near-infrared spectroscopy were calculated by nonlinear iterative partial least squares (NIPALS) before building the back propagation artificial neural networks method (BP-ANN), and the numbers of principal components were calculated by the method of cross validation. The calculated principal components were used as the inputs of the artificial neural networks model, and the artificial neural networks model was used to find the relation between chlorophyll in winter wheat and reflective spectrum, which can predict the content of chlorophyll in winter wheat. The correlation coefficient (r) of calibration set was 0.9604, while the standard deviation (SD) and relative standard deviation (RSD) was 0.187 and 5.18% respectively. The correlation coefficient (r) of predicted set was 0.9600, and the standard deviation (SD) and relative standard deviation (RSD) was 0.145 and 4.21% respectively. It means that the MSC-ANN algorithm can reject noises in the original spectra produced by the environmental physical factor effectively and set up an exact model to predict the contents of chlorophyll in living leaves veraciously to replace the classical method and meet the needs of fast analysis of agricultural products.

[Geographical distribution of left ventricular Tei index based on principal component analysis].

PubMed

Xu, Jinhui; Ge, Miao; He, Jinwei; Xue, Ranyin; Yang, Shaofang; Jiang, Jilin

2014-11-01

To provide a scientific standard of left ventricular Tei index for healthy people from various region of China, and to lay a reliable foundation for the evaluation of left ventricular diastolic and systolic function. The correlation and principal component analysis were used to explore the left ventricular Tei index, which based on the data of 3 562 samples from 50 regions of China by means of literature retrieval. Th e nine geographical factors were longitude(X₁), latitude(X₂), altitude(X₃), annual sunshine hours (X₄), the annual average temperature (X₅), annual average relative humidity (X₆), annual precipitation (X₇), annual temperature range (X₈) and annual average wind speed (X₉). ArcGIS soft ware was applied to calculate the spatial distribution regularities of left ventricular Tei index. There is a significant correlation between the healthy people's left ventricular Tei index and geographical factors, and the correlation coefficients were -0.107 (r₁), -0.301 (r₂), -0.029 (r₃), -0.277 (r₄), -0.256(r₅), -0.289(r₆), -0.320(r₇), -0.310 (r₈) and -0.117 (r₉), respectively. A linear equation between the Tei index and the geographical factor was obtained by regression analysis based on the three extracting principal components. The geographical distribution tendency chart for healthy people's left Tei index was fitted out by the ArcGIS spatial interpolation analysis. The geographical distribution for left ventricular Tei index in China follows certain pattern. The reference value in North is higher than that in South, while the value in East is higher than that in West.

Subjective Well-Being in a Multicultural Urban Population: Structural, and Multivariate Analyses of the Ontario Health Survey Well-Being Scale

ERIC Educational Resources Information Center

John, Lindsay Herbert

2004-01-01

The validity of a scale, from the Ontario Health Survey, measuring the subjective sense of well-being, for a large multicultural population in Metropolitan Toronto, is examined through principal components analysis with oblique rotation. Four factors are extracted. Factor 1, is a stress and strain factor, and consists of health worries, feeling…

Development of a glottal area index that integrates glottal gap size and open quotient

PubMed Central

Chen, Gang; Kreiman, Jody; Gerratt, Bruce R.; Neubauer, Juergen; Shue, Yen-Liang; Alwan, Abeer

2013-01-01

Because voice signals result from vocal fold vibration, perceptually meaningful vibratory measures should quantify those aspects of vibration that correspond to differences in voice quality. In this study, glottal area waveforms were extracted from high-speed videoendoscopy of the vocal folds. Principal component analysis was applied to these waveforms to investigate the factors that vary with voice quality. Results showed that the first principal component derived from tokens without glottal gaps was significantly (p < 0.01) associated with the open quotient (OQ). The alternating-current (AC) measure had a significant effect (p < 0.01) on the first principal component among tokens exhibiting glottal gaps. A measure AC/OQ, defined as the ratio of AC to OQ, was proposed to combine both amplitude and temporal characteristics of the glottal area waveform for both complete and incomplete glottal closures. Analyses of “glide” phonations in which quality varied continuously from breathy to pressed showed that the AC/OQ measure was able to characterize the corresponding continuum of glottal area waveform variation, regardless of the presence or absence of glottal gaps. PMID:23464035

Determination of the rotational diffusion tensor of macromolecules in solution from nmr relaxation data with a combination of exact and approximate methods--application to the determination of interdomain orientation in multidomain proteins.

PubMed

Ghose, R; Fushman, D; Cowburn, D

2001-04-01

In this paper we present a method for determining the rotational diffusion tensor from NMR relaxation data using a combination of approximate and exact methods. The approximate method, which is computationally less intensive, computes values of the principal components of the diffusion tensor and estimates the Euler angles, which relate the principal axis frame of the diffusion tensor to the molecular frame. The approximate values of the principal components are then used as starting points for an exact calculation by a downhill simplex search for the principal components of the tensor over a grid of the space of Euler angles relating the diffusion tensor frame to the molecular frame. The search space of Euler angles is restricted using the tensor orientations calculated using the approximate method. The utility of this approach is demonstrated using both simulated and experimental relaxation data. A quality factor that determines the extent of the agreement between the measured and predicted relaxation data is provided. This approach is then used to estimate the relative orientation of SH3 and SH2 domains in the SH(32) dual-domain construct of Abelson kinase complexed with a consolidated ligand. Copyright 2001 Academic Press.

Determination of the Rotational Diffusion Tensor of Macromolecules in Solution from NMR Relaxation Data with a Combination of Exact and Approximate Methods—Application to the Determination of Interdomain Orientation in Multidomain Proteins

NASA Astrophysics Data System (ADS)

Ghose, Ranajeet; Fushman, David; Cowburn, David

2001-04-01

In this paper we present a method for determining the rotational diffusion tensor from NMR relaxation data using a combination of approximate and exact methods. The approximate method, which is computationally less intensive, computes values of the principal components of the diffusion tensor and estimates the Euler angles, which relate the principal axis frame of the diffusion tensor to the molecular frame. The approximate values of the principal components are then used as starting points for an exact calculation by a downhill simplex search for the principal components of the tensor over a grid of the space of Euler angles relating the diffusion tensor frame to the molecular frame. The search space of Euler angles is restricted using the tensor orientations calculated using the approximate method. The utility of this approach is demonstrated using both simulated and experimental relaxation data. A quality factor that determines the extent of the agreement between the measured and predicted relaxation data is provided. This approach is then used to estimate the relative orientation of SH3 and SH2 domains in the SH(32) dual-domain construct of Abelson kinase complexed with a consolidated ligand.

Environmental conditions and reproductive health outcomes

EPA Science Inventory

Environmental exposures range across multiple domains to affect human health. In an effort to learn how environmental factors combine to contribute to health outcomes we constructed a multiple environmental domain index (MEDI) for use in health research. We used principal compone...

Discrimination of gender-, speed-, and shoe-dependent movement patterns in runners using full-body kinematics.

PubMed

Maurer, Christian; Federolf, Peter; von Tscharner, Vinzenz; Stirling, Lisa; Nigg, Benno M

2012-05-01

Changes in gait kinematics have often been analyzed using pattern recognition methods such as principal component analysis (PCA). It is usually just the first few principal components that are analyzed, because they describe the main variability within a dataset and thus represent the main movement patterns. However, while subtle changes in gait pattern (for instance, due to different footwear) may not change main movement patterns, they may affect movements represented by higher principal components. This study was designed to test two hypotheses: (1) speed and gender differences can be observed in the first principal components, and (2) small interventions such as changing footwear change the gait characteristics of higher principal components. Kinematic changes due to different running conditions (speed - 3.1m/s and 4.9 m/s, gender, and footwear - control shoe and adidas MicroBounce shoe) were investigated by applying PCA and support vector machine (SVM) to a full-body reflective marker setup. Differences in speed changed the basic movement pattern, as was reflected by a change in the time-dependent coefficient derived from the first principal. Gender was differentiated by using the time-dependent coefficient derived from intermediate principal components. (Intermediate principal components are characterized by limb rotations of the thigh and shank.) Different shoe conditions were identified in higher principal components. This study showed that different interventions can be analyzed using a full-body kinematic approach. Within the well-defined vector space spanned by the data of all subjects, higher principal components should also be considered because these components show the differences that result from small interventions such as footwear changes. Crown Copyright © 2012. Published by Elsevier B.V. All rights reserved.

Factor Structure, Stability, and Congruence in the Functional Movement Screen

ERIC Educational Resources Information Center

Kelleher, Leila K.; Beach, Tyson A. C.; Frost, David M.; Johnson, Andrew M.; Dickey, James P.

2018-01-01

The scoring scheme for the functional movement screen implicitly assumes that the factor structure is consistent, stable, and congruent across different populations. To determine if this is the case, we compared principal components analyses of three samples: a healthy, general population (n = 100), a group of varsity athletes (n = 101), and a…

Factors Affecting Faculty Use of Learning Technologies: Implications for Models of Technology Adoption

ERIC Educational Resources Information Center

Buchanan, Tom; Sainter, Phillip; Saunders, Gunter

2013-01-01

This study examines factors associated with the use of learning technologies by higher education faculty. In an online survey in a UK university, 114 faculty respondents completed a measure of Internet self-efficacy, and reported on their use of learning technologies along with barriers to their adoption. Principal components analysis suggested…

Principal Component Relaxation Mode Analysis of an All-Atom Molecular Dynamics Simulation of Human Lysozyme

NASA Astrophysics Data System (ADS)

Nagai, Toshiki; Mitsutake, Ayori; Takano, Hiroshi

2013-02-01

A new relaxation mode analysis method, which is referred to as the principal component relaxation mode analysis method, has been proposed to handle a large number of degrees of freedom of protein systems. In this method, principal component analysis is carried out first and then relaxation mode analysis is applied to a small number of principal components with large fluctuations. To reduce the contribution of fast relaxation modes in these principal components efficiently, we have also proposed a relaxation mode analysis method using multiple evolution times. The principal component relaxation mode analysis method using two evolution times has been applied to an all-atom molecular dynamics simulation of human lysozyme in aqueous solution. Slow relaxation modes and corresponding relaxation times have been appropriately estimated, demonstrating that the method is applicable to protein systems.

Functional principal component analysis of glomerular filtration rate curves after kidney transplant.

PubMed

Dong, Jianghu J; Wang, Liangliang; Gill, Jagbir; Cao, Jiguo

2017-01-01

This article is motivated by some longitudinal clinical data of kidney transplant recipients, where kidney function progression is recorded as the estimated glomerular filtration rates at multiple time points post kidney transplantation. We propose to use the functional principal component analysis method to explore the major source of variations of glomerular filtration rate curves. We find that the estimated functional principal component scores can be used to cluster glomerular filtration rate curves. Ordering functional principal component scores can detect abnormal glomerular filtration rate curves. Finally, functional principal component analysis can effectively estimate missing glomerular filtration rate values and predict future glomerular filtration rate values.

Assessing the Structure of the Ways of Coping Questionnaire in Fibromyalgia Patients Using Common Factor Analytic Approaches.

PubMed

Van Liew, Charles; Santoro, Maya S; Edwards, Larissa; Kang, Jeremy; Cronan, Terry A

2016-01-01

The Ways of Coping Questionnaire (WCQ) is a widely used measure of coping processes. Despite its use in a variety of populations, there has been concern about the stability and structure of the WCQ across different populations. This study examines the factor structure of the WCQ in a large sample of individuals diagnosed with fibromyalgia. The participants were 501 adults (478 women) who were part of a larger intervention study. Participants completed the WCQ at their 6-month assessment. Foundational factoring approaches were performed on the data (i.e., maximum likelihood factoring [MLF], iterative principal factoring [IPF], principal axis factoring (PAF), and principal components factoring [PCF]) with oblique oblimin rotation. Various criteria were evaluated to determine the number of factors to be extracted, including Kaiser's rule, Scree plot visual analysis, 5 and 10% unique variance explained, 70 and 80% communal variance explained, and Horn's parallel analysis (PA). It was concluded that the 4-factor PAF solution was the preferable solution, based on PA extraction and the fact that this solution minimizes nonvocality and multivocality. The present study highlights the need for more research focused on defining the limits of the WCQ and the degree to which population-specific and context-specific subscale adjustments are needed.

Assessing the Structure of the Ways of Coping Questionnaire in Fibromyalgia Patients Using Common Factor Analytic Approaches

PubMed Central

Edwards, Larissa; Kang, Jeremy

2016-01-01

The Ways of Coping Questionnaire (WCQ) is a widely used measure of coping processes. Despite its use in a variety of populations, there has been concern about the stability and structure of the WCQ across different populations. This study examines the factor structure of the WCQ in a large sample of individuals diagnosed with fibromyalgia. The participants were 501 adults (478 women) who were part of a larger intervention study. Participants completed the WCQ at their 6-month assessment. Foundational factoring approaches were performed on the data (i.e., maximum likelihood factoring [MLF], iterative principal factoring [IPF], principal axis factoring (PAF), and principal components factoring [PCF]) with oblique oblimin rotation. Various criteria were evaluated to determine the number of factors to be extracted, including Kaiser's rule, Scree plot visual analysis, 5 and 10% unique variance explained, 70 and 80% communal variance explained, and Horn's parallel analysis (PA). It was concluded that the 4-factor PAF solution was the preferable solution, based on PA extraction and the fact that this solution minimizes nonvocality and multivocality. The present study highlights the need for more research focused on defining the limits of the WCQ and the degree to which population-specific and context-specific subscale adjustments are needed. PMID:28070160

[Complexity and its integrative effects of the time lags of environment factors affecting Larix gmelinii stem sap flow].

PubMed

Wang, Hui-Mei; Sun, Wei; Zu, Yuan-Gang; Wang, Wen-Jie

2011-12-01

Based on the one-year (2005) observations with a frequency of half hour on the stem sap flow of Larix gmelinii plantation trees planted in 1969 and the related environmental factors air humidity (RH), air temperature (T(air)), photosynthetic components active radiation (PAR), soil temperature (T(soil)), and soil moisture (TDR), principal analysis (PCA) and correction analysis were made on the time lag effect of the stem flow in different seasons (26 days of each season) and in a year via dislocation analysis, with the complexity and its integrative effects of the time lags of environment factors affecting the stem sap flow approached. The results showed that in different seasons and for different environmental factors, the time lag effect varied obviously. In general, the time lag of PAR was 0.5-1 hour ahead of sap flow, that of T(air) and RH was 0-2 hours ahead of or behind the sap flow, and the time lags of T(soil) and TDR were much longer or sometimes undetectable. Because of the complexity of the time lags, no evident improvements were observed in the linear correlations (R2, slope, and intercept) when the time lags based on short-term (20 days) data were used to correct the time lags based on whole year data. However, obvious improvements were found in the standardized and non-standardized correlation coefficients in stepwise multiple regressions, i.e., the time lag corrections could improve the effects of RH, but decreased the effects of PAR, T(air), and T(soil). PCA could be used to simplify the complexity. The first and the second principal components could stand for over 75% information of all the environmental factors in different seasons and in whole year. The time lags of both the first and the second principal components were 1-1.5 hours in advance of the sap flow, except in winter (no time lag effect).

Principal component analysis to assess the composition and fate of impurities in a large river-embedded reservoir: Qingcaosha Reservoir.

PubMed

Ou, Hua-Se; Wei, Chao-Hai; Deng, Yang; Gao, Nai-Yun

2013-08-01

Qingcaosha Reservoir (QR) is the largest river-embedded reservoir in east China, which receives its source water from the Yangtze River (YR). The temporal and spatial variations in dissolved organic matter (DOM), chromophoric DOM (CDOM), nitrogen, phosphorus and phytoplankton biomass were investigated from June to September in 2012 and were integrated by principal component analysis (PCA). Three PCA factors were identified: (1) phytoplankton related factor 1, (2) total DOM related factor 2, and (3) eutrophication related factor 3. Factor 1 was a lake-type parameter which correlated with chlorophyll-a and protein-like CDOM (r = 0.793 and r = 0.831, respectively). Factor 2 was a river-type parameter which correlated with total DOC and humic-like CDOM (r = 0.668 and r = 0.726, respectively). Factor 3 correlated with total nitrogen and phosphorus (r = 0.864 and r = 0.621, respectively). The low flow speed, self-sedimentation and nutrient accumulation in QR resulted in increases in PCA factor 1 scores (phytoplankton biomass and derived CDOM) in the spatial scale, indicating a change of river-type water (YR) to lake-type water (QR). In summer, the water temperature variation induced a growth-bloom-decay process of phytoplankton combined with the increase of PCA factor 2 (humic-like CDOM) in the QR, which was absent in the YR.

Psychometric analysis of the Brisbane Practice Environment Measure (B-PEM).

PubMed

Flint, Anndrea; Farrugia, Charles; Courtney, Mary; Webster, Joan

2010-03-01

To undertake rigorous psychometric testing of the newly developed contemporary work environment measure (the Brisbane Practice Environment Measure [B-PEM]) using exploratory factor analysis and confirmatory factor analysis. Content validity of the 33-item measure was established by a panel of experts. Initial testing involved 195 nursing staff using principal component factor analysis with varimax rotation (orthogonal) and Cronbach's alpha coefficients. Confirmatory factor analysis was conducted using data from a further 983 nursing staff. Principal component factor analysis yielded a four-factor solution with eigenvalues greater than 1 that explained 52.53% of the variance. These factors were then verified using confirmatory factor analysis. Goodness-of-fit indices showed an acceptable fit overall with the full model, explaining 21% to 73% of the variance. Deletion of items took place throughout the evolution of the instrument, resulting in a 26-item, four-factor measure called the Brisbane Practice Environment Measure-Tested. The B-PEM has undergone rigorous psychometric testing, providing evidence of internal consistency and goodness-of-fit indices within acceptable ranges. The measure can be utilised as a subscale or total score reflective of a contemporary nursing work environment. An up-to-date instrument to measure practice environment may be useful for nursing leaders to monitor the workplace and to assist in identifying areas for improvement, facilitating greater job satisfaction and retention.

The Butterflies of Principal Components: A Case of Ultrafine-Grained Polyphase Units

NASA Astrophysics Data System (ADS)

Rietmeijer, F. J. M.

1996-03-01

Dusts in the accretion regions of chondritic interplanetary dust particles [IDPs] consisted of three principal components: carbonaceous units [CUs], carbon-bearing chondritic units [GUs] and carbon-free silicate units [PUs]. Among others, differences among chondritic IDP morphologies and variable bulk C/Si ratios reflect variable mixtures of principal components. The spherical shapes of the initially amorphous principal components remain visible in many chondritic porous IDPs but fusion was documented for CUs, GUs and PUs. The PUs occur as coarse- and ultrafine-grained units that include so called GEMS. Spherical principal components preserved in an IDP as recognisable textural units have unique proporties with important implications for their petrological evolution from pre-accretion processing to protoplanet alteration and dynamic pyrometamorphism. Throughout their lifetime the units behaved as closed-systems without chemical exchange with other units. This behaviour is reflected in their mineralogies while the bulk compositions of principal components define the environments wherein they were formed.

Animal reservoir, natural and socioeconomic variations and the transmission of hemorrhagic fever with renal syndrome in Chenzhou, China, 2006-2010.

PubMed

Xiao, Hong; Tian, Huai-Yu; Gao, Li-Dong; Liu, Hai-Ning; Duan, Liang-Song; Basta, Nicole; Cazelles, Bernard; Li, Xiu-Jun; Lin, Xiao-Ling; Wu, Hong-Wei; Chen, Bi-Yun; Yang, Hui-Suo; Xu, Bing; Grenfell, Bryan

2014-01-01

China has the highest incidence of hemorrhagic fever with renal syndrome (HFRS) worldwide. Reported cases account for 90% of the total number of global cases. By 2010, approximately 1.4 million HFRS cases had been reported in China. This study aimed to explore the effect of the rodent reservoir, and natural and socioeconomic variables, on the transmission pattern of HFRS. Data on monthly HFRS cases were collected from 2006 to 2010. Dynamic rodent monitoring data, normalized difference vegetation index (NDVI) data, climate data, and socioeconomic data were also obtained. Principal component analysis was performed, and the time-lag relationships between the extracted principal components and HFRS cases were analyzed. Polynomial distributed lag (PDL) models were used to fit and forecast HFRS transmission. Four principal components were extracted. Component 1 (F1) represented rodent density, the NDVI, and monthly average temperature. Component 2 (F2) represented monthly average rainfall and monthly average relative humidity. Component 3 (F3) represented rodent density and monthly average relative humidity. The last component (F4) represented gross domestic product and the urbanization rate. F2, F3, and F4 were significantly correlated, with the monthly HFRS incidence with lags of 4 months (r = -0.289, P<0.05), 5 months (r = -0.523, P<0.001), and 0 months (r = -0.376, P<0.01), respectively. F1 was correlated with the monthly HFRS incidence, with a lag of 4 months (r = 0.179, P = 0.192). Multivariate PDL modeling revealed that the four principal components were significantly associated with the transmission of HFRS. The monthly trend in HFRS cases was significantly associated with the local rodent reservoir, climatic factors, the NDVI, and socioeconomic conditions present during the previous months. The findings of this study may facilitate the development of early warning systems for the control and prevention of HFRS and similar diseases.

The viscoelastic behavior of the principal compliance matrix of a unidirectional graphite/epoxy composite

NASA Technical Reports Server (NTRS)

Morris, D. H.; Yeow, Y. T.

1979-01-01

The time-temperature response of the principal compliances of a unidirectional graphite/epoxy composite was determined. It is shown that two components of the compliance matrix are time and temperature independent and that the compliance matrix is symmetric for the viscoelastic composite. The time-temperature superposition principle is used to determine shift factors which are independent of fiber orientation, for fiber angles that vary from 10 D to 90 D with respect to the load direction.

Multivariate Analysis of Remains of Molluscan Foods Consumed by Latest Pleistocene and Holocene Humans in Nerja Cave, Málaga, Spain

NASA Astrophysics Data System (ADS)

Serrano, Francisco; Guerra-Merchán, Antonio; Lozano-Francisco, Carmen; Vera-Peláez, José Luis

1997-09-01

Nerja Cave is a karstic cavity used by humans from Late Paleolithic to post-Chalcolithic times. Remains of molluscan foods in the uppermost Pleistocene and Holocene sediments were studied with cluster analysis and principal components analysis, in both Qand Rmodes. The results from cluster analysis distinguished interval groups mainly in accordance with chronology and distinguished assemblages of species mainly according to habitat. Significant changes in the shellfish diet through time were revealed. In the Late Magdalenian, most molluscs consumed consisted of pulmonate gastropods and species from sandy sea bottoms. The Epipaleolithic diet was more varied and included species from rocky shorelines. From the Neolithic onward most molluscs consumed were from rocky shorelines. From the principal components analysis in Qmode, the first factor reflected mainly changes in the predominant capture environment, probably because of major paleogeographic changes. The second factor may reflect selective capture along rocky coastlines during certain times. The third factor correlated well with the sea-surface temperature curve in the western Mediterranean (Alboran Sea) during the late Quaternary.

Controlling factors for infiltration on undisturbed hillslopes in unmanaged plantation forests

NASA Astrophysics Data System (ADS)

Hiraoka, Marino; Onda, Yuichi; Gomi, Takashi; Mizugaki, Shigeru; Nanko, Kazuki; Kato, Hiroaki

2017-04-01

Infiltration into the soil is a crucial factor for predicting overland flow generation. Infiltration capacity strongly relates to ground vegetation, soil characteristics, or both. For revealing controlling factors for infiltration capacity, we conducted in-situ rainfall simulation using an oscillating-nozzle type rainfall simulator at 26 plots with different ground cover conditions of unmanaged Japanese cypress (Chamaecyparis obtusa) plantations. For wide-ranging vegetation cover condition (0-100%), infiltration capacity widely varied (5-322 mm/h) and had positive correlations with indices of ground vegetation and ground litter (p < 0.01). For a limited vegetation cover condition (0-20%), the range of infiltration capacity (7-114 mm/h) was associated with ground litter thickness (p < 0.05), and difference in soil organic matter and difference in soil bulk density. Principal component analysis showed that the first and second principal components (70% of total variation) related to changes in above- and below-ground biomass and changes in pores in soil. Our findings showed that development of ground vegetation alters hydrological processes of surface soil through changes in soil characteristics via the propagation of belowground biomass development.

Further Validation of the Inventory of Mental Toughness Factors in Sport (IMTF-S)

ERIC Educational Resources Information Center

Stonkus, Mark A.; Royal, Kenneth D.

2015-01-01

The purpose of this study was to provide further validation a new measure of mental toughness in sport. The Inventory of Mental Toughness Factors in Sport (IMTF-S) was originally developed and validated using principal component analysis. For the present study, the psychometric properties of the IMTF-S were again evaluated, but by way of the Rasch…

Construction and Validation of the Career and Educational Decision Self-Efficacy Inventory for Secondary Students (CEDSIS)

ERIC Educational Resources Information Center

Ho, Esther Sui Chu; Sum, Kwok Wing

2018-01-01

This study aims to construct and validate the Career and Educational Decision Self-Efficacy Inventory for Secondary Students (CEDSIS) by using a sample of 2,631 students in Hong Kong. Principal component analysis yielded a three-factor structure, which demonstrated good model fit in confirmatory factor analysis. High reliability was found for the…

Background recovery via motion-based robust principal component analysis with matrix factorization

NASA Astrophysics Data System (ADS)

Pan, Peng; Wang, Yongli; Zhou, Mingyuan; Sun, Zhipeng; He, Guoping

2018-03-01

Background recovery is a key technique in video analysis, but it still suffers from many challenges, such as camouflage, lighting changes, and diverse types of image noise. Robust principal component analysis (RPCA), which aims to recover a low-rank matrix and a sparse matrix, is a general framework for background recovery. The nuclear norm is widely used as a convex surrogate for the rank function in RPCA, which requires computing the singular value decomposition (SVD), a task that is increasingly costly as matrix sizes and ranks increase. However, matrix factorization greatly reduces the dimension of the matrix for which the SVD must be computed. Motion information has been shown to improve low-rank matrix recovery in RPCA, but this method still finds it difficult to handle original video data sets because of its batch-mode formulation and implementation. Hence, in this paper, we propose a motion-assisted RPCA model with matrix factorization (FM-RPCA) for background recovery. Moreover, an efficient linear alternating direction method of multipliers with a matrix factorization (FL-ADM) algorithm is designed for solving the proposed FM-RPCA model. Experimental results illustrate that the method provides stable results and is more efficient than the current state-of-the-art algorithms.

RECENT APPLICATIONS OF SOURCE APPORTIONMENT METHODS AND RELATED NEEDS

EPA Science Inventory

Traditional receptor modeling studies have utilized factor analysis (like principal component analysis, PCA) and/or Chemical Mass Balance (CMB) to assess source influences. The limitations with these approaches is that PCA is qualitative and CMB requires the input of source pr...

Identification of regional activation by factorization of high-density surface EMG signals: A comparison of Principal Component Analysis and Non-negative Matrix factorization.

PubMed

Gallina, Alessio; Garland, S Jayne; Wakeling, James M

2018-05-22

In this study, we investigated whether principal component analysis (PCA) and non-negative matrix factorization (NMF) perform similarly for the identification of regional activation within the human vastus medialis. EMG signals from 64 locations over the VM were collected from twelve participants while performing a low-force isometric knee extension. The envelope of the EMG signal of each channel was calculated by low-pass filtering (8 Hz) the monopolar EMG signal after rectification. The data matrix was factorized using PCA and NMF, and up to 5 factors were considered for each algorithm. Association between explained variance, spatial weights and temporal scores between the two algorithms were compared using Pearson correlation. For both PCA and NMF, a single factor explained approximately 70% of the variance of the signal, while two and three factors explained just over 85% or 90%. The variance explained by PCA and NMF was highly comparable (R > 0.99). Spatial weights and temporal scores extracted with non-negative reconstruction of PCA and NMF were highly associated (all p < 0.001, mean R > 0.97). Regional VM activation can be identified using high-density surface EMG and factorization algorithms. Regional activation explains up to 30% of the variance of the signal, as identified through both PCA and NMF. Copyright © 2018 Elsevier Ltd. All rights reserved.

New predictor of aortic enlargement in uncomplicated type B aortic dissection based on elliptic Fourier analysis.

PubMed

Sato, Hiroshi; Ito, Toshiro; Kuroda, Yosuke; Uchiyama, Hiroki; Watanabe, Toshitaka; Yasuda, Naomi; Nakazawa, Junji; Harada, Ryo; Kawaharada, Nobuyoshi

2017-12-01

This study aimed to re-examine the conventional predictive factors for dissected aortic enlargement, such as the aortic and false lumen diameter and to consider whether the morphological elements of the dissected aorta could be predictors by quantifying the 'shape' of the true lumen based on elliptic Fourier analysis. A total of 80 patients with uncomplicated type B aortic dissection were included. The patients were divided into 'Enlargement group' and 'No Change group.' Between the 2 groups, the mean systolic blood pressure during follow-up, aortic and false lumen maximum diameters, and analysed morphological data were compared using each statistical method. The maximum aortic and false lumen diameters were significantly larger in the Enlargement group than in the No Change group (39.3 vs 35.9 mm; P = 0.0058) (23.5 vs 18.2 mm; P = 0.000095). The principal component 1, which is the data calculated by elliptic Fourier analysis, was significantly lower in the Enlargement group than in the No Change group (0.020 vs - 0.072; P = 0.000049). The mean systolic blood pressure ≥130 mmHg, aortic diameter, false lumen diameter and principal component 1 were included in the Cox proportional hazard model as covariates to determine the significant predictive variable. Principal component 1 demonstrated the only significance with aortic enlargement on multivariate analysis (odds ratio = 0.32; P = 0.048). The analysed and calculated morphological data of the shape of the true lumen can be more effective predictive factors of aortic enlargement of type B dissection than the conventional factors. © The Author 2017. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

Readers' opinions of romantic poetry are consistent with emotional measures based on the Dictionary of Affect in Language.

PubMed

Whissell, Cynthia

2003-06-01

A principal components analysis of 68 volunteers' subjective ratings of 20 excerpts of Romantic poetry and of Dictionary of Affect scores for the same excerpts produced four components representing Pleasantness, Activation, Romanticism, and Nature. Dictionary measures and subjective ratings of the same constructs loaded on the same factor. Results are interpreted as providing construct validity for the Dictionary of Affect.

Spectral discrimination of bleached and healthy submerged corals based on principal components analysis

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

Holden, H.; LeDrew, E.

1997-06-01

Remote discrimination of substrate types in relatively shallow coastal waters has been limited by the spatial and spectral resolution of available sensors. An additional limiting factor is the strong attenuating influence of the water column over the substrate. As a result, there have been limited attempts to map submerged ecosystems such as coral reefs based on spectral characteristics. Both healthy and bleached corals were measured at depth with a hand-held spectroradiometer, and their spectra compared. Two separate principal components analyses (PCA) were performed on two sets of spectral data. The PCA revealed that there is indeed a spectral difference basedmore » on health. In the first data set, the first component (healthy coral) explains 46.82%, while the second component (bleached coral) explains 46.35% of the variance. In the second data set, the first component (bleached coral) explained 46.99%; the second component (healthy coral) explained 36.55%; and the third component (healthy coral) explained 15.44 % of the total variance in the original data. These results are encouraging with respect to using an airborne spectroradiometer to identify areas of bleached corals thus enabling accurate monitoring over time.« less

Principal component analysis to assess the efficiency and mechanism for enhanced coagulation of natural algae-laden water using a novel dual coagulant system.

PubMed

Ou, Hua-Se; Wei, Chao-Hai; Deng, Yang; Gao, Nai-Yun; Ren, Yuan; Hu, Yun

2014-02-01

A novel dual coagulant system of polyaluminum chloride sulfate (PACS) and polydiallyldimethylammonium chloride (PDADMAC) was used to treat natural algae-laden water from Meiliang Gulf, Lake Taihu. PACS (Aln(OH)mCl3n-m-2k(SO4)k) has a mass ratio of 10 %, a SO4 (2-)/Al3 (+) mole ratio of 0.0664, and an OH/Al mole ratio of 2. The PDADMAC ([C8H16NCl]m) has a MW which ranges from 5 × 10(5) to 20 × 10(5) Da. The variations of contaminants in water samples during treatments were estimated in the form of principal component analysis (PCA) factor scores and conventional variables (turbidity, DOC, etc.). Parallel factor analysis determined four chromophoric dissolved organic matters (CDOM) components, and PCA identified four integrated principle factors. PCA factor 1 had significant correlations with chlorophyll-a (r=0.718), protein-like CDOM C1 (0.689), and C2 (0.756). Factor 2 correlated with UV254 (0.672), humic-like CDOM component C3 (0.716), and C4 (0.758). Factors 3 and 4 had correlations with NH3-N (0.748) and T-P (0.769), respectively. The variations of PCA factors scores revealed that PACS contributed less aluminum dissolution than PAC to obtain equivalent removal efficiency of contaminants. This might be due to the high cationic charge and pre-hydrolyzation of PACS. Compared with PACS coagulation (20 mg L(-1)), the removal of PCA factors 1, 2, and 4 increased 45, 33, and 12 %, respectively, in combined PACS-PDADMAC treatment (0.8 mg L(-1) +20 mg L(-1)). Since PAC contained more Al (0.053 g/1 g) than PACS (0.028 g/1 g), the results indicated that PACS contributed less Al dissolution into the water to obtain equivalent removal efficiency.

Fine structure of the low-frequency spectra of heart rate and blood pressure

PubMed Central

Kuusela, Tom A; Kaila, Timo J; Kähönen, Mika

2003-01-01

Background The aim of this study was to explore the principal frequency components of the heart rate and blood pressure variability in the low frequency (LF) and very low frequency (VLF) band. The spectral composition of the R–R interval (RRI) and systolic arterial blood pressure (SAP) in the frequency range below 0.15 Hz were carefully analyzed using three different spectral methods: Fast Fourier transform (FFT), Wigner-Ville distribution (WVD), and autoregression (AR). All spectral methods were used to create time–frequency plots to uncover the principal spectral components that are least dependent on time. The accurate frequencies of these components were calculated from the pole decomposition of the AR spectral density after determining the optimal model order – the most crucial factor when using this method – with the help of FFT and WVD methods. Results Spectral analysis of the RRI and SAP of 12 healthy subjects revealed that there are always at least three spectral components below 0.15 Hz. The three principal frequency components are 0.026 ± 0.003 (mean ± SD) Hz, 0.076 ± 0.012 Hz, and 0.117 ± 0.016 Hz. These principal components vary only slightly over time. FFT-based coherence and phase-function analysis suggests that the second and third components are related to the baroreflex control of blood pressure, since the phase difference between SAP and RRI was negative and almost constant, whereas the origin of the first component is different since no clear SAP–RRI phase relationship was found. Conclusion The above data indicate that spontaneous fluctuations in heart rate and blood pressure within the standard low-frequency range of 0.04–0.15 Hz typically occur at two frequency components rather than only at one as widely believed, and these components are not harmonically related. This new observation in humans can help explain divergent results in the literature concerning spontaneous low-frequency oscillations. It also raises methodological and computational questions regarding the usability and validity of the low-frequency spectral band when estimating sympathetic activity and baroreflex gain. PMID:14552660

Fine structure of the low-frequency spectra of heart rate and blood pressure.

PubMed

Kuusela, Tom A; Kaila, Timo J; Kähönen, Mika

2003-10-13

The aim of this study was to explore the principal frequency components of the heart rate and blood pressure variability in the low frequency (LF) and very low frequency (VLF) band. The spectral composition of the R-R interval (RRI) and systolic arterial blood pressure (SAP) in the frequency range below 0.15 Hz were carefully analyzed using three different spectral methods: Fast Fourier transform (FFT), Wigner-Ville distribution (WVD), and autoregression (AR). All spectral methods were used to create time-frequency plots to uncover the principal spectral components that are least dependent on time. The accurate frequencies of these components were calculated from the pole decomposition of the AR spectral density after determining the optimal model order--the most crucial factor when using this method--with the help of FFT and WVD methods. Spectral analysis of the RRI and SAP of 12 healthy subjects revealed that there are always at least three spectral components below 0.15 Hz. The three principal frequency components are 0.026 +/- 0.003 (mean +/- SD) Hz, 0.076 +/- 0.012 Hz, and 0.117 +/- 0.016 Hz. These principal components vary only slightly over time. FFT-based coherence and phase-function analysis suggests that the second and third components are related to the baroreflex control of blood pressure, since the phase difference between SAP and RRI was negative and almost constant, whereas the origin of the first component is different since no clear SAP-RRI phase relationship was found. The above data indicate that spontaneous fluctuations in heart rate and blood pressure within the standard low-frequency range of 0.04-0.15 Hz typically occur at two frequency components rather than only at one as widely believed, and these components are not harmonically related. This new observation in humans can help explain divergent results in the literature concerning spontaneous low-frequency oscillations. It also raises methodological and computational questions regarding the usability and validity of the low-frequency spectral band when estimating sympathetic activity and baroreflex gain.

Intraoperative impaction of total knee replacements: an explicit finite-element-analysis of principal stresses in ceramic vs. cobalt-chromium femoral components.

PubMed

Kluess, Daniel; Mittelmeier, Wolfram; Bader, Rainer

2010-12-01

In connection with technological advances in the manufacturing of medical ceramics, a newly developed ceramic femoral component was introduced in total knee arthroplasty. We generated an explicit finite-element-model to calculate the stresses developed under the highly dynamic intraoperative impaction with regard to cobalt-chromium and ceramic implant material as well as application of a silicone cover in order to reduce stress. The impaction was calculated with the hammer hitting the backside of the impactor at previously measured initial velocities. Subsequently the impactor, consisting of a steel handhold and a polyoxymethylene head, hit the femoral component. Instead of modelling femoral bone, the implant was mounted on four spring elements with spring constants previously determined in an experimental impaction model. The maximum principal stresses in the implants were evaluated at 8000 increments during the first 4 ms of impact. The ceramic implant showed principal stresses 10% to 48% higher than the cobalt chromium femoral component. The simulation of a 5mm thick silicone layer between the impactor and the femoral component showed a strong decrease of vibration resulting in a reduction of 54% to 68% of the maximum stress amounts. The calculated amounts of principal stress were beneath the ultimate bending strengths of each material. Based on the results, intraoperative fracture of femoral components in total knee replacement may not be caused solely by impaction, but also by contributing geometrical factors such as inadequate preparation of the distal femur. In order to minimize the influence of impaction related stress peaks we recommend limiting the velocity as well as the weight of the impaction hammer when inserting femoral components. The silicone cover seems to deliver a strong decrease of implant stress and should be considered in surgery technique in the future. Copyright © 2010 Elsevier Ltd. All rights reserved.

Quality assurance of the clinical learning environment in Austria: Construct validity of the Clinical Learning Environment, Supervision and Nurse Teacher Scale (CLES+T scale).

PubMed

Mueller, Gerhard; Mylonas, Demetrius; Schumacher, Petra

2018-07-01

Within nursing education, the clinical learning environment is of a high importance in regards to the development of competencies and abilities. The organization, atmosphere, and supervision in the clinical learning environment are only a few factors that influence this development. In Austria there is currently no valid instrument available for the evaluation of influencing factors. The aim of the study was to test the construct validity with principal component analysis as well as the internal consistency of the German Clinical Learning Environment, Supervision and Teacher Scale (CLES+T scale) in Austria. The present validation study has a descriptive-quantitative cross-sectional design. The sample consisted of 385 nursing students from thirteen training institutions in Austria. The data collection was carried out online between March and April 2016. Starting with a polychoric correlation matrix, a parallel analysis with principal component extraction and promax rotation was carried out due to the ordinal data. The exploratory ordinal factor analysis supported a four-component solution and explained 73% of the total variance. The internal consistency of all 25 items reached a Cronbach's α of 0.95 and the four components ranged between 0.83 and 0.95. The German version of the CLES+T scale seems to be a useful instrument for identifying potential areas of improvement in clinical practice in order to derive specific quality measures for the practical learning environment. Copyright © 2018 Elsevier Ltd. All rights reserved.

Support vector machine based classification of fast Fourier transform spectroscopy of proteins

NASA Astrophysics Data System (ADS)

Lazarevic, Aleksandar; Pokrajac, Dragoljub; Marcano, Aristides; Melikechi, Noureddine

2009-02-01

Fast Fourier transform spectroscopy has proved to be a powerful method for study of the secondary structure of proteins since peak positions and their relative amplitude are affected by the number of hydrogen bridges that sustain this secondary structure. However, to our best knowledge, the method has not been used yet for identification of proteins within a complex matrix like a blood sample. The principal reason is the apparent similarity of protein infrared spectra with actual differences usually masked by the solvent contribution and other interactions. In this paper, we propose a novel machine learning based method that uses protein spectra for classification and identification of such proteins within a given sample. The proposed method uses principal component analysis (PCA) to identify most important linear combinations of original spectral components and then employs support vector machine (SVM) classification model applied on such identified combinations to categorize proteins into one of given groups. Our experiments have been performed on the set of four different proteins, namely: Bovine Serum Albumin, Leptin, Insulin-like Growth Factor 2 and Osteopontin. Our proposed method of applying principal component analysis along with support vector machines exhibits excellent classification accuracy when identifying proteins using their infrared spectra.

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.

Identification of factors most important for ammonia emission from fertilized soils for potato production using principal component analysis

Treesearch

Guodoong Liu; Yuncong Li; Kati W. Migliaccio; Ying Ouyang; Ashok K. Alva

2011-01-01

Ammonia (NH3) emissions from fertilized soils are a costly problem that is undermining agricultural and ecological sustainability worldwide. Ammonia emissions from crop production have been reliably documented in recent years. However, insufficient efforts have been made to determine the factors most influential in facilitating NH3 emissions. The goal of this study was...

Validation of the Sexual Assault Symptom Scale II (SASS II) Using a Panel Research Design

ERIC Educational Resources Information Center

Ruch, Libby O.; Wang, Chang-Hwai

2006-01-01

To examine the utility of a self-report scale of sexual assault trauma, 223 female victims were interviewed with the 43-item Sexual Assault Symptom Scale II (SASS II) at 1, 3, 7, 11, and 15 months postassault. Factor analyses using principal-components extraction with an oblimin rotation yielded 7 common factors with 31 items. The internal…

An Investigation of Psychometric Properties of Coping Styles Scale Brief Form: A Study of Validity and Reliability

ERIC Educational Resources Information Center

Bacanli, Hasan; Surucu, Mustafa; Ilhan, Tahsin

2013-01-01

The aim of the current study was to develop a short form of Coping Styles Scale based on COPE Inventory. A total of 275 undergraduate students (114 female, and 74 male) were administered in the first study. In order to test factors structure of Coping Styles Scale Brief Form, principal components factor analysis and direct oblique rotation was…

An application of principal component analysis to the clavicle and clavicle fixation devices.

PubMed

Daruwalla, Zubin J; Courtis, Patrick; Fitzpatrick, Clare; Fitzpatrick, David; Mullett, Hannan

2010-03-26

Principal component analysis (PCA) enables the building of statistical shape models of bones and joints. This has been used in conjunction with computer assisted surgery in the past. However, PCA of the clavicle has not been performed. Using PCA, we present a novel method that examines the major modes of size and three-dimensional shape variation in male and female clavicles and suggests a method of grouping the clavicle into size and shape categories. Twenty-one high-resolution computerized tomography scans of the clavicle were reconstructed and analyzed using a specifically developed statistical software package. After performing statistical shape analysis, PCA was applied to study the factors that account for anatomical variation. The first principal component representing size accounted for 70.5 percent of anatomical variation. The addition of a further three principal components accounted for almost 87 percent. Using statistical shape analysis, clavicles in males have a greater lateral depth and are longer, wider and thicker than in females. However, the sternal angle in females is larger than in males. PCA confirmed these differences between genders but also noted that men exhibit greater variance and classified clavicles into five morphological groups. This unique approach is the first that standardizes a clavicular orientation. It provides information that is useful to both, the biomedical engineer and clinician. Other applications include implant design with regard to modifying current or designing future clavicle fixation devices. Our findings support the need for further development of clavicle fixation devices and the questioning of whether gender-specific devices are necessary.

Method for Automatic Selection of Parameters in Normal Tissue Complication Probability Modeling.

PubMed

Christophides, Damianos; Appelt, Ane L; Gusnanto, Arief; Lilley, John; Sebag-Montefiore, David

2018-07-01

To present a fully automatic method to generate multiparameter normal tissue complication probability (NTCP) models and compare its results with those of a published model, using the same patient cohort. Data were analyzed from 345 rectal cancer patients treated with external radiation therapy to predict the risk of patients developing grade 1 or ≥2 cystitis. In total, 23 clinical factors were included in the analysis as candidate predictors of cystitis. Principal component analysis was used to decompose the bladder dose-volume histogram into 8 principal components, explaining more than 95% of the variance. The data set of clinical factors and principal components was divided into training (70%) and test (30%) data sets, with the training data set used by the algorithm to compute an NTCP model. The first step of the algorithm was to obtain a bootstrap sample, followed by multicollinearity reduction using the variance inflation factor and genetic algorithm optimization to determine an ordinal logistic regression model that minimizes the Bayesian information criterion. The process was repeated 100 times, and the model with the minimum Bayesian information criterion was recorded on each iteration. The most frequent model was selected as the final "automatically generated model" (AGM). The published model and AGM were fitted on the training data sets, and the risk of cystitis was calculated. The 2 models had no significant differences in predictive performance, both for the training and test data sets (P value > .05) and found similar clinical and dosimetric factors as predictors. Both models exhibited good explanatory performance on the training data set (P values > .44), which was reduced on the test data sets (P values < .05). The predictive value of the AGM is equivalent to that of the expert-derived published model. It demonstrates potential in saving time, tackling problems with a large number of parameters, and standardizing variable selection in NTCP modeling. Crown Copyright © 2018. Published by Elsevier Inc. All rights reserved.

Nonlinear Principal Components Analysis: Introduction and Application

ERIC Educational Resources Information Center

Linting, Marielle; Meulman, Jacqueline J.; Groenen, Patrick J. F.; van der Koojj, Anita J.

2007-01-01

The authors provide a didactic treatment of nonlinear (categorical) principal components analysis (PCA). This method is the nonlinear equivalent of standard PCA and reduces the observed variables to a number of uncorrelated principal components. The most important advantages of nonlinear over linear PCA are that it incorporates nominal and ordinal…

Selective principal component regression analysis of fluorescence hyperspectral image to assess aflatoxin contamination in corn

USDA-ARS?s Scientific Manuscript database

Selective principal component regression analysis (SPCR) uses a subset of the original image bands for principal component transformation and regression. For optimal band selection before the transformation, this paper used genetic algorithms (GA). In this case, the GA process used the regression co...

Similarities between principal components of protein dynamics and random diffusion

NASA Astrophysics Data System (ADS)

Hess, Berk

2000-12-01

Principal component analysis, also called essential dynamics, is a powerful tool for finding global, correlated motions in atomic simulations of macromolecules. It has become an established technique for analyzing molecular dynamics simulations of proteins. The first few principal components of simulations of large proteins often resemble cosines. We derive the principal components for high-dimensional random diffusion, which are almost perfect cosines. This resemblance between protein simulations and noise implies that for many proteins the time scales of current simulations are too short to obtain convergence of collective motions.

Post-inflammatory fatigue in sarcoidosis: personality profiles, psychological symptoms and stress hormones.

PubMed

Korenromp, Ingrid H E; Grutters, Jan C; van den Bosch, Jules M M; Heijnen, Cobi J

2012-02-01

Chronic fatigue following inflammatory diseases has been well documented. However, little is known about possible risk factors of chronic post-inflammatory fatigue. The aim of this study was to investigate whether chronic post-inflammatory fatigue after clinical remission of the disease sarcoidosis is associated with specific dimensions of personality, psychological symptoms and baseline levels of stress hormones. Thirty-seven non-fatigued and 33 fatigued patients in clinical remission of sarcoidosis were evaluated with the Temperament and Character Inventory-short form (TCI); the Symptom CheckList-90 (SCL), and the Checklist Individual Strength (CIS). Baseline levels of ACTH and cortisol were measured in plasma. Principal component analysis with orthogonal rotation (varimax) was conducted on all personality, psychological and stress hormone data in order to obtain a smaller set of components. Logistic regression was performed to associate these components with chronic post-inflammatory fatigue. Principal component analyses identified 5 components, of which two components were significantly associated with chronic post-inflammatory fatigue. The first component comprised the personality trait Harm Avoidance and all SCL-subscales except Sleep. The second component consisted of baseline levels ACTH and cortisol, and showed an inverse association with chronic post-inflammatory fatigue. The 3 other components, consisting of respectively SCL-Sleep, TCI-Novelty Seeking-Reward Dependence-Self Transcendence, and TCI-Persistence, were not significantly associated with chronic fatigue. Chronic post-inflammatory fatigue after clinical remission of sarcoidosis is associated with a triad of risk factors: a specific personality profile with profound neurotic characteristics in combination with high levels of psychological distress, and decreased baseline ACTH/cortisol levels. Copyright © 2011 Elsevier Inc. All rights reserved.

Multivariate Statistical Analysis of MSL APXS Bulk Geochemical Data

NASA Astrophysics Data System (ADS)

Hamilton, V. E.; Edwards, C. S.; Thompson, L. M.; Schmidt, M. E.

2014-12-01

We apply cluster and factor analyses to bulk chemical data of 130 soil and rock samples measured by the Alpha Particle X-ray Spectrometer (APXS) on the Mars Science Laboratory (MSL) rover Curiosity through sol 650. Multivariate approaches such as principal components analysis (PCA), cluster analysis, and factor analysis compliment more traditional approaches (e.g., Harker diagrams), with the advantage of simultaneously examining the relationships between multiple variables for large numbers of samples. Principal components analysis has been applied with success to APXS, Pancam, and Mössbauer data from the Mars Exploration Rovers. Factor analysis and cluster analysis have been applied with success to thermal infrared (TIR) spectral data of Mars. Cluster analyses group the input data by similarity, where there are a number of different methods for defining similarity (hierarchical, density, distribution, etc.). For example, without any assumptions about the chemical contributions of surface dust, preliminary hierarchical and K-means cluster analyses clearly distinguish the physically adjacent rock targets Windjana and Stephen as being distinctly different than lithologies observed prior to Curiosity's arrival at The Kimberley. In addition, they are separated from each other, consistent with chemical trends observed in variation diagrams but without requiring assumptions about chemical relationships. We will discuss the variation in cluster analysis results as a function of clustering method and pre-processing (e.g., log transformation, correction for dust cover) and implications for interpreting chemical data. Factor analysis shares some similarities with PCA, and examines the variability among observed components of a dataset so as to reveal variations attributable to unobserved components. Factor analysis has been used to extract the TIR spectra of components that are typically observed in mixtures and only rarely in isolation; there is the potential for similar results with data from APXS. These techniques offer new ways to understand the chemical relationships between the materials interrogated by Curiosity, and potentially their relation to materials observed by APXS instruments on other landed missions.

Scientific Elitism and the Information System of Science

ERIC Educational Resources Information Center

Amick, Daniel James

1973-01-01

Scientific elitism must be viewed as a multidimensional phenomenon. Ten variables of elitism are considered and a principal components factor analysis is used to scale this multivariate domain. Two significant dimensions of elitism were found; one in basic and one in applied science. (20 references) (Author)

Development of the Academic Stereotype Threat Inventory

ERIC Educational Resources Information Center

Pseekos, A. Chantelle; Dahlen, Eric R.; Levy, Jacob J.

2008-01-01

The authors describe the development and preliminary validation of the Academic Stereotype Threat Inventory, a self-report measurement of math-related stereotype threat among women. A preliminary version of the instrument was administered to 308 undergraduate women. Principal component analysis yielded a 3-factor solution. Convergent and…

Using Interactive Graphics to Teach Multivariate Data Analysis to Psychology Students

ERIC Educational Resources Information Center

Valero-Mora, Pedro M.; Ledesma, Ruben D.

2011-01-01

This paper discusses the use of interactive graphics to teach multivariate data analysis to Psychology students. Three techniques are explored through separate activities: parallel coordinates/boxplots; principal components/exploratory factor analysis; and cluster analysis. With interactive graphics, students may perform important parts of the…

Detecting Multidimensionality: Which Residual Data-Type Works Best?

ERIC Educational Resources Information Center

Linacre, John Michael

1998-01-01

Simulation studies indicate that, for responses to complete tests, construction of Rasch measures from observational data, followed by principal components factor analysis of Rasch residuals, provides an effective means of identifying multidimensionality. The most diagnostically useful residual form was found to be the standardized residual. (SLD)

Attitudes toward Anger Management Scale: Development and Initial Validation

ERIC Educational Resources Information Center

Boudreaux, David J.; Dahlen, Eric R.; Madson, Michael B.; Bullock-Yowell, Emily

2014-01-01

This article describes the development and preliminary validation of the Attitudes Toward Anger Management Scale (ATAMS), a self-report measure of attitudes toward anger management services. Undergraduate volunteers ("N" = 415) completed an initial version of the instrument. Principal components analysis yielded a two-factor solution.…

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

Common factor analysis versus principal component analysis: choice for symptom cluster research.

PubMed

Kim, Hee-Ju

2008-03-01

The purpose of this paper is to examine differences between two factor analytical methods and their relevance for symptom cluster research: common factor analysis (CFA) versus principal component analysis (PCA). Literature was critically reviewed to elucidate the differences between CFA and PCA. A secondary analysis (N = 84) was utilized to show the actual result differences from the two methods. CFA analyzes only the reliable common variance of data, while PCA analyzes all the variance of data. An underlying hypothetical process or construct is involved in CFA but not in PCA. PCA tends to increase factor loadings especially in a study with a small number of variables and/or low estimated communality. Thus, PCA is not appropriate for examining the structure of data. If the study purpose is to explain correlations among variables and to examine the structure of the data (this is usual for most cases in symptom cluster research), CFA provides a more accurate result. If the purpose of a study is to summarize data with a smaller number of variables, PCA is the choice. PCA can also be used as an initial step in CFA because it provides information regarding the maximum number and nature of factors. In using factor analysis for symptom cluster research, several issues need to be considered, including subjectivity of solution, sample size, symptom selection, and level of measure.

Screening Protocol for Early Identification of Brazilian Children at Risk for Dyslexia

PubMed Central

Germano, Giseli D.; César, Alexandra B. P. de C.; Capellini, Simone A.

2017-01-01

Early identification of students at risk of dyslexia has been an educational challenge in the past years. This research had two main goals. First, we aimed to develop a screening protocol for early identification of Brazilian children at risk for dyslexia; second, we aimed to identify the predictive variables of this protocol using Principal Component Analysis. The major step involved in developing this protocol was the selection of variables, which were chosen based on the literature review and linguistic criteria. The screening protocol was composed of seven cognitive-linguistic skills: Letter naming; Phonological Awareness (which comprises the following subtests: Rhyme production, Rhyme identification, Syllabic segmentation, Production of words from a given phoneme, Phonemic Synthesis, and Phonemic analysis); Phonological Working memory, Rapid naming Speed; Silent reading; Reading of words and non-words; and Auditory Comprehension of sentences from pictures. A total of 149 children, aged from 6 years to 6 and 11, of both genders who were enrolled in the 1st grade of elementary public schools were submitted to the screening protocol. Principal Component Analysis revealed four factors, accounting for 64.45% of the variance of the Protocol variables: first factor (“pre-reading”), second factor (“decoding”), third factor (“Reading”), and fourth factor “Auditory processing.” The factors found corroborate those reported in the National and International literature and have been described as early signs of dyslexia and reading problems. PMID:29163246

A principal component regression model to forecast airborne concentration of Cupressaceae pollen in the city of Granada (SE Spain), during 1995-2006.

PubMed

Ocaña-Peinado, Francisco M; Valderrama, Mariano J; Bouzas, Paula R

2013-05-01

The problem of developing a 2-week-on ahead forecast of atmospheric cypress pollen levels is tackled in this paper by developing a principal component multiple regression model involving several climatic variables. The efficacy of the proposed model is validated by means of an application to real data of Cupressaceae pollen concentration in the city of Granada (southeast of Spain). The model was applied to data from 11 consecutive years (1995-2005), with 2006 being used to validate the forecasts. Based on the work of different authors, factors as temperature, humidity, hours of sun and wind speed were incorporated in the model. This methodology explains approximately 75-80% of the variability in the airborne Cupressaceae pollen concentration.

Physicochemical properties of quinoa starch.

PubMed

Li, Guantian; Wang, Sunan; Zhu, Fan

2016-02-10

Physicochemical properties of quinoa starches isolated from 26 commercial samples from a wide range of collection were studied. Swelling power (SP), water solubility index (WSI), amylose leaching (AML), enzyme susceptibility, pasting, thermal and textural properties were analyzed. Apparent amylose contents (AAM) ranged from 7.7 to 25.7%. Great variations in the diverse physicochemical properties were observed. Correlation analysis showed that AAM was the most significant factor related to AML, WSI, and pasting parameters. Correlations among diverse physicochemical parameters were analyzed. Principal component analysis using twenty three variables were used to visualize the difference among samples. Six principal components were extracted which could explain 88.8% of the total difference. The wide variations in physicochemical properties could contribute to innovative utilization of quinoa starch for food and non-food applications. Copyright © 2015 Elsevier Ltd. All rights reserved.

Geochemistry of sediments in the Northern and Central Adriatic Sea

NASA Astrophysics Data System (ADS)

De Lazzari, A.; Rampazzo, G.; Pavoni, B.

2004-03-01

Major, minor and trace elements, loss of ignition, specific surface area, quantities of calcite and dolomite, qualitative mineralogical composition, grain-size distribution and organic micropollutants (PAH, PCB, DDT) were determined on surficial marine sediments sampled during the 1990 ASCOP (Adriatic Scientific Cooperative Program) cruise. Mineralogical composition and carbonate content of the samples were found to be comparable with data previously reported in the literature, whereas geochemical composition and distribution of major, minor and trace elements for samples in international waters and in the central basin have never been reported before. The large amount of information contained in the variables of different origin has been processed by means of a comprehensive approach which establishes the relations among the components through the mathematical-statistical calculation of principal components (factors). These account for the major part of data variance loosing only marginal parts of information and are independent from the units of measure. The sample descriptors concerning natural components and contamination load are discussed by means of a statistical model based on an R-mode Factor analysis calculating four significant factors which explain 86.8% of the total variance, and represent important relationships between grain size, mineralogy, geochemistry and organic micropollutants. A description and an interpretation of factor composition is discussed on the basis of pollution inputs, basin geology and hydrodynamics. The areal distribution of the factors showed that it is the fine grain-size fraction, with oxides and hydroxides of colloidal origin, which are the main means of transport and thus the principal link between chemical, physical and granulometric elements in the Adriatic.

An Introductory Application of Principal Components to Cricket Data

ERIC Educational Resources Information Center

Manage, Ananda B. W.; Scariano, Stephen M.

2013-01-01

Principal Component Analysis is widely used in applied multivariate data analysis, and this article shows how to motivate student interest in this topic using cricket sports data. Here, principal component analysis is successfully used to rank the cricket batsmen and bowlers who played in the 2012 Indian Premier League (IPL) competition. In…

Least Principal Components Analysis (LPCA): An Alternative to Regression Analysis.

ERIC Educational Resources Information Center

Olson, Jeffery E.

Often, all of the variables in a model are latent, random, or subject to measurement error, or there is not an obvious dependent variable. When any of these conditions exist, an appropriate method for estimating the linear relationships among the variables is Least Principal Components Analysis. Least Principal Components are robust, consistent,…

Identifying apple surface defects using principal components analysis and artifical neural networks

USDA-ARS?s Scientific Manuscript database

Artificial neural networks and principal components were used to detect surface defects on apples in near-infrared images. Neural networks were trained and tested on sets of principal components derived from columns of pixels from images of apples acquired at two wavelengths (740 nm and 950 nm). I...

Finding Planets in K2: A New Method of Cleaning the Data

NASA Astrophysics Data System (ADS)

Currie, Miles; Mullally, Fergal; Thompson, Susan E.

2017-01-01

We present a new method of removing systematic flux variations from K2 light curves by employing a pixel-level principal component analysis (PCA). This method decomposes the light curves into its principal components (eigenvectors), each with an associated eigenvalue, the value of which is correlated to how much influence the basis vector has on the shape of the light curve. This method assumes that the most influential basis vectors will correspond to the unwanted systematic variations in the light curve produced by K2’s constant motion. We correct the raw light curve by automatically fitting and removing the strongest principal components. The strongest principal components generally correspond to the flux variations that result from the motion of the star in the field of view. Our primary method of calculating the strongest principal components to correct for in the raw light curve estimates the noise by measuring the scatter in the light curve after using an algorithm for Savitsy-Golay detrending, which computes the combined photometric precision value (SG-CDPP value) used in classic Kepler. We calculate this value after correcting the raw light curve for each element in a list of cumulative sums of principal components so that we have as many noise estimate values as there are principal components. We then take the derivative of the list of SG-CDPP values and take the number of principal components that correlates to the point at which the derivative effectively goes to zero. This is the optimal number of principal components to exclude from the refitting of the light curve. We find that a pixel-level PCA is sufficient for cleaning unwanted systematic and natural noise from K2’s light curves. We present preliminary results and a basic comparison to other methods of reducing the noise from the flux variations.

A Model of Objective Weighting for EIA.

ERIC Educational Resources Information Center

Ying, Long Gen; Liu, You Ci

1995-01-01

In the research of environmental impact assessment (EIA), the problem of weight distribution for a set of parameters has not yet been properly solved. Presents an approach of objective weighting by using a procedure of Pij principal component-factor analysis (Pij PCFA), which suits specifically those parameters measured directly by physical…

A Factor Analytic Validation of Holland's Vocational Preference Inventory

ERIC Educational Resources Information Center

Di Scipio, William J.

1974-01-01

A principal components analysis was applied to a 135-item pool of the Holland Vocational Preference Inventory, Sixth Revision. The a priori clinical scales were partially upheld with differences attributed to the characteristics of the sample and sociopolitical time context during which the test was administered. (Author)

Reexamining the Correlates of Adolescent Violent Victimization: The Importance of Exposure, Guardianship, and Target Characteristics

ERIC Educational Resources Information Center

Tillyer, Marie Skubak; Tillyer, Rob; Miller, Holly Ventura; Pangrac, Rebekah

2011-01-01

The present study examines the relative contributions of various theoretical constructs to violent victimization by operationalizing multiple measures of exposure to motivated offenders, guardianship, and target characteristics. Using a nationally representative sample of American adolescents, we conducted principal components factor analysis and…

Outdoor Leader Career Development: Exploration of a Career Path

ERIC Educational Resources Information Center

Wagstaff, Mark

2016-01-01

The purpose of this study was to assess the efficacy of the proposed Outdoor Leader Career Development Model (OLCDM) through the development of the Outdoor Leader Career Development Inventory (OLCDI). I assessed the reliability and validity of the OLCDI through exploratory factor analysis, principal component analysis, and varimax rotation, based…

Mapping "StrengthsQuest" Themes to Holland's Vocational Personality Types

ERIC Educational Resources Information Center

Carson, Andrew D.; Evans, Karen; Gitin, Elena; Eads, Jonathan

2011-01-01

A sample of 1,747 students attending undergraduate classes in legal education at an online university completed the "StrengthsQuest" assessment as part of a larger investigation; of this number, 117 students also completed the "Kuder Career Search". Exploratory factor analysis (principal components followed varimax rotation) of…

The Circumplex Pattern of the Life Styles Inventory: A Reanalysis.

ERIC Educational Resources Information Center

Levin, Joseph

1991-01-01

A reanalysis of the intercorrelation matrix from a principal components analysis of the Life Styles Inventory was conducted using a Canadian sample. Using nonmetric multidimensional scaling, analyses show an almost perfect circumplex pattern. Results illustrate the inadequacy of factor analytic procedures for the analysis and representation of a…

A Principal Components Analysis of the Rathus Assertiveness Schedule.

ERIC Educational Resources Information Center

Law, H. G.; And Others

1979-01-01

Investigated the adequacy of the Rathus Assertiveness Schedule (RAS) as a global measure of assertiveness. Analysis indicated that the RAS does not provide a unidimensional index of assertiveness, but rather measures a number of factors including situation-specific assertive behavior, aggressiveness, and a more general assertiveness. (Author)

Factors Associated with Implementation of the South Carolina Students Health and Fitness Act of 2005: Elementary School Principals' and Physical Activity Directors' Ratings

ERIC Educational Resources Information Center

Evans, Miriam Jones

2012-01-01

This study explores factors associated with implementation of the physical education and physical activity standards of the South Carolina Students Health and Fitness Act of 2005 in Title I elementary schools. The study was framed using selected components of the diffusion of innovations theory, which looked at characteristics of the law and their…

Overlap and distinction between measures of insight and self-stigma.

PubMed

Hasson-Ohayon, Ilanit

2018-05-24

Multiple studies on insight into one's illness and self-stigma among patients with serious mental illness and their relatives have shown that these constructs are related to one another and that they affect outcome. However, a critical exploration of the items used to assess both constructs raises questions with regard to the possible overlapping and centrality of items. The current study used five different samples to explore the possible overlap and distinction between insight and self-stigma, and to identify central items, via network analyses and principal component factor analysis. Findings from the network analyses showed overlap between insight and self-stigma exist with a relatively clearer observational distinction between the constructs among the two parent samples in comparison to the patient samples. Principal component factor analysis constrained to two factors showed that a relatively high percentage of items were not loaded on either factor, and in a few datasets, several insight items were loaded on the self-stigma scale and vice versa. The author discusses implications for research and calls for rethinking the way insight is assessed. Clinical implications are also discussed in reference to central items of social isolation, future worries and stereotype endorsement among the different study groups. Copyright © 2018 Elsevier B.V. All rights reserved.

The DASS-14: Improving the Construct Validity and Reliability of the Depression, Anxiety, and Stress Scale in a Cohort of Health Professionals.

PubMed

Wise, Frances M; Harris, Darren W; Olver, John H

2017-01-01

Considerable research has been undertaken in evaluating the DASS-21 in a variety of clinical populations, but studies of the instrument's psychometric adequacy in healthcare professionals is lacking. This study aimed to establish and improve the construct validity and reliability of the DASS-21 in a cohort of Australian health professionals. 343 rehabilitation health professionals completed the DASS-21, along with a demographic questionnaire. Principal components analysis was performed to identify potential factors in the DASS-21. Factors were interpreted against theoretical constructs underlying the instrument. Items loading on separate factors were then subjected to reliability analysis to determine internal consistency of subscales. Items that demonstrated poor fit, or loaded onto more than one factor, were deleted to maximise the reliability of each subscale. Principal components analysis identified three dimensions (depression, anxiety, stress) in a modified version of the DASS-21 (renamed DASS-14), with appropriate construct validity and good reliability (a=0.73 to 0.88). The three dimensions accounted for over 62% of variance between items. The modified DASS-14 scale is a more parsimonious measure of depression, anxiety, and stress, with acceptable reliability and construct validity, in rehabilitation health professionals and is appropriate for use in studies of similar populations.

40 CFR 60.2998 - What are the principal components of the model rule?

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 6 2010-07-01 2010-07-01 false What are the principal components of... December 9, 2004 Model Rule-Use of Model Rule § 60.2998 What are the principal components of the model rule... management plan. (c) Operator training and qualification. (d) Emission limitations and operating limits. (e...

40 CFR 60.2570 - What are the principal components of the model rule?

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 6 2010-07-01 2010-07-01 false What are the principal components of... Construction On or Before November 30, 1999 Use of Model Rule § 60.2570 What are the principal components of... (k) of this section. (a) Increments of progress toward compliance. (b) Waste management plan. (c...

PM10 and gaseous pollutants trends from air quality monitoring networks in Bari province: principal component analysis and absolute principal component scores on a two years and half data set

PubMed Central

2014-01-01

Background The chemical composition of aerosols and particle size distributions are the most significant factors affecting air quality. In particular, the exposure to finer particles can cause short and long-term effects on human health. In the present paper PM10 (particulate matter with aerodynamic diameter lower than 10 μm), CO, NOx (NO and NO2), Benzene and Toluene trends monitored in six monitoring stations of Bari province are shown. The data set used was composed by bi-hourly means for all parameters (12 bi-hourly means per day for each parameter) and it’s referred to the period of time from January 2005 and May 2007. The main aim of the paper is to provide a clear illustration of how large data sets from monitoring stations can give information about the number and nature of the pollutant sources, and mainly to assess the contribution of the traffic source to PM10 concentration level by using multivariate statistical techniques such as Principal Component Analysis (PCA) and Absolute Principal Component Scores (APCS). Results Comparing the night and day mean concentrations (per day) for each parameter it has been pointed out that there is a different night and day behavior for some parameters such as CO, Benzene and Toluene than PM10. This suggests that CO, Benzene and Toluene concentrations are mainly connected with transport systems, whereas PM10 is mostly influenced by different factors. The statistical techniques identified three recurrent sources, associated with vehicular traffic and particulate transport, covering over 90% of variance. The contemporaneous analysis of gas and PM10 has allowed underlining the differences between the sources of these pollutants. Conclusions The analysis of the pollutant trends from large data set and the application of multivariate statistical techniques such as PCA and APCS can give useful information about air quality and pollutant’s sources. These knowledge can provide useful advices to environmental policies in order to reach the WHO recommended levels. PMID:24555534

Free energy landscape of a biomolecule in dihedral principal component space: sampling convergence and correspondence between structures and minima.

PubMed

Maisuradze, Gia G; Leitner, David M

2007-05-15

Dihedral principal component analysis (dPCA) has recently been developed and shown to display complex features of the free energy landscape of a biomolecule that may be absent in the free energy landscape plotted in principal component space due to mixing of internal and overall rotational motion that can occur in principal component analysis (PCA) [Mu et al., Proteins: Struct Funct Bioinfo 2005;58:45-52]. Another difficulty in the implementation of PCA is sampling convergence, which we address here for both dPCA and PCA using a tetrapeptide as an example. We find that for both methods the sampling convergence can be reached over a similar time. Minima in the free energy landscape in the space of the two largest dihedral principal components often correspond to unique structures, though we also find some distinct minima to correspond to the same structure. 2007 Wiley-Liss, Inc.

[Validation of a questionnaire to evaluate patient safety in clinical laboratories].

PubMed

Giménez Marín, Ángeles; Rivas-Ruiz, Francisco

2012-01-01

The aim of this study was to prepare, pilot and validate a questionnaire to evaluate patient safety in the specific context of clinical laboratories. A specific questionnaire on patient safety in the laboratory, with 62 items grouped into six areas, was developed, taking into consideration the diverse human and laboratory contextual factors which may contribute to producing errors. A pilot study of 30 interviews was carried out, including validity and reliability analyses using principal components factor analysis and Cronbach's alpha. Subsequently, 240 questionnaires were sent to 21 hospitals, followed by a test-retest of 41 questionnaires with the definitive version. The sample analyzed was composed of 225 questionnaires (an overall response rate of 80%). Of the 62 items initially assessed, 17 were eliminated due to non-compliance with the criteria established before the principal components factor analysis was performed. For the 45 remaining items, 12 components were identified, with an cumulative variance of 69.5%. In seven of the 10 components with two or more items, Cronbach's alpha was higher than 0.7. The questionnaire items assessed in the test-retest were found to be stable. We present the first questionnaire with sufficiently proven validity and reliability for evaluating patient safety in the specific context of clinical laboratories. This questionnaire provides a useful instrument to perform a subsequent macrostudy of hospital clinical laboratories in Spain. The questionnaire can also be used to monitor and promote commitment to patient safety within the search for continuous quality improvement. Copyright © 2011 SESPAS. Published by Elsevier Espana. All rights reserved.

Validation and factor structure of the Thai version of the EURO-D scale for depression among older psychiatric patients.

PubMed

Jirapramukpitak, Tawanchai; Darawuttimaprakorn, Niphon; Punpuing, Sureeporn; Abas, Melanie

2009-11-01

To assess the concurrent and the construct validity of the Euro-D in older Thai persons. Eight local psychiatrists used the major depressive episode section of the Mini International Neuropsychiatric Interview to interview 150 consecutive psychiatric clinic attendees. A trained interviewer administered the Euro-D. We used receiver operating characteristic (ROC) analysis to assess the overall discriminability of the Euro-D scale and principal components factor analysis to assess its construct validity. The area under the ROC curve for the Euro-D with respect to major depressive episode was 0.78 [95% confidence interval (CI) 0.70-0.90] indicating moderately good discriminability. At a cut-point of 5/6 the sensitivity for major depressive episodes is 84.3%, specificity 58.6%, and kappa 0.37 (95% CI 0.22-0.52) indicating fair concordance. However, at the 3/4 cut-point recommended from European studies there is high sensitivity (94%) but poor specificity (34%). The principal components analysis suggested four factors. The first two factors conformed to affective suffering (depression, suicidality and tearfulness) and motivation (interest, concentration and enjoyment). Sleep and appetite constituted a separate factor, whereas pessimism loaded on its own factor. Among Thai psychiatric clinic attendees Euro-D is moderately valid for major depression. A much higher cut-point may be required than that which is usually advocated. The Thai version also shares two common factors as reported from most of previous studies.

Mapping Common Aphasia Assessments to Underlying Cognitive Processes and Their Neural Substrates.

PubMed

Lacey, Elizabeth H; Skipper-Kallal, Laura M; Xing, Shihui; Fama, Mackenzie E; Turkeltaub, Peter E

2017-05-01

Understanding the relationships between clinical tests, the processes they measure, and the brain networks underlying them, is critical in order for clinicians to move beyond aphasia syndrome classification toward specification of individual language process impairments. To understand the cognitive, language, and neuroanatomical factors underlying scores of commonly used aphasia tests. Twenty-five behavioral tests were administered to a group of 38 chronic left hemisphere stroke survivors and a high-resolution magnetic resonance image was obtained. Test scores were entered into a principal components analysis to extract the latent variables (factors) measured by the tests. Multivariate lesion-symptom mapping was used to localize lesions associated with the factor scores. The principal components analysis yielded 4 dissociable factors, which we labeled Word Finding/Fluency, Comprehension, Phonology/Working Memory Capacity, and Executive Function. While many tests loaded onto the factors in predictable ways, some relied heavily on factors not commonly associated with the tests. Lesion symptom mapping demonstrated discrete brain structures associated with each factor, including frontal, temporal, and parietal areas extending beyond the classical language network. Specific functions mapped onto brain anatomy largely in correspondence with modern neural models of language processing. An extensive clinical aphasia assessment identifies 4 independent language functions, relying on discrete parts of the left middle cerebral artery territory. A better understanding of the processes underlying cognitive tests and the link between lesion and behavior may lead to improved aphasia diagnosis, and may yield treatments better targeted to an individual's specific pattern of deficits and preserved abilities.

Spectral compression algorithms for the analysis of very large multivariate images

DOEpatents

Keenan, Michael R.

2007-10-16

A method for spectrally compressing data sets enables the efficient analysis of very large multivariate images. The spectral compression algorithm uses a factored representation of the data that can be obtained from Principal Components Analysis or other factorization technique. Furthermore, a block algorithm can be used for performing common operations more efficiently. An image analysis can be performed on the factored representation of the data, using only the most significant factors. The spectral compression algorithm can be combined with a spatial compression algorithm to provide further computational efficiencies.

Fast, Exact Bootstrap Principal Component Analysis for p > 1 million

PubMed Central

Fisher, Aaron; Caffo, Brian; Schwartz, Brian; Zipunnikov, Vadim

2015-01-01

Many have suggested a bootstrap procedure for estimating the sampling variability of principal component analysis (PCA) results. However, when the number of measurements per subject (p) is much larger than the number of subjects (n), calculating and storing the leading principal components from each bootstrap sample can be computationally infeasible. To address this, we outline methods for fast, exact calculation of bootstrap principal components, eigenvalues, and scores. Our methods leverage the fact that all bootstrap samples occupy the same n-dimensional subspace as the original sample. As a result, all bootstrap principal components are limited to the same n-dimensional subspace and can be efficiently represented by their low dimensional coordinates in that subspace. Several uncertainty metrics can be computed solely based on the bootstrap distribution of these low dimensional coordinates, without calculating or storing the p-dimensional bootstrap components. Fast bootstrap PCA is applied to a dataset of sleep electroencephalogram recordings (p = 900, n = 392), and to a dataset of brain magnetic resonance images (MRIs) (p ≈ 3 million, n = 352). For the MRI dataset, our method allows for standard errors for the first 3 principal components based on 1000 bootstrap samples to be calculated on a standard laptop in 47 minutes, as opposed to approximately 4 days with standard methods. PMID:27616801

Cognitive load, emotion, and performance in high-fidelity simulation among beginning nursing students: a pilot study.

PubMed

Schlairet, Maura C; Schlairet, Timothy James; Sauls, Denise H; Bellflowers, Lois

2015-03-01

Establishing the impact of the high-fidelity simulation environment on student performance, as well as identifying factors that could predict learning, would refine simulation outcome expectations among educators. The purpose of this quasi-experimental pilot study was to explore the impact of simulation on emotion and cognitive load among beginning nursing students. Forty baccalaureate nursing students participated in teaching simulations, rated their emotional state and cognitive load, and completed evaluation simulations. Two principal components of emotion were identified representing the pleasant activation and pleasant deactivation components of affect. Mean rating of cognitive load following simulation was high. Linear regression identiffed slight but statistically nonsignificant positive associations between principal components of emotion and cognitive load. Logistic regression identified a negative but statistically nonsignificant effect of cognitive load on assessment performance. Among lower ability students, a more pronounced effect of cognitive load on assessment performance was observed; this also was statistically non-significant. Copyright 2015, SLACK Incorporated.

Principal components analysis of an evaluation of the hemiplegic subject based on the Bobath approach.

PubMed

Corriveau, H; Arsenault, A B; Dutil, E; Lepage, Y

1992-01-01

An evaluation based on the Bobath approach to treatment has previously been developed and partially validated. The purpose of the present study was to verify the content validity of this evaluation with the use of a statistical approach known as principal components analysis. Thirty-eight hemiplegic subjects participated in the study. Analysis of the scores on each of six parameters (sensorium, active movements, muscle tone, reflex activity, postural reactions, and pain) was evaluated on three occasions across a 2-month period. Each time this produced three factors that contained 70% of the variation in the data set. The first component mainly reflected variations in mobility, the second mainly variations in muscle tone, and the third mainly variations in sensorium and pain. The results of such exploratory analysis highlight the fact that some of the parameters are not only important but also interrelated. These results seem to partially support the conceptual framework substantiating the Bobath approach to treatment.

Principal Workload: Components, Determinants and Coping Strategies in an Era of Standardization and Accountability

ERIC Educational Resources Information Center

Oplatka, Izhar

2017-01-01

Purpose: In order to fill the gap in theoretical and empirical knowledge about the characteristics of principal workload, the purpose of this paper is to explore the components of principal workload as well as its determinants and the coping strategies commonly used by principals to face this personal state. Design/methodology/approach:…

The development of summary components for the Disablement in the Physically Active scale in collegiate athletes.

PubMed

Houston, Megan N; Hoch, Johanna M; Van Lunen, Bonnie L; Hoch, Matthew C

2015-11-01

The Disablement in the Physically Active scale (DPA) is a generic patient-reported outcome designed to evaluate constructs of disability in physically active populations. The purpose of this study was to analyze the DPA scale structure for summary components. Four hundred and fifty-six collegiate athletes completed a demographic form and the DPA. A principal component analysis (PCA) was conducted with oblique rotation. Factors with eigenvalues >1 that explained >5 % of the variance were retained. The PCA revealed a two-factor structure consistent with paradigms used to develop the original DPA. Items 1-12 loaded on Factors 1 and Items 13-16 loaded on Factor 2. Items 1-12 pertain to impairment, activity limitations, and participation restrictions. Items 13-16 address psychosocial and emotional well-being. Consideration of item content suggested Factor 1 concerned physical function, while Factor 2 concerned mental well-being. Thus, items clustered around Factor 1 and 2 were identified as physical (DPA-PSC) and mental (DPA-MSC) summary components, respectively. Together, the factors accounted for 65.1 % of the variance. The PCA revealed a two-factor structure for the DPA that resulted in DPA-PSC and DPA-MSC. Analyzing the DPA as separate constructs may provide distinct information that could help to prescribe treatment and rehabilitation strategies.

Identification of atmospheric organic sources using the carbon hollow tube-gas chromatography method and factor analysis

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

Cobb, G.P.; Braman, R.S.; Gilbert, R.A.

Atmospheric organics were sampled and analyzed by using the carbon hollow tube-gas chromatography method. Chromatograms from spice mixtures, cigarettes, and ambient air were analyzed. Principal factor analysis of row order chromatographic data produces factors which are eigenchromatograms of the components in the samples. Component sources are identified from the eigenchromatograms in all experiments and the individual eigenchromatogram corresponding to a particular source is determined in most cases. Organic sources in ambient air and in cigaretts are identified with 87% certainty. Analysis of clove cigarettes allows the determination of the relative amount of clove in different cigarettes. A new nondestructive qualitymore » control method using the hollow tube-gas chromatography analysis is discussed.« less

Assessing rumination in eating disorders: principal component analysis of a minimally modified ruminative response scale.

PubMed

Cowdrey, Felicity A; Park, Rebecca J

2011-12-01

A process account of eating disorders (EDs) (Park et al., in press-a) proposes that preoccupation with ruminative themes of eating, weight and shape may be important in ED maintenance. No self-report measure exists to capture disorder-specific rumination in EDs. 275 healthy participants rated rumination items and completed self-report measures of ED symptoms, depression and anxiety. Principal component analysis revealed two factors, reflection and brooding. The final nine-item Ruminative Response Scale for Eating Disorders (RRS-ED) demonstrated good convergent and discriminant validity and test-retest reliability. The psychometric properties were replicated in an anorexia nervosa sample. The findings support the notion that rumination in EDs is distinct from rumination in depression and is not adequately captured by existing measures. Copyright © 2011 Elsevier Ltd. All rights reserved.

Determination of Disaster Awareness, Attitude Levels and Individual Priorities at Kocaeli University

ERIC Educational Resources Information Center

Gerdan, Serpil

2014-01-01

Problem Statement: In disaster prone countries, preparedness is an important factor in disaster mitigation. There are various disaster management approaches. However, one common point of these approaches is that they are "preventive." First and foremost of the principal components of the preventive approach is preparedness and education.…

Validation of the Consumer Values versus Perceived Product Attributes Model Measuring the Purchase of Athletic Team Merchandise

ERIC Educational Resources Information Center

Lee, Donghun; Byon, Kevin K.; Schoenstedt, Linda; Johns, Gary; Bussell, Leigh Ann; Choi, Hwansuk

2012-01-01

Various consumer values and perceived product attributes trigger consumptive behaviors of athletic team merchandise (Lee, Trail, Kwon, & Anderson, 2011). Likewise, using a principal component analysis technique on a student sample, a measurement scale was proposed that consisted of nine factors affecting the purchase of athletic team…

Attitudes of French-Canadian university students toward use of condoms: a structural analysis.

PubMed

Bernard, J; Hébert, Y; de Man, A; Farrar, D

1989-12-01

164 French-Canadian university students took part in a study analyzing the factorial pattern of attitudes toward the use of condoms. A principal component analysis identified 7 factors, namely, positive general attitude toward use of condoms, contraceptive security, inhibition of sexual pleasure, inconvenience, embarrassment, responsibility, and lessening of physical pleasure.

Measuring Staff Perceptions of University Identity and Activities: The Mission and Values Inventory

ERIC Educational Resources Information Center

Ferrari, Joseph R.; Velcoff, Jessica

2006-01-01

Higher education institutions need to ascertain whether their stakeholders understand the school's mission, vision, and values. In the present study, the psychometric properties of a mission identity and activity measure were investigated with two staff samples. Using a principal component factor analysis (varimax rotation), respondents in Sample…

Social Support for Exercise and Dietary Habits among College Students

ERIC Educational Resources Information Center

Gruber, Kenneth J.

2008-01-01

An assessment inventory (the Friend/Peer Support-Health Eating Physical Activity Scale-FPS-HEPAS) was developed to measure social influence patterns of college student physical activity and food consumption habits. Principal components analysis of 50 items with two referent sets (friends and peers) produced two scales with common factors:…

The Cross-Cultural Validity of the Learning Disability Index: A Reanalysis of Mishra's Data.

ERIC Educational Resources Information Center

Inglis, James; Lawson, J. S.

1985-01-01

Investigated nature of a learning disability index (LDI) for the objective assessment of verbal-nonverbal patterns of intellectual deficit on the Wechsler Intelligence Scale for Children-Revised using Factor II score coefficients derived from an unrotated principal components analysis of normative data, and average scaled scores. The…

Communication as an Essential Component of Effective Leadership across Low Socio-Economic Groups

ERIC Educational Resources Information Center

Marriott, Sarah E.; Bradfield, Gail A.; Wingert, Jennifer L.

2013-01-01

This project describes a problem-based learning project focusing on communication between high school principals and students and families from poverty. Review of current research indicated a lack of effective interactions between school administrators and families in poverty. There were many identified factors which contribute to this…

Validation of the Marijuana Effect Expectancy Questionnaire-Brief

ERIC Educational Resources Information Center

Torrealday, O.; Stein, L. A. R.; Barnett, N.; Golembeske, C.; Lebeau, R.; Colby, S. M.; Monti, P. M.

2008-01-01

The purpose of this study was to evaluate a brief version of the Marijuana Effect Expectancy Questionnaire (MEEQ; Schafer & Brown, 1991). The original MEEQ was reduced to 6 items (MEEQ-B). Principal component analysis (PCA) was performed and two factors were identified (positive effects and negative effects) accounting for 52.3% of the variance.…

Classification Techniques for Multivariate Data Analysis.

DTIC Science & Technology

1980-03-28

analysis among biologists, botanists, and ecologists, while some social scientists may refer "typology". Other frequently encountered terms are pattern...the determinantal equation: lB -XW 0 (42) 49 The solutions X. are the eigenvalues of the matrix W-1 B 1 as in discriminant analysis. There are t non...Statistical Package for Social Sciences (SPSS) (14) subprogram FACTOR was used for the principal components analysis. It is designed both for the factor

Assessment of sediment quality in the Mediterranean Sea-Boughrara lagoon exchange areas (southeastern Tunisia): GIS approach-based chemometric methods.

PubMed

Kharroubi, Adel; Gargouri, Dorra; Baati, Houda; Azri, Chafai

2012-06-01

Concentrations of selected heavy metals (Cd, Pb, Zn, Cu, Mn, and Fe) in surface sediments from 66 sites in both northern and eastern Mediterranean Sea-Boughrara lagoon exchange areas (southeastern Tunisia) were studied in order to understand current metal contamination due to the urbanization and economic development of nearby several coastal regions of the Gulf of Gabès. Multiple approaches were applied for the sediment quality assessment. These approaches were based on GIS coupled with chemometric methods (enrichment factors, geoaccumulation index, principal component analysis, and cluster analysis). Enrichment factors and principal component analysis revealed two distinct groups of metals. The first group corresponded to Fe and Mn derived from natural sources, and the second group contained Cd, Pb, Zn, and Cu originated from man-made sources. For these latter metals, cluster analysis showed two distinct distributions in the selected areas. They were attributed to temporal and spatial variations of contaminant sources input. The geoaccumulation index (I (geo)) values explained that only Cd, Pb, and Cu can be considered as moderate to extreme pollutants in the studied sediments.

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.

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.

[Priorization of facilitators for the implementation of medication review with follow-up service in Spanish community pharmacies through exploratory factor analysis].

PubMed

Gil, Modesta Inmaculada; Benrimoj, Shalom Isaac; Martínez-Martínez, Fernando; Cardero, Manuel; Gastelurrutia, Miguel Ángel

2013-01-01

to prioritize previously identified in Spain facilitators for the implementation of new Pharmaceutical Services that allow designing strategies for the implementation of Medication Review with follow-up (MRFup) service. Exploratory factor analysis (EFA). A draft of a questionnaire was performed based on a previous literature review and following the RAND/UCLA methodology. An expert panel worked with it and generated a definitive questionnaire which, after piloting, was used with a representative sample of pharmacists, owners or staff members, who were working in community pharmacy, using computer-assisted telephone interviewing (CATI) methodology. To understand underlying constructs in the questionnaire an EFA was performed. Different approaches were tested such as principal components factor analysis and principal axis factoring method. The best interpretability was achieved using the Factorization of Principal axis method with Direct Oblimin rotation, which explained the 40.0% of total variance. This produced four factors defined as: «Incentives», «External campaigns», «Expert in MRFup» and «Professionalism of the pharmacist». It can be stated that for implementation and sustainability of MRFup Service it is necessary being paid; also it must be explained to health professional and society in general. Practice of MRFup service demands pharmacists receiving a more clinical education and assuming more responsibilities as health professionals. Copyright © 2012 Elsevier España, S.L. All rights reserved.

Water characterization and seasonal heavy metal distribution in the Odiel River (Huelva, Spain) by means of principal component analysis.

PubMed

Montes-Botella, C; Tenorio, M D

2003-11-01

The Iberian Pyrite Belt is the largest mass of sulfide and manganese ores in Western Europe. Its sulfide oxidation is the origin of a heavily acidic drainage that affects the Odiel River in southwestern Huelva (Spain). To assess physicochemical, contamination parameters, heavy metal distribution and its seasonal variation in the upper Odiel River and in El Lomero mines, three water samplings were undertaken and analyzed between July 1998 and November 1999. Water from the Odiel River in the polluted zone showed low pH values (2.76-3.51), high heavy metal content, and high values of conductivity (1410-3648 microS/cm) and dissolved solids (1484-5602 mg/L). Principal Component Analysis (PCA) showed that variables related with the products of the pyrite oxidation and the salts that are solubilized by the high acidity generated in the oxidation of sulfides, grouped in the first component, accounted for 40.88% of total variance, and were the main influential factor in physicochemical water sample properties. The second influential factor was minority metals (nickel, cobalt, cadmium). Heavy metals showed three different seasonal patterns, closely related with saline efflorescences formed next to the river bed: majority metals (iron, copper, manganese, zinc); minority metals (lead, nickel, cobalt, cadmium); and chromium, which had a distinctive behavior.

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

Kravtsov, A.I.

To determine the effect of geologic factors on the composition of abyssal derivates (complementing existing information on the geochemistry of volcanic gases) isotopic analysis of carbon was used to obtain physicochemical criteria of the origin of gases, independent of geologic-petrographic data. The investigations include component analysis of all the gases, particularly hydrocarbon compounds, repeatedly found in the fumarole emanations of pyroclastic streams. Volcanic carbon dioxide which is the principal component of gases of active volcanoes and hot springs in the Kuril-Kamchatka volcanic arc and of other volcanoes was investigated.

Psychometric properties of the Spanish version of the Cocaine Selective Severity Assessment to evaluate cocaine withdrawal in treatment-seeking individuals.

PubMed

Pérez de los Cobos, José; Trujols, Joan; Siñol, Núria; Vasconcelos e Rego, Lisiane; Iraurgi, Ioseba; Batlle, Francesca

2014-09-01

Reliable and valid assessment of cocaine withdrawal is relevant for treating cocaine-dependent patients. This study examined the psychometric properties of the Spanish version of the Cocaine Selective Severity Assessment (CSSA), an instrument that measures cocaine withdrawal. Participants were 170 cocaine-dependent inpatients receiving detoxification treatment. Principal component analysis revealed a 4-factor structure for CSSA that included the following components: 'Cocaine Craving and Psychological Distress', 'Lethargy', 'Carbohydrate Craving and Irritability', and 'Somatic Depressive Symptoms'. These 4 components accounted for 56.0% of total variance. Internal reliability for these components ranged from unacceptable to good (Chronbach's alpha: 0.87, 0.65, 0.55, and 0.22, respectively). All components except Somatic Depressive Symptoms presented concurrent validity with cocaine use. In summary, while some properties of the Spanish version of the CSSA are satisfactory, such as interpretability of factor structure and test-retest reliability, other properties, such as internal reliability and concurrent validity of some factors, are inadequate. Copyright © 2014 Elsevier Inc. All rights reserved.

Development of a scale for attitude toward condom use for migrant workers in India.

PubMed

Talukdar, Arunansu; Bal, Runa; Sanyal, Debasis; Roy, Krishnendu; Talukdar, Payel Sengupta

2008-02-01

The propaganda for the use of condoms remains one of the mainstay for prevention of human immunodeficiency virus (HIV) transmission. In spite of the proven efficacy of condom, some moral, social and psychological obstacles are still prevalent, hindering the use of condoms. The study tried to construct a short condom-attitude scale for use among the migrant workers, a major bridge population in India. The study was conducted among the male migrant workers who were 18-49 years old, sexually active and had heard about condoms and were engaged in nonformal jobs. We recruited 234 and 280 candidates for Phase 1 and Phase 2 respectively. Ten items from the original 40-item Brown's ATC (attitude towards condom) scale were selected in Phase 1. After analysis of Phase 1 results, using principal component analysis six items were found appropriate for measuring attitude towards condom use. These six items were then administered in another group in Phase 2. Utilizing Pearson's correlations, scale items were examined in terms of their mean response scores and the correlation matrix between items. Cornbach's alpha and construct validity were also assessed for the entire sample. Study subjects were categorized as condom users and nonusers. The scale structure was explored by analyzing response scores with respect to the items, using principal component analysis followed by varimax rotation analysis. Principal component analysis revealed that the first factor accounted for 71% of the variance, with eigenvalue greater than one. Eigenvalues of the second factor was less than one. Application of screen test suggests only one factor was dominant. Mean score of six items among condom users was 20.45 and that among nonusers was 16.67, which was statistically significant (P<0.01). Cornbach's alpha coefficient was 0.92. This tailor-made attitude-toward-condom-use scale, targeted for most vulnerable people in India, can be included in any rapid survey for assessing the existing beliefs and attitudes toward condoms and also for evaluating efficacy of an intervention program.

The Influence Function of Principal Component Analysis by Self-Organizing Rule.

PubMed

Higuchi; Eguchi

1998-07-28

This article is concerned with a neural network approach to principal component analysis (PCA). An algorithm for PCA by the self-organizing rule has been proposed and its robustness observed through the simulation study by Xu and Yuille (1995). In this article, the robustness of the algorithm against outliers is investigated by using the theory of influence function. The influence function of the principal component vector is given in an explicit form. Through this expression, the method is shown to be robust against any directions orthogonal to the principal component vector. In addition, a statistic generated by the self-organizing rule is proposed to assess the influence of data in PCA.

Whole-body patterns of the range of joint motion in young adults: masculine type and feminine type.

PubMed

Moromizato, Keiichi; Kimura, Ryosuke; Fukase, Hitoshi; Yamaguchi, Kyoko; Ishida, Hajime

2016-10-01

Understanding the whole-body patterns of joint flexibility and their related biological and physical factors contributes not only to clinical assessments but also to the fields of human factors and ergonomics. In this study, ranges of motion (ROMs) at limb and trunk joints of young adults were analysed to understand covariation patterns of different joint motions and to identify factors associated with the variation in ROM. Seventy-eight healthy volunteers (42 males and 36 females) living on Okinawa Island, Japan, were recruited. Passive ROM was measured at multiple joints through the whole body (31 measurements) including the left and right side limbs and trunk. Comparisons between males and females, dominant and non-dominant sides, and antagonistic motions indicated that body structures influence ROMs. In principal component analysis (PCA) on the ROM data, the first principal component (PC1) represented the sex difference and a similar covariation pattern appeared in the analysis within each sex. Multiple regression analysis showed that this component was associated with sex, age, body fat %, iliospinale height, and leg extension strength. The present study identified that there is a spectrum of "masculine" and "feminine" types in the whole-body patterns of joint flexibility. This study also suggested that body proportion and composition, muscle mass and strength, and possibly skeletal structures partly explain such patterns. These results would be important to understand individual variation in susceptibility to joint injuries and diseases and in one's suitable and effective postures and motions.

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

USGS Publications Warehouse

Brown, C. Erwin

1993-01-01

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

The Specific Level of Functioning Scale: construct validity, internal consistency and factor structure in a large Italian sample of people with schizophrenia living in the community.

PubMed

Mucci, Armida; Rucci, Paola; Rocca, Paola; Bucci, Paola; Gibertoni, Dino; Merlotti, Eleonora; Galderisi, Silvana; Maj, Mario

2014-10-01

The study aimed to assess the construct validity, internal consistency and factor structure of the Specific Levels of Functioning Scale (SLOF), a multidimensional instrument assessing real life functioning. The study was carried out in 895 Italian people with schizophrenia, all living in the community and attending the outpatient units of 26 university psychiatric clinics and/or community mental health departments. The construct validity of the SLOF was analyzed by means of the multitrait-multimethod approach, using the Personal and Social Performance (PSP) Scale as the gold standard. The factor structure of the SLOF was examined using both an exploratory principal component analysis and a confirmatory factor analysis. The six factors identified using exploratory principal component analysis explained 57.1% of the item variance. The examination of the multitrait-multimethod matrix revealed that the SLOF factors had high correlations with PSP factors measuring the same constructs and low correlations with PSP factors measuring different constructs. The confirmatory factor analysis (CFA) corroborated the 6-factor structure reported in the original validation study. Loadings were all significant and ranged from a minimum of 0.299 to a maximum of 0.803. The CFA model was adequately powered and had satisfactory goodness of fit indices (comparative fit index=0.927, Tucker-Lewis index=0.920 and root mean square error of approximation=0.047, 95% CI 0.045-0.049). The present study confirms, in a large sample of Italian people with schizophrenia living in the community, that the SLOF is a reliable and valid instrument for the assessment of social functioning. It has good construct validity and internal consistency, and a well-defined factor structure. Copyright © 2014 Elsevier B.V. All rights reserved.

A New Tool for Nutrition App Quality Evaluation (AQEL): Development, Validation, and Reliability Testing

PubMed Central

Huang, Wenhao; Chapman-Novakofski, Karen M

2017-01-01

Background The extensive availability and increasing use of mobile apps for nutrition-based health interventions makes evaluation of the quality of these apps crucial for integration of apps into nutritional counseling. Objective The goal of this research was the development, validation, and reliability testing of the app quality evaluation (AQEL) tool, an instrument for evaluating apps’ educational quality and technical functionality. Methods Items for evaluating app quality were adapted from website evaluations, with additional items added to evaluate the specific characteristics of apps, resulting in 79 initial items. Expert panels of nutrition and technology professionals and app users reviewed items for face and content validation. After recommended revisions, nutrition experts completed a second AQEL review to ensure clarity. On the basis of 150 sets of responses using the revised AQEL, principal component analysis was completed, reducing AQEL into 5 factors that underwent reliability testing, including internal consistency, split-half reliability, test-retest reliability, and interrater reliability (IRR). Two additional modifiable constructs for evaluating apps based on the age and needs of the target audience as selected by the evaluator were also tested for construct reliability. IRR testing using intraclass correlations (ICC) with all 7 constructs was conducted, with 15 dietitians evaluating one app. Results Development and validation resulted in the 51-item AQEL. These were reduced to 25 items in 5 factors after principal component analysis, plus 9 modifiable items in two constructs that were not included in principal component analysis. Internal consistency and split-half reliability of the following constructs derived from principal components analysis was good (Cronbach alpha >.80, Spearman-Brown coefficient >.80): behavior change potential, support of knowledge acquisition, app function, and skill development. App purpose split half-reliability was .65. Test-retest reliability showed no significant change over time (P>.05) for all but skill development (P=.001). Construct reliability was good for items assessing age appropriateness of apps for children, teens, and a general audience. In addition, construct reliability was acceptable for assessing app appropriateness for various target audiences (Cronbach alpha >.70). For the 5 main factors, ICC (1,k) was >.80, with a P value of <.05. When 15 nutrition professionals evaluated one app, ICC (2,15) was .98, with a P value of <.001 for all 7 constructs when the modifiable items were specified for adults seeking weight loss support. Conclusions Our preliminary effort shows that AQEL is a valid, reliable instrument for evaluating nutrition apps’ qualities for clinical interventions by nutrition clinicians, educators, and researchers. Further efforts in validating AQEL in various contexts are needed. PMID:29079554

Exploring functional data analysis and wavelet principal component analysis on ecstasy (MDMA) wastewater data.

PubMed

Salvatore, Stefania; Bramness, Jørgen G; Røislien, Jo

2016-07-12

Wastewater-based epidemiology (WBE) is a novel approach in drug use epidemiology which aims to monitor the extent of use of various drugs in a community. In this study, we investigate functional principal component analysis (FPCA) as a tool for analysing WBE data and compare it to traditional principal component analysis (PCA) and to wavelet principal component analysis (WPCA) which is more flexible temporally. We analysed temporal wastewater data from 42 European cities collected daily over one week in March 2013. The main temporal features of ecstasy (MDMA) were extracted using FPCA using both Fourier and B-spline basis functions with three different smoothing parameters, along with PCA and WPCA with different mother wavelets and shrinkage rules. The stability of FPCA was explored through bootstrapping and analysis of sensitivity to missing data. The first three principal components (PCs), functional principal components (FPCs) and wavelet principal components (WPCs) explained 87.5-99.6 % of the temporal variation between cities, depending on the choice of basis and smoothing. The extracted temporal features from PCA, FPCA and WPCA were consistent. FPCA using Fourier basis and common-optimal smoothing was the most stable and least sensitive to missing data. FPCA is a flexible and analytically tractable method for analysing temporal changes in wastewater data, and is robust to missing data. WPCA did not reveal any rapid temporal changes in the data not captured by FPCA. Overall the results suggest FPCA with Fourier basis functions and common-optimal smoothing parameter as the most accurate approach when analysing WBE data.

Principal components analysis to identify influences on research communication and engagement during an environmental disaster.

PubMed

Winters, Charlene A; Moore, Colleen F; Kuntz, Sandra W; Weinert, Clarann; Hernandez, Tanis; Black, Brad

2016-08-09

To discern community attitudes towards research engagement in Libby, Montana, the only Superfund site for which a public health emergency has been declared. Survey study of convenience samples of residents near the Libby, Montana Superfund site. Residents of the Libby, Montana area were recruited from a local retail establishment (N=120, survey 1) or a community event (N=127, survey 2). Two surveys were developed in consultation with a Community Advisory Panel. Principal components of survey 1 showed four dimensions of community members' attitudes towards research engagement: (1) researcher communication and contributions to the community, (2) identity and affiliation of the researchers requesting participation, (3) potential personal barriers, including data confidentiality, painful or invasive procedures and effects on health insurance and (4) research benefits for the community, oneself or family. The score on the first factor was positively related to desire to participate in research (r=0.31, p=0.01). Scores on factors 2 and 3 were higher for those with diagnosis of asbestos-related disease (ARD) in the family (Cohen's d=0.41, 0.57). Survey 2 also found more positive attitudes towards research when a family member had ARD (Cohen's d=0.48). Principal components analysis shows different dimensions of attitudes towards research engagement. The different dimensions are related to community members' desire to be invited to participate in research, awareness of past research in the community and having been screened or diagnosed with a health condition related to the Superfund contaminant. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Patient feature based dosimetric Pareto front prediction in esophageal cancer radiotherapy.

PubMed

Wang, Jiazhou; Jin, Xiance; Zhao, Kuaike; Peng, Jiayuan; Xie, Jiang; Chen, Junchao; Zhang, Zhen; Studenski, Matthew; Hu, Weigang

2015-02-01

To investigate the feasibility of the dosimetric Pareto front (PF) prediction based on patient's anatomic and dosimetric parameters for esophageal cancer patients. Eighty esophagus patients in the authors' institution were enrolled in this study. A total of 2928 intensity-modulated radiotherapy plans were obtained and used to generate PF for each patient. On average, each patient had 36.6 plans. The anatomic and dosimetric features were extracted from these plans. The mean lung dose (MLD), mean heart dose (MHD), spinal cord max dose, and PTV homogeneity index were recorded for each plan. Principal component analysis was used to extract overlap volume histogram (OVH) features between PTV and other organs at risk. The full dataset was separated into two parts; a training dataset and a validation dataset. The prediction outcomes were the MHD and MLD. The spearman's rank correlation coefficient was used to evaluate the correlation between the anatomical features and dosimetric features. The stepwise multiple regression method was used to fit the PF. The cross validation method was used to evaluate the model. With 1000 repetitions, the mean prediction error of the MHD was 469 cGy. The most correlated factor was the first principal components of the OVH between heart and PTV and the overlap between heart and PTV in Z-axis. The mean prediction error of the MLD was 284 cGy. The most correlated factors were the first principal components of the OVH between heart and PTV and the overlap between lung and PTV in Z-axis. It is feasible to use patients' anatomic and dosimetric features to generate a predicted Pareto front. Additional samples and further studies are required improve the prediction model.

Quantifying Burnout among Emergency Medicine Professionals

PubMed Central

Wilson, William; Raj, Jeffrey Pradeep; Narayan, Girish; Ghiya, Murtuza; Murty, Shakuntala; Joseph, Bobby

2017-01-01

Background: Burnout is a syndrome explained as serious emotional depletion with poor adaptation at work due to prolonged occupational stress. It has three principal components namely emotional exhaustion(EE), depersonalization(DP) and diminished feelings of personal accomplishment(PA). Thus, we aimed at measuring the degree of burnout in doctors and nurses working in emergency medicine department (EMD) of 4 select tertiary care teaching hospitals in South India. Methods: A cross sectional survey was conducted among EMD professionals using a 30-item standardized pilot tested questionnaire as well as the Maslach burnout inventory. Univariate and Multivariate analyses were conducted using binary logistic regression models to identify predictors of burnout. Results: Total number of professionals interviewed were 105 of which 71.5% were women and 51.4% were doctors. Majority (78.1%) belonged to the age group 20-30 years. Prevalence of moderate to severe burnout in the 3 principal components EE, DP and PA were 64.8%, 71.4% and 73.3% respectively. After multivariate analysis, the risk factors [adjusted odds ratio (95% confidence intervals) for DP included facing more criticism [3.57(1.25,10.19)], disturbed sleep [6.44(1.45,28.49)] and being short tempered [3.14(1.09,9.09)]. While there were no statistically significant risk factors for EE, being affected by mortality [2.35(1.12,3.94)] and fear of medication errors [3.61(1.26, 10.37)] appeared to be significant predictors of PA. Conclusion: Degree of burn out among doctors and nurses is moderately high in all of the three principal components and some of the predictors identified were criticism, disturbed sleep, short tempered nature, fear of committing errors and witnessing death in EMD. PMID:29097859

Quantifying Burnout among Emergency Medicine Professionals.

PubMed

Wilson, William; Raj, Jeffrey Pradeep; Narayan, Girish; Ghiya, Murtuza; Murty, Shakuntala; Joseph, Bobby

2017-01-01

Burnout is a syndrome explained as serious emotional depletion with poor adaptation at work due to prolonged occupational stress. It has three principal components namely emotional exhaustion(EE), depersonalization(DP) and diminished feelings of personal accomplishment(PA). Thus, we aimed at measuring the degree of burnout in doctors and nurses working in emergency medicine department (EMD) of 4 select tertiary care teaching hospitals in South India. A cross sectional survey was conducted among EMD professionals using a 30-item standardized pilot tested questionnaire as well as the Maslach burnout inventory. Univariate and Multivariate analyses were conducted using binary logistic regression models to identify predictors of burnout. Total number of professionals interviewed were 105 of which 71.5% were women and 51.4% were doctors. Majority (78.1%) belonged to the age group 20-30 years. Prevalence of moderate to severe burnout in the 3 principal components EE, DP and PA were 64.8%, 71.4% and 73.3% respectively. After multivariate analysis, the risk factors [adjusted odds ratio (95% confidence intervals) for DP included facing more criticism [3.57(1.25,10.19)], disturbed sleep [6.44(1.45,28.49)] and being short tempered [3.14(1.09,9.09)]. While there were no statistically significant risk factors for EE, being affected by mortality [2.35(1.12,3.94)] and fear of medication errors [3.61(1.26, 10.37)] appeared to be significant predictors of PA. Degree of burn out among doctors and nurses is moderately high in all of the three principal components and some of the predictors identified were criticism, disturbed sleep, short tempered nature, fear of committing errors and witnessing death in EMD.

TU-C-17A-10: Patient Features Based Dosimetric Pareto Front Prediction In Esophagus Cancer Radiotherapy

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

Wang, J; Zhao, K; Peng, J

2014-06-15

Purpose: The purpose of this study is to study the feasibility of the dosimetric pareto front (PF) prediction based on patient anatomic and dosimetric parameters for esophagus cancer patients. Methods: Sixty esophagus patients in our institution were enrolled in this study. A total 2920 IMRT plans were created to generated PF for each patient. On average, each patient had 48 plans. The anatomic and dosimetric features were extracted from those plans. The mean lung dose (MLD), mean heart dose (MHD), spinal cord max dose and PTV homogeneous index (PTVHI) were recorded for each plan. The principal component analysis (PCA) wasmore » used to extract overlap volume histogram (OVH) features between PTV and other critical organs. The full dataset was separated into two parts include the training dataset and the validation dataset. The prediction outcomes were the MHD and MLD for the current study. The spearman rank correlation coefficient was used to evaluate the correlation between the anatomical features and dosimetric features. The PF was fit by the the stepwise multiple regression method. The cross-validation method was used to evaluation the model. Results: The mean prediction error of the MHD was 465 cGy with 100 repetitions. The most correlated factors were the first principal components of the OVH between heart and PTV, and the overlap between heart and PTV in Z-axis. The mean prediction error of the MLD was 195 cGy. The most correlated factors were the first principal components of the OVH between lung and PTV, and the overlap between lung and PTV in Z-axis. Conclusion: It is feasible to use patients anatomic and dosimetric features to generate a predicted PF. Additional samples and further studies were required to get a better prediction model.« less

Mapping brain activity in gradient-echo functional MRI using principal component analysis

NASA Astrophysics Data System (ADS)

Khosla, Deepak; Singh, Manbir; Don, Manuel

1997-05-01

The detection of sites of brain activation in functional MRI has been a topic of immense research interest and many technique shave been proposed to this end. Recently, principal component analysis (PCA) has been applied to extract the activated regions and their time course of activation. This method is based on the assumption that the activation is orthogonal to other signal variations such as brain motion, physiological oscillations and other uncorrelated noises. A distinct advantage of this method is that it does not require any knowledge of the time course of the true stimulus paradigm. This technique is well suited to EPI image sequences where the sampling rate is high enough to capture the effects of physiological oscillations. In this work, we propose and apply tow methods that are based on PCA to conventional gradient-echo images and investigate their usefulness as tools to extract reliable information on brain activation. The first method is a conventional technique where a single image sequence with alternating on and off stages is subject to a principal component analysis. The second method is a PCA-based approach called the common spatial factor analysis technique (CSF). As the name suggests, this method relies on common spatial factors between the above fMRI image sequence and a background fMRI. We have applied these methods to identify active brain ares during visual stimulation and motor tasks. The results from these methods are compared to those obtained by using the standard cross-correlation technique. We found good agreement in the areas identified as active across all three techniques. The results suggest that PCA and CSF methods have good potential in detecting the true stimulus correlated changes in the presence of other interfering signals.

Patient feature based dosimetric Pareto front prediction in esophageal cancer radiotherapy

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

Wang, Jiazhou; Zhao, Kuaike; Peng, Jiayuan

2015-02-15

Purpose: To investigate the feasibility of the dosimetric Pareto front (PF) prediction based on patient’s anatomic and dosimetric parameters for esophageal cancer patients. Methods: Eighty esophagus patients in the authors’ institution were enrolled in this study. A total of 2928 intensity-modulated radiotherapy plans were obtained and used to generate PF for each patient. On average, each patient had 36.6 plans. The anatomic and dosimetric features were extracted from these plans. The mean lung dose (MLD), mean heart dose (MHD), spinal cord max dose, and PTV homogeneity index were recorded for each plan. Principal component analysis was used to extract overlapmore » volume histogram (OVH) features between PTV and other organs at risk. The full dataset was separated into two parts; a training dataset and a validation dataset. The prediction outcomes were the MHD and MLD. The spearman’s rank correlation coefficient was used to evaluate the correlation between the anatomical features and dosimetric features. The stepwise multiple regression method was used to fit the PF. The cross validation method was used to evaluate the model. Results: With 1000 repetitions, the mean prediction error of the MHD was 469 cGy. The most correlated factor was the first principal components of the OVH between heart and PTV and the overlap between heart and PTV in Z-axis. The mean prediction error of the MLD was 284 cGy. The most correlated factors were the first principal components of the OVH between heart and PTV and the overlap between lung and PTV in Z-axis. Conclusions: It is feasible to use patients’ anatomic and dosimetric features to generate a predicted Pareto front. Additional samples and further studies are required improve the prediction model.« less

40 CFR 62.14505 - What are the principal components of this subpart?

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 8 2010-07-01 2010-07-01 false What are the principal components of this subpart? 62.14505 Section 62.14505 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... components of this subpart? This subpart contains the eleven major components listed in paragraphs (a...

Principal component analysis of cardiovascular risk traits in three generations cohort among Indian Punjabi population.

PubMed

Badaruddoza; Kumar, Raman; Kaur, Manpreet

2015-09-01

The current study focused to determine significant cardiovascular risk factors through principal component factor analysis (PCFA) among three generations on 1827 individuals in three generations including 911 males (378 from offspring, 439 from parental and 94 from grand-parental generations) and 916 females (261 from offspring, 515 from parental and 140 from grandparental generations). The study performed PCFA with orthogonal rotation to reduce 12 inter-correlated variables into groups of independent factors. The factors have been identified as 2 for male grandparents, 3 for male offspring, female parents and female grandparents each, 4 for male parents and 5 for female offspring. This data reduction method identified these factors that explained 72%, 84%, 79%, 69%, 70% and 73% for male and female offspring, male and female parents and male and female grandparents respectively, of the variations in original quantitative traits. The factor 1 accounting for the largest portion of variations was strongly loaded with factors related to obesity (body mass index (BMI), waist circumference (WC), waist to hip ratio (WHR), and thickness of skinfolds) among all generations with both sexes, which has been known to be an independent predictor for cardiovascular morbidity and mortality. The second largest components, factor 2 and factor 3 for almost all generations reflected traits of blood pressure phenotypes loaded, however, in male offspring generation it was observed that factor 2 was loaded with blood pressure phenotypes as well as obesity. This study not only confirmed but also extended prior work by developing a cumulative risk scale from factor scores. Till today, such a cumulative and extensive scale has not been used in any Indian studies with individuals of three generations. These findings and study highlight the importance of global approach for assessing the risk and need for studies that elucidate how these different cardiovascular risk factors interact with each other over the time to create clinical disease. The findings also added depth to the negligible amount of literature of factor analysis of cardiovascular risk in any Indian ethnic population.

STUDY OF THE INELASTIC SCATTERING OF ELECTRONS BY THE NUCLEI $sup 6$Li AND $sup 7$Li (in French)

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

Bernheim, M.; Bishop, G.R.

1963-11-01

We have measured the form factors for transitions to the following excited states by the inelastic scattering of electrons: 2.189, 3.57, and 4.52 Mev of /sup 6/Li; and 0.478, 4.61, 5.76, and 6.8 Mev of /sup 7/Li. The dependence of the form factors on the momentum transfer indicates the principal components of the wave functions describing these states. (auth)

An Analysis of Turnover Intentions: A Reexamination of Air Force Civil Engineering Company Grade Officers

DTIC Science & Technology

2012-03-01

edu 75 Appendix C Factor Analysis of Measurement Items Interrole conflict Factor Analysis (FA): Table: KMO and Bartlett’s Test Kaiser-Meyer...Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. 77 POS FA: Table: KMO and Bartlett’s...Tempo FA: Table: KMO and Bartlett’s Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .733 Bartlett’s Test of Sphericity Approx. Chi-Square

Postconcussive Symptoms in OEF-OIF Veterans: Factor Structure and Impact of Posttraumatic Stress

DTIC Science & Technology

2009-06-03

correlations between NSI full items are presented in Appendix A. Visual inspection of the correlation matrix, the Kaiser - Meyer - Olkin coefficient of .92, and...Spearman rho correlations between NSI residuals are pre- sented in Appendix B. Again, visual inspection of the correla- tion matrix, the Kaiser - Meyer ... Olkin coefficient of .83, and Bartlett’s test of sphericity (x2 5 1,936.0, p , .01) suggested that the matrix could be factored. Principal-components

Dietary intake of palmitate and oleate has broad impact on systemic and tissue lipid profiles in humans.

PubMed

Kien, C Lawrence; Bunn, Janice Y; Stevens, Robert; Bain, James; Ikayeva, Olga; Crain, Karen; Koves, Timothy R; Muoio, Deborah M

2014-03-01

Epidemiologic evidence has suggested that diets with a high ratio of palmitic acid (PA) to oleic acid (OA) increase risk of cardiovascular disease (CVD). To gain additional insights into the relative effect of dietary fatty acids and their metabolism on CVD risk, we sought to identify a metabolomic signature that tracks with diet-induced changes in blood lipid concentrations and whole-body fat oxidation. We applied comprehensive metabolomic profiling tools to biological specimens collected from 18 healthy adults enrolled in a crossover trial that compared a 3-wk high-palmitic acid (HPA) with a low-palmitic acid and high-oleic acid (HOA) diet. A principal components analysis of the data set including 329 variables measured in 15 subjects in the fasted state identified one factor, the principal components analysis factor in the fasted state (PCF1-Fasted), which was heavily weighted by the PA:OA ratio of serum and muscle lipids, that was affected by diet (P < 0.0001; HPA greater than HOA). One other factor, the additional principal components analysis factor in the fasted state (PCF2-Fasted), reflected a wide range of acylcarnitines and was affected by diet in women only (P = 0.0198; HPA greater than HOA). HOA lowered the ratio of serum low-density lipoprotein to high-density lipoprotein (LDL:HDL) in men and women, and adjustment for the PCF1-Fasted abolished the effect. In women only, adjustment for the PCF2-Fasted eliminated the HOA-diet effect on serum total- and LDL-cholesterol concentrations. The respiratory exchange ratio in the fasted state was lower with the HPA diet (P = 0.04), and the diet effect was eliminated after adjustment for the PCF1-Fasted. The messenger RNA expression of the cholesterol regulatory gene insulin-induced gene-1 was higher with the HOA diet (P = 0.008). These results suggest that replacing dietary PA with OA reduces the blood LDL concentration and whole-body fat oxidation by modifying the saturation index of circulating and tissue lipids. In women, these effects are also associated with a higher production and accumulation of acylcarnitines, possibly reflecting a shift in fat catabolism.

Principal component analysis of dietary and lifestyle patterns in relation to risk of subtypes of esophageal and gastric cancer

PubMed Central

Silvera, Stephanie A. Navarro; Mayne, Susan T; Risch, Harvey A.; Gammon, Marilie D; Vaughan, Thomas; Chow, Wong-Ho; Dubin, Joel A; Dubrow, Robert; Schoenberg, Janet; Stanford, Janet L; West, A. Brian; Rotterdam, Heidrun; Blot, William J

2011-01-01

Purpose To perform pattern analyses of dietary and lifestyle factors in relation to risk of esophageal and gastric cancers. Methods We evaluated risk factors for esophageal adenocarcinoma (EA), esophageal squamous cell carcinoma (ESCC), gastric cardia adenocarcinoma (GCA), and other gastric cancers (OGA) using data from a population-based case-control study conducted in Connecticut, New Jersey, and western Washington state. Dietary/lifestyle patterns were created using principal component analysis (PCA). Impact of the resultant scores on cancer risk was estimated through logistic regression. Results PCA identified six patterns: meat/nitrite, fruit/vegetable, smoking/alcohol, legume/meat alternate, GERD/BMI, and fish/vitamin C. Risk of each cancer under study increased with rising meat/nitrite score. Risk of EA increased with increasing GERD/BMI score, and risk of ESCC rose with increasing smoking/alcohol score and decreasing GERD/BMI score. Fruit/vegetable scores were inversely associated with EA, ESCC, and GCA. Conclusions PCA may provide a useful approach for summarizing extensive dietary/lifestyle data into fewer interpretable combinations that discriminate between cancer cases and controls. The analyses suggest that meat/nitrite intake is associated with elevated risk of each cancer under study, while fruit/vegetable intake reduces risk of EA, ESCC, and GCA. GERD/obesity were confirmed as risk factors for EA and smoking/alcohol as risk factors for ESCC. PMID:21435900

Principal component analysis of the Norwegian version of the quality of life in late-stage dementia scale.

PubMed

Mjørud, Marit; Kirkevold, Marit; Røsvik, Janne; Engedal, Knut

2014-01-01

To investigate which factors the Quality of Life in Late-Stage Dementia (QUALID) scale holds when used among people with dementia (pwd) in nursing homes and to find out how the symptom load varies across the different severity levels of dementia. We included 661 pwd [mean age ± SD, 85.3 ± 8.6 years; 71.4% women]. The QUALID and the Clinical Dementia Rating (CDR) scale were applied. A principal component analysis (PCA) with varimax rotation and Kaiser normalization was applied to test the factor structure. Nonparametric analyses were applied to examine differences of symptom load across the three CDR groups. The mean QUALID score was 21.5 (±7.1), and the CDR scores of the three groups were 1 in 22.5%, 2 in 33.6% and 3 in 43.9%. The results of the statistical measures employed were the following: Crohnbach's α of QUALID, 0.74; Bartlett's test of sphericity, p <0.001; the Kaiser-Meyer-Olkin measure, 0.77. The PCA analysis resulted in three components accounting for 53% of the variance. The first component was 'tension' ('facial expression of discomfort', 'appears physically uncomfortable', 'verbalization suggests discomfort', 'being irritable and aggressive', 'appears calm', Crohnbach's α = 0.69), the second was 'well-being' ('smiles', 'enjoys eating', 'enjoys touching/being touched', 'enjoys social interaction', Crohnbach's α = 0.62) and the third was 'sadness' ('appears sad', 'cries', 'facial expression of discomfort', Crohnbach's α 0.65). The mean score on the components 'tension' and 'well-being' increased significantly with increasing severity levels of dementia. Three components of quality of life (qol) were identified. Qol decreased with increasing severity of dementia. © 2013 S. Karger AG, Basel.

[In vitro transdermal delivery of the active fraction of xiangfusiwu decoction based on principal component analysis].

PubMed

Li, Zhen-Hao; Liu, Pei; Qian, Da-Wei; Li, Wei; Shang, Er-Xin; Duan, Jin-Ao

2013-06-01

The objective of the present study was to establish a method based on principal component analysis (PCA) for the study of transdermal delivery of multiple components in Chinese medicine, and to choose the best penetration enhancers for the active fraction of Xiangfusiwu decoction (BW) with this method. Improved Franz diffusion cells with isolated rat abdomen skins were carried out to experiment on the transdermal delivery of six active components, including ferulic acid, paeoniflorin, albiflorin, protopine, tetrahydropalmatine and tetrahydrocolumbamine. The concentrations of these components were determined by LC-MS/MS, then the total factor scores of the concentrations at different times were calculated using PCA and were employed instead of the concentrations to compute the cumulative amounts and steady fluxes, the latter of which were considered as the indexes for optimizing penetration enhancers. The results showed that compared to the control group, the steady fluxes of the other groups increased significantly and furthermore, 4% azone with 1% propylene glycol manifested the best effect. The six components could penetrate through skin well under the action of penetration enhancers. The method established in this study has been proved to be suitable for the study of transdermal delivery of multiple components, and it provided a scientific basis for preparation research of Xiangfusiwu decoction and moreover, it could be a reference for Chinese medicine research.

Regional differences in low birth weight in Spain: biological, demographic and socioeconomic variables.

PubMed

Fuster, Vicente; Zuluaga, Pilar; Colantonio, S E; Román-Busto, J

2015-01-01

The geographic and demographic dimensions of Spain, in terms of surface and number of inhabitants, and its heterogeneous socioeconomic development offer an adequate opportunity to study the provincial differences in birth weight from 1996 to 2010, focusing on possible factors determining the relative frequency of low birth weight. The study analysed geographic differences with regard to biological, demographic and socioeconomic factors that interfere with the female reproductive pattern. The variables considered here were: birth order, proportion of premature deliveries, mother's age, multiparity, mother's country of origin and professional qualifications. Two periods (1996-2000 and 2006-2010) were compared by means of principal components analysis. An increase in the relative frequency of deliveries weighing less than 2500 g occurred in most of the 52 geographic units studied, differences being significant in 42. Only in five cases was there a non-significant reduction in the proportion of low weight births. The first component after principal component analysis indicated that low birth weight was positively related to maternal age and to multiple deliveries, and negatively to the mother's low professional qualification. The second component related positively to the incidence of premature deliveries and to non-Spanish status and negatively in the case of primiparous mothers. The progressive increase in low birth weight incidence observed in Spain from 1996 onwards has occurred with considerable variation in each province. In part, this diversity can be attributed to the unequal reproductive patterns of immigrant mothers.

Factor structure of the Spanish version of the Object-Spatial Imagery and Verbal Questionnaire.

PubMed

Campos, Alfredo; Pérez-Fabello, María José

2011-04-01

The reliability and factor structure of the Spanish version of the Object-Spatial Imagery and Verbal Questionnaire (OSIVQ) were assessed in a sample of 213 Spanish university graduates. The questionnaire measures three types of processing preferences (verbal, object imagery, and spatial imagery). Principal components analysis with varimax rotation identified three factors, corresponding to the three scales proposed in the original version, explaining 33.1% of the overall variance. Cronbach's alphas were .72, .77, and .81 for the verbal, object imagery, and spatial imagery scales, respectively.

Principal Components Analyses of the MMPI-2 PSY-5 Scales. Identification of Facet Subscales

ERIC Educational Resources Information Center

Arnau, Randolph C.; Handel, Richard W.; Archer, Robert P.

2005-01-01

The Personality Psychopathology Five (PSY-5) is a five-factor personality trait model designed for assessing personality pathology using quantitative dimensions. Harkness, McNulty, and Ben-Porath developed Minnesota Multiphasic Personality Inventory-2 (MMPI-2) scales based on the PSY-5 model, and these scales were recently added to the standard…

Selection into Medicine Using Interviews and Other Measures: Much Remains to Be Learned

ERIC Educational Resources Information Center

Ma, Colleen; Harris, Peter; Cole, Andrew; Jones, Phil; Shulruf, Boaz

2016-01-01

The objectives of this study were to identify the effectiveness of the panel admission interview as a selection tool for the medical program and identify improvements in the selection tools battery. Data from 1024 students, representing four cohorts of students were used in this study. Exploratory factor analysis using principal component analysis…

What Do Phonological Processing Errors Tell about Students' Skills in Reading, Writing, and Oral Language?

ERIC Educational Resources Information Center

Choi, Dowon; Hatcher, Ryan C.; Dulong-Langley, Susan; Liu, Xiaochen; Bray, Melissa A.; Courville, Troy; O'Brien, Rebecca; DeBiase, Emily

2017-01-01

The kinds of errors that children and adolescents make on phonological processing tasks were studied with a large sample between ages 4 and 19 (N = 3,842) who were tested on the Kaufman Test of Educational Achievement-Third Edition (KTEA-3). Principal component analysis identified two phonological processing factors: Basic Phonological Awareness…

Developing and Evaluating Creativity Gamification Rehabilitation System: The Application of PCA-ANFIS Based Emotions Model

ERIC Educational Resources Information Center

Su, Chung-Ho; Cheng, Ching-Hsue

2016-01-01

This study aims to explore the factors in a patient's rehabilitation achievement after a total knee replacement (TKR) patient exercises, using a PCA-ANFIS emotion model-based game rehabilitation system, which combines virtual reality (VR) and motion capture technology. The researchers combine a principal component analysis (PCA) and an adaptive…

Toward a Tripartite Model of Research Motivation: Development and Initial Validation of the Research Motivation Scale

ERIC Educational Resources Information Center

Deemer, Eric D.; Martens, Matthew P.; Buboltz, Walter C.

2010-01-01

An instrument designed to measure a 3-factor model of research motivation was developed and psychometrically examined in the present research. Participants were 437 graduate students in biology, chemistry/biochemistry, physics/astronomy, and psychology. A principal components analysis supported the retention of 20 items representing the 3-factor…

Mapping common aphasia assessments to underlying cognitive processes and their neural substrates

PubMed Central

Lacey, Elizabeth H.; Skipper-Kallal, LM; Xing, S; Fama, ME; Turkeltaub, PE

2017-01-01

Background Understanding the relationships between clinical tests, the processes they measure, and the brain networks underlying them, is critical in order for clinicians to move beyond aphasia syndrome classification toward specification of individual language process impairments. Objective To understand the cognitive, language, and neuroanatomical factors underlying scores of commonly used aphasia tests. Methods 25 behavioral tests were administered to a group of 38 chronic left hemisphere stroke survivors and a high resolution MRI was obtained. Test scores were entered into a principal components analysis to extract the latent variables (factors) measured by the tests. Multivariate lesion-symptom mapping was used to localize lesions associated with the factor scores. Results The principal components analysis yielded four dissociable factors, which we labeled Word Finding/Fluency, Comprehension, Phonology/Working Memory Capacity, and Executive Function. While many tests loaded onto the factors in predictable ways, some relied heavily on factors not commonly associated with the tests. Lesion symptom mapping demonstrated discrete brain structures associated with each factor, including frontal, temporal, and parietal areas extending beyond the classical language network. Specific functions mapped onto brain anatomy largely in correspondence with modern neural models of language processing. Conclusions An extensive clinical aphasia assessment identifies four independent language functions, relying on discrete parts of the left middle cerebral artery territory. A better understanding of the processes underlying cognitive tests and the link between lesion and behavior may lead to improved aphasia diagnosis, and may yield treatments better targeted to an individual’s specific pattern of deficits and preserved abilities. PMID:28135902

Hierarchical Regularity in Multi-Basin Dynamics on Protein Landscapes

NASA Astrophysics Data System (ADS)

Matsunaga, Yasuhiro; Kostov, Konstatin S.; Komatsuzaki, Tamiki

2004-04-01

We analyze time series of potential energy fluctuations and principal components at several temperatures for two kinds of off-lattice 46-bead models that have two distinctive energy landscapes. The less-frustrated "funnel" energy landscape brings about stronger nonstationary behavior of the potential energy fluctuations at the folding temperature than the other, rather frustrated energy landscape at the collapse temperature. By combining principal component analysis with an embedding nonlinear time-series analysis, it is shown that the fast fluctuations with small amplitudes of 70-80% of the principal components cause the time series to become almost "random" in only 100 simulation steps. However, the stochastic feature of the principal components tends to be suppressed through a wide range of degrees of freedom at the transition temperature.

Dimensions of professional labor support for intrapartum practice.

PubMed

Sauls, Donna J

2006-01-01

To define and describe the dimensions of Professional Labor Support (PLS). A factor-analytic study was conducted with a random sample of 146 intrapartum nurses in Texas. Nurses' responses to the Labor Support Questionnaire (LSQ) were subjected to principal components analysis and descriptive analysis. A six-factor solution indicated the dimensions of PLS: Tangible Support, Advocacy, Emotional Support-Reassurance, Emotional Support-Creating Control, Security and Comfort, Emotional Support-Nurse Caring Behavior, and Informational Support. Although the presence of four dimensions was theorized, six dimensions were found. The emotional support dimension was identified by nurses as being an important component of labor support as indicated by the identification of three separate emotional support dimensions.

Temporal trends and bioavailability assessment of heavy metals in the sediments of Deception Bay, Queensland, Australia.

PubMed

Brady, James P; Ayoko, Godwin A; Martens, Wayde N; Goonetilleke, Ashantha

2014-12-15

Thirteen sites in Deception Bay, Queensland, Australia were sampled three times over a period of 7 months and assessed for contamination by a range of heavy metals, primarily As, Cd, Cr, Cu, Pb and Hg. Fraction analysis, enrichment factors and Principal Components Analysis-Absolute Principal Component Scores (PCA-APCS) analysis were conducted in order to identify the potential bioavailability of these elements of concern and their sources. Hg and Te were identified as the elements of highest enrichment in Deception Bay while marine sediments, shipping and antifouling agents were identified as the sources of the Weak Acid Extractable Metals (WE-M), with antifouling agents showing long residence time for mercury contamination. This has significant implications for the future of monitoring and regulation of heavy metal contamination within Deception Bay. Copyright © 2014 Elsevier Ltd. All rights reserved.

Potentiality Prediction of Electric Power Replacement Based on Power Market Development Strategy

NASA Astrophysics Data System (ADS)

Miao, Bo; Yang, Shuo; Liu, Qiang; Lin, Jingyi; Zhao, Le; Liu, Chang; Li, Bin

2017-05-01

The application of electric power replacement plays an important role in promoting the development of energy conservation and emission reduction in our country. To exploit the potentiality of regional electric power replacement, the regional GDP (gross domestic product) and energy consumption are taken as potentiality evaluation indicators. The principal component factors are extracted with PCA (principal component analysis), and the integral potentiality analysis is made to the potentiality of electric power replacement in the national various regions; a region is taken as a research object, and the potentiality of electric power replacement is defined and quantified. The analytical model for the potentiality of multi-scenario electric power replacement is developed, and prediction is made to the energy consumption with the grey prediction model. The relevant theoretical research is utilized to realize prediction analysis on the potentiality amount of multi-scenario electric power replacement.

The Coach-Athlete Relationship Questionnaire (CART-Q): development and initial validation.

PubMed

Jowett, Sophia; Ntoumanis, Nikos

2004-08-01

The purpose of the present study was to develop and validate a self-report instrument that measures the nature of the coach-athlete relationship. Jowett et al.'s (Jowett & Meek, 2000; Jowett, in press) qualitative case studies and relevant literature were used to generate items for an instrument that measures affective, cognitive, and behavioral aspects of the coach-athlete relationship. Two studies were carried out in an attempt to assess content, predictive, and construct validity, as well as internal consistency, of the Coach-Athlete Relationship Questionnaire (CART-Q), using two independent British samples. Principal component analysis and confirmatory factor analysis were used to reduce the number of items, identify principal components, and confirm the latent structure of the CART-Q. Results supported the multidimensional nature of the coach-athlete relationship. The latent structure of the CART-Q was underlined by the latent variables of coaches' and athletes' Closeness (emotions), Commitment (cognitions), and Complementarity (behaviors).

Classification of narcotics in solid mixtures using principal component analysis and Raman spectroscopy.

PubMed

Ryder, Alan G

2002-03-01

Eighty-five solid samples consisting of illegal narcotics diluted with several different materials were analyzed by near-infrared (785 nm excitation) Raman spectroscopy. Principal Component Analysis (PCA) was employed to classify the samples according to narcotic type. The best sample discrimination was obtained by using the first derivative of the Raman spectra. Furthermore, restricting the spectral variables for PCA to 2 or 3% of the original spectral data according to the most intense peaks in the Raman spectrum of the pure narcotic resulted in a rapid discrimination method for classifying samples according to narcotic type. This method allows for the easy discrimination between cocaine, heroin, and MDMA mixtures even when the Raman spectra are complex or very similar. This approach of restricting the spectral variables also decreases the computational time by a factor of 30 (compared to the complete spectrum), making the methodology attractive for rapid automatic classification and identification of suspect materials.

Principals' Perceptions Regarding Their Supervision and Evaluation

ERIC Educational Resources Information Center

Hvidston, David J.; Range, Bret G.; McKim, Courtney Ann

2015-01-01

This study examined the perceptions of principals concerning principal evaluation and supervisory feedback. Principals were asked two open-ended questions. Respondents included 82 principals in the Rocky Mountain region. The emerging themes were "Superintendent Performance," "Principal Evaluation Components," "Specific…

Bidding factors-the reduction of the data dimension with the use of PCA

NASA Astrophysics Data System (ADS)

Leśniak, Agnieszka

2017-07-01

Making the decision to participate in the tender is subject to a number of factors, affects the health of the company and is an important aspect in its quest for success. Efforts to select bidding factors have been repeatedly undertaken in various countries and in numerous construction markets. Researchers usually give a long list of factors, also called criteria, which in their opinion may significantly influence the bidding decision. The paper presents an attempt to reduce a proposed set of bidding factorsdefined in Poland with the use of thePrincipal Component Analysis.

Conformational states and folding pathways of peptides revealed by principal-independent component analyses.

PubMed

Nguyen, Phuong H

2007-05-15

Principal component analysis is a powerful method for projecting multidimensional conformational space of peptides or proteins onto lower dimensional subspaces in which the main conformations are present, making it easier to reveal the structures of molecules from e.g. molecular dynamics simulation trajectories. However, the identification of all conformational states is still difficult if the subspaces consist of more than two dimensions. This is mainly due to the fact that the principal components are not independent with each other, and states in the subspaces cannot be visualized. In this work, we propose a simple and fast scheme that allows one to obtain all conformational states in the subspaces. The basic idea is that instead of directly identifying the states in the subspace spanned by principal components, we first transform this subspace into another subspace formed by components that are independent of one other. These independent components are obtained from the principal components by employing the independent component analysis method. Because of independence between components, all states in this new subspace are defined as all possible combinations of the states obtained from each single independent component. This makes the conformational analysis much simpler. We test the performance of the method by analyzing the conformations of the glycine tripeptide and the alanine hexapeptide. The analyses show that our method is simple and quickly reveal all conformational states in the subspaces. The folding pathways between the identified states of the alanine hexapeptide are analyzed and discussed in some detail. 2007 Wiley-Liss, Inc.

Reliability and validity of the Malay Version of the Breast- Impact of Treatment Scale (MVBITS) in breast cancer women undergoing chemotherapy.

PubMed

Zainal, Nor Zuraida; Shuib, Norley; Bustam, Anita Zarina; Sabki, Zuraida Ahmad; Guan, Ng Chong

2013-01-01

Body image dissatisfaction among breast cancer survivors has been associated with psychological stress resultant from breast cancer and resultant surgery. This study aimed to examine the psychometric properties of the Malay Version of the Breast-Impact of Treatment Scale (MVBITS) and to investigate the associations of retained factors with the Hospital Anxiety and Depression Scale (HADS) and the Rosenberg Self-Esteem Scale (RSES). The MVBITS was 'forward-backward' translated from English to Malay and then administered to 70 female breast cancer patients who came to the Oncology Clinic of University Malaya Medical Centre, Kuala Lumpur, Malaysia to undergo chemotherapy. Principal component analysis (PCA) with varimax rotation was performed to explore the factor structure of the MVBITS. Associations of retained factors were estimated with reference to Spearman correlation coefficients. The internal consistency reliability of MVBITS was good (Cronbach's alpha 0.945) and showed temporal stability over a 3-week period. Principal component analysis suggested two factors termed as 'Intrusion' and 'Avoidance' domains. These factors explained 70.3% of the variance. Factor 1 comprised the effects of breast cancer treatment on the emotion and thought, while Factor 2 informed attempts to limit exposure of the body to self or others. The Factor 1 of MVBITS was positively correlated with total, depression and anxiety sub-scores of HADS. Factor 2 was positively correlated with total and anxiety sub-scores of HADS. MVBITS was also positively correlated with the RSES scores. The results showed that the Malay Version of Breast-Impact of Treatment Scale possesses satisfactory psychometric properties suggesting that this instrument is appropriate for assessment of body change stress among female breast cancer patients in Malaysia.

Metabolic Profiles Predict Adverse Events Following Coronary Artery Bypass Grafting

PubMed Central

Shah, Asad A.; Craig, Damian M.; Sebek, Jacqueline K.; Haynes, Carol; Stevens, Robert C.; Muehlbauer, Michael J.; Granger, Christopher B.; Hauser, Elizabeth R.; Newby, L. Kristin; Newgard, Christopher B.; Kraus, William E.; Hughes, G. Chad; Shah, Svati H.

2012-01-01

Objectives Clinical models incompletely predict outcomes following coronary artery bypass grafting. Novel molecular technologies may identify biomarkers to improve risk stratification. We examined whether metabolic profiles can predict adverse events in patients undergoing coronary artery bypass grafting. Methods The study population comprised 478 subjects from the CATHGEN biorepository of patients referred for cardiac catheterization who underwent coronary artery bypass grafting after enrollment. Targeted mass spectrometry-based profiling of 69 metabolites was performed in frozen, fasting plasma samples collected prior to surgery. Principal-components analysis and Cox proportional hazards regression modeling were used to assess the relation between metabolite factor levels and a composite outcome of post-coronary artery bypass grafting myocardial infarction, need for percutaneous coronary intervention, repeat coronary artery bypass grafting, or death. Results Over a mean follow-up of 4.3 ± 2.4 years, 126 subjects (26.4%) suffered an adverse event. Three principal-components analysis-derived factors were significantly associated with adverse outcome in univariable analysis: short-chain dicarboxylacylcarnitines (factor 2, P=0.001); ketone-related metabolites (factor 5, P=0.02); and short-chain acylcarnitines (factor 6, P=0.004). These three factors remained independently predictive of adverse outcome after multivariable adjustment: factor 2 (adjusted hazard ratio 1.23; 95% confidence interval [1.10-1.38]; P<0.001), factor 5 (1.17 [1.01-1.37], P=0.04), and factor 6 (1.14 [1.02-1.27], P=0.03). Conclusions Metabolic profiles are independently associated with adverse outcomes following coronary artery bypass grafting. These profiles may represent novel biomarkers of risk that augment existing tools for risk stratification of coronary artery bypass grafting patients and may elucidate novel biochemical pathways that mediate risk. PMID:22306227

Functional Data Analysis in NTCP Modeling: A New Method to Explore the Radiation Dose-Volume Effects

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

Benadjaoud, Mohamed Amine, E-mail: mohamedamine.benadjaoud@gustaveroussy.fr; Université Paris sud, Le Kremlin-Bicêtre; Institut Gustave Roussy, Villejuif

2014-11-01

Purpose/Objective(s): To describe a novel method to explore radiation dose-volume effects. Functional data analysis is used to investigate the information contained in differential dose-volume histograms. The method is applied to the normal tissue complication probability modeling of rectal bleeding (RB) for patients irradiated in the prostatic bed by 3-dimensional conformal radiation therapy. Methods and Materials: Kernel density estimation was used to estimate the individual probability density functions from each of the 141 rectum differential dose-volume histograms. Functional principal component analysis was performed on the estimated probability density functions to explore the variation modes in the dose distribution. The functional principalmore » components were then tested for association with RB using logistic regression adapted to functional covariates (FLR). For comparison, 3 other normal tissue complication probability models were considered: the Lyman-Kutcher-Burman model, logistic model based on standard dosimetric parameters (LM), and logistic model based on multivariate principal component analysis (PCA). Results: The incidence rate of grade ≥2 RB was 14%. V{sub 65Gy} was the most predictive factor for the LM (P=.058). The best fit for the Lyman-Kutcher-Burman model was obtained with n=0.12, m = 0.17, and TD50 = 72.6 Gy. In PCA and FLR, the components that describe the interdependence between the relative volumes exposed at intermediate and high doses were the most correlated to the complication. The FLR parameter function leads to a better understanding of the volume effect by including the treatment specificity in the delivered mechanistic information. For RB grade ≥2, patients with advanced age are significantly at risk (odds ratio, 1.123; 95% confidence interval, 1.03-1.22), and the fits of the LM, PCA, and functional principal component analysis models are significantly improved by including this clinical factor. Conclusion: Functional data analysis provides an attractive method for flexibly estimating the dose-volume effect for normal tissues in external radiation therapy.« less

Molecular dynamics in principal component space.

PubMed

Michielssens, Servaas; van Erp, Titus S; Kutzner, Carsten; Ceulemans, Arnout; de Groot, Bert L

2012-07-26

A molecular dynamics algorithm in principal component space is presented. It is demonstrated that sampling can be improved without changing the ensemble by assigning masses to the principal components proportional to the inverse square root of the eigenvalues. The setup of the simulation requires no prior knowledge of the system; a short initial MD simulation to extract the eigenvectors and eigenvalues suffices. Independent measures indicated a 6-7 times faster sampling compared to a regular molecular dynamics simulation.

Optimized principal component analysis on coronagraphic images of the fomalhaut system

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

Meshkat, Tiffany; Kenworthy, Matthew A.; Quanz, Sascha P.

We present the results of a study to optimize the principal component analysis (PCA) algorithm for planet detection, a new algorithm complementing angular differential imaging and locally optimized combination of images (LOCI) for increasing the contrast achievable next to a bright star. The stellar point spread function (PSF) is constructed by removing linear combinations of principal components, allowing the flux from an extrasolar planet to shine through. The number of principal components used determines how well the stellar PSF is globally modeled. Using more principal components may decrease the number of speckles in the final image, but also increases themore » background noise. We apply PCA to Fomalhaut Very Large Telescope NaCo images acquired at 4.05 μm with an apodized phase plate. We do not detect any companions, with a model dependent upper mass limit of 13-18 M {sub Jup} from 4-10 AU. PCA achieves greater sensitivity than the LOCI algorithm for the Fomalhaut coronagraphic data by up to 1 mag. We make several adaptations to the PCA code and determine which of these prove the most effective at maximizing the signal-to-noise from a planet very close to its parent star. We demonstrate that optimizing the number of principal components used in PCA proves most effective for pulling out a planet signal.« less

[Soil microbial community structure in Picea asperata plantations with different ages in subalpine of western Sichuan, Southwest China.

PubMed

Luo, Da; Liu, Shun; Shi, Zuo Min; Feng, Qiu Hong; Liu, Qian Li; Zhang, Li; Huang, Quan; He, Jian She

2017-02-01

The effects of four Picea asperata plantations with different ages (50-, 38-, 27- and 20-year-old), in subalpine of western Sichuan, on the characteristics of soil microbial diversity and microbial community structure were studied by the method of phospholipid fatty acid (PLFA) profiles. The results showed that, with the increase of age, the contents of soil organic carbon and total nitrogen gradually improved, while Shannon's diversity index and Pielou's evenness index of soil microorganisms increased at first and then decreased. The amounts of microbial total PLFAs, bacterial PLFAs, fungal PLFAs, actinobacterial PLFAs, and arbuscular mycorrhizal fungal (AMF) PLFAs in soils consistently increased with increasing age. The principal component analysis (PCA) indicated that the soil microbial communities in different plantations were structurally distinct from each other. The first principal component (PC1) and the second principal component (PC2) together accounted for 66.8% of total variation of the soil microbial community structure. The redundancy analysis (RDA) of soil microbial community structure and environmental factors showed that soil organic carbon, total nitrogen, total potassium, and fine root mass were the key determinants influencing the microbial community structure. Our study suggested that, with the extension of artificialafforestation time, the soil fertility and microbial biomass were enhanced, and the restoration processes of forest ecosystem were stable.

[Confirmative study of a French version of the Exercise Dependence Scale-revised with a French population].

PubMed

Allegre, B; Therme, P

2008-10-01

Since the first writings on excessive exercise, there has been an increased interest in exercise dependence. One of the major consequences of this increased interest has been the development of several definitions and measures of exercise dependence. The work of Veale [Does primary exercise dependence really exist? In: Annet J, Cripps B, Steinberg H, editors. Exercise addiction: Motivation for participation in sport and exercise.Leicester, UK: Br Psychol Soc; 1995. p. 1-5.] provides an advance for the definition and measure of exercise dependence. These studies have adapted the DSM-IV criteria for substance dependence to measure exercise dependence. The Exercise Dependence Scale-Revised is based on these diagnostic criteria, which are: tolerance; withdrawal effects; intention effect; lack of control; time; reductions in other activities; continuance. Confirmatory factor analyses of EDS-R provided support to present a measurement model (21 items loaded in seven factors) of EDS-R (Comparative Fit Index=0.97; Root mean Square Error of Approximation=0.05; Tucker-Lewis Index=0.96). The aim of this study was to examine the psychometric properties of a French version of the EDS-R [Factorial validity and psychometric examination of the exercise dependence scale-revised. Meas Phys Educ Exerc Sci 2004;8(4):183-201.] to test the stability of the seven-factor model of the original version with a French population. A total of 516 half-marathoners ranged in age from 17 to 74 years old (Mean age=39.02 years, ET=10.64), with 402 men (77.9%) and 114 women (22.1%) participated in the study. The principal component analysis results in a six-factor structure, which accounts for 68.60% of the total variance. Because principal component analysis presents a six-factor structure differing from the original seven-factor structure, two models were tested, using confirmatory factor analysis. The first model is the seven-factor model of the original version of the EDS-R and the second is the model produced by the principal component analysis. The results of confirmatory factor analysis presented the original model (with a seven-factor structure) as a good model and fit indices were good (X(2)/ddl=2.89, Root Mean Square Error of Approximation (RMSEA)=0.061, Expected Cross Validation Index (ECVI)=1.20, Goodness-of-Fit Index (GFI)=0.92, Comparative Fit Index (CFI)=0.94, Standardized Root Mean Square (SRMS)=0.048). These results showed that the French version of EDS-R has an identical factor structure to the original. Therefore, the French version of EDS-R was an acceptable scale to measure exercise dependence and can be used on a French population.

[A study of Boletus bicolor from different areas using Fourier transform infrared spectrometry].

PubMed

Zhou, Zai-Jin; Liu, Gang; Ren, Xian-Pei

2010-04-01

It is hard to differentiate the same species of wild growing mushrooms from different areas by macromorphological features. In this paper, Fourier transform infrared (FTIR) spectroscopy combined with principal component analysis was used to identify 58 samples of boletus bicolor from five different areas. Based on the fingerprint infrared spectrum of boletus bicolor samples, principal component analysis was conducted on 58 boletus bicolor spectra in the range of 1 350-750 cm(-1) using the statistical software SPSS 13.0. According to the result, the accumulated contributing ratio of the first three principal components accounts for 88.87%. They included almost all the information of samples. The two-dimensional projection plot using first and second principal component is a satisfactory clustering effect for the classification and discrimination of boletus bicolor. All boletus bicolor samples were divided into five groups with a classification accuracy of 98.3%. The study demonstrated that wild growing boletus bicolor at species level from different areas can be identified by FTIR spectra combined with principal components analysis.

SPSS and SAS programs for determining the number of components using parallel analysis and velicer's MAP test.

PubMed

O'Connor, B P

2000-08-01

Popular statistical software packages do not have the proper procedures for determining the number of components in factor and principal components analyses. Parallel analysis and Velicer's minimum average partial (MAP) test are validated procedures, recommended widely by statisticians. However, many researchers continue to use alternative, simpler, but flawed procedures, such as the eigenvalues-greater-than-one rule. Use of the proper procedures might be increased if these procedures could be conducted within familiar software environments. This paper describes brief and efficient programs for using SPSS and SAS to conduct parallel analyses and the MAP test.

Reliability, factor analysis and internal consistency calculation of the Insomnia Severity Index (ISI) in French and in English among Lebanese adolescents.

PubMed

Chahoud, M; Chahine, R; Salameh, P; Sauleau, E A

2017-06-01

Our goal is to validate and to verify the reliability of the French and English versions of the Insomnia Severity Index (ISI) in Lebanese adolescents. A cross-sectional study was implemented. 104 Lebanese students aged between 14 and 19 years participated in the study. The English version of the questionnaire was distributed to English-speaking students and the French version was administered to French-speaking students. A scale (1 to 7 with 1 = very well understood and 7 = not at all) was used to identify the level of the students' understanding of each instruction, question and answer of the ISI. The scale's structural validity was assessed. The factor structure of ISI was evaluated by principal component analysis. The internal consistency of this scale was evaluated by Cronbach's alpha. To assess test-retest reliability the intraclass correlation coefficient (ICC) was used. The principal component analysis confirmed the presence of a two-component factor structure in the English version and a three-component factor structure in the French version with eigenvalues > 1. The English version of the ISI had an excellent internal consistency (α = 0.90), while the French version had a good internal consistency (α = 0.70). The ICC presented an excellent agreement in the French version (ICC = 0.914, CI = 0.856-0.949) and a good agreement in the English one (ICC = 0.762, CI = 0.481-890). The Bland-Altman plots of the two versions of the ISI showed that the responses over two weeks' were comparable and very few outliers were detected. The results of our analyses reveal that both English and French versions of the ISI scale have good internal consistency and are reproducible and reliable. Therefore, it can be used to assess the prevalence of insomnia in Lebanese adolescents.

Tool development to assess the work related neck and upper limb musculoskeletal disorders among female garment workers in Sri-Lanka.

PubMed

Amarasinghe, Nirmalie Champika; De AlwisSenevirathne, Rohini

2016-10-17

Musculoskeletal disorders (MSDs) have been identified as a predisposing factor for lesser productivity, but no validated tool has been developed to assess them in the Sri- Lankan context. To develop a validated tool to assess the neck and upper limb MSDs. It comprises three components: item selections, item reduction using principal component analysis, and validation. A tentative self-administrated questionnaire was developed, translated, and pre-tested. Four important domains - neck, shoulder, elbow and wrist - were identified through principal component analysis. Prevalence of any MSDs was 38.1% and prevalence of neck, shoulder, elbow and wrist MSDs are 12.85%, 13.71%, 12%, 13.71% respectively. Content and criterion validity of the tool was assessed. Separate ROC curves were produced and sensitivity and specificity of neck (83.1%, 71.7%), shoulder (97.6%, 91.9%), elbow (98.2%, 87.2%), and wrist (97.6%, 94.9%) was determined. Cronbach's Alpha and correlation coefficient was above 0.7. The tool has high sensitivity, specificity, internal consistency, and test re-test reliability.

Temperament and problem solving in a population of adolescent guide dogs.

PubMed

Bray, Emily E; Sammel, Mary D; Seyfarth, Robert M; Serpell, James A; Cheney, Dorothy L

2017-09-01

It is often assumed that measures of temperament within individuals are more correlated to one another than to measures of problem solving. However, the exact relationship between temperament and problem-solving tasks remains unclear because large-scale studies have typically focused on each independently. To explore this relationship, we tested 119 prospective adolescent guide dogs on a battery of 11 temperament and problem-solving tasks. We then summarized the data using both confirmatory factor analysis and exploratory principal components analysis. Results of confirmatory analysis revealed that a priori separation of tests as measuring either temperament or problem solving led to weak results, poor model fit, some construct validity, and no predictive validity. In contrast, results of exploratory analysis were best summarized by principal components that mixed temperament and problem-solving traits. These components had both construct and predictive validity (i.e., association with success in the guide dog training program). We conclude that there is complex interplay between tasks of "temperament" and "problem solving" and that the study of both together will be more informative than approaches that consider either in isolation.

Daytime Symptoms in Primary Insomnia: A Prospective Analysis Using Ecological Momentary Assessment

PubMed Central

Buysse, Daniel J.; Thompson, Wesley; Scott, John; Franzen, Peter L.; Germain, Anne; Hall, Martica L.; Moul, Douglas E.; Nofzinger, Eric A.; Kupfer, David J.

2007-01-01

Objectives To prospectively characterize and compare daytime symptoms in primary insomnia (PI) and good sleeper control (GSC) subjects using ecological momentary assessment; to examine relationships between daytime symptom factors, retrospective psychological and sleep reports, and concurrent sleep diary reports. Methods Subjects included 47 PI and 18 GSC. Retrospective self-reports of daytime and sleep symptoms were collected. Daytime symptoms and sleep diary information were then collected for one week on hand-held computers. The Daytime Insomnia Symptom Scale (DISS) consisted of 19 visual analog scales completed four times per day. Factors for the DISS were derived using functional principal components analysis. Nonparametric tests were used to contrast DISS, retrospective symptom ratings, and sleep diary results in PI and GSC subjects, and to examine relationships among them. Results Four principal components were identified for the DISS: Alert Cognition, Negative Mood, Positive Mood, and Sleepiness/Fatigue. PI scored significantly worse than GSC on all four factors (p < .0003 for each). Among PI subjects DISS scales and retrospective psychological symptoms were related to each other in plausible ways. DISS factors were also related to self-report measures of sleep, whereas retrospective psychological symptom measures were not. Conclusions Daytime symptom factors of alertness, positive and negative mood, and sleepiness/fatigue, collected with ecological momentary assessment, showed impairment in PI versus GSC. DISS factors showed stronger relationships to retrospective sleep symptoms and concurrent sleep diary reports than retrospective psychological symptoms. The diurnal pattern of symptoms may inform studies of the pathophysiology and treatment outcome of insomnia. PMID:17368098

How multi segmental patterns deviate in spastic diplegia from typical developed.

PubMed

Zago, Matteo; Sforza, Chiarella; Bona, Alessia; Cimolin, Veronica; Costici, Pier Francesco; Condoluci, Claudia; Galli, Manuela

2017-10-01

The relationship between gait features and coordination in children with Cerebral Palsy is not sufficiently analyzed yet. Principal Component Analysis can help in understanding motion patterns decomposing movement into its fundamental components (Principal Movements). This study aims at quantitatively characterizing the functional connections between multi-joint gait patterns in Cerebral Palsy. 65 children with spastic diplegia aged 10.6 (SD 3.7) years participated in standardized gait analysis trials; 31 typically developing adolescents aged 13.6 (4.4) years were also tested. To determine if posture affects gait patterns, patients were split into Crouch and knee Hyperextension group according to knee flexion angle at standing. 3D coordinates of hips, knees, ankles, metatarsal joints, pelvis and shoulders were submitted to Principal Component Analysis. Four Principal Movements accounted for 99% of global variance; components 1-3 explained major sagittal patterns, components 4-5 referred to movements on frontal plane and component 6 to additional movement refinements. Dimensionality was higher in patients than in controls (p<0.01), and the Crouch group significantly differed from controls in the application of components 1 and 4-6 (p<0.05), while the knee Hyperextension group in components 1-2 and 5 (p<0.05). Compensatory strategies of children with Cerebral Palsy (interactions between main and secondary movement patterns), were objectively determined. Principal Movements can reduce the effort in interpreting gait reports, providing an immediate and quantitative picture of the connections between movement components. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Swedish P-CAT: modification and exploration of psychometric properties of two different versions.

PubMed

Selan, Denis; Jakobsson, Ulf; Condelius, Anna

2017-09-01

The aim of this study was to further investigate the psychometric properties (with focus on construct validity and scale function) of the Swedish version of the Person-centred Care Assessment Tool (P-CAT) in a sample consisting of staff working in elderly care units (N = 142). The aim was also to further develop and psychometrically test a modified, noncontext-specific version of the instrument (mP-CAT) in a sample consisting of staff working in primary health care or within home care for older people (N = 182). Principal component analysis with varimax rotation initially suggested a three-factor solution for the P-CAT, explaining 55.96% of variance. Item 13 solely represented one factor wherefore this solution was rejected. A final 2-factor solution, without item 13, had a cumulative explained variance of 50.03%. All communalities were satisfactory (>0.3), and alpha values for both first factor (items 1-6, 11) and second factor (items 7-10, 12) were found to be acceptable. Principal component analysis with varimax rotation suggested a final 2-factor solution for the mP-CAT explaining 46.15% of the total variance with communalities ranging from 0.263 to 0.712. Cronbach's α for both factors was found to be acceptable (>0.7). This study suggests a 2-factor structure for the P-CAT and an exclusion of item 13. The results indicated that the modified noncontext-specific version, mP-CAT, seems to be a valid measure. Further psychometric testing of the mP-CAT is however needed in order to establish the instrument's validity and reliability in various contexts. © 2016 Nordic College of Caring Science.

Principal component for metabolic syndrome risk maps to chromosome 4p in Mexican Americans: the San Antonio Family Heart Study.

PubMed

Cai, Guowen; Cole, Shelley A; Freeland-Graves, Jeanne H; MacCluer, Jean W; Blangero, John; Comuzzie, Anthony G

2004-10-01

Metabolic syndrome refers to the clustering of disease conditions such as insulin resistance, hyperinsulinemia, dyslipidemia, hypertension, and obesity. To explore the genetic predispositions of this complex syndrome, we conducted a principal components analysis using data on 14 phenotypes related to the risk of developing metabolic syndrome. The subjects were 566 nondiabetic Mexican Americans, distributed in 41 extended families from the San Antonio Family Heart Study. The factor scores obtained from these 14 phenotypes were used in multipoint linkage analysis using SOLAR. Factors were identified that accounted for 73% of the total variance of the original variables: body size-adiposity, insulin-glucose, blood pressure, and lipid levels. Each factor exhibited evidence for either significant or suggestive linkage involving four factor-specific chromosomal regions relating to chromosomes 1, 3, 4, and 6. Significant evidence for linkage of the lipid factor was found on chromosome 4 near marker D4S403 (LOD = 3.52), where the cholecystokinin A receptor (CCKAR) and ADP-ribosyl cyclase 1 (CD38) genes are located. Suggestive evidence for linkage of the body size-adiposity factor to chromosome 1 near marker D1S1597 (LOD = 2.53) in the region containing the nuclear receptor subfamily 0, group B, member 2 gene (NROB2) also was observed. The insulin-glucose and blood pressure factors were linked suggestively to regions on chromosome 3 near marker D3S1595 (LOD = 2.20) and on chromosome 6 near marker D6S 1031 (LOD = 2.08), respectively. In summary, our findings suggest that the factor structures for the risk of metabolic syndrome are influenced by multiple distinct genes across the genome.

Principal Components Analysis of Triaxial Vibration Data From Helicopter Transmissions

NASA Technical Reports Server (NTRS)

Tumer, Irem Y.; Huff, Edward M.

2001-01-01

Research on the nature of the vibration data collected from helicopter transmissions during flight experiments has led to several crucial observations believed to be responsible for the high rates of false alarms and missed detections in aircraft vibration monitoring systems. This work focuses on one such finding, namely, the need to consider additional sources of information about system vibrations. In this light, helicopter transmission vibration data, collected using triaxial accelerometers, were explored in three different directions, analyzed for content, and then combined using Principal Components Analysis (PCA) to analyze changes in directionality. In this paper, the PCA transformation is applied to 176 test conditions/data sets collected from an OH58C helicopter to derive the overall experiment-wide covariance matrix and its principal eigenvectors. The experiment-wide eigenvectors. are then projected onto the individual test conditions to evaluate changes and similarities in their directionality based on the various experimental factors. The paper will present the foundations of the proposed approach, addressing the question of whether experiment-wide eigenvectors accurately model the vibration modes in individual test conditions. The results will further determine the value of using directionality and triaxial accelerometers for vibration monitoring and anomaly detection.

Hydrochemical and multivariate analysis of groundwater quality in the northwest of Sinai, Egypt.

PubMed

El-Shahat, M F; Sadek, M A; Salem, W M; Embaby, A A; Mohamed, F A

2017-08-01

The northwestern coast of Sinai is home to many economic activities and development programs, thus evaluation of the potentiality and vulnerability of water resources is important. The present work has been conducted on the groundwater resources of this area for describing the major features of groundwater quality and the principal factors that control salinity evolution. The major ionic content of 39 groundwater samples collected from the Quaternary aquifer shows high coefficients of variation reflecting asymmetry of aquifer recharge. The groundwater samples have been classified into four clusters (using hierarchical cluster analysis), these match the variety of total dissolvable solids, water types and ionic orders. The principal component analysis combined the ionic parameters of the studied groundwater samples into two principal components. The first represents about 56% of the whole sample variance reflecting a salinization due to evaporation, leaching, dissolution of marine salts and/or seawater intrusion. The second represents about 15.8% reflecting dilution with rain water and the El-Salam Canal. Most groundwater samples were not suitable for human consumption and about 41% are suitable for irrigation. However, all groundwater samples are suitable for cattle, about 69% and 15% are suitable for horses and poultry, respectively.

A reduction in ag/residential signature conflict using principal components analysis of LANDSAT temporal data

NASA Technical Reports Server (NTRS)

Williams, D. L.; Borden, F. Y.

1977-01-01

Methods to accurately delineate the types of land cover in the urban-rural transition zone of metropolitan areas were considered. The application of principal components analysis to multidate LANDSAT imagery was investigated as a means of reducing the overlap between residential and agricultural spectral signatures. The statistical concepts of principal components analysis were discussed, as well as the results of this analysis when applied to multidate LANDSAT imagery of the Washington, D.C. metropolitan area.

Factors supporting dentist leaders' retention in leadership.

PubMed

Tuononen, T; Lammintakanen, J; Suominen, A L

2017-12-01

The aim was to study factors associated with staying in a dentist leadership position. We used an electronic questionnaire to gather data from 156 current or former Finnish dentist leaders in 2014. Principal component analysis categorized statements regarding time usage and opportunities in managerial work into five main components. Associations between these main component scores and the tendency to stay as a leader were analyzed with logistic regression. Out of the five main components, two were significantly associated with staying as a leader: 'career intentions', which represented intent to continue or to leave the leadership position; and 'work time control opportunities', which represented how leaders could control their own work time. Other factors that supported staying were leadership education, more work time available for leadership work, and lower age. The main component 'work pressure' decreased, although not significantly, the odds of continuing; it included lack of leadership work time, and pressure from superiors or subordinates. Leaders have important roles in health care, ensuring everyday operations as well as developing their organizations to meet future challenges. Knowledge of these supporting factors will enable dentist leaders and their organizations to improve working conditions in order to recruit and retain motivated and competent persons. In addition, well-designed education is important to inspire and encourage future leaders. Copyright© 2017 Dennis Barber Ltd.

Constrained Principal Component Analysis: Various Applications.

ERIC Educational Resources Information Center

Hunter, Michael; Takane, Yoshio

2002-01-01

Provides example applications of constrained principal component analysis (CPCA) that illustrate the method on a variety of contexts common to psychological research. Two new analyses, decompositions into finer components and fitting higher order structures, are presented, followed by an illustration of CPCA on contingency tables and the CPCA of…

Psychometric evaluation of the canine brief pain inventory in a Swedish sample of dogs with pain related to osteoarthritis.

PubMed

Essner, Ann; Zetterberg, Lena; Hellström, Karin; Gustås, Pia; Högberg, Hans; Sjöström, Rita

2017-07-01

To evaluate intervention, implement evidence-based practice and enhance the welfare of dogs with naturally occurring osteoarthritis (OA), access to valid, reliable and clinically relevant outcome measures is crucial for researchers, veterinarians and rehabilitation practitioners. The objectives of the present study were to translate and evaluate psychometric properties, in terms of internal consistency and construct validity, of the owner-reported measure canine brief pain inventory (CBPI) in a Swedish sample of dogs with pain related to OA. Twenty-one owners of clinically sound dogs and 58 owners of dogs with pain related to OA were included in this observational and cross-sectional study. After being translated according to the guidelines for patient-reported outcome measures, the CBPI was completed by the canine owners. Construct validity was assessed by confirmatory factor analysis, by repeating the principal component analysis and by assessing for differences between clinically sound dogs and dogs with pain related to OA. Internal consistency was estimated by Cronbach's α. Confirmatory factor analysis was not able to confirm the factor-structure models tested in our sample. Principal component analysis showed a two-component structure, pain severity and pain interference of function. Two components accounted for 76.8% of the total variance, suggesting an acceptable fit of a two-component structure. The ratings from the clinically sound dogs differed from OA dogs and showed significantly lower CBPI total sum. Cronbach's α was 0.94 for the total CBPI, 0.91 for the pain severity and 0.91 for the pain interference of function. The results indicate that the translated version of the CBPI is valid for use in the Swedish language. The findings suggest satisfying psychometric properties in terms of high internal consistencies and ability to discriminate clinically sound dogs from OA dogs. However, based on the confirmatory factor analysis, the original factor structure in the CBPI is not ideally suited to measure pain related to OA in our sample and the hypothesis of the presented two-factor structure was rejected. Further research needs to be conducted to determine whether the original psychometric results from CBPI can be replicated across different target groups and particularly with larger sample size.

Assessment of Disgust Sensitivity in Children with an Age-Downward Version of the Disgust Emotion Scale

ERIC Educational Resources Information Center

Muris, Peter; Huijding, Jorg; Mayer, Birgit; Langkamp, Marit; Reyhan, Ela; Olatunji, Bunmi

2012-01-01

The present study examined the psychometric properties of the Disgust Emotion Scale for Children (DES-C). Principal components analysis of the DES-C data revealed five factors reflecting disgust toward (a) rotting foods, (b) injection and blood, (c) odors, (d) mutilation and death, and (e) animals, which were largely in keeping with the intended…

Genetic variation and seed transfer guidelines for ponderosa pine in central Oregon.

Treesearch

Frank C. Sorensen

1994-01-01

Adaptive genetic variation in seed and seedling traits for ponderosa pine from the east slopes of the Cascade Range in Oregon was analyzed by using 307 families from 227 locations. Factor scores from three principal components based on seed and seedling traits were related by multiple regression to latitude, distance from the Cascade crest, elevation, slope, and...

IQ, Skin Color, Crime, HIV/AIDS, and Income in 50 U.S. States

ERIC Educational Resources Information Center

Templer, Donald I.; Rushton, J. Philippe

2011-01-01

In 50 U.S. states, we found a positive manifold across 11 measures including IQ, skin color, birth rate, infant mortality, life expectancy, HIV/AIDS, violent crime, and state income with the first principal component accounting for 33% of the variance (median factor loading = 0.34). The correlation with a composite of total violent crime was…

Development and Validation of an Exploratory Measure to Assess Student Coping: The Student Coping Scale

ERIC Educational Resources Information Center

Boujut, Emile

2013-01-01

Students is a very specific population according to their manner to cope with stress. A coping questionnaire for students was developed and administered to 1100 French students at the beginning of the term (T1). Principal Component Analysis of responses, followed by varimax rotations, yielded three factors accounting for 50.5% of the total…

Screening ADHD Problems in the Sports Behavior Checklist: Factor Structure, Convergent and Divergent Validity, and Group Differences

ERIC Educational Resources Information Center

Clendenin, Aaron A.; Businelle, Michael S.; Kelley, Mary Lou

2005-01-01

The Sports Behavior Checklist (SBC) is subjected to a principal components analysis, and subscales are correlated with subscales of the Conners' Revised Parent Form and the Social Skills Rating System. Both of these analyses are conducted to determine the construct validity of the instrument. A subsample of lower socioeconomic status individuals…

Principal component analysis on molecular descriptors as an alternative point of view in the search of new Hsp90 inhibitors.

PubMed

Lauria, Antonino; Ippolito, Mario; Almerico, Anna Maria

2009-10-01

Inhibiting a protein that regulates multiple signal transduction pathways in cancer cells is an attractive goal for cancer therapy. Heat shock protein 90 (Hsp90) is one of the most promising molecular targets for such an approach. In fact, Hsp90 is a ubiquitous molecular chaperone protein that is involved in folding, activating and assembling of many key mediators of signal transduction, cellular growth, differentiation, stress-response and apoptothic pathways. With the aim to analyze which molecular descriptors have the higher importance in the binding interactions of these classes, we first performed molecular docking experiments on the 187 Hsp90 inhibitors included in the BindingDB, a public database of measured binding affinities. Further, for each frozen conformation obtained from the docking, a set of 250 molecular descriptors was calculated, and the resulting Structure/Descriptors matrix was submitted to Principal Component Analysis. From the factor scores it emerged a good clusterization among similar compounds both in terms of structural class and activity spectrum, while examination of the loadings of the first two factors also allowed to study the classes of descriptors which mainly contribute to each one.

A model of objective weighting for EIA.

PubMed

Ying, L G; Liu, Y C

1995-06-01

In spite of progress achieved in the research of environmental impact assessment (EIA), the problem of weight distribution for a set of parameters has not as yet, been properly solved. This paper presents an approach of objective weighting by using a procedure of P ij principal component-factor analysis (P ij PCFA), which suits specifically those parameters measured directly by physical scales. The P ij PCFA weighting procedure reforms the conventional weighting practice in two aspects: first, the expert subjective judgment is replaced by the standardized measure P ij as the original input of weight processing and, secondly, the principal component-factor analysis is introduced to approach the environmental parameters for their respective contributions to the totality of the regional ecosystem. Not only is the P ij PCFA weighting logical in theoretical reasoning, it also suits practically all levels of professional routines in natural environmental assessment and impact analysis. Having been assured of objectivity and accuracy in the EIA case study of the Chuansha County in Shanghai, China, the P ij PCFA weighting procedure has the potential to be applied in other geographical fields that need assigning weights to parameters that are measured by physical scales.

Characterizing Variability of Modular Brain Connectivity with Constrained Principal Component Analysis

PubMed Central

Hirayama, Jun-ichiro; Hyvärinen, Aapo; Kiviniemi, Vesa; Kawanabe, Motoaki; Yamashita, Okito

2016-01-01

Characterizing the variability of resting-state functional brain connectivity across subjects and/or over time has recently attracted much attention. Principal component analysis (PCA) serves as a fundamental statistical technique for such analyses. However, performing PCA on high-dimensional connectivity matrices yields complicated “eigenconnectivity” patterns, for which systematic interpretation is a challenging issue. Here, we overcome this issue with a novel constrained PCA method for connectivity matrices by extending the idea of the previously proposed orthogonal connectivity factorization method. Our new method, modular connectivity factorization (MCF), explicitly introduces the modularity of brain networks as a parametric constraint on eigenconnectivity matrices. In particular, MCF analyzes the variability in both intra- and inter-module connectivities, simultaneously finding network modules in a principled, data-driven manner. The parametric constraint provides a compact module-based visualization scheme with which the result can be intuitively interpreted. We develop an optimization algorithm to solve the constrained PCA problem and validate our method in simulation studies and with a resting-state functional connectivity MRI dataset of 986 subjects. The results show that the proposed MCF method successfully reveals the underlying modular eigenconnectivity patterns in more general situations and is a promising alternative to existing methods. PMID:28002474

Rapid fingerprinting of spilled petroleum products using fluorescence spectroscopy coupled with parallel factor and principal component analysis.

PubMed

Mirnaghi, Fatemeh S; Soucy, Nicholas; Hollebone, Bruce P; Brown, Carl E

2018-05-19

The characterization of spilled petroleum products in an oil spill is necessary for identifying the spill source, selection of clean-up strategies, and evaluating potential environmental and ecological impacts. Existing standard methods for the chemical characterization of spilled oils are time-consuming due to the lengthy sample preparation for analysis. The main objective of this study is the development of a rapid screening method for the fingerprinting of spilled petroleum products using excitation/emission matrix (EEM) fluorescence spectroscopy, thereby delivering a preliminary evaluation of the petroleum products within hours after a spill. In addition, the developed model can be used for monitoring the changes of aromatic compositions of known spilled oils over time. This study involves establishing a fingerprinting model based on the composition of polycyclic and heterocyclic aromatic hydrocarbons (PAH and HAHs, respectively) of 130 petroleum products at different states of evaporative weathering. The screening model was developed using parallel factor analysis (PARAFAC) of a large EEM dataset. The significant fluorescing components for each sample class were determined. After which, through principal component analysis (PCA), the variation of scores of their modeled factors was discriminated based on the different classes of petroleum products. This model was then validated using gas chromatography-mass spectrometry (GC-MS) analysis. The rapid fingerprinting and the identification of unknown and new spilled oils occurs through matching the spilled product with the products of the developed model. Finally, it was shown that HAH compounds in asphaltene and resins contribute to ≥4-ring PAHs compounds in petroleum products. Copyright © 2018. Published by Elsevier Ltd.

Development of an instrument to understand the child protective services decision-making process, with a focus on placement decisions.

PubMed

Dettlaff, Alan J; Christopher Graham, J; Holzman, Jesse; Baumann, Donald J; Fluke, John D

2015-11-01

When children come to the attention of the child welfare system, they become involved in a decision-making process in which decisions are made that have a significant effect on their future and well-being. The decision to remove children from their families is particularly complex; yet surprisingly little is understood about this decision-making process. This paper presents the results of a study to develop an instrument to explore, at the caseworker level, the context of the removal decision, with the objective of understanding the influence of the individual and organizational factors on this decision, drawing from the Decision Making Ecology as the underlying rationale for obtaining the measures. The instrument was based on the development of decision-making scales used in prior decision-making studies and administered to child protection caseworkers in several states. Analyses included reliability analyses, principal components analyses, and inter-correlations among the resulting scales. For one scale regarding removal decisions, a principal components analysis resulted in the extraction of two components, jointly identified as caseworkers' decision-making orientation, described as (1) an internal reference to decision-making and (2) an external reference to decision-making. Reliability analyses demonstrated acceptable to high internal consistency for 9 of the 11 scales. Full details of the reliability analyses, principal components analyses, and inter-correlations among the seven scales are discussed, along with implications for practice and the utility of this instrument to support the understanding of decision-making in child welfare. Copyright © 2015 Elsevier Ltd. All rights reserved.

A measure for objects clustering in principal component analysis biplot: A case study in inter-city buses maintenance cost data

NASA Astrophysics Data System (ADS)

Ginanjar, Irlandia; Pasaribu, Udjianna S.; Indratno, Sapto W.

2017-03-01

This article presents the application of the principal component analysis (PCA) biplot for the needs of data mining. This article aims to simplify and objectify the methods for objects clustering in PCA biplot. The novelty of this paper is to get a measure that can be used to objectify the objects clustering in PCA biplot. Orthonormal eigenvectors, which are the coefficients of a principal component model representing an association between principal components and initial variables. The existence of the association is a valid ground to objects clustering based on principal axes value, thus if m principal axes used in the PCA, then the objects can be classified into 2m clusters. The inter-city buses are clustered based on maintenance costs data by using two principal axes PCA biplot. The buses are clustered into four groups. The first group is the buses with high maintenance costs, especially for lube, and brake canvass. The second group is the buses with high maintenance costs, especially for tire, and filter. The third group is the buses with low maintenance costs, especially for lube, and brake canvass. The fourth group is buses with low maintenance costs, especially for tire, and filter.

Survey to Identify Substandard and Falsified Tablets in Several Asian Countries with Pharmacopeial Quality Control Tests and Principal Component Analysis of Handheld Raman Spectroscopy.

PubMed

Kakio, Tomoko; Nagase, Hitomi; Takaoka, Takashi; Yoshida, Naoko; Hirakawa, Junichi; Macha, Susan; Hiroshima, Takashi; Ikeda, Yukihiro; Tsuboi, Hirohito; Kimura, Kazuko

2018-06-01

The World Health Organization has warned that substandard and falsified medical products (SFs) can harm patients and fail to treat the diseases for which they were intended, and they affect every region of the world, leading to loss of confidence in medicines, health-care providers, and health systems. Therefore, development of analytical procedures to detect SFs is extremely important. In this study, we investigated the quality of pharmaceutical tablets containing the antihypertensive candesartan cilexetil, collected in China, Indonesia, Japan, and Myanmar, using the Japanese pharmacopeial analytical procedures for quality control, together with principal component analysis (PCA) of Raman spectrum obtained with handheld Raman spectrometer. Some samples showed delayed dissolution and failed to meet the pharmacopeial specification, whereas others failed the assay test. These products appeared to be substandard. Principal component analysis showed that all Raman spectra could be explained in terms of two components: the amount of the active pharmaceutical ingredient and the kinds of excipients. Principal component analysis score plot indicated one substandard, and the falsified tablets have similar principal components in Raman spectra, in contrast to authentic products. The locations of samples within the PCA score plot varied according to the source country, suggesting that manufacturers in different countries use different excipients. Our results indicate that the handheld Raman device will be useful for detection of SFs in the field. Principal component analysis of that Raman data clarify the difference in chemical properties between good quality products and SFs that circulate in the Asian market.

Principal component analysis and the locus of the Fréchet mean in the space of phylogenetic trees.

PubMed

Nye, Tom M W; Tang, Xiaoxian; Weyenberg, Grady; Yoshida, Ruriko

2017-12-01

Evolutionary relationships are represented by phylogenetic trees, and a phylogenetic analysis of gene sequences typically produces a collection of these trees, one for each gene in the analysis. Analysis of samples of trees is difficult due to the multi-dimensionality of the space of possible trees. In Euclidean spaces, principal component analysis is a popular method of reducing high-dimensional data to a low-dimensional representation that preserves much of the sample's structure. However, the space of all phylogenetic trees on a fixed set of species does not form a Euclidean vector space, and methods adapted to tree space are needed. Previous work introduced the notion of a principal geodesic in this space, analogous to the first principal component. Here we propose a geometric object for tree space similar to the [Formula: see text]th principal component in Euclidean space: the locus of the weighted Fréchet mean of [Formula: see text] vertex trees when the weights vary over the [Formula: see text]-simplex. We establish some basic properties of these objects, in particular showing that they have dimension [Formula: see text], and propose algorithms for projection onto these surfaces and for finding the principal locus associated with a sample of trees. Simulation studies demonstrate that these algorithms perform well, and analyses of two datasets, containing Apicomplexa and African coelacanth genomes respectively, reveal important structure from the second principal components.

Fast and Accurate Radiative Transfer Calculations Using Principal Component Analysis for (Exo-)Planetary Retrieval Models

NASA Astrophysics Data System (ADS)

Kopparla, P.; Natraj, V.; Shia, R. L.; Spurr, R. J. D.; Crisp, D.; Yung, Y. L.

2015-12-01

Radiative transfer (RT) computations form the engine of atmospheric retrieval codes. However, full treatment of RT processes is computationally expensive, prompting usage of two-stream approximations in current exoplanetary atmospheric retrieval codes [Line et al., 2013]. Natraj et al. [2005, 2010] and Spurr and Natraj [2013] demonstrated the ability of a technique using principal component analysis (PCA) to speed up RT computations. In the PCA method for RT performance enhancement, empirical orthogonal functions are developed for binned sets of inherent optical properties that possess some redundancy; costly multiple-scattering RT calculations are only done for those few optical states corresponding to the most important principal components, and correction factors are applied to approximate radiation fields. Kopparla et al. [2015, in preparation] extended the PCA method to a broadband spectral region from the ultraviolet to the shortwave infrared (0.3-3 micron), accounting for major gas absorptions in this region. Here, we apply the PCA method to a some typical (exo-)planetary retrieval problems. Comparisons between the new model, called Universal Principal Component Analysis Radiative Transfer (UPCART) model, two-stream models and line-by-line RT models are performed, for spectral radiances, spectral fluxes and broadband fluxes. Each of these are calculated at the top of the atmosphere for several scenarios with varying aerosol types, extinction and scattering optical depth profiles, and stellar and viewing geometries. We demonstrate that very accurate radiance and flux estimates can be obtained, with better than 1% accuracy in all spectral regions and better than 0.1% in most cases, as compared to a numerically exact line-by-line RT model. The accuracy is enhanced when the results are convolved to typical instrument resolutions. The operational speed and accuracy of UPCART can be further improved by optimizing binning schemes and parallelizing the codes, work on which is under way.

Public knowledge and beliefs about depression among urban and rural Malays in Malaysia.

PubMed

Swami, Viren; Loo, Phik-Wern; Furnham, Adrian

2010-09-01

This study examined knowledge and beliefs about depression among Malaysian Malays varying in socioeconomic status. A total of 153 urban and 189 rural participants completed a questionnaire in which they had to identify two cases of depression and rate a series of items about the causes and best treatments for depression. Results showed that urban participants were more likely to use psychiatric labels ('depression') for the two vignettes, whereas rural participants tended to use more generic terms ('emotional stress'). Principal components analysis (PCA) showed that beliefs about the causes of depression factored into five components, of which stressful life events was most strongly endorsed by both groups. PCA of treatment items revealed four stable components, of which religious factors were most strongly endorsed. There were also a number of significant between-group differences in the endorsement of these factors (eta(p) (2) = .03-.11), with rural participants generally rating supernatural and religious factors more strongly than urban Malays. These results are discussed in relation to mental health literacy programmes in Malaysia.

Medical tourists: who goes and what motivates them?

PubMed

Gan, Lydia L; Frederick, James R

2013-01-01

This study relates consumers' attitudes toward medical tourism to a number of consumer characteristics, such as age, education, income, and insurance status. Principal components analysis of the attitudes of 289 consumers from various communities of North Carolina resulted in three attitude-related factors: economic, treatment-related, and travel-related. Major findings include: (a) the uninsured and low-income consumers are more sensitive to economic factors than the insured and the middle-income consumers; (b) the 51- to 64-year-olds are less motivated by economic factors than young adults; (c) surprisingly, the better one's health, the more one is motivated by treatment-related factors.

Figures of merit for present and future dark energy probes

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

Mortonson, Michael J.; Huterer, Dragan; Hu, Wayne

2010-09-15

We compare current and forecasted constraints on dynamical dark energy models from Type Ia supernovae and the cosmic microwave background using figures of merit based on the volume of the allowed dark energy parameter space. For a two-parameter dark energy equation of state that varies linearly with the scale factor, and assuming a flat universe, the area of the error ellipse can be reduced by a factor of {approx}10 relative to current constraints by future space-based supernova data and CMB measurements from the Planck satellite. If the dark energy equation of state is described by a more general basis ofmore » principal components, the expected improvement in volume-based figures of merit is much greater. While the forecasted precision for any single parameter is only a factor of 2-5 smaller than current uncertainties, the constraints on dark energy models bounded by -1{<=}w{<=}1 improve for approximately 6 independent dark energy parameters resulting in a reduction of the total allowed volume of principal component parameter space by a factor of {approx}100. Typical quintessence models can be adequately described by just 2-3 of these parameters even given the precision of future data, leading to a more modest but still significant improvement. In addition to advances in supernova and CMB data, percent-level measurement of absolute distance and/or the expansion rate is required to ensure that dark energy constraints remain robust to variations in spatial curvature.« less

Measuring Acceptance of Sleep Difficulties: The Development of the Sleep Problem Acceptance Questionnaire.

PubMed

Bothelius, Kristoffer; Jernelöv, Susanna; Fredrikson, Mats; McCracken, Lance M; Kaldo, Viktor

2015-11-01

Acceptance may be an important therapeutic process in sleep medicine, but valid psychometric instruments measuring acceptance related to sleep difficulties are lacking. The purpose of this study was to develop a measure of acceptance in insomnia, and to examine its factor structure as well as construct validity. In a cross-sectional design, a principal component analysis for item reduction was conducted on a first sample (A) and a confirmatory factor analysis on a second sample (B). Construct validity was tested on a combined sample (C). Questionnaire items were derived from a measure of acceptance in chronic pain, and data were gathered through screening or available from pretreatment assessments in four insomnia treatment trials, administered online, via bibliotherapy and in primary care. Adults with insomnia: 372 in sample A and 215 in sample B. Sample C (n = 820) included sample A and B with another 233 participants added. Construct validity was assessed through relations with established acceptance and sleep scales. The principal component analysis presented a two-factor solution with eight items, explaining 65.9% of the total variance. The confirmatory factor analysis supported the solution. Acceptance of sleep problems was more closely related to subjective symptoms and consequences of insomnia than to diary description of sleep, or to acceptance of general private events. The Sleep Problem Acceptance Questionnaire (SPAQ), containing the subscales "Activity Engagement" and "Willingness", is a valid tool to assess acceptance of insomnia. © 2015 Associated Professional Sleep Societies, LLC.

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

Recognition of units in coarse, unconsolidated braided-stream deposits from geophysical log data with principal components analysis

USGS Publications Warehouse

Morin, R.H.

1997-01-01

Returns from drilling in unconsolidated cobble and sand aquifers commonly do not identify lithologic changes that may be meaningful for Hydrogeologic investigations. Vertical resolution of saturated, Quaternary, coarse braided-slream deposits is significantly improved by interpreting natural gamma (G), epithermal neutron (N), and electromagnetically induced resistivity (IR) logs obtained from wells at the Capital Station site in Boise, Idaho. Interpretation of these geophysical logs is simplified because these sediments are derived largely from high-gamma-producing source rocks (granitics of the Boise River drainage), contain few clays, and have undergone little diagenesis. Analysis of G, N, and IR data from these deposits with principal components analysis provides an objective means to determine if units can be recognized within the braided-stream deposits. In particular, performing principal components analysis on G, N, and IR data from eight wells at Capital Station (1) allows the variable system dimensionality to be reduced from three to two by selecting the two eigenvectors with the greatest variance as axes for principal component scatterplots, (2) generates principal components with interpretable physical meanings, (3) distinguishes sand from cobble-dominated units, and (4) provides a means to distinguish between cobble-dominated units.

Analysis and Evaluation of the Characteristic Taste Components in Portobello Mushroom.

PubMed

Wang, Jinbin; Li, Wen; Li, Zhengpeng; Wu, Wenhui; Tang, Xueming

2018-05-10

To identify the characteristic taste components of the common cultivated mushroom (brown; Portobello), Agaricus bisporus, taste components in the stipe and pileus of Portobello mushroom harvested at different growth stages were extracted and identified, and principal component analysis (PCA) and taste active value (TAV) were used to reveal the characteristic taste components during the each of the growth stages of Portobello mushroom. In the stipe and pileus, 20 and 14 different principal taste components were identified, respectively, and they were considered as the principal taste components of Portobello mushroom fruit bodies, which included most amino acids and 5'-nucleotides. Some taste components that were found at high levels, such as lactic acid and citric acid, were not detected as Portobello mushroom principal taste components through PCA. However, due to their high content, Portobello mushroom could be used as a source of organic acids. The PCA and TAV results revealed that 5'-GMP, glutamic acid, malic acid, alanine, proline, leucine, and aspartic acid were the characteristic taste components of Portobello mushroom fruit bodies. Portobello mushroom was also found to be rich in protein and amino acids, so it might also be useful in the formulation of nutraceuticals and functional food. The results in this article could provide a theoretical basis for understanding and regulating the characteristic flavor components synthesis process of Portobello mushroom. © 2018 Institute of Food Technologists®.

Applications of principal component analysis to breath air absorption spectra profiles classification

NASA Astrophysics Data System (ADS)

Kistenev, Yu. V.; Shapovalov, A. V.; Borisov, A. V.; Vrazhnov, D. A.; Nikolaev, V. V.; Nikiforova, O. Y.

2015-12-01

The results of numerical simulation of application principal component analysis to absorption spectra of breath air of patients with pulmonary diseases are presented. Various methods of experimental data preprocessing are analyzed.

[The principal components analysis--method to classify the statistical variables with applications in medicine].

PubMed

Dascălu, Cristina Gena; Antohe, Magda Ecaterina

2009-01-01

Based on the eigenvalues and the eigenvectors analysis, the principal component analysis has the purpose to identify the subspace of the main components from a set of parameters, which are enough to characterize the whole set of parameters. Interpreting the data for analysis as a cloud of points, we find through geometrical transformations the directions where the cloud's dispersion is maximal--the lines that pass through the cloud's center of weight and have a maximal density of points around them (by defining an appropriate criteria function and its minimization. This method can be successfully used in order to simplify the statistical analysis on questionnaires--because it helps us to select from a set of items only the most relevant ones, which cover the variations of the whole set of data. For instance, in the presented sample we started from a questionnaire with 28 items and, applying the principal component analysis we identified 7 principal components--or main items--fact that simplifies significantly the further data statistical analysis.

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…

A Note on McDonald's Generalization of Principal Components Analysis

ERIC Educational Resources Information Center

Shine, Lester C., II

1972-01-01

It is shown that McDonald's generalization of Classical Principal Components Analysis to groups of variables maximally channels the totalvariance of the original variables through the groups of variables acting as groups. An equation is obtained for determining the vectors of correlations of the L2 components with the original variables.…

A New Tool for Nutrition App Quality Evaluation (AQEL): Development, Validation, and Reliability Testing.

PubMed

DiFilippo, Kristen Nicole; Huang, Wenhao; Chapman-Novakofski, Karen M

2017-10-27

The extensive availability and increasing use of mobile apps for nutrition-based health interventions makes evaluation of the quality of these apps crucial for integration of apps into nutritional counseling. The goal of this research was the development, validation, and reliability testing of the app quality evaluation (AQEL) tool, an instrument for evaluating apps' educational quality and technical functionality. Items for evaluating app quality were adapted from website evaluations, with additional items added to evaluate the specific characteristics of apps, resulting in 79 initial items. Expert panels of nutrition and technology professionals and app users reviewed items for face and content validation. After recommended revisions, nutrition experts completed a second AQEL review to ensure clarity. On the basis of 150 sets of responses using the revised AQEL, principal component analysis was completed, reducing AQEL into 5 factors that underwent reliability testing, including internal consistency, split-half reliability, test-retest reliability, and interrater reliability (IRR). Two additional modifiable constructs for evaluating apps based on the age and needs of the target audience as selected by the evaluator were also tested for construct reliability. IRR testing using intraclass correlations (ICC) with all 7 constructs was conducted, with 15 dietitians evaluating one app. Development and validation resulted in the 51-item AQEL. These were reduced to 25 items in 5 factors after principal component analysis, plus 9 modifiable items in two constructs that were not included in principal component analysis. Internal consistency and split-half reliability of the following constructs derived from principal components analysis was good (Cronbach alpha >.80, Spearman-Brown coefficient >.80): behavior change potential, support of knowledge acquisition, app function, and skill development. App purpose split half-reliability was .65. Test-retest reliability showed no significant change over time (P>.05) for all but skill development (P=.001). Construct reliability was good for items assessing age appropriateness of apps for children, teens, and a general audience. In addition, construct reliability was acceptable for assessing app appropriateness for various target audiences (Cronbach alpha >.70). For the 5 main factors, ICC (1,k) was >.80, with a P value of <.05. When 15 nutrition professionals evaluated one app, ICC (2,15) was .98, with a P value of <.001 for all 7 constructs when the modifiable items were specified for adults seeking weight loss support. Our preliminary effort shows that AQEL is a valid, reliable instrument for evaluating nutrition apps' qualities for clinical interventions by nutrition clinicians, educators, and researchers. Further efforts in validating AQEL in various contexts are needed. ©Kristen Nicole DiFilippo, Wenhao Huang, Karen M. Chapman-Novakofski. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 27.10.2017.

Factor Structure of the Internet Addiction Test in Online Gamers and Poker Players.

PubMed

Khazaal, Yasser; Achab, Sophia; Billieux, Joel; Thorens, Gabriel; Zullino, Daniele; Dufour, Magali; Rothen, Stéphane

2015-01-01

The Internet Addiction Test (IAT) is the most widely used questionnaire to screen for problematic Internet use. Nevertheless, its factorial structure is still debated, which complicates comparisons among existing studies. Most previous studies were performed with students or community samples despite the probability of there being more problematic Internet use among users of specific applications, such as online gaming or gambling. To assess the factorial structure of a modified version of the IAT that addresses specific applications, such as video games and online poker. Two adult samples-one sample of Internet gamers (n=920) and one sample of online poker players (n=214)-were recruited and completed an online version of the modified IAT. Both samples were split into two subsamples. Two principal component analyses (PCAs) followed by two confirmatory factor analyses (CFAs) were run separately. The results of principal component analysis indicated that a one-factor model fit the data well across both samples. In consideration of the weakness of some IAT items, a 17-item modified version of the IAT was proposed. This study assessed, for the first time, the factorial structure of a modified version of an Internet-administered IAT on a sample of Internet gamers and a sample of online poker players. The scale seems appropriate for the assessment of such online behaviors. Further studies on the modified 17-item IAT version are needed.

Attachment to Life: Psychometric Analyses of the Valuation of Life Scale and Differences Among Older Adults

PubMed Central

Gitlin, Laura N.; Parisi, Jeanine; Huang, Jin; Winter, Laraine; Roth, David L.

2016-01-01

Purpose of study: Examine psychometric properties of Lawton’s Valuation of Life (VOL) scale, a measure of an older adults’ assessment of the perceived value of their lives; and whether ratings differ by race (White, Black/African American) and sex. Design and Methods: The 13-item VOL scale was administered at baseline in 2 separate randomized trials (Advancing Better Living for Elders, ABLE; Get Busy Get Better, GBGB) for a total of 527 older adults. Principal component analyses were applied to a subset of ABLE data (subsample 1) and confirmatory factor analyses were conducted on remaining data (subsample 2 and GBGB). Once the factor structure was identified and confirmed, 2 subscales were created, corresponding to optimism and engagement. Convergent validity of total and subscale scores were examined using measures of depressive symptoms, social support, control-oriented strategies, mastery, and behavioral activation. For discriminant validity, indices of health status, physical function, financial strain, cognitive status, and number of falls were examined. Results: Trial samples (ABLE vs. GBGB) differed by age, race, marital status, education, and employment. Principal component analysis on ABLE subsample 1 (n = 156) yielded two factors subsequently confirmed in confirmatory factor analyses on ABLE subsample 2 (n = 163) and GBGB sample (N = 208) separately. Adequate fit was found for the 2-factor model. Correlational analyses supported strong convergent and discriminant validity. Some statistically significant race and sex differences in subscale scores were found. Implications: VOL measures subjective appraisals of perceived value of life. Consisting of two interrelated subscales, it offers an efficient approach to ascertain personal attributions. PMID:26874189

Validating a Cantonese short version of the Zarit Burden Interview (CZBI-Short) for dementia caregivers.

PubMed

Tang, Jennifer Yee-Man; Ho, Andy Hau-Yan; Luo, Hao; Wong, Gloria Hoi-Yan; Lau, Bobo Hi-Po; Lum, Terry Yat-Sang; Cheung, Karen Siu-Lan

2016-09-01

The present study aimed to develop and validate a Cantonese short version of the Zarit Burden Interview (CZBI-Short) for Hong Kong Chinese dementia caregivers. The 12-item Zarit Burden Interview (ZBI) was translated into spoken Cantonese and back-translated by two bilingual research assistants and face validated by a panel of experts. Five hundred Chinese dementia caregivers showing signs of stress reported their burden using the translated ZBI and rated their depressive symptoms, overall health, and care recipients' physical functioning and behavioral problems. The factor structure of the translated scale was identified using principal component analysis and confirmatory factor analysis; internal consistency and item-total correlations were assessed; and concurrent validity was tested by correlating the ZBI with depressive symptoms, self-rated health, and care recipients' physical functioning and behavioral problems. The principal component analysis resulted in 11 items loading on a three-factor model comprised role strain, self-criticism, and negative emotion, which accounted for 59% of the variance. The confirmatory factor analysis supported the three-factor model (CZBI-Short) that explained 61% of the total variance. Cronbach's alpha (0.84) and item-total correlations (rho = 0.39-0.71) indicated CZBI-Short had good reliability. CZBI-Short showed correlations with depressive symptoms (r = 0.50), self-rated health (r = -0.26) and care recipients' physical functioning (r = 0.18-0.26) and disruptive behaviors (r = 0.36). The 12-item CZBI-Short is a concise, reliable, and valid instrument to assess burden in Chinese dementia caregivers in clinical and social care settings.

The Complexity of Human Walking: A Knee Osteoarthritis Study

PubMed Central

Kotti, Margarita; Duffell, Lynsey D.; Faisal, Aldo A.; McGregor, Alison H.

2014-01-01

This study proposes a framework for deconstructing complex walking patterns to create a simple principal component space before checking whether the projection to this space is suitable for identifying changes from the normality. We focus on knee osteoarthritis, the most common knee joint disease and the second leading cause of disability. Knee osteoarthritis affects over 250 million people worldwide. The motivation for projecting the highly dimensional movements to a lower dimensional and simpler space is our belief that motor behaviour can be understood by identifying a simplicity via projection to a low principal component space, which may reflect upon the underlying mechanism. To study this, we recruited 180 subjects, 47 of which reported that they had knee osteoarthritis. They were asked to walk several times along a walkway equipped with two force plates that capture their ground reaction forces along 3 axes, namely vertical, anterior-posterior, and medio-lateral, at 1000 Hz. Data when the subject does not clearly strike the force plate were excluded, leaving 1–3 gait cycles per subject. To examine the complexity of human walking, we applied dimensionality reduction via Probabilistic Principal Component Analysis. The first principal component explains 34% of the variance in the data, whereas over 80% of the variance is explained by 8 principal components or more. This proves the complexity of the underlying structure of the ground reaction forces. To examine if our musculoskeletal system generates movements that are distinguishable between normal and pathological subjects in a low dimensional principal component space, we applied a Bayes classifier. For the tested cross-validated, subject-independent experimental protocol, the classification accuracy equals 82.62%. Also, a novel complexity measure is proposed, which can be used as an objective index to facilitate clinical decision making. This measure proves that knee osteoarthritis subjects exhibit more variability in the two-dimensional principal component space. PMID:25232949

Anxiety sensitivity among Cambodian refugees with panic disorder: A factor analytic investigation.

PubMed

Hinton, Devon E; Pich, Vuth; Safren, Steven A; Pollack, Mark H; McNally, Richard J

2006-01-01

Among Cambodian refugees with panic disorder (N = 208), we performed two factor analyses, one with the ASI, another with an Augmented ASI (consisting of the 16-item ASI supplemented with a 9-item addendum that assesses additional Cambodian concerns about anxiety-related sensations). The principal component analysis of the ASI yielded a 3-factor solution (I, "Weak Heart Concerns"; II, "Social Concerns"; III, "Control Concerns"); the Augmented ASI, a 4-factor solution: I, "Wind Attack Concerns"; II, "Weak Heart Concerns"; III, "Social Concerns"; and IV, "Control Concerns." The item clustering within the factor solution of both the ASI and Augmented ASI illustrates the role of cultural syndromes in generating fear of mental and bodily events.

Principal Components Analysis of a JWST NIRSpec Detector Subsystem

NASA Technical Reports Server (NTRS)

Arendt, Richard G.; Fixsen, D. J.; Greenhouse, Matthew A.; Lander, Matthew; Lindler, Don; Loose, Markus; Moseley, S. H.; Mott, D. Brent; Rauscher, Bernard J.; Wen, Yiting;

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.

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...

A Native American Community's Involvement and Empowerment to Guide Their Children's Development in the School Setting

ERIC Educational Resources Information Center

Rivera, Hector H.; Tharp, Roland G.

2006-01-01

This study provides an empirical description of the dimensions of community values, beliefs, and opinions through a survey conducted in the Pueblo Indian community of Zuni in New Mexico. The sample was composed of 200 randomly chosen community members ranging from 21 to 103 years old. A principal component factor analysis was conducted, as well as…

Maternal Control and Sensitivity, Child Gender, and Maternal Education in Relation to Children's Behavioral Outcomes in African American Families

ERIC Educational Resources Information Center

Tamis-LeMonda, Catherine S.; Briggs, Rahil D.; McClowry, Sandra G.; Snow, David L.

2009-01-01

This study examined relationships between mother-child interactions and children's behaviors in 119 urban African American mothers and their 6-7 year old children. Interactions during a cooking task and a follow-up child clean-up task were videotaped. Principal components analyses of behaviors during the cooking task yielded two factors in mothers…

A search for specificity in DNA-drug interactions.

PubMed

Cruciani, G; Goodford, P J

1994-06-01

The GRID force field and a principal component analysis have been used in order to predict the interactions of small chemical groups with all 64 different triplet sequences of B-DNA. Factors that favor binding to guanine-cytosine base pairs have been identified, and a dictionary of ligand groups and their locations is presented as a guide to the design of specific DNA ligands.

Validation of the Finnegan neonatal abstinence syndrome tool-short form.

PubMed

Maguire, Denise; Cline, Genieveve J; Parnell, Lisa; Tai, Chun-Yi

2013-12-01

The purpose of this study was to reduce the number of items in the Modified Finnegan Neonatal Abstinence Syndrome Tool (M-FNAST) to the minimum possible while retaining or improving its validity in a short version. All infants with a diagnosis of neonatal abstinence syndrome (171) who were admitted to a large neonatal intensive care unit in southwest Florida between September 2010 and October 2012 comprised the sample. This was a psychometric evaluation of 33 856 M-FNAST assessments that were downloaded from the electronic medical record. Principal axis factoring extraction with varimax rotation was performed on the M-FNAST data. Principal components extraction was used before principal factors extraction to estimate the number of factors with the scree test and factorability of the correlation matrices with Bartlett's chi-square test, and Kaiser-Meyer-Olkin Measure of Sampling Adequacy. The M-FNAST scores ranged from 0 to 29, with a mean of 3.5 (SD = 2.5). Less than 1% (21) of infants had scores of 17 or more. Nearly all (97.7%) scores fell between 0 and 9. Most subjects were full-term gestation, but 11 were preterm between 28 and 37 weeks' gestational age. The 2-factor solution explained 23.74% of the total variance and consists of 2 factors, mild/early and moderate/advanced signs. The 2-factor solution was significantly correlated with the total score on the MFNAST (r = 0.917; P < .001). Among infants who scored 8 or greater, the total score on the 2-factor solution short form FNAST was significantly correlated with the total score on the M-FNAST (r = 0.629; P < .001).

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.

IMPROVED SEARCH OF PRINCIPAL COMPONENT ANALYSIS DATABASES FOR SPECTRO-POLARIMETRIC INVERSION

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

Casini, R.; Lites, B. W.; Ramos, A. Asensio

2013-08-20

We describe a simple technique for the acceleration of spectro-polarimetric inversions based on principal component analysis (PCA) of Stokes profiles. This technique involves the indexing of the database models based on the sign of the projections (PCA coefficients) of the first few relevant orders of principal components of the four Stokes parameters. In this way, each model in the database can be attributed a distinctive binary number of 2{sup 4n} bits, where n is the number of PCA orders used for the indexing. Each of these binary numbers (indices) identifies a group of ''compatible'' models for the inversion of amore » given set of observed Stokes profiles sharing the same index. The complete set of the binary numbers so constructed evidently determines a partition of the database. The search of the database for the PCA inversion of spectro-polarimetric data can profit greatly from this indexing. In practical cases it becomes possible to approach the ideal acceleration factor of 2{sup 4n} as compared to the systematic search of a non-indexed database for a traditional PCA inversion. This indexing method relies on the existence of a physical meaning in the sign of the PCA coefficients of a model. For this reason, the presence of model ambiguities and of spectro-polarimetric noise in the observations limits in practice the number n of relevant PCA orders that can be used for the indexing.« less

An Analysis of the Perceptions and Resources of Large University Classes

PubMed Central

Cash, Ceilidh Barlow; Letargo, Jessa; Graether, Steffen P.; Jacobs, Shoshanah R.

2017-01-01

Large class learning is a reality that is not exclusive to the first-year experience at midsized, comprehensive universities; upper-year courses have similarly high enrollment, with many class sizes greater than 200 students. Research into the efficacy and deficiencies of large undergraduate classes has been ongoing for more than 100 years, with most research associating large classes with weak student engagement, decreased depth of learning, and ineffective interactions. This study used a multidimensional research approach to survey student and instructor perceptions of large biology classes and to characterize the courses offered by a department according to resources and course structure using a categorical principal components analysis. Both student and instructor survey results indicated that a large class begins around 240 students. Large classes were identified as impersonal and classified using extrinsic qualifiers; however, students did identify techniques that made the classes feel smaller. In addition to the qualitative survey, we also attempted to quantify courses by collecting data from course outlines and analyzed the data using categorical principal component analysis. The analysis maps institutional change in resource allocation and teaching structure from 2010 through 2014 and validates the use of categorical principal components analysis in educational research. We examine what perceptions and factors are involved in a large class that is perceived to feel small. Our analysis suggests that it is not the addition of resources or difference in the lecturing method, but it is the instructor that determines whether a large class can feel small. PMID:28495937

Survey of whole air data from the second airborne Biomass Burning and Lightning Experiment using principal component analysis

NASA Astrophysics Data System (ADS)

Choi, Yunsoo; Elliott, Scott; Simpson, Isobel J.; Blake, Donald R.; Colman, Jonah J.; Dubey, Manvendra K.; Meinardi, Simone; Rowland, F. Sherwood; Shirai, Tomoko; Smith, Felisa A.

2003-03-01

Hydrocarbon and halocarbon measurements collected during the second airborne Biomass Burning and Lightning Experiment (BIBLE-B) were subjected to a principal component analysis (PCA), to test the capability for identifying intercorrelated compounds within a large whole air data set. The BIBLE expeditions have sought to quantify and understand the products of burning, electrical discharge, and general atmospheric chemical processes during flights arrayed along the western edge of the Pacific. Principal component analysis was found to offer a compact method for identifying the major modes of composition encountered in the regional whole air data set. Transecting the continental monsoon, urban and industrial tracers (e.g., combustion byproducts, chlorinated methanes and ethanes, xylenes, and longer chain alkanes) dominated the observed variability. Pentane enhancements reflected vehicular emissions. In general, ethyl and propyl nitrate groupings indicated oxidation under nitrogen oxide (NOx) rich conditions and hence city or lightning influences. Over the tropical ocean, methyl nitrate grouped with brominated compounds and sometimes with dimethyl sulfide and methyl iodide. Biomass burning signatures were observed during flights over the Australian continent. Strong indications of wetland anaerobics (methane) or liquefied petroleum gas leakage (propane) were conspicuous by their absence. When all flights were considered together, sources attributable to human activity emerged as the most important. We suggest that factor reductions in general and PCA in particular may soon play a vital role in the analysis of regional whole air data sets, as a complement to more familiar methods.

Progress Towards Improved Analysis of TES X-ray Data Using Principal Component Analysis

NASA Technical Reports Server (NTRS)

Busch, S. E.; Adams, J. S.; Bandler, S. R.; Chervenak, J. A.; Eckart, M. E.; Finkbeiner, F. M.; Fixsen, D. J.; Kelley, R. L.; Kilbourne, C. A.; Lee, S.-J.;

Phenotypic Characterization and Multivariate Analysis to Explain Body Conformation in Lesser Known Buffalo (Bubalus bubalis) from North India

PubMed Central

Vohra, V.; Niranjan, S. K.; Mishra, A. K.; Jamuna, V.; Chopra, A.; Sharma, Neelesh; Jeong, Dong Kee

2015-01-01

Phenotypic characterization and body biometric in 13 traits (height at withers, body length, chest girth, paunch girth, ear length, tail length, length of tail up to switch, face length, face width, horn length, circumference of horn at base, distances between pin bone and hip bone) were recorded in 233 adult Gojri buffaloes from Punjab and Himachal Pradesh states of India. Traits were analysed by using varimax rotated principal component analysis (PCA) with Kaiser Normalization to explain body conformation. PCA revealed four components which explained about 70.9% of the total variation. First component described the general body conformation and explained 31.5% of total variation. It was represented by significant positive high loading of height at wither, body length, heart girth, face length and face width. The communality ranged from 0.83 (hip bone distance) to 0.45 (horn length) and unique factors ranged from 0.16 to 0.55 for all these 13 different biometric traits. Present study suggests that first principal component can be used in the evaluation and comparison of body conformation in buffaloes and thus provides an opportunity to distinguish between early and late maturing to adult, based on a small group of biometric traits to explain body conformation in adult buffaloes. PMID:25656215

Snapshot hyperspectral imaging probe with principal component analysis and confidence ellipse for classification

NASA Astrophysics Data System (ADS)

Lim, Hoong-Ta; Murukeshan, Vadakke Matham

2017-06-01

Hyperspectral imaging combines imaging and spectroscopy to provide detailed spectral information for each spatial point in the image. This gives a three-dimensional spatial-spatial-spectral datacube with hundreds of spectral images. Probe-based hyperspectral imaging systems have been developed so that they can be used in regions where conventional table-top platforms would find it difficult to access. A fiber bundle, which is made up of specially-arranged optical fibers, has recently been developed and integrated with a spectrograph-based hyperspectral imager. This forms a snapshot hyperspectral imaging probe, which is able to form a datacube using the information from each scan. Compared to the other configurations, which require sequential scanning to form a datacube, the snapshot configuration is preferred in real-time applications where motion artifacts and pixel misregistration can be minimized. Principal component analysis is a dimension-reducing technique that can be applied in hyperspectral imaging to convert the spectral information into uncorrelated variables known as principal components. A confidence ellipse can be used to define the region of each class in the principal component feature space and for classification. This paper demonstrates the use of the snapshot hyperspectral imaging probe to acquire data from samples of different colors. The spectral library of each sample was acquired and then analyzed using principal component analysis. Confidence ellipse was then applied to the principal components of each sample and used as the classification criteria. The results show that the applied analysis can be used to perform classification of the spectral data acquired using the snapshot hyperspectral imaging probe.

Pepper seed variety identification based on visible/near-infrared spectral technology

NASA Astrophysics Data System (ADS)

Li, Cuiling; Wang, Xiu; Meng, Zhijun; Fan, Pengfei; Cai, Jichen

2016-11-01

Pepper is a kind of important fruit vegetable, with the expansion of pepper hybrid planting area, detection of pepper seed purity is especially important. This research used visible/near infrared (VIS/NIR) spectral technology to detect the variety of single pepper seed, and chose hybrid pepper seeds "Zhuo Jiao NO.3", "Zhuo Jiao NO.4" and "Zhuo Jiao NO.5" as research sample. VIS/NIR spectral data of 80 "Zhuo Jiao NO.3", 80 "Zhuo Jiao NO.4" and 80 "Zhuo Jiao NO.5" pepper seeds were collected, and the original spectral data was pretreated with standard normal variable (SNV) transform, first derivative (FD), and Savitzky-Golay (SG) convolution smoothing methods. Principal component analysis (PCA) method was adopted to reduce the dimension of the spectral data and extract principal components, according to the distribution of the first principal component (PC1) along with the second principal component(PC2) in the twodimensional plane, similarly, the distribution of PC1 coupled with the third principal component(PC3), and the distribution of PC2 combined with PC3, distribution areas of three varieties of pepper seeds were divided in each twodimensional plane, and the discriminant accuracy of PCA was tested through observing the distribution area of samples' principal components in validation set. This study combined PCA and linear discriminant analysis (LDA) to identify single pepper seed varieties, results showed that with the FD preprocessing method, the discriminant accuracy of pepper seed varieties was 98% for validation set, it concludes that using VIS/NIR spectral technology is feasible for identification of single pepper seed varieties.

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.

Italian regional health system structure and expected cancer survival.

PubMed

Vercelli, Marina; Lillini, Roberto; Quaglia, Alberto; Capocaccia, Riccardo

2014-01-01

Few studies deal with the association of socioeconomic and health system resource variables with cancer survival at the Italian regional level, where the greatest number of decisions about social and health policies and resource allocations are taken. The present study aimed to describe the causal relationships between socioeconomic and health system resource factors and regional cancer survival and to compute the expected cancer survival at provincial, regional and area levels. Age-standardized relative survival at 5 years from diagnosis of cases incident in 1995-1998 and followed up to 2004 were derived by gender for 11 sites from the Italian Association of Cancer Registries data bank. The socioeconomic and health system resource variables, describing at a regional level the macro-economy, demography, labor market, and health resources for 1995-2005, came from the Health for All database. A principal components factor analysis was applied to the socioeconomic and health system resource variables. For every site, linear regression models were computed considering the relative survival at 5 years as a dependent variable and the principal components factor analysis factors as independent variables. The factors described the socioeconomic and health-related features of the regional systems and were causally related to the characteristics of the patient taken in charge. The models built by the factors allowed computation of the expected relative survival at 5 years with very good concordance with those observed at regional, macro-regional and national levels. In the regions without any cancer registry, survival was coherent with that of neighboring regions with similar socioeconomic and health system resources characteristics. The models highlighted the causal correlations between socioeconomic and health system resources and cancer survival, suggesting that they could be good evaluation tools for the efficiency of the resources allocation and use.

Multivariate analysis of selected metals in tannery effluents and related soil.

PubMed

Tariq, Saadia R; Shah, Munir H; Shaheen, N; Khalique, A; Manzoor, S; Jaffar, M

2005-06-30

Effluent and relevant soil samples from 38 tanning units housed in Kasur, Pakistan, were obtained for metal analysis by flame atomic absorption spectrophotometric method. The levels of 12 metals, Na, Ca, K, Mg, Fe, Mn, Cr, Co, Cd, Ni, Pb and Zn were determined in the two media. The data were evaluated towards metal distribution and metal-to-metal correlations. The study evidenced enhanced levels of Cr (391, 16.7 mg/L) and Na (25,519, 9369 mg/L) in tannery effluents and relevant soil samples, respectively. The effluent versus soil trace metal content relationship confirmed that the effluent Cr was strongly correlated with soil Cr. For metal source identification the techniques of principal component analysis, and cluster analysis were applied. The principal component analysis yielded two factors for effluents: factor 1 (49.6% variance) showed significant loading for Ca, Fe, Mn, Cr, Cd, Ni, Pb and Zn, referring to a tanning related source for these metals, and factor 2 (12.6% variance) with higher loadings of Na, K, Mg and Co, was associated with the processes during the skin/hide treatment. Similarly, two factors with a cumulative variance of 34.8% were obtained for soil samples: factor 1 manifested the contribution from Mg, Mn, Co, Cd, Ni and Pb, which though soil-based is basically effluent-derived, while factor 2 was found associated with Na, K, Ca, Cr and Zn which referred to a tannery-based source. The dendograms obtained from cluster analysis, also support the observed results. The study exhibits a gross pollution of soils with Cr at levels far exceeding the stipulated safe limit laid down for tannery effluents.

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.

Principal components of wrist circumduction from electromagnetic surgical tracking.

PubMed

Rasquinha, Brian J; Rainbow, Michael J; Zec, Michelle L; Pichora, David R; Ellis, Randy E

2017-02-01

An electromagnetic (EM) surgical tracking system was used for a functionally calibrated kinematic analysis of wrist motion. Circumduction motions were tested for differences in subject gender and for differences in the sense of the circumduction as clockwise or counter-clockwise motion. Twenty subjects were instrumented for EM tracking. Flexion-extension motion was used to identify the functional axis. Subjects performed unconstrained wrist circumduction in a clockwise and counter-clockwise sense. Data were decomposed into orthogonal flexion-extension motions and radial-ulnar deviation motions. PCA was used to concisely represent motions. Nonparametric Wilcoxon tests were used to distinguish the groups. Flexion-extension motions were projected onto a direction axis with a root-mean-square error of [Formula: see text]. Using the first three principal components, there was no statistically significant difference in gender (all [Formula: see text]). For motion sense, radial-ulnar deviation distinguished the sense of circumduction in the first principal component ([Formula: see text]) and in the third principal component ([Formula: see text]); flexion-extension distinguished the sense in the second principal component ([Formula: see text]). The clockwise sense of circumduction could be distinguished by a multifactorial combination of components; there were no gender differences in this small population. These data constitute a baseline for normal wrist circumduction. The multifactorial PCA findings suggest that a higher-dimensional method, such as manifold analysis, may be a more concise way of representing circumduction in human joints.

Dietary intake of palmitate and oleate has broad impact on systemic and tissue lipid profiles in humans123

PubMed Central

Kien, C Lawrence; Bunn, Janice Y; Stevens, Robert; Bain, James; Ikayeva, Olga; Crain, Karen; Koves, Timothy R; Muoio, Deborah M

2014-01-01

Background: Epidemiologic evidence has suggested that diets with a high ratio of palmitic acid (PA) to oleic acid (OA) increase risk of cardiovascular disease (CVD). Objective: To gain additional insights into the relative effect of dietary fatty acids and their metabolism on CVD risk, we sought to identify a metabolomic signature that tracks with diet-induced changes in blood lipid concentrations and whole-body fat oxidation. Design: We applied comprehensive metabolomic profiling tools to biological specimens collected from 18 healthy adults enrolled in a crossover trial that compared a 3-wk high–palmitic acid (HPA) with a low–palmitic acid and high–oleic acid (HOA) diet. Results: A principal components analysis of the data set including 329 variables measured in 15 subjects in the fasted state identified one factor, the principal components analysis factor in the fasted state (PCF1-Fasted), which was heavily weighted by the PA:OA ratio of serum and muscle lipids, that was affected by diet (P < 0.0001; HPA greater than HOA). One other factor, the additional principal components analysis factor in the fasted state (PCF2-Fasted), reflected a wide range of acylcarnitines and was affected by diet in women only (P = 0.0198; HPA greater than HOA). HOA lowered the ratio of serum low-density lipoprotein to high-density lipoprotein (LDL:HDL) in men and women, and adjustment for the PCF1-Fasted abolished the effect. In women only, adjustment for the PCF2-Fasted eliminated the HOA-diet effect on serum total- and LDL-cholesterol concentrations. The respiratory exchange ratio in the fasted state was lower with the HPA diet (P = 0.04), and the diet effect was eliminated after adjustment for the PCF1-Fasted. The messenger RNA expression of the cholesterol regulatory gene insulin-induced gene-1 was higher with the HOA diet (P = 0.008). Conclusions: These results suggest that replacing dietary PA with OA reduces the blood LDL concentration and whole-body fat oxidation by modifying the saturation index of circulating and tissue lipids. In women, these effects are also associated with a higher production and accumulation of acylcarnitines, possibly reflecting a shift in fat catabolism. PMID:24429541

Exploring Innovation Capabilities of Hospital CIOs: An Empirical Assessment.

PubMed

Esdar, Moritz; Liebe, Jan-David; Weiß, Jan-Patrick; Hübner, Ursula

2017-01-01

Hospital CIOs play a central role in the adoption of innovative health IT. Until now, it remained unclear which particular conditions constitute their capability to innovate in terms of intrapersonal as well as organisational factors. An inventory of 20 items was developed to capture these conditions and examined by analysing data obtained from 164 German hospital CIOs. Principal component analysis resulted in three internally consistent components that constitute large portions of the CIOs innovation capability: organisational innovation culture, entrepreneurship personality and openness towards users. Results were used to build composite indicators that allow further evaluations.

Structured assessment of current mental state in clinical practice: an international study of the reliability and validity of the Current Psychiatric State interview, CPS-50.

PubMed

Falloon, I R H; Mizuno, M; Murakami, M; Roncone, R; Unoka, Z; Harangozo, J; Pullman, J; Gedye, R; Held, T; Hager, B; Erickson, D; Burnett, K

2005-01-01

To develop a reliable standardized assessment of psychiatric symptoms for use in clinical practice. A 50-item interview, the Current Psychiatric State 50 (CPS-50), was used to assess 237 patients with a range of psychiatric diagnoses. Ratings were made by interviewers after a 2-day training. Comparisons of inter-rater reliability on each item and on eight clinical subscales were made across four international centres and between psychiatrists and non-psychiatrists. A principal components analysis was used to validate these clinical scales. Acceptable inter-rater reliability (intra-class coefficient > 0.80) was found for 46 of the 50 items, and for all eight subscales. There was no difference between centres or between psychiatrists and non-psychiatrists. The principal components analysis factors were similar to the clinical scales. The CPS-50 is a reliable standardized assessment of current mental status that can be used in clinical practice by all mental health professionals after brief training. Blackwell Munksgaard 2004

The assessment of source attribution of soil pollution in a typical e-waste recycling town and its surrounding regions using the combined organic and inorganic dataset.

PubMed

Luo, Jie; Qi, Shihua; Xie, Xianming; Gu, X W Sophie; Wang, Jinji

2017-01-01

Guiyu is a well-known electronic waste dismantling and recycling town in south China. Concentrations and distribution of the 21 mineral elements and 16 polycyclic aromatic hydrocarbons (PAHs) collected there were evaluated. Principal component analyses (PCA) applied to the data matrix of PAHs in the soil extracted three major factors explaining 85.7% of the total variability identified as traffic emission, coal combustion, and an unidentified source. By using metallic or metalloid element concentrations as variables, five principal components (PCs) were identified and accounted for 70.4% of the information included in the initial data matrix, which can be denoted as e-waste dismantling-related contamination, two different geological origins, anthropogenic influenced source, and marine aerosols. Combining the 21 metallic and metalloid element datasets with the 16 PAH concentrations can narrow down the coarse source and decrease the unidentified contribution to soil in the present study and therefore effectively assists the source identification process.

Weak acid extractable metals in Bramble Bay, Queensland, Australia: temporal behaviour, enrichment and source apportionment.

PubMed

Brady, James P; Ayoko, Godwin A; Martens, Wayde N; Goonetilleke, Ashantha

2015-02-15

Sediment samples were taken from six sampling sites in Bramble Bay, Queensland, Australia between February and November in 2012. They were analysed for a range of heavy metals including Al, Fe, Mn, Ti, Ce, Th, U, V, Cr, Co, Ni, Cu, Zn, As, Cd, Sb, Te, Hg, Tl and Pb. Fraction analysis, Enrichment Factors and Principal Component Analysis-Absolute Principal Component Scores (PCA-APCS) were carried out in order to assess metal pollution, potential bioavailability and source apportionment. Cr and Ni exceeded the Australian Interim Sediment Quality Guidelines at some sampling sites, while Hg was found to be the most enriched metal. Fraction analysis identified increased weak acid soluble Hg and Cd during the sampling period. Source apportionment via PCA-APCS found four sources of metals pollution, namely, marine sediments, shipping, antifouling coatings and a mixed source. These sources need to be considered in any metal pollution control measure within Bramble Bay. Copyright © 2014 Elsevier Ltd. All rights reserved.

Improved GSO Optimized ESN Soft-Sensor Model of Flotation Process Based on Multisource Heterogeneous Information Fusion

PubMed Central

Wang, Jie-sheng; Han, Shuang; Shen, Na-na

2014-01-01

For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, an echo state network (ESN) based fusion soft-sensor model optimized by the improved glowworm swarm optimization (GSO) algorithm is proposed. Firstly, the color feature (saturation and brightness) and texture features (angular second moment, sum entropy, inertia moment, etc.) based on grey-level co-occurrence matrix (GLCM) are adopted to describe the visual characteristics of the flotation froth image. Then the kernel principal component analysis (KPCA) method is used to reduce the dimensionality of the high-dimensional input vector composed by the flotation froth image characteristics and process datum and extracts the nonlinear principal components in order to reduce the ESN dimension and network complex. The ESN soft-sensor model of flotation process is optimized by the GSO algorithm with congestion factor. Simulation results show that the model has better generalization and prediction accuracy to meet the online soft-sensor requirements of the real-time control in the flotation process. PMID:24982935

A Principal Component Analysis of 39 Scientific Impact Measures

PubMed Central

Bollen, Johan; Van de Sompel, Herbert

2009-01-01

Background The impact of scientific publications has traditionally been expressed in terms of citation counts. However, scientific activity has moved online over the past decade. To better capture scientific impact in the digital era, a variety of new impact measures has been proposed on the basis of social network analysis and usage log data. Here we investigate how these new measures relate to each other, and how accurately and completely they express scientific impact. Methodology We performed a principal component analysis of the rankings produced by 39 existing and proposed measures of scholarly impact that were calculated on the basis of both citation and usage log data. Conclusions Our results indicate that the notion of scientific impact is a multi-dimensional construct that can not be adequately measured by any single indicator, although some measures are more suitable than others. The commonly used citation Impact Factor is not positioned at the core of this construct, but at its periphery, and should thus be used with caution. PMID:19562078

[Factor structure of the H.S.C.L. in a sample of French anxious-depressed patients].

PubMed

Guelfi, J D; Barthelet, G; Lancrenon, S; Fermanian, J

1984-06-01

The 58 items version of the Hopkins Symptom Check List (Derogatis et al.) was scored on 85 depressed outpatients treated by general practitioners. The data were obtained before any anti-depressant treatment. A principal component factorial analysis with Varimax rotation was used. 41 of the 58 items of the check list were assigned to seven independent factors: interpersonal sensitivity, somatisation (pain), retardation, digestive disorders, autonomic symptoms, sleep and obsessive disorders, depressive mood. Our results are compared to those published by Derogatis.

Sparse PCA corrects for cell type heterogeneity in epigenome-wide association studies.

PubMed

Rahmani, Elior; Zaitlen, Noah; Baran, Yael; Eng, Celeste; Hu, Donglei; Galanter, Joshua; Oh, Sam; Burchard, Esteban G; Eskin, Eleazar; Zou, James; Halperin, Eran

2016-05-01

In epigenome-wide association studies (EWAS), different methylation profiles of distinct cell types may lead to false discoveries. We introduce ReFACTor, a method based on principal component analysis (PCA) and designed for the correction of cell type heterogeneity in EWAS. ReFACTor does not require knowledge of cell counts, and it provides improved estimates of cell type composition, resulting in improved power and control for false positives in EWAS. Corresponding software is available at http://www.cs.tau.ac.il/~heran/cozygene/software/refactor.html.

Development of a brief measure of college stress: the college student stress scale.

PubMed

Feldt, Ronald C

2008-06-01

The study included assessment of the psychometric properties of an 11-item measure of perceived stress and control in 273 first-year college students. Results indicated good internal consistency and stability over a 5-week interval, and the total score was highly correlated with another measure of perceived stress. Principal components analysis with varimax rotation indicated two possible factors which explained 55% of the variance. However, given the small number of items and low internal consistency of the second factor (alpha=.60), use of the Total score is recommended.

Confirmatory Factor Analysis of the Delirium Rating Scale Revised-98 (DRS-R98).

PubMed

Thurber, Steven; Kishi, Yasuhiro; Trzepacz, Paula T; Franco, Jose G; Meagher, David J; Lee, Yanghyun; Kim, Jeong-Lan; Furlanetto, Leticia M; Negreiros, Daniel; Huang, Ming-Chyi; Chen, Chun-Hsin; Kean, Jacob; Leonard, Maeve

2015-01-01

Principal components analysis applied to the Delirium Rating Scale-Revised-98 contributes to understanding the delirium construct. Using a multisite pooled international delirium database, the authors applied confirmatory factor analysis to Delirium Rating Scale-Revised-98 scores from 859 adult patients evaluated by delirium experts (delirium, N=516; nondelirium, N=343). Confirmatory factor analysis found all diagnostic features and core symptoms (cognitive, language, thought process, sleep-wake cycle, motor retardation), except motor agitation, loaded onto factor 1. Motor agitation loaded onto factor 2 with noncore symptoms (delusions, affective lability, and perceptual disturbances). Factor 1 loading supports delirium as a single construct, but when accompanied by psychosis, motor agitation's role may not be solely as a circadian activity indicator.

Fouling analysis of membrane bioreactor treating antibiotic production wastewater at different hydraulic retention times.

PubMed

Yu, Dawei; Chen, Yutao; Wei, Yuansong; Wang, Jianxing; Wang, Yawei; Li, Kun

2017-04-01

Membrane fouling, including foulants and factors, was investigated during hydraulic retention time (HRT) optimization of a membrane bioreactor (MBR) that treated wastewater from the production of antibiotics. The results showed that HRT played an important role in membrane fouling. Trans-membrane pressure (TMP), membrane flux, and resistance were stable at -6 kPa, 76 L m -2  h -1  bar -1 , and 4.5 × 10 12  m -1 when HRT was at 60, 48, and 36 h, respectively. Using Fourier transform infrared spectroscopy, foulants were identified as carbohydrates and proteins, which correlated with effluent organic matter and effluent chemical oxygen demand (COD) compounds. Therefore, membrane fouling trends would benefit from low supernatant COD (378 mg L -1 ) and a low membrane removal rate (26 %) at a HRT of 36 h. Serious membrane fouling at 72 and 24 h was related to soluble microbial products and extracellular polymeric substances in mixed liquor, respectively. Based on the TMP decrease and flux recovery after physical and chemical cleaning, irremovable fouling aggravation was related to extracellular polymeric substances' increase and soluble microbial products' decrease. According to changes in the specific oxygen uptake rate (SOUR) and mixed liquor suspended solids (MLSSs) during HRT optimization in this study, antibiotic production wastewater largely inhibited MLSS growth, which only increased from 4.5 to 5.0 g L -1 when HRT was decreased from 72 to 24 h, but did not limit sludge activity. The results of a principal component analysis highlighted both proteins and carbohydrates in extracellular polymeric substances as the primary foulants. Membrane fouling associated with the first principal component was positively related to extracellular polymeric substances and negatively related to soluble microbial products. Principal component 2 was primarily related to proteins in the influent. Additional membrane fouling factors included biomass characteristics, operational conditions, and feed characteristics.

Principal component analysis of physicochemical and sensory characteristics of beef rounds extended with gum arabic from Acacia senegal var. kerensis.

PubMed

Mwove, Johnson K; Gogo, Lilian A; Chikamai, Ben N; Omwamba, Mary; Mahungu, Symon M

2018-03-01

Principal component analysis (PCA) was carried out to study the relationship between 24 meat quality measurements taken from beef round samples that were injected with curing brines containing gum arabic (1%, 1.5%, 2%, 2.5%, and 3%) and soy protein concentrate (SPC) (3.5%) at two injection levels (30% and 35%). The measurements used to describe beef round quality were expressible moisture, moisture content, cook yield, possible injection, achieved gum arabic level in beef round, and protein content, as well as descriptive sensory attributes for flavor, texture, basic tastes, feeling factors, color, and overall acceptability. Several significant correlations were found between beef round quality parameters. The highest significant negative and positive correlations were recorded between color intensity and gray color and between color intensity and brown color, respectively. The first seven principal components (PCs) were extracted explaining over 95% of the total variance. The first PC was characterized by texture attributes (hardness and denseness), feeling factors (chemical taste and chemical burn), and two physicochemical properties (expressible moisture and achieved gum arabic level). Taste attribute (saltiness), physicochemical attributes (cook yield and possible injection), and overall acceptability were useful in defining the second PC, while the third PC was characterized by metallic taste, gray color, brown color, and physicochemical attributes (moisture and protein content). The correlation loading plot showed that the distribution of the samples on the axes of the first two PCs allowed for differentiation of samples injected to 30% injection level which were placed on the upper side of the biplot from those injected to 35% which were placed on the lower side. Similarly, beef samples extended with gum arabic and those containing SPC were also visible when scores for the first and third PCs were plotted. Thus, PCA was efficient in analyzing the quality characteristics of beef rounds extended with gum arabic.

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.

Principal component analysis of phenolic acid spectra

USDA-ARS?s Scientific Manuscript database

Phenolic acids are common plant metabolites that exhibit bioactive properties and have applications in functional food and animal feed formulations. The ultraviolet (UV) and infrared (IR) spectra of four closely related phenolic acid structures were evaluated by principal component analysis (PCA) to...

Optimal pattern synthesis for speech recognition based on principal component analysis

NASA Astrophysics Data System (ADS)

Korsun, O. N.; Poliyev, A. V.

2018-02-01

The algorithm for building an optimal pattern for the purpose of automatic speech recognition, which increases the probability of correct recognition, is developed and presented in this work. The optimal pattern forming is based on the decomposition of an initial pattern to principal components, which enables to reduce the dimension of multi-parameter optimization problem. At the next step the training samples are introduced and the optimal estimates for principal components decomposition coefficients are obtained by a numeric parameter optimization algorithm. Finally, we consider the experiment results that show the improvement in speech recognition introduced by the proposed optimization algorithm.

Facilitating in vivo tumor localization by principal component analysis based on dynamic fluorescence molecular imaging

NASA Astrophysics Data System (ADS)

Gao, Yang; Chen, Maomao; Wu, Junyu; Zhou, Yuan; Cai, Chuangjian; Wang, Daliang; Luo, Jianwen

2017-09-01

Fluorescence molecular imaging has been used to target tumors in mice with xenograft tumors. However, tumor imaging is largely distorted by the aggregation of fluorescent probes in the liver. A principal component analysis (PCA)-based strategy was applied on the in vivo dynamic fluorescence imaging results of three mice with xenograft tumors to facilitate tumor imaging, with the help of a tumor-specific fluorescent probe. Tumor-relevant features were extracted from the original images by PCA and represented by the principal component (PC) maps. The second principal component (PC2) map represented the tumor-related features, and the first principal component (PC1) map retained the original pharmacokinetic profiles, especially of the liver. The distribution patterns of the PC2 map of the tumor-bearing mice were in good agreement with the actual tumor location. The tumor-to-liver ratio and contrast-to-noise ratio were significantly higher on the PC2 map than on the original images, thus distinguishing the tumor from its nearby fluorescence noise of liver. The results suggest that the PC2 map could serve as a bioimaging marker to facilitate in vivo tumor localization, and dynamic fluorescence molecular imaging with PCA could be a valuable tool for future studies of in vivo tumor metabolism and progression.

Geochemical differentiation processes for arc magma of the Sengan volcanic cluster, Northeastern Japan, constrained from principal component analysis

NASA Astrophysics Data System (ADS)

Ueki, Kenta; Iwamori, Hikaru

2017-10-01

In this study, with a view of understanding the structure of high-dimensional geochemical data and discussing the chemical processes at work in the evolution of arc magmas, we employed principal component analysis (PCA) to evaluate the compositional variations of volcanic rocks from the Sengan volcanic cluster of the Northeastern Japan Arc. We analyzed the trace element compositions of various arc volcanic rocks, sampled from 17 different volcanoes in a volcanic cluster. The PCA results demonstrated that the first three principal components accounted for 86% of the geochemical variation in the magma of the Sengan region. Based on the relationships between the principal components and the major elements, the mass-balance relationships with respect to the contributions of minerals, the composition of plagioclase phenocrysts, geothermal gradient, and seismic velocity structure in the crust, the first, the second, and the third principal components appear to represent magma mixing, crystallizations of olivine/pyroxene, and crystallizations of plagioclase, respectively. These represented 59%, 20%, and 6%, respectively, of the variance in the entire compositional range, indicating that magma mixing accounted for the largest variance in the geochemical variation of the arc magma. Our result indicated that crustal processes dominate the geochemical variation of magma in the Sengan volcanic cluster.

Identifying Items to Assess Methodological Quality in Physical Therapy Trials: A Factor Analysis

PubMed Central

Cummings, Greta G.; Fuentes, Jorge; Saltaji, Humam; Ha, Christine; Chisholm, Annabritt; Pasichnyk, Dion; Rogers, Todd

2014-01-01

Background Numerous tools and individual items have been proposed to assess the methodological quality of randomized controlled trials (RCTs). The frequency of use of these items varies according to health area, which suggests a lack of agreement regarding their relevance to trial quality or risk of bias. Objective The objectives of this study were: (1) to identify the underlying component structure of items and (2) to determine relevant items to evaluate the quality and risk of bias of trials in physical therapy by using an exploratory factor analysis (EFA). Design A methodological research design was used, and an EFA was performed. Methods Randomized controlled trials used for this study were randomly selected from searches of the Cochrane Database of Systematic Reviews. Two reviewers used 45 items gathered from 7 different quality tools to assess the methodological quality of the RCTs. An exploratory factor analysis was conducted using the principal axis factoring (PAF) method followed by varimax rotation. Results Principal axis factoring identified 34 items loaded on 9 common factors: (1) selection bias; (2) performance and detection bias; (3) eligibility, intervention details, and description of outcome measures; (4) psychometric properties of the main outcome; (5) contamination and adherence to treatment; (6) attrition bias; (7) data analysis; (8) sample size; and (9) control and placebo adequacy. Limitation Because of the exploratory nature of the results, a confirmatory factor analysis is needed to validate this model. Conclusions To the authors' knowledge, this is the first factor analysis to explore the underlying component items used to evaluate the methodological quality or risk of bias of RCTs in physical therapy. The items and factors represent a starting point for evaluating the methodological quality and risk of bias in physical therapy trials. Empirical evidence of the association among these items with treatment effects and a confirmatory factor analysis of these results are needed to validate these items. PMID:24786942

Identifying items to assess methodological quality in physical therapy trials: a factor analysis.

PubMed

Armijo-Olivo, Susan; Cummings, Greta G; Fuentes, Jorge; Saltaji, Humam; Ha, Christine; Chisholm, Annabritt; Pasichnyk, Dion; Rogers, Todd

2014-09-01

Numerous tools and individual items have been proposed to assess the methodological quality of randomized controlled trials (RCTs). The frequency of use of these items varies according to health area, which suggests a lack of agreement regarding their relevance to trial quality or risk of bias. The objectives of this study were: (1) to identify the underlying component structure of items and (2) to determine relevant items to evaluate the quality and risk of bias of trials in physical therapy by using an exploratory factor analysis (EFA). A methodological research design was used, and an EFA was performed. Randomized controlled trials used for this study were randomly selected from searches of the Cochrane Database of Systematic Reviews. Two reviewers used 45 items gathered from 7 different quality tools to assess the methodological quality of the RCTs. An exploratory factor analysis was conducted using the principal axis factoring (PAF) method followed by varimax rotation. Principal axis factoring identified 34 items loaded on 9 common factors: (1) selection bias; (2) performance and detection bias; (3) eligibility, intervention details, and description of outcome measures; (4) psychometric properties of the main outcome; (5) contamination and adherence to treatment; (6) attrition bias; (7) data analysis; (8) sample size; and (9) control and placebo adequacy. Because of the exploratory nature of the results, a confirmatory factor analysis is needed to validate this model. To the authors' knowledge, this is the first factor analysis to explore the underlying component items used to evaluate the methodological quality or risk of bias of RCTs in physical therapy. The items and factors represent a starting point for evaluating the methodological quality and risk of bias in physical therapy trials. Empirical evidence of the association among these items with treatment effects and a confirmatory factor analysis of these results are needed to validate these items. © 2014 American Physical Therapy Association.

Performance-based measures associate with frailty in patients with end-stage liver disease

PubMed Central

Lai, Jennifer C.; Volk, Michael L; Strasburg, Debra; Alexander, Neil

2016-01-01

Background Physical frailty, as measured by the Fried Frailty Index, is increasingly recognized as a critical determinant of outcomes in cirrhotics. However, its utility is limited by the inclusion of self-reported components. We aimed to identify performance-based measures associated with frailty in patients with cirrhosis. Methods Cirrhotics ≥50 years underwent: 6-minute walk test (6MWT, cardiopulmonary endurance), chair stands in 30 seconds (muscle endurance), isometric knee extension (lower extremity strength), unipedal stance time (static balance), and maximal step length (dynamic balance/coordination). Linear regression associated each physical performance test with frailty. Principal components exploratory factor analysis evaluated the inter-relatedness of frailty and the 5 physical performance tests. Results Of forty cirrhotics, with a median age of 64 years and Model for End-stage Liver Disease (MELD) MELD of 12,10 (25%) were frail by Fried Frailty Index ≥3. Frail cirrhotics had poorer performance in 6MWT distance (231 vs. 338 meters), 30 second chair stands (7 vs. 10), isometric knee extension (86 vs. 122 Newton meters), and maximal step length (22 vs. 27 inches) [p≤0.02 for each]. Each physical performance test was significantly associated with frailty (p<0.01), even after adjustment for MELD or hepatic encephalopathy. Principal component factor analysis demonstrated substantial, but unique, clustering of each physical performance test to a single factor – frailty. Conclusion Frailty in cirrhosis is a multi-dimensional construct that is distinct from liver dysfunction and incorporates endurance, strength, and balance. Our data provide specific targets for prehabilitation interventions aimed at reducing frailty in cirrhotics in preparation for liver transplantation. PMID:27495749

Performance-Based Measures Associate With Frailty in Patients With End-Stage Liver Disease.

PubMed

Lai, Jennifer C; Volk, Michael L; Strasburg, Debra; Alexander, Neil

2016-12-01

Physical frailty, as measured by the Fried Frailty Index, is increasingly recognized as a critical determinant of outcomes in patients with cirrhosis. However, its utility is limited by the inclusion of self-reported components. We aimed to identify performance-based measures associated with frailty in patients with cirrhosis. Patients with cirrhosis, aged 50 years or older, underwent: 6-minute walk test (cardiopulmonary endurance), chair stands in 30 seconds (muscle endurance), isometric knee extension (lower extremity strength), unipedal stance time (static balance), and maximal step length (dynamic balance/coordination). Linear regression associated each physical performance test with frailty. Principal components exploratory factor analysis evaluated the interrelatedness of frailty and the 5 physical performance tests. Of 40 patients with cirrhosis, with a median age of 64 years and Model for End-stage Liver Disease (MELD) MELD of 12.10 (25%) were frail by Fried Frailty Index ≥3. Frail patients with cirrhosis had poorer performance in 6-minute walk test distance (231 vs 338 m), 30-second chair stands (7 vs 10), isometric knee extension (86 vs 122 Newton meters), and maximal step length (22 vs 27 in. (P ≤ 0.02 for each). Each physical performance test was significantly associated with frailty (P < 0.01), even after adjustment for MELD or hepatic encephalopathy. Principal component factor analysis demonstrated substantial, but unique, clustering of each physical performance test to a single factor-frailty. Frailty in cirrhosis is a multidimensional construct that is distinct from liver dysfunction and incorporates endurance, strength, and balance. Our data provide specific targets for prehabilitation interventions aimed at reducing frailty in patients with cirrhosis in preparation for liver transplantation.

Identifying maternal and infant factors associated with newborn size in rural Bangladesh by partial least squares (PLS) regression analysis

PubMed Central

Rahman, Md. Jahanur; Shamim, Abu Ahmed; Klemm, Rolf D. W.; Labrique, Alain B.; Rashid, Mahbubur; Christian, Parul; West, Keith P.

2017-01-01

Birth weight, length and circumferences of the head, chest and arm are key measures of newborn size and health in developing countries. We assessed maternal socio-demographic factors associated with multiple measures of newborn size in a large rural population in Bangladesh using partial least squares (PLS) regression method. PLS regression, combining features from principal component analysis and multiple linear regression, is a multivariate technique with an ability to handle multicollinearity while simultaneously handling multiple dependent variables. We analyzed maternal and infant data from singletons (n = 14,506) born during a double-masked, cluster-randomized, placebo-controlled maternal vitamin A or β-carotene supplementation trial in rural northwest Bangladesh. PLS regression results identified numerous maternal factors (parity, age, early pregnancy MUAC, living standard index, years of education, number of antenatal care visits, preterm delivery and infant sex) significantly (p<0.001) associated with newborn size. Among them, preterm delivery had the largest negative influence on newborn size (Standardized β = -0.29 − -0.19; p<0.001). Scatter plots of the scores of first two PLS components also revealed an interaction between newborn sex and preterm delivery on birth size. PLS regression was found to be more parsimonious than both ordinary least squares regression and principal component regression. It also provided more stable estimates than the ordinary least squares regression and provided the effect measure of the covariates with greater accuracy as it accounts for the correlation among the covariates and outcomes. Therefore, PLS regression is recommended when either there are multiple outcome measurements in the same study, or the covariates are correlated, or both situations exist in a dataset. PMID:29261760

Identifying maternal and infant factors associated with newborn size in rural Bangladesh by partial least squares (PLS) regression analysis.

PubMed

Kabir, Alamgir; Rahman, Md Jahanur; Shamim, Abu Ahmed; Klemm, Rolf D W; Labrique, Alain B; Rashid, Mahbubur; Christian, Parul; West, Keith P

2017-01-01

Birth weight, length and circumferences of the head, chest and arm are key measures of newborn size and health in developing countries. We assessed maternal socio-demographic factors associated with multiple measures of newborn size in a large rural population in Bangladesh using partial least squares (PLS) regression method. PLS regression, combining features from principal component analysis and multiple linear regression, is a multivariate technique with an ability to handle multicollinearity while simultaneously handling multiple dependent variables. We analyzed maternal and infant data from singletons (n = 14,506) born during a double-masked, cluster-randomized, placebo-controlled maternal vitamin A or β-carotene supplementation trial in rural northwest Bangladesh. PLS regression results identified numerous maternal factors (parity, age, early pregnancy MUAC, living standard index, years of education, number of antenatal care visits, preterm delivery and infant sex) significantly (p<0.001) associated with newborn size. Among them, preterm delivery had the largest negative influence on newborn size (Standardized β = -0.29 - -0.19; p<0.001). Scatter plots of the scores of first two PLS components also revealed an interaction between newborn sex and preterm delivery on birth size. PLS regression was found to be more parsimonious than both ordinary least squares regression and principal component regression. It also provided more stable estimates than the ordinary least squares regression and provided the effect measure of the covariates with greater accuracy as it accounts for the correlation among the covariates and outcomes. Therefore, PLS regression is recommended when either there are multiple outcome measurements in the same study, or the covariates are correlated, or both situations exist in a dataset.

Psychometric Evaluation of the Team Member Perspectives of Person-Centered Care (TM-PCC) Survey for Long-Term Care Homes.

PubMed

Boscart, Veronique M; Davey, Meaghan; Ploeg, Jenny; Heckman, George; Dupuis, Sherry; Sheiban, Linda; Luh Kim, Jessica; Brown, Paul; Sidani, Souraya

2018-06-06

Person-centered care (PCC) is fundamental for providing high-quality care in long-term care homes. This study aimed to evaluate the psychometric properties of an 11-item Team Member Perspectives of Person-Centered Care (TM-PCC) survey, adapted from White and colleagues (2008). In a cross-sectional study, 461 staff from four long-term care homes in Ontario, Canada, completed the TM-PCC. Construct validity and internal consistency of the TM-PCC were examined with a principal component analysis and Cronbach’s alpha coefficient. Findings revealed a three-component structure with factor 1, Supporting Social Relationships; factor 2, Familiarity with Residents’ Preferences; and factor 3, Meaningful Resident⁻Staff Relationships. The TM-PCC, as compared to the original survey, presented with less components (i.e., did not address Resident Autonomy, Personhood, Comfort, Work with Residents, Personal Environment, and Management Structure), yet included one new component (Meaningful Resident⁻Staff Relationships). The TM-PCC has a similar internal consistency (Cronbach’s alpha coefficient 0.82 vs. White et al. 0.74⁻0.91). The TM-PCC can be used to assess PCC from the staff’s perspective in long-term care homes.

Source identification and apportionment of heavy metals in urban soil profiles.

PubMed

Luo, Xiao-San; Xue, Yan; Wang, Yan-Ling; Cang, Long; Xu, Bo; Ding, Jing

2015-05-01

Because heavy metals (HMs) occurring naturally in soils accumulate continuously due to human activities, identifying and apportioning their sources becomes a challenging task for pollution prevention in urban environments. Besides the enrichment factors (EFs) and principal component analysis (PCA) for source classification, the receptor model (Absolute Principal Component Scores-Multiple Linear Regression, APCS-MLR) and Pb isotopic mixing model were also developed to quantify the source contribution for typical HMs (Cd, Co, Cr, Cu, Mn, Ni, Pb, Zn) in urban park soils of Xiamen, a representative megacity in southeast China. Furthermore, distribution patterns of their concentrations and sources in 13 soil profiles (top 20 cm) were investigated by different depths (0-5, 5-10, 10-20 cm). Currently the principal anthropogenic source for HMs in urban soil of China is atmospheric deposition from coal combustion rather than vehicle exhaust. Specifically for Pb source by isotopic model ((206)Pb/(207)Pb and (208)Pb/(207)Pb), the average contributions were natural (49%)>coal combustion (45%)≫traffic emissions (6%). Although the urban surface soils are usually more contaminated owing to recent and current human sources, leaching effects and historic vehicle emissions can also make deep soil layer contaminated by HMs. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

Dimensions Underlying Measures of Disability, Personal Factors, and Health Status in Cervical Radiculopathy

PubMed Central

Halvorsen, Marie; Kierkegaard, Marie; Harms-Ringdahl, Karin; Peolsson, Anneli; Dedering, Åsa

2015-01-01

Abstract This cross-sectional study sought to identify dimensions underlying measures of impairment, disability, personal factors, and health status in patients with cervical radiculopathy. One hundred twenty-four patients with magnetic resonance imaging-verified cervical radiculopathy, attending a neurosurgery clinic in Sweden, participated. Data from clinical tests and questionnaires on disability, personal factors, and health status were used in a principal-component analysis (PCA) with oblique rotation. The PCA supported a 3-component model including 14 variables from clinical tests and questionnaires, accounting for 73% of the cumulative percentage. The first component, pain and disability, explained 56%. The second component, health, fear-avoidance beliefs, kinesiophobia, and self-efficacy, explained 9.2%. The third component including anxiety, depression, and catastrophizing explained 7.6%. The strongest-loading variables of each dimension were “present neck pain intensity,” “fear avoidance,” and “anxiety.” The three underlying dimensions identified and labeled Pain and functioning, Health, beliefs, and kinesiophobia, and Mood state and catastrophizing captured aspects of importance for cervical radiculopathy. Since the variables “present neck pain intensity,” “fear avoidance,” and “anxiety” had the strongest loading in each of the three dimensions; it may be important to include them in a reduced multidimensional measurement set in cervical radiculopathy. PMID:26091482

Dimensions Underlying Measures of Disability, Personal Factors, and Health Status in Cervical Radiculopathy: A Cross-Sectional Study.

PubMed

Halvorsen, Marie; Kierkegaard, Marie; Harms-Ringdahl, Karin; Peolsson, Anneli; Dedering, Åsa

2015-06-01

This cross-sectional study sought to identify dimensions underlying measures of impairment, disability, personal factors, and health status in patients with cervical radiculopathy. One hundred twenty-four patients with magnetic resonance imaging-verified cervical radiculopathy, attending a neurosurgery clinic in Sweden, participated. Data from clinical tests and questionnaires on disability, personal factors, and health status were used in a principal-component analysis (PCA) with oblique rotation. The PCA supported a 3-component model including 14 variables from clinical tests and questionnaires, accounting for 73% of the cumulative percentage. The first component, pain and disability, explained 56%. The second component, health, fear-avoidance beliefs, kinesiophobia, and self-efficacy, explained 9.2%. The third component including anxiety, depression, and catastrophizing explained 7.6%. The strongest-loading variables of each dimension were "present neck pain intensity," "fear avoidance," and "anxiety." The three underlying dimensions identified and labeled Pain and functioning, Health, beliefs, and kinesiophobia, and Mood state and catastrophizing captured aspects of importance for cervical radiculopathy. Since the variables "present neck pain intensity," "fear avoidance," and "anxiety" had the strongest loading in each of the three dimensions; it may be important to include them in a reduced multidimensional measurement set in cervical radiculopathy.

Assessment of Supportive, Conflicted, and Controlling Dimensions of Family Functioning: A Principal Components Analysis of Family Environment Scale Subscales in a College Sample.

ERIC Educational Resources Information Center

Kronenberger, William G.; Thompson, Robert J., Jr.; Morrow, Catherine

1997-01-01

A principal components analysis of the Family Environment Scale (FES) (R. Moos and B. Moos, 1994) was performed using 113 undergraduates. Research supported 3 broad components encompassing the 10 FES subscales. These results supported previous research and the generalization of the FES to college samples. (SLD)

The Development and Validation of the Empathy Components Questionnaire (ECQ).

PubMed

Batchelder, Laurie; Brosnan, Mark; Ashwin, Chris

2017-01-01

Key research suggests that empathy is a multidimensional construct comprising of both cognitive and affective components. More recent theories and research suggest even further factors within these components of empathy, including the ability to empathize with others versus the drive towards empathizing with others. While numerous self-report measures have been developed to examine empathy, none of them currently index all of these wider components together. The aim of the present research was to develop and validate the Empathy Components Questionnaire (ECQ) to measure cognitive and affective components, as well as ability and drive components within each. Study one utilized items measuring cognitive and affective empathy taken from various established questionnaires to create an initial version of the ECQ. Principal component analysis (PCA) was used to examine the underlying components of empathy within the ECQ in a sample of 101 typical adults. Results revealed a five-component model consisting of cognitive ability, cognitive drive, affective ability, affective drive, and a fifth factor assessing affective reactivity. This five-component structure was then validated and confirmed using confirmatory factor analysis (CFA) in an independent sample of 211 typical adults. Results also showed that females scored higher than males overall on the ECQ, and on specific components, which is consistent with previous findings of a female advantage on self-reported empathy. Findings also showed certain components predicted scores on an independent measure of social behavior, which provided good convergent validity of the ECQ. Together, these findings validate the newly developed ECQ as a multidimensional measure of empathy more in-line with current theories of empathy. The ECQ provides a useful new tool for quick and easy measurement of empathy and its components for research with both healthy and clinical populations.

The Development and Validation of the Empathy Components Questionnaire (ECQ)

PubMed Central

Batchelder, Laurie; Brosnan, Mark; Ashwin, Chris

2017-01-01

Key research suggests that empathy is a multidimensional construct comprising of both cognitive and affective components. More recent theories and research suggest even further factors within these components of empathy, including the ability to empathize with others versus the drive towards empathizing with others. While numerous self-report measures have been developed to examine empathy, none of them currently index all of these wider components together. The aim of the present research was to develop and validate the Empathy Components Questionnaire (ECQ) to measure cognitive and affective components, as well as ability and drive components within each. Study one utilized items measuring cognitive and affective empathy taken from various established questionnaires to create an initial version of the ECQ. Principal component analysis (PCA) was used to examine the underlying components of empathy within the ECQ in a sample of 101 typical adults. Results revealed a five-component model consisting of cognitive ability, cognitive drive, affective ability, affective drive, and a fifth factor assessing affective reactivity. This five-component structure was then validated and confirmed using confirmatory factor analysis (CFA) in an independent sample of 211 typical adults. Results also showed that females scored higher than males overall on the ECQ, and on specific components, which is consistent with previous findings of a female advantage on self-reported empathy. Findings also showed certain components predicted scores on an independent measure of social behavior, which provided good convergent validity of the ECQ. Together, these findings validate the newly developed ECQ as a multidimensional measure of empathy more in-line with current theories of empathy. The ECQ provides a useful new tool for quick and easy measurement of empathy and its components for research with both healthy and clinical populations. PMID:28076406

Burst and Principal Components Analyses of MEA Data Separates Chemicals by Class

EPA Science Inventory

Microelectrode arrays (MEAs) detect drug and chemical induced changes in action potential "spikes" in neuronal networks and can be used to screen chemicals for neurotoxicity. Analytical "fingerprinting," using Principal Components Analysis (PCA) on spike trains recorded from prim...

EVALUATION OF ACID DEPOSITION MODELS USING PRINCIPAL COMPONENT SPACES

EPA Science Inventory

An analytical technique involving principal components analysis is proposed for use in the evaluation of acid deposition models. elationships among model predictions are compared to those among measured data, rather than the more common one-to-one comparison of predictions to mea...

Association of dietary patterns with sociodemographic and health-related factors among coronary artery disease (CAD) patients.

PubMed

Esmaili, Haleh; Mohd Yusof, Rokiah; Abu Saad, Hazizi; Ghaemian, Ali; Darani Zad, Nasrin

2015-01-01

This study aimed to identify the association of dietary patterns with sociodemographic and health-related characteristics among coronary artery disease patients. In this cross-sectional study, the participants were 250 patients coronary artery disease aged ≥ 40 years old. Data collection was done using questionnaires related to sociodemographics, health-related factors, and food-frequency intake information. Three dietary patterns (traditional, western, and healthy) were obtained using principal component analysis. The result showed that dietary patterns were associated with sociodemographic and health-related factors. According to the result, all the factors were taken very seriously when planning a promotional program for healthy lifestyle in prevention of CAD.

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.

Factorial and Hierarchical Cluster Analysis of the Adaptive Behavior Scales (Part I & II) in a Population of Older People (50 Years +) with Severe Intellectual Impairment (Mental Handicap).

ERIC Educational Resources Information Center

Moss, S. C.; Hogg, J.

1990-01-01

Principal components analysis was employed on the Adaptive Behavior Scales with scores of 122 older (mean age 63.5) individuals with severe intellectual impairment living in England. The study found the structure of adaptive skills and interpersonal maladaptive behaviors similar to that found for younger retarded adults. Two factors, personal…

A Second Look at Dwyer's Studies by Means of Meta-Analysis: The Effects of Pictorial Realism on Text Comprehension and Vocabulary.

ERIC Educational Resources Information Center

Reinwein, Joachim; Huberdeau, Lucie

A meta-analysis examined a series of studies by F.M. Dwyer on the effect of illustrations on text comprehension. Principal component analysis was used to reduce the four posttests used by Dwyer to more fundamental factors of learning, followed by analyses of variance. All nine studies (involving secondary-school and college students) in which…

[Support of the nursing process through electronic nursing documentation systems (UEPD) – Initial validation of an instrument].

PubMed

Hediger, Hannele; Müller-Staub, Maria; Petry, Heidi

2016-01-01

Electronic nursing documentation systems, with standardized nursing terminology, are IT-based systems for recording the nursing processes. These systems have the potential to improve the documentation of the nursing process and to support nurses in care delivery. This article describes the development and initial validation of an instrument (known by its German acronym UEPD) to measure the subjectively-perceived benefits of an electronic nursing documentation system in care delivery. The validity of the UEPD was examined by means of an evaluation study carried out in an acute care hospital (n = 94 nurses) in German-speaking Switzerland. Construct validity was analyzed by principal components analysis. Initial references of validity of the UEPD could be verified. The analysis showed a stable four factor model (FS = 0.89) scoring in 25 items. All factors loaded ≥ 0.50 and the scales demonstrated high internal consistency (Cronbach's α = 0.73 – 0.90). Principal component analysis revealed four dimensions of support: establishing nursing diagnosis and goals; recording a case history/an assessment and documenting the nursing process; implementation and evaluation as well as information exchange. Further testing with larger control samples and with different electronic documentation systems are needed. Another potential direction would be to employ the UEPD in a comparison of various electronic documentation systems.

Seasonal and Spatial Variability of Anthropogenic and Natural Factors Influencing Groundwater Quality Based on Source Apportionment

PubMed Central

Guo, Xueru; Zuo, Rui; Meng, Li; Wang, Jinsheng; Teng, Yanguo; Liu, Xin; Chen, Minhua

2018-01-01

Globally, groundwater resources are being deteriorated by rapid social development. Thus, there is an urgent need to assess the combined impacts of natural and enhanced anthropogenic sources on groundwater chemistry. The aim of this study was to identify seasonal characteristics and spatial variations in anthropogenic and natural effects, to improve the understanding of major hydrogeochemical processes based on source apportionment. 34 groundwater points located in a riverside groundwater resource area in northeast China were sampled during the wet and dry seasons in 2015. Using principal component analysis and factor analysis, 4 principal components (PCs) were extracted from 16 groundwater parameters. Three of the PCs were water-rock interaction (PC1), geogenic Fe and Mn (PC2), and agricultural pollution (PC3). A remarkable difference (PC4) was organic pollution originating from negative anthropogenic effects during the wet season, and geogenic F enrichment during the dry season. Groundwater exploitation resulted in dramatic depression cone with higher hydraulic gradient around the water source area. It not only intensified dissolution of calcite, dolomite, gypsum, Fe, Mn and fluorine minerals, but also induced more surface water recharge for the water source area. The spatial distribution of the PCs also suggested the center of the study area was extremely vulnerable to contamination by Fe, Mn, COD, and F−. PMID:29415516

Lipolysis, lipid peroxidation, and color characteristics of Serrano Hams from Duroc and large white pigs during dry-curing.

PubMed

del Olmo, Ana; Calzada, Javier; Nuñez, Manuel

2013-11-01

Lipolysis, lipid peroxidation, and colorimetric characteristics of Serrano hams from Duroc and Large White pigs along a 15-mo curing period were investigated. Physicochemical parameters of both types of hams evolved similarly during curing. Twelve of 13 free fatty acids (FFAs) increased during curing, eicosatrienoic acid being the only exception. Linoleic, stearic, and arachidonic acids and the minor heptadecanoic acid reached lower concentrations, and the rest of minor FFAs higher concentrations, in Duroc hams than in Large White hams. The index measuring the early stage of lipid peroxidation declined from month 5 onwards, indicating that the phenomenon had been completed by month 5, while the index of the secondary stage of lipid peroxidation increased with curing time. Higher values were found for the 1st index in Duroc hams. Curing affected color parameters. Lightness decreased and redness increased in both types of hams, while yellowness decreased only in Duroc hams. Lower redness values were found for Duroc hams. Major differences in color parameters were found between muscles. Principal components analysis of FFAs yielded 2 main principal components. The 1st factor, correlated with all FFAs excepting eicosatrienoic acid, allowed discrimination between curing times. The 2nd factor, correlated with eicosatrienoic acid, permitted discrimination between breeds. © 2013 Institute of Food Technologists®

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.

Local Prediction Models on Mid-Atlantic Ridge MORB by Principal Component Regression

NASA Astrophysics Data System (ADS)

Ling, X.; Snow, J. E.; Chin, W.

2017-12-01

The isotopic compositions of the daughter isotopes of long-lived radioactive systems (Sr, Nd, Hf and Pb ) can be used to map the scale and history of mantle heterogeneities beneath mid-ocean ridges. Our goal is to relate the multidimensional structure in the existing isotopic dataset with an underlying physical reality of mantle sources. The numerical technique of Principal Component Analysis is useful to reduce the linear dependence of the data to a minimum set of orthogonal eigenvectors encapsulating the information contained (cf Agranier et al 2005). The dataset used for this study covers almost all the MORBs along mid-Atlantic Ridge (MAR), from 54oS to 77oN and 8.8oW to -46.7oW, including replicating the dataset of Agranier et al., 2005 published plus 53 basalt samples dredged and analyzed since then (data from PetDB). The principal components PC1 and PC2 account for 61.56% and 29.21%, respectively, of the total isotope ratios variability. The samples with similar compositions to HIMU and EM and DM are identified to better understand the PCs. PC1 and PC2 are accountable for HIMU and EM whereas PC2 has limited control over the DM source. PC3 is more strongly controlled by the depleted mantle source than PC2. What this means is that all three principal components have a high degree of significance relevant to the established mantle sources. We also tested the relationship between mantle heterogeneity and sample locality. K-means clustering algorithm is a type of unsupervised learning to find groups in the data based on feature similarity. The PC factor scores of each sample are clustered into three groups. Cluster one and three are alternating on the north and south MAR. Cluster two exhibits on 45.18oN to 0.79oN and -27.9oW to -30.40oW alternating with cluster one. The ridge has been preliminarily divided into 16 sections considering both the clusters and ridge segments. The principal component regression models the section based on 6 isotope ratios and PCs. The prediction residual is about 1-2km. It means that the combined 5 isotopes are a strong predictor of geographic location along the ridge, a slightly surprising result. PCR is a robust and powerful method for both visualizing and manipulating the multidimensional representation of isotope data.

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.

Pattern identification in time-course gene expression data with the CoGAPS matrix factorization.

PubMed

Fertig, Elana J; Stein-O'Brien, Genevieve; Jaffe, Andrew; Colantuoni, Carlo

2014-01-01

Patterns in time-course gene expression data can represent the biological processes that are active over the measured time period. However, the orthogonality constraint in standard pattern-finding algorithms, including notably principal components analysis (PCA), confounds expression changes resulting from simultaneous, non-orthogonal biological processes. Previously, we have shown that Markov chain Monte Carlo nonnegative matrix factorization algorithms are particularly adept at distinguishing such concurrent patterns. One such matrix factorization is implemented in the software package CoGAPS. We describe the application of this software and several technical considerations for identification of age-related patterns in a public, prefrontal cortex gene expression dataset.

A comparison of Italian, Japanese and American students' responses to the Adolescent Reinforcement Survey Schedule.

PubMed

Galeazzi, A; Franceschina, E; Cautela, J; Holmes, G R; Sakano, Y

1998-02-01

The Italian form of the Adolescent Reinforcement Survey Schedule (ARSS-I) was administered to (N = 648) high school boys and girls from northern and central Italy. Their responses were factor analyzed using a principal component. VARIMAX rotation procedure (SAS Institute, Inc., 1990). The 10 interpretable factors from the Italian data were compared and contrasted to factor analytic results from Holmes (1991, 1994) studies using American and Japanese students. Additionally, the Italian data analyses includes an examination by gender using t tests for each of the ARSS-I items and an ANOVA for age and age-gender effects on responses to the ARSS-I.

Complexity of free energy landscapes of peptides revealed by nonlinear principal component analysis.

PubMed

Nguyen, Phuong H

2006-12-01

Employing the recently developed hierarchical nonlinear principal component analysis (NLPCA) method of Saegusa et al. (Neurocomputing 2004;61:57-70 and IEICE Trans Inf Syst 2005;E88-D:2242-2248), the complexities of the free energy landscapes of several peptides, including triglycine, hexaalanine, and the C-terminal beta-hairpin of protein G, were studied. First, the performance of this NLPCA method was compared with the standard linear principal component analysis (PCA). In particular, we compared two methods according to (1) the ability of the dimensionality reduction and (2) the efficient representation of peptide conformations in low-dimensional spaces spanned by the first few principal components. The study revealed that NLPCA reduces the dimensionality of the considered systems much better, than did PCA. For example, in order to get the similar error, which is due to representation of the original data of beta-hairpin in low dimensional space, one needs 4 and 21 principal components of NLPCA and PCA, respectively. Second, by representing the free energy landscapes of the considered systems as a function of the first two principal components obtained from PCA, we obtained the relatively well-structured free energy landscapes. In contrast, the free energy landscapes of NLPCA are much more complicated, exhibiting many states which are hidden in the PCA maps, especially in the unfolded regions. Furthermore, the study also showed that many states in the PCA maps are mixed up by several peptide conformations, while those of the NLPCA maps are more pure. This finding suggests that the NLPCA should be used to capture the essential features of the systems. (c) 2006 Wiley-Liss, Inc.

Application of principal component regression and partial least squares regression in ultraviolet spectrum water quality detection

NASA Astrophysics Data System (ADS)

Li, Jiangtong; Luo, Yongdao; Dai, Honglin

2018-01-01

Water is the source of life and the essential foundation of all life. With the development of industrialization, the phenomenon of water pollution is becoming more and more frequent, which directly affects the survival and development of human. Water quality detection is one of the necessary measures to protect water resources. Ultraviolet (UV) spectral analysis is an important research method in the field of water quality detection, which partial least squares regression (PLSR) analysis method is becoming predominant technology, however, in some special cases, PLSR's analysis produce considerable errors. In order to solve this problem, the traditional principal component regression (PCR) analysis method was improved by using the principle of PLSR in this paper. The experimental results show that for some special experimental data set, improved PCR analysis method performance is better than PLSR. The PCR and PLSR is the focus of this paper. Firstly, the principal component analysis (PCA) is performed by MATLAB to reduce the dimensionality of the spectral data; on the basis of a large number of experiments, the optimized principal component is extracted by using the principle of PLSR, which carries most of the original data information. Secondly, the linear regression analysis of the principal component is carried out with statistic package for social science (SPSS), which the coefficients and relations of principal components can be obtained. Finally, calculating a same water spectral data set by PLSR and improved PCR, analyzing and comparing two results, improved PCR and PLSR is similar for most data, but improved PCR is better than PLSR for data near the detection limit. Both PLSR and improved PCR can be used in Ultraviolet spectral analysis of water, but for data near the detection limit, improved PCR's result better than PLSR.

Short communication: Discrimination between retail bovine milks with different fat contents using chemometrics and fatty acid profiling.

PubMed

Vargas-Bello-Pérez, Einar; Toro-Mujica, Paula; Enriquez-Hidalgo, Daniel; Fellenberg, María Angélica; Gómez-Cortés, Pilar

2017-06-01

We used a multivariate chemometric approach to differentiate or associate retail bovine milks with different fat contents and non-dairy beverages, using fatty acid profiles and statistical analysis. We collected samples of bovine milk (whole, semi-skim, and skim; n = 62) and non-dairy beverages (n = 27), and we analyzed them using gas-liquid chromatography. Principal component analysis of the fatty acid data yielded 3 significant principal components, which accounted for 72% of the total variance in the data set. Principal component 1 was related to saturated fatty acids (C4:0, C6:0, C8:0, C12:0, C14:0, C17:0, and C18:0) and monounsaturated fatty acids (C14:1 cis-9, C16:1 cis-9, C17:1 cis-9, and C18:1 trans-11); whole milk samples were clearly differentiated from the rest using this principal component. Principal component 2 differentiated semi-skim milk samples by n-3 fatty acid content (C20:3n-3, C20:5n-3, and C22:6n-3). Principal component 3 was related to C18:2 trans-9,trans-12 and C20:4n-6, and its lower scores were observed in skim milk and non-dairy beverages. A cluster analysis yielded 3 groups: group 1 consisted of only whole milk samples, group 2 was represented mainly by semi-skim milks, and group 3 included skim milk and non-dairy beverages. Overall, the present study showed that a multivariate chemometric approach is a useful tool for differentiating or associating retail bovine milks and non-dairy beverages using their fatty acid profile. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Use of multivariate statistics to identify unreliable data obtained using CASA.

PubMed

Martínez, Luis Becerril; Crispín, Rubén Huerta; Mendoza, Maximino Méndez; Gallegos, Oswaldo Hernández; Martínez, Andrés Aragón

2013-06-01

In order to identify unreliable data in a dataset of motility parameters obtained from a pilot study acquired by a veterinarian with experience in boar semen handling, but without experience in the operation of a computer assisted sperm analysis (CASA) system, a multivariate graphical and statistical analysis was performed. Sixteen boar semen samples were aliquoted then incubated with varying concentrations of progesterone from 0 to 3.33 µg/ml and analyzed in a CASA system. After standardization of the data, Chernoff faces were pictured for each measurement, and a principal component analysis (PCA) was used to reduce the dimensionality and pre-process the data before hierarchical clustering. The first twelve individual measurements showed abnormal features when Chernoff faces were drawn. PCA revealed that principal components 1 and 2 explained 63.08% of the variance in the dataset. Values of principal components for each individual measurement of semen samples were mapped to identify differences among treatment or among boars. Twelve individual measurements presented low values of principal component 1. Confidence ellipses on the map of principal components showed no statistically significant effects for treatment or boar. Hierarchical clustering realized on two first principal components produced three clusters. Cluster 1 contained evaluations of the two first samples in each treatment, each one of a different boar. With the exception of one individual measurement, all other measurements in cluster 1 were the same as observed in abnormal Chernoff faces. Unreliable data in cluster 1 are probably related to the operator inexperience with a CASA system. These findings could be used to objectively evaluate the skill level of an operator of a CASA system. This may be particularly useful in the quality control of semen analysis using CASA systems.

[Spatial distribution characteristics of the physical and chemical properties of water in the Kunes River after the supply of snowmelt during spring].

PubMed

Liu, Xiang; Guo, Ling-Peng; Zhang, Fei-Yun; Ma, Jie; Mu, Shu-Yong; Zhao, Xin; Li, Lan-Hai

2015-02-01

Eight physical and chemical indicators related to water quality were monitored from nineteen sampling sites along the Kunes River at the end of snowmelt season in spring. To investigate the spatial distribution characteristics of water physical and chemical properties, cluster analysis (CA), discriminant analysis (DA) and principal component analysis (PCA) are employed. The result of cluster analysis showed that the Kunes River could be divided into three reaches according to the similarities of water physical and chemical properties among sampling sites, representing the upstream, midstream and downstream of the river, respectively; The result of discriminant analysis demonstrated that the reliability of such a classification was high, and DO, Cl- and BOD5 were the significant indexes leading to this classification; Three principal components were extracted on the basis of the principal component analysis, in which accumulative variance contribution could reach 86.90%. The result of principal component analysis also indicated that water physical and chemical properties were mostly affected by EC, ORP, NO3(-) -N, NH4(+) -N, Cl- and BOD5. The sorted results of principal component scores in each sampling sites showed that the water quality was mainly influenced by DO in upstream, by pH in midstream, and by the rest of indicators in downstream. The order of comprehensive scores for principal components revealed that the water quality degraded from the upstream to downstream, i.e., the upstream had the best water quality, followed by the midstream, while the water quality at downstream was the worst. This result corresponded exactly to the three reaches classified using cluster analysis. Anthropogenic activity and the accumulation of pollutants along the river were probably the main reasons leading to this spatial difference.

Evidence for age-associated disinhibition of the wake drive provided by scoring principal components of the resting EEG spectrum in sleep-provoking conditions.

PubMed

Putilov, Arcady A; Donskaya, Olga G

2016-01-01

Age-associated changes in different bandwidths of the human electroencephalographic (EEG) spectrum are well documented, but their functional significance is poorly understood. This spectrum seems to represent summation of simultaneous influences of several sleep-wake regulatory processes. Scoring of its orthogonal (uncorrelated) principal components can help in separation of the brain signatures of these processes. In particular, the opposite age-associated changes were documented for scores on the two largest (1st and 2nd) principal components of the sleep EEG spectrum. A decrease of the first score and an increase of the second score can reflect, respectively, the weakening of the sleep drive and disinhibition of the opposing wake drive with age. In order to support the suggestion of age-associated disinhibition of the wake drive from the antagonistic influence of the sleep drive, we analyzed principal component scores of the resting EEG spectra obtained in sleep deprivation experiments with 81 healthy young adults aged between 19 and 26 and 40 healthy older adults aged between 45 and 66 years. At the second day of the sleep deprivation experiments, frontal scores on the 1st principal component of the EEG spectrum demonstrated an age-associated reduction of response to eyes closed relaxation. Scores on the 2nd principal component were either initially increased during wakefulness or less responsive to such sleep-provoking conditions (frontal and occipital scores, respectively). These results are in line with the suggestion of disinhibition of the wake drive with age. They provide an explanation of why older adults are less vulnerable to sleep deprivation than young adults.

DREEM on: validation of the Dundee Ready Education Environment Measure in Pakistan.

PubMed

Khan, Junaid Sarfraz; Tabasum, Saima; Yousafzai, Usman Khalil; Fatima, Mehreen

2011-09-01

To validate DREEM in medical education environment of Punjab, Pakistan. The DREEM questionnaire was anonymously collected from Final year Baccalaureate of Medicine; Baccalaureate of Surgery students in the private and public medical colleges affiliated with the University of Health Sciences, Lahore. Data was analyzed using Principal Component Analysis with Varimax Rotation. The response rate was 84.14 %. The average DREEM score was 125. Confirmatory and Exploratory Factor Analysis was applied under the conditions of eigenvalues >1 and loadings > or = 0.3. In CONFIRMATORY FACTOR ANALYSIS, Five components were extracted accounting for 40.10% of variance and in EXPLORATORY FACTOR ANALYSIS, Ten components were extracted accounting for 52.33% of variance. Total 50 items had internal consistency reliability of 0.91 (Cronbach's Alpha). The value of Spearman-Brown was 0.868 showing the reliability of the analysis. In both analyses the subscales produced were sensible but the mismatch from the original was largely due to the English-Pakistan contextual and cultural differences. DREEM is a generic instrument that will do well with regional modifications to suit individual, contextual and cultural settings.

Metabolic syndrome, adherence to the Mediterranean diet and 10-year cardiovascular disease incidence: The ATTICA study.

PubMed

Kastorini, Christina-Maria; Panagiotakos, Demosthenes B; Chrysohoou, Christina; Georgousopoulou, Ekavi; Pitaraki, Evangelia; Puddu, Paolo Emilio; Tousoulis, Dimitrios; Stefanadis, Christodoulos; Pitsavos, Christos

2016-03-01

To better understand the metabolic syndrome (MS) spectrum through principal components analysis and further evaluate the role of the Mediterranean diet on MS presence. During 2001-2002, 1514 men and 1528 women (>18 y) without any clinical evidence of CVD or any other chronic disease, at baseline, living in greater Athens area, Greece, were enrolled. In 2011-2012, the 10-year follow-up was performed in 2583 participants (15% of the participants were lost to follow-up). Incidence of fatal or non-fatal CVD was defined according to WHO-ICD-10 criteria. MS was defined by the National Cholesterol Education Program Adult Treatment panel III (revised NCEP ATP III) definition. Adherence to the Mediterranean diet was assessed using the MedDietScore (range 0-55). Five principal components were derived, explaining 73.8% of the total variation, characterized by the: a) body weight and lipid profile, b) blood pressure, c) lipid profile, d) glucose profile, e) inflammatory factors. All components were associated with higher likelihood of CVD incidence. After adjusting for various potential confounding factors, adherence to the Mediterranean dietary pattern for each 10% increase in the MedDietScore, was associated with 15% lower odds of CVD incidence (95%CI: 0.71-1.06). For the participants with low adherence to the Mediterranean diet all five components were significantly associated with increased likelihood of CVD incidence. However, for the ones following closely the Mediterranean pattern positive, yet not significant associations were observed. Results of the present work propose a wider MS definition, while highlighting the beneficial role of the Mediterranean dietary pattern. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Effects of growing location on the production of main active components and antioxidant activity of Dasiphora fruticosa (L.) Rydb. by chemometric methods.

PubMed

Liu, Wei; Wang, Dongmei; Hou, Xiaogai; Yang, Yueqin; Xue, Xian; Jia, Qishi; Zhang, Lixia; Zhao, Wei; Yin, Dongxue

2018-05-17

Traditional Chinese medicine (TCM) plays a very important role in the health system of China. The content and activity of active component are main indexes that evaluate the quality of TCM, however they may vary with environmental factors in their growing locations. In this study, effects of environmental factors on the contents of active components and antioxidant activity of Dasiphora fruticosa from the five main production areas of China were investigated. The contents of tannin, total flavonoid and rutin were determined and varied within the range of 7.65-10.69%, 2.30-5.39% and 0.18-0.81%, respectively. Antioxidant activity was determined by DPPH assay, with the DPPH IC 50 values ranged from 8.791 to 32.534μg mL -1 . In order to further explore the cause of these significant geographical variations, the chemometric methods including correlation analysis, principal component analysis, gray correlation analysis, and path analysis were conducted. The results showed environmental factors had significant effect on the active component contents and antioxidant activity. Rapidly available phosphorus (RAP) and rapidly available nitrogen (RAN) were common dominant factors, and a significant positive correlation was observed between RAP and active components and antioxidant activity (P<0.05). Contributed by their high active components and strong antioxidant activity, Bange in Tibet and Geermu in Qinghai Province was selected as a favorable growing location, respectively. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

[Spatial distribution of birth defects among children aged 0 to 5 years and its relationship with soil chemical elements in Chongqing].

PubMed

Dong, Yan; Zhong, Zhao-hui; Li, Hong; Li, Jie; Wang, Ying-xiong; Peng, Bin; Zhang, Mao-zhong; Huang, Qiao; Yan, Ju; Xu, Fei-long

2013-10-01

To explore the correlation between the incidence of birth defects and the contents of soil elements so as to provide a scientific basis for screening the related pathogenic factors that inducing birth defects for the development of related preventive and control strategies. MapInfo 7.0 software was used to draw the maps on spatial distribution regarding the incidence rates of birth defects and the contents of 11 chemical elements in soil in the 33 studied areas. Variables on the two maps were superposed for analyzing the spatial correlation. SAS 8.0 software was used to analyze single factor, multi-factors and principal components as well as to comprehensively evaluate the degrees of relevance. Different incidence rates of birth defects showed in the maps of spatial distribution presented certain degrees of negative correlation with anomalies of soil chemical elements, including copper, chrome, iodine, selenium, zinc while positively correlated with the levels of lead. Results from the principal component regression equation indicating that the contents of copper(0.002), arsenic(-0.07), cadmium(0.05), chrome (-0.001), zinc (0.001), iodine(-0.03), lead (0.08), fluorine(-0.002)might serve as important factors that related to the prevalence of birth defects. Through the study on spatial distribution, we noticed that the incidence rates of birth defects were related to the contents of copper, chrome, iodine, selenium, zinc, lead in soil while the contents of chrome, iodine and lead might lead to the occurrence of birth defects.

The dimensions of responsiveness of a health system: a Taiwanese perspective.

PubMed

Hsu, Chih-Cheng; Chen, Likwang; Hu, Yu-Whuei; Yip, Winnie; Shu, Chen-Chun

2006-03-17

Responsiveness is an indicator used to measure how well a health system performs relative to non-health aspects. This study assessed whether seven dimensions proposed by the World Health Organization (WHO) to measure responsiveness (dignity, autonomy, confidentiality, prompt attention, social support, basic amenities, and choices of providers) are applicable in evaluating the health system of Taiwan. A key informant survey and focus group research were used in this study. The translated WHO proposed questionnaire was sent to 205 nominated key informants by mail, and 132 (64.4%) were returned. We used principal component analysis to extract factors. Linear regression analysis was used to assess the relationship between the total score and the extracted factors. A qualitative content analysis was also carried out in focus group research. Principal component analysis produced five factors (respect, access, confidentiality, basic amenities, and social support) that explained 63.5% of the total variances. These five factors demonstrated acceptable internal consistency and four of them (except social support) were significantly correlated with the total responsiveness score. The focus group interviews revealed health providers' communication ability and medical ethics were also highly appraised by Taiwanese. When the performance of a health system is to be evaluated, elements of responsiveness proposed by WHO may have to be tailored to fit different cultural backgrounds. Four key features illustrate the uniqueness of Taiwanese perspectives: the idea of autonomy may not be conceptualized, prompt attention and choice of providers are on the same track, social support during care is trivially correlated to the total responsiveness score, and accountability of health providers is deemed essential to a health system.

Systems genetic analysis of multivariate response to iron deficiency in mice

PubMed Central

Yin, Lina; Unger, Erica L.; Jellen, Leslie C.; Earley, Christopher J.; Allen, Richard P.; Tomaszewicz, Ann; Fleet, James C.

2012-01-01

The aim of this study was to identify genes that influence iron regulation under varying dietary iron availability. Male and female mice from 20+ BXD recombinant inbred strains were fed iron-poor or iron-adequate diets from weaning until 4 mo of age. At death, the spleen, liver, and blood were harvested for the measurement of hemoglobin, hematocrit, total iron binding capacity, transferrin saturation, and liver, spleen and plasma iron concentration. For each measure and diet, we found large, strain-related variability. A principal-components analysis (PCA) was performed on the strain means for the seven parameters under each dietary condition for each sex, followed by quantitative trait loci (QTL) analysis on the factors. Compared with the iron-adequate diet, iron deficiency altered the factor structure of the principal components. QTL analysis, combined with PosMed (a candidate gene searching system) published gene expression data and literature citations, identified seven candidate genes, Ptprd, Mdm1, Picalm, lip1, Tcerg1, Skp2, and Frzb based on PCA factor, diet, and sex. Expression of each of these is cis-regulated, significantly correlated with the corresponding PCA factor, and previously reported to regulate iron, directly or indirectly. We propose that polymorphisms in multiple genes underlie individual differences in iron regulation, especially in response to dietary iron challenge. This research shows that iron management is a highly complex trait, influenced by multiple genes. Systems genetics analysis of iron homeostasis holds promise for developing new methods for prevention and treatment of iron deficiency anemia and related diseases. PMID:22461179

Subscales measuring symptoms of non-specific depression, anhedonia, and anxiety in the Edinburgh Postnatal Depression Scale.

PubMed

Tuohy, Alan; McVey, Cynthia

2008-06-01

There has been considerable research and clinical interest in the comorbidity of anxiety and depression in the post-partum period, and specifically in the possibility that the commonly used Edinburgh Postnatal Depression Scale (EPDS) incorporates an anxiety component. We hypothesized that the recommended version of factor analysis (Fabrigar, Wegener, MacCallum, & Strahan, 1999) would identify such covert dimensions more reliably than the commonly used principal components analysis with varimax rotation and eigenvalues greater than 1. Principal axis factor extraction with parallel analysis and oblique (direct quartimin) factor rotation was applied to the 10 EPDS items. The study used a sample of recent mothers recruited and assessed via e-mail and the Internet (N=440). In addition to the EPDS, the Hospital Anxiety and Depression Scale (HADS) and the Positive and Negative Affect Scales (PANAS) were also administered. Three factors were found, which were identified as 'non-specific depressive symptoms', 'anhedonia', and 'anxietal symptoms' subscales, respectively. These subscales were regressed on the HADS anxiety and depression and the PANAS positive and negative affectivity scales, with results substantially consistent with current structural models of the taxonomy of the emotional disorders. The data were obtained from a self-selected non-clinical sample. In addition, it is known that the use of computer-based assessment may tend to inflate self-report scores. It was concluded that there is now sufficient evidence that clinicians should not assume the EPDS to be unidimensional, but should assess all three subscales when screening for susceptibility to post-partum depression and/or post-partum anxiety.

A HIERARCHIAL STOCHASTIC MODEL OF LARGE SCALE ATMOSPHERIC CIRCULATION PATTERNS AND MULTIPLE STATION DAILY PRECIPITATION

EPA Science Inventory

A stochastic model of weather states and concurrent daily precipitation at multiple precipitation stations is described. our algorithms are invested for classification of daily weather states; k means, fuzzy clustering, principal components, and principal components coupled with ...

Rosacea assessment by erythema index and principal component analysis segmentation maps

NASA Astrophysics Data System (ADS)

Kuzmina, Ilona; Rubins, Uldis; Saknite, Inga; Spigulis, Janis

2017-12-01

RGB images of rosacea were analyzed using segmentation maps of principal component analysis (PCA) and erythema index (EI). Areas of segmented clusters were compared to Clinician's Erythema Assessment (CEA) values given by two dermatologists. The results show that visible blood vessels are segmented more precisely on maps of the erythema index and the third principal component (PC3). In many cases, a distribution of clusters on EI and PC3 maps are very similar. Mean values of clusters' areas on these maps show a decrease of the area of blood vessels and erythema and an increase of lighter skin area after the therapy for the patients with diagnosis CEA = 2 on the first visit and CEA=1 on the second visit. This study shows that EI and PC3 maps are more useful than the maps of the first (PC1) and second (PC2) principal components for indicating vascular structures and erythema on the skin of rosacea patients and therapy monitoring.

Airborne electromagnetic data levelling using principal component analysis based on flight line difference

NASA Astrophysics Data System (ADS)

Zhang, Qiong; Peng, Cong; Lu, Yiming; Wang, Hao; Zhu, Kaiguang

2018-04-01

A novel technique is developed to level airborne geophysical data using principal component analysis based on flight line difference. In the paper, flight line difference is introduced to enhance the features of levelling error for airborne electromagnetic (AEM) data and improve the correlation between pseudo tie lines. Thus we conduct levelling to the flight line difference data instead of to the original AEM data directly. Pseudo tie lines are selected distributively cross profile direction, avoiding the anomalous regions. Since the levelling errors of selective pseudo tie lines show high correlations, principal component analysis is applied to extract the local levelling errors by low-order principal components reconstruction. Furthermore, we can obtain the levelling errors of original AEM data through inverse difference after spatial interpolation. This levelling method does not need to fly tie lines and design the levelling fitting function. The effectiveness of this method is demonstrated by the levelling results of survey data, comparing with the results from tie-line levelling and flight-line correlation levelling.

Multilevel sparse functional principal component analysis.

PubMed

Di, Chongzhi; Crainiceanu, Ciprian M; Jank, Wolfgang S

2014-01-29

We consider analysis of sparsely sampled multilevel functional data, where the basic observational unit is a function and data have a natural hierarchy of basic units. An example is when functions are recorded at multiple visits for each subject. Multilevel functional principal component analysis (MFPCA; Di et al. 2009) was proposed for such data when functions are densely recorded. Here we consider the case when functions are sparsely sampled and may contain only a few observations per function. We exploit the multilevel structure of covariance operators and achieve data reduction by principal component decompositions at both between and within subject levels. We address inherent methodological differences in the sparse sampling context to: 1) estimate the covariance operators; 2) estimate the functional principal component scores; 3) predict the underlying curves. Through simulations the proposed method is able to discover dominating modes of variations and reconstruct underlying curves well even in sparse settings. Our approach is illustrated by two applications, the Sleep Heart Health Study and eBay auctions.

[Content of mineral elements of Gastrodia elata by principal components analysis].

PubMed

Li, Jin-ling; Zhao, Zhi; Liu, Hong-chang; Luo, Chun-li; Huang, Ming-jin; Luo, Fu-lai; Wang, Hua-lei

2015-03-01

To study the content of mineral elements and the principal components in Gastrodia elata. Mineral elements were determined by ICP and the data was analyzed by SPSS. K element has the highest content-and the average content was 15.31 g x kg(-1). The average content of N element was 8.99 g x kg(-1), followed by K element. The coefficient of variation of K and N was small, but the Mn was the biggest with 51.39%. The highly significant positive correlation was found among N, P and K . Three principal components were selected by principal components analysis to evaluate the quality of G. elata. P, B, N, K, Cu, Mn, Fe and Mg were the characteristic elements of G. elata. The content of K and N elements was higher and relatively stable. The variation of Mn content was biggest. The quality of G. elata in Guizhou and Yunnan was better from the perspective of mineral elements.

Visualizing Hyolaryngeal Mechanics in Swallowing Using Dynamic MRI

PubMed Central

Pearson, William G.; Zumwalt, Ann C.

2013-01-01

Introduction Coordinates of anatomical landmarks are captured using dynamic MRI to explore whether a proposed two-sling mechanism underlies hyolaryngeal elevation in pharyngeal swallowing. A principal components analysis (PCA) is applied to coordinates to determine the covariant function of the proposed mechanism. Methods Dynamic MRI (dMRI) data were acquired from eleven healthy subjects during a repeated swallows task. Coordinates mapping the proposed mechanism are collected from each dynamic (frame) of a dynamic MRI swallowing series of a randomly selected subject in order to demonstrate shape changes in a single subject. Coordinates representing minimum and maximum hyolaryngeal elevation of all 11 subjects were also mapped to demonstrate shape changes of the system among all subjects. MophoJ software was used to perform PCA and determine vectors of shape change (eigenvectors) for elements of the two-sling mechanism of hyolaryngeal elevation. Results For both single subject and group PCAs, hyolaryngeal elevation accounted for the first principal component of variation. For the single subject PCA, the first principal component accounted for 81.5% of the variance. For the between subjects PCA, the first principal component accounted for 58.5% of the variance. Eigenvectors and shape changes associated with this first principal component are reported. Discussion Eigenvectors indicate that two-muscle slings and associated skeletal elements function as components of a covariant mechanism to elevate the hyolaryngeal complex. Morphological analysis is useful to model shape changes in the two-sling mechanism of hyolaryngeal elevation. PMID:25090608

Obesity, metabolic syndrome, impaired fasting glucose, and microvascular dysfunction: a principal component analysis approach.

PubMed

Panazzolo, Diogo G; Sicuro, Fernando L; Clapauch, Ruth; Maranhão, Priscila A; Bouskela, Eliete; Kraemer-Aguiar, Luiz G

2012-11-13

We aimed to evaluate the multivariate association between functional microvascular variables and clinical-laboratorial-anthropometrical measurements. Data from 189 female subjects (34.0 ± 15.5 years, 30.5 ± 7.1 kg/m2), who were non-smokers, non-regular drug users, without a history of diabetes and/or hypertension, were analyzed by principal component analysis (PCA). PCA is a classical multivariate exploratory tool because it highlights common variation between variables allowing inferences about possible biological meaning of associations between them, without pre-establishing cause-effect relationships. In total, 15 variables were used for PCA: body mass index (BMI), waist circumference, systolic and diastolic blood pressure (BP), fasting plasma glucose, levels of total cholesterol, high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), triglycerides (TG), insulin, C-reactive protein (CRP), and functional microvascular variables measured by nailfold videocapillaroscopy. Nailfold videocapillaroscopy was used for direct visualization of nutritive capillaries, assessing functional capillary density, red blood cell velocity (RBCV) at rest and peak after 1 min of arterial occlusion (RBCV(max)), and the time taken to reach RBCV(max) (TRBCV(max)). A total of 35% of subjects had metabolic syndrome, 77% were overweight/obese, and 9.5% had impaired fasting glucose. PCA was able to recognize that functional microvascular variables and clinical-laboratorial-anthropometrical measurements had a similar variation. The first five principal components explained most of the intrinsic variation of the data. For example, principal component 1 was associated with BMI, waist circumference, systolic BP, diastolic BP, insulin, TG, CRP, and TRBCV(max) varying in the same way. Principal component 1 also showed a strong association among HDL-c, RBCV, and RBCV(max), but in the opposite way. Principal component 3 was associated only with microvascular variables in the same way (functional capillary density, RBCV and RBCV(max)). Fasting plasma glucose appeared to be related to principal component 4 and did not show any association with microvascular reactivity. In non-diabetic female subjects, a multivariate scenario of associations between classic clinical variables strictly related to obesity and metabolic syndrome suggests a significant relationship between these diseases and microvascular reactivity.

Swedish attitudes towards persons with mental illness.

PubMed

Högberg, Torbjörn; Magnusson, Annabella; Lützén, Kim; Ewalds-Kvist, Béatrice

2012-04-01

Negative and stigmatizing attitudes towards persons with mental illness must be dealt with to facilitate the sufferers' social acceptance. The present study aimed at survey Swedish attitudes towards persons with mental illness related to factors impacting these attitudes. New CAMI-S based on the questionnaire "Community Attitudes to Mental Illness in Sweden" ([CAMI] Taylor & Dear, 1981) was developed with nine behavioral-intention items and thus comprised a total of 29 items. Of 5000 Swedish people, 2391 agreed to complete the questionnaire. Principal component analysis rendered four factors reflecting attitudes towards the mentally ill: Intention to Interact, Fearful and Avoidant, Open-minded and Pro-Integration, as well as Community Mental Health Ideology. The factors were analyzed for trends in attitudes. By MANOVA, the experience of mental illness effects on mind-set towards the sufferers was assessed. By means of logistic regression, demographic factors contributing to positive attitudes towards persons with mental illness residing in the neighborhood were assessed. By New CAMI-S, the Swedish attitudes towards the mentally ill were surveyed and trends in agreement with living next to a person with mental illness were revealed in three out of four factors derived by principal component analysis. Aspects impacting the Swedish attitudes towards persons with mental illness and willingness to have him/her residing in the neighborhood comprised experience of mental illness, female gender, age (31-50 years), born in Scandinavia or outside Europe, only 9 years of compulsory school and accommodation in flat. The New CAMI-S came out as a useful tool to screen Swedish attitudes towards persons with mental illness. Most Swedes were prepared to live next to the mentally ill.

Validation of a questionnaire to measure sexual health knowledge and understanding (Sexual Health Questionnaire) in Nepalese secondary school: A psychometric process.

PubMed

Acharya, Dev Raj; Thomas, Malcolm; Cann, Rosemary

2016-01-01

School-based sex education has the potential to prevent unwanted pregnancy and to promote positive sexual health at the individual, family and community level. To develop and validate a sexual health questionnaire to measure young peoples' sexual health knowledge and understanding (SHQ) in Nepalese secondary school. Secondary school students (n = 259, male = 43.63%, female = 56.37%) and local experts (n = 9, male = 90%, female = 10%) were participated in this study. Evaluation processes were; content validity (>0.89), plausibility check (>95), item-total correlation (>0.3), factor loading (>0.4), principal component analysis (4 factors Kaiser's criterion), Chronbach's alpha (>0.65), face validity and internal consistency using test-retest reliability (P > 0.05). The principal component analysis revealed four factors to be extracted; sexual health norms and beliefs, source of sexual health information, sexual health knowledge and understanding, and level of sexual awareness. Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy demonstrated that the patterns of correlations are relatively compact (>0.80). Chronbach's alpha for each factors were above the cut-off point (0.65). Face validity indicated that the questions were clear to the majority of the respondent. Moreover, there were no significant differences (P > 0.05) in the responses to the items at two time points at seven weeks later. The finding suggests that SHQ is a valid and reliable instrument to be used in schools to measure sexual health knowledge and understanding. Further analysis such as structured equation modelling (SEM) and confirmatory factor analysis could make the questionnaire more robust and applicable to the wider school population.

Validation of a questionnaire to measure sexual health knowledge and understanding (Sexual Health Questionnaire) in Nepalese secondary school: A psychometric process

PubMed Central

Acharya, Dev Raj; Thomas, Malcolm; Cann, Rosemary

2016-01-01

Background: School-based sex education has the potential to prevent unwanted pregnancy and to promote positive sexual health at the individual, family and community level. Objectives: To develop and validate a sexual health questionnaire to measure young peoples’ sexual health knowledge and understanding (SHQ) in Nepalese secondary school. Materials and Methods: Secondary school students (n = 259, male = 43.63%, female = 56.37%) and local experts (n = 9, male = 90%, female = 10%) were participated in this study. Evaluation processes were; content validity (>0.89), plausibility check (>95), item-total correlation (>0.3), factor loading (>0.4), principal component analysis (4 factors Kaiser's criterion), Chronbach's alpha (>0.65), face validity and internal consistency using test-retest reliability (P > 0.05). Results: The principal component analysis revealed four factors to be extracted; sexual health norms and beliefs, source of sexual health information, sexual health knowledge and understanding, and level of sexual awareness. Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy demonstrated that the patterns of correlations are relatively compact (>0.80). Chronbach's alpha for each factors were above the cut-off point (0.65). Face validity indicated that the questions were clear to the majority of the respondent. Moreover, there were no significant differences (P > 0.05) in the responses to the items at two time points at seven weeks later. Conclusions: The finding suggests that SHQ is a valid and reliable instrument to be used in schools to measure sexual health knowledge and understanding. Further analysis such as structured equation modelling (SEM) and confirmatory factor analysis could make the questionnaire more robust and applicable to the wider school population. PMID:27500171

The Derivation of Job Compensation Index Values from the Position Analysis Questionnaire (PAQ). Report No. 6.

ERIC Educational Resources Information Center

McCormick, Ernest J.; And Others

The study deals with the job component method of establishing compensation rates. The basic job analysis questionnaire used in the study was the Position Analysis Questionnaire (PAQ) (Form B). On the basis of a principal components analysis of PAQ data for a large sample (2,688) of jobs, a number of principal components (job dimensions) were…

Perceptions of the Principal Evaluation Process and Performance Criteria: A Qualitative Study of the Challenge of Principal Evaluation

ERIC Educational Resources Information Center

Faginski-Stark, Erica; Casavant, Christopher; Collins, William; McCandless, Jason; Tencza, Marilyn

2012-01-01

Recent federal and state mandates have tasked school systems to move beyond principal evaluation as a bureaucratic function and to re-imagine it as a critical component to improve principal performance and compel school renewal. This qualitative study investigated the district leaders' and principals' perceptions of the performance evaluation…

2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications.

PubMed

Du, Qi-Shi; Wang, Shu-Qing; Xie, Neng-Zhong; Wang, Qing-Yan; Huang, Ri-Bo; Chou, Kuo-Chen

2017-09-19

A two-level principal component predictor (2L-PCA) was proposed based on the principal component analysis (PCA) approach. It can be used to quantitatively analyze various compounds and peptides about their functions or potentials to become useful drugs. One level is for dealing with the physicochemical properties of drug molecules, while the other level is for dealing with their structural fragments. The predictor has the self-learning and feedback features to automatically improve its accuracy. It is anticipated that 2L-PCA will become a very useful tool for timely providing various useful clues during the process of drug development.

Lower- extremity biomechanics and maintenance of vertical-jump height during prolonged intermittent exercise.

PubMed

Schmitz, Randy J; Cone, John C; Copple, Timothy J; Henson, Robert A; Shultz, Sandra J

2014-11-01

Potential biomechanical compensations allowing for maintenance of maximal explosive performance during prolonged intermittent exercise, with respect to the corresponding rise in injury rates during the later stages of exercise or competition, are relatively unknown. To identify lower-extremity countermovement-jump (CMJ) biomechanical factors using a principal-components approach and then examine how these factors changed during a 90-min intermittent-exercise protocol (IEP) while maintaining maximal jump height. Mixed-model design. Laboratory. Fifty-nine intermittent-sport athletes (30 male, 29 female) participated in experimental and control conditions. Before and after a dynamic warm-up and every 15 min during the 1st and 2nd halves of an individually prescribed 90-min IEP, participants were assessed on rating of perceived exertion, sprint/cut speed, and 3-dimensional CMJ biomechanics (experimental). On a separate day, the same measures were obtained every 15 min during 90 min of quiet rest (control). Univariate piecewise growth models analyzed progressive changes in CMJ performance and biomechanical factors extracted from a principal-components analysis of the individual biomechanical dependent variables. While CMJ height was maintained during the 1st and 2nd halves, the body descended less and knee kinetic and energetic magnitudes decreased as the IEP progressed. The results indicate that vertical-jump performance is maintained along with progressive biomechanical changes commonly associated with decreased performance. A better understanding of lower-extremity biomechanics during explosive actions in response to IEP allows us to further develop and individualize performance training programs.

Factor Structure of the Internet Addiction Test in Online Gamers and Poker Players

PubMed Central

Achab, Sophia; Billieux, Joel; Thorens, Gabriel; Zullino, Daniele; Dufour, Magali; Rothen, Stéphane

2015-01-01

Background The Internet Addiction Test (IAT) is the most widely used questionnaire to screen for problematic Internet use. Nevertheless, its factorial structure is still debated, which complicates comparisons among existing studies. Most previous studies were performed with students or community samples despite the probability of there being more problematic Internet use among users of specific applications, such as online gaming or gambling. Objective To assess the factorial structure of a modified version of the IAT that addresses specific applications, such as video games and online poker. Methods Two adult samples—one sample of Internet gamers (n=920) and one sample of online poker players (n=214)—were recruited and completed an online version of the modified IAT. Both samples were split into two subsamples. Two principal component analyses (PCAs) followed by two confirmatory factor analyses (CFAs) were run separately. Results The results of principal component analysis indicated that a one-factor model fit the data well across both samples. In consideration of the weakness of some IAT items, a 17-item modified version of the IAT was proposed. Conclusions This study assessed, for the first time, the factorial structure of a modified version of an Internet-administered IAT on a sample of Internet gamers and a sample of online poker players. The scale seems appropriate for the assessment of such online behaviors. Further studies on the modified 17-item IAT version are needed. PMID:26543917

Chemical information obtained from Auger depth profiles by means of advanced factor analysis (MLCFA)

NASA Astrophysics Data System (ADS)

De Volder, P.; Hoogewijs, R.; De Gryse, R.; Fiermans, L.; Vennik, J.

1993-01-01

The advanced multivariate statistical technique "maximum likelihood common factor analysis (MLCFA)" is shown to be superior to "principal component analysis (PCA)" for decomposing overlapping peaks into their individual component spectra of which neither the number of components nor the peak shape of the component spectra is known. An examination of the maximum resolving power of both techniques, MLCFA and PCA, by means of artificially created series of multicomponent spectra confirms this finding unambiguously. Substantial progress in the use of AES as a chemical-analysis technique is accomplished through the implementation of MLCFA. Chemical information from Auger depth profiles is extracted by investigating the variation of the line shape of the Auger signal as a function of the changing chemical state of the element. In particular, MLCFA combined with Auger depth profiling has been applied to problems related to steelcord-rubber tyre adhesion. MLCFA allows one to elucidate the precise nature of the interfacial layer of reaction products between natural rubber vulcanized on a thin brass layer. This study reveals many interesting chemical aspects of the oxi-sulfidation of brass undetectable with classical AES.

Single-trial event-related potentials to significant stimuli.

PubMed

Rushby, Jacqueline A; Barry, Robert J

2009-11-01

The stimulus-response pattern of the skin conductance response (SCR) was used as a model of the Orienting Reflex (OR) to assess the P1, N1, P2, N2 and late positive complex (LPC/P300) components of the ERP in a simple habituation paradigm, in which a single series of 12 innocuous tones were presented at a very long interstimulus interval (2 min). To maintain their waking state during this boring task, participants were instructed to alternately close or open their eyes to each stimulus. None of the baseline-to-peak ERP measures showed trials effects comparable with the marked habituation over trials shown by the SCRs. Principal Components Analysis was used to decompose the ERP, yielding factors identified as the N1, N2, P3a, P3b and Novelty P3 components. An additional factor represented later eye-movement activity. No trial effects were apparent for the N1, N2, P3a or P3b components. The Novelty P3 showed marked response decrement over trials. These results are discussed in relation to current conceptualisations of the OR.

Hydrochemical characteristics and water quality assessment of surface water and groundwater in Songnen plain, Northeast China.

PubMed

Zhang, Bing; Song, Xianfang; Zhang, Yinghua; Han, Dongmei; Tang, Changyuan; Yu, Yilei; Ma, Ying

2012-05-15

Water quality is the critical factor that influence on human health and quantity and quality of grain production in semi-humid and semi-arid area. Songnen plain is one of the grain bases in China, as well as one of the three major distribution regions of soda saline-alkali soil in the world. To assess the water quality, surface water and groundwater were sampled and analyzed by fuzzy membership analysis and multivariate statistics. The surface water were gather into class I, IV and V, while groundwater were grouped as class I, II, III and V by fuzzy membership analysis. The water samples were grouped into four categories according to irrigation water quality assessment diagrams of USDA. Most water samples distributed in category C1-S1, C2-S2 and C3-S3. Three groups were generated from hierarchical cluster analysis. Four principal components were extracted from principal component analysis. The indicators to water quality assessment were Na, HCO(3), NO(3), Fe, Mn and EC from principal component analysis. We conclude that surface water and shallow groundwater are suitable for irrigation, the reservoir and deep groundwater in upstream are the resources for drinking. The water for drinking should remove of the naturally occurring ions of Fe and Mn. The control of sodium and salinity hazard is required for irrigation. The integrated management of surface water and groundwater for drinking and irrigation is to solve the water issues. Copyright © 2012 Elsevier Ltd. All rights reserved.

Rapid 3D Reconstruction for Image Sequence Acquired from UAV Camera

PubMed Central

Qu, Yufu; Huang, Jianyu; Zhang, Xuan

2018-01-01

In order to reconstruct three-dimensional (3D) structures from an image sequence captured by unmanned aerial vehicles’ camera (UAVs) and improve the processing speed, we propose a rapid 3D reconstruction method that is based on an image queue, considering the continuity and relevance of UAV camera images. The proposed approach first compresses the feature points of each image into three principal component points by using the principal component analysis method. In order to select the key images suitable for 3D reconstruction, the principal component points are used to estimate the interrelationships between images. Second, these key images are inserted into a fixed-length image queue. The positions and orientations of the images are calculated, and the 3D coordinates of the feature points are estimated using weighted bundle adjustment. With this structural information, the depth maps of these images can be calculated. Next, we update the image queue by deleting some of the old images and inserting some new images into the queue, and a structural calculation of all the images can be performed by repeating the previous steps. Finally, a dense 3D point cloud can be obtained using the depth–map fusion method. The experimental results indicate that when the texture of the images is complex and the number of images exceeds 100, the proposed method can improve the calculation speed by more than a factor of four with almost no loss of precision. Furthermore, as the number of images increases, the improvement in the calculation speed will become more noticeable. PMID:29342908

Rapid 3D Reconstruction for Image Sequence Acquired from UAV Camera.

PubMed

Qu, Yufu; Huang, Jianyu; Zhang, Xuan

2018-01-14

In order to reconstruct three-dimensional (3D) structures from an image sequence captured by unmanned aerial vehicles' camera (UAVs) and improve the processing speed, we propose a rapid 3D reconstruction method that is based on an image queue, considering the continuity and relevance of UAV camera images. The proposed approach first compresses the feature points of each image into three principal component points by using the principal component analysis method. In order to select the key images suitable for 3D reconstruction, the principal component points are used to estimate the interrelationships between images. Second, these key images are inserted into a fixed-length image queue. The positions and orientations of the images are calculated, and the 3D coordinates of the feature points are estimated using weighted bundle adjustment. With this structural information, the depth maps of these images can be calculated. Next, we update the image queue by deleting some of the old images and inserting some new images into the queue, and a structural calculation of all the images can be performed by repeating the previous steps. Finally, a dense 3D point cloud can be obtained using the depth-map fusion method. The experimental results indicate that when the texture of the images is complex and the number of images exceeds 100, the proposed method can improve the calculation speed by more than a factor of four with almost no loss of precision. Furthermore, as the number of images increases, the improvement in the calculation speed will become more noticeable.

Experimental Researches on the Durability Indicators and the Physiological Comfort of Fabrics using the Principal Component Analysis (PCA) Method

NASA Astrophysics Data System (ADS)

Hristian, L.; Ostafe, M. M.; Manea, L. R.; Apostol, L. L.

2017-06-01

The work pursued the distribution of combed wool fabrics destined to manufacturing of external articles of clothing in terms of the values of durability and physiological comfort indices, using the mathematical model of Principal Component Analysis (PCA). Principal Components Analysis (PCA) applied in this study is a descriptive method of the multivariate analysis/multi-dimensional data, and aims to reduce, under control, the number of variables (columns) of the matrix data as much as possible to two or three. Therefore, based on the information about each group/assortment of fabrics, it is desired that, instead of nine inter-correlated variables, to have only two or three new variables called components. The PCA target is to extract the smallest number of components which recover the most of the total information contained in the initial data.

Information extraction from multivariate images

NASA Technical Reports Server (NTRS)

Park, S. K.; Kegley, K. A.; Schiess, J. R.

1986-01-01

An overview of several multivariate image processing techniques is presented, with emphasis on techniques based upon the principal component transformation (PCT). Multiimages in various formats have a multivariate pixel value, associated with each pixel location, which has been scaled and quantized into a gray level vector, and the bivariate of the extent to which two images are correlated. The PCT of a multiimage decorrelates the multiimage to reduce its dimensionality and reveal its intercomponent dependencies if some off-diagonal elements are not small, and for the purposes of display the principal component images must be postprocessed into multiimage format. The principal component analysis of a multiimage is a statistical analysis based upon the PCT whose primary application is to determine the intrinsic component dimensionality of the multiimage. Computational considerations are also discussed.

Principal Component Clustering Approach to Teaching Quality Discriminant Analysis

ERIC Educational Resources Information Center

Xian, Sidong; Xia, Haibo; Yin, Yubo; Zhai, Zhansheng; Shang, Yan

2016-01-01

Teaching quality is the lifeline of the higher education. Many universities have made some effective achievement about evaluating the teaching quality. In this paper, we establish the Students' evaluation of teaching (SET) discriminant analysis model and algorithm based on principal component clustering analysis. Additionally, we classify the SET…

Analysis of the principal component algorithm in phase-shifting interferometry.

PubMed

Vargas, J; Quiroga, J Antonio; Belenguer, T

2011-06-15

We recently presented a new asynchronous demodulation method for phase-sampling interferometry. The method is based in the principal component analysis (PCA) technique. In the former work, the PCA method was derived heuristically. In this work, we present an in-depth analysis of the PCA demodulation method.

Psychometric Measurement Models and Artificial Neural Networks

ERIC Educational Resources Information Center

Sese, Albert; Palmer, Alfonso L.; Montano, Juan J.

2004-01-01

The study of measurement models in psychometrics by means of dimensionality reduction techniques such as Principal Components Analysis (PCA) is a very common practice. In recent times, an upsurge of interest in the study of artificial neural networks apt to computing a principal component extraction has been observed. Despite this interest, the…

Burst and Principal Components Analyses of MEA Data for 16 Chemicals Describe at Least Three Effects Classes.

EPA Science Inventory

Microelectrode arrays (MEAs) detect drug and chemical induced changes in neuronal network function and have been used for neurotoxicity screening. As a proof-•of-concept, the current study assessed the utility of analytical "fingerprinting" using Principal Components Analysis (P...

Incremental principal component pursuit for video background modeling

DOEpatents

Rodriquez-Valderrama, Paul A.; Wohlberg, Brendt

2017-03-14

An incremental Principal Component Pursuit (PCP) algorithm for video background modeling that is able to process one frame at a time while adapting to changes in background, with a computational complexity that allows for real-time processing, having a low memory footprint and is robust to translational and rotational jitter.

Genetic and Environmental Influences on Female Sexual Orientation, Childhood Gender Typicality and Adult Gender Identity

PubMed Central

Burri, Andrea; Cherkas, Lynn; Spector, Timothy; Rahman, Qazi

2011-01-01

Background Human sexual orientation is influenced by genetic and non-shared environmental factors as are two important psychological correlates – childhood gender typicality (CGT) and adult gender identity (AGI). However, researchers have been unable to resolve the genetic and non-genetic components that contribute to the covariation between these traits, particularly in women. Methodology/Principal Findings Here we performed a multivariate genetic analysis in a large sample of British female twins (N = 4,426) who completed a questionnaire assessing sexual attraction, CGT and AGI. Univariate genetic models indicated modest genetic influences on sexual attraction (25%), AGI (11%) and CGT (31%). For the multivariate analyses, a common pathway model best fitted the data. Conclusions/Significance This indicated that a single latent variable influenced by a genetic component and common non-shared environmental component explained the association between the three traits but there was substantial measurement error. These findings highlight common developmental factors affecting differences in sexual orientation. PMID:21760939

Exploratory Development Research of U.S. Navy Marine Corps Personnel, Phase 1. Factors Affecting Attrition

DTIC Science & Technology

1977-09-30

havior. He has taught courses in research and has had con- siderable experience in the design and analysis of qualitative and quantitative research...name, serial number, unit, rank, or other identifying information was asked. Research pesign The research design was developed to determine if causes of...attrition have similar impach on men in various situations. The design includes three principal components. First, both attriters and adjusted men

Dynamic competitive probabilistic principal components analysis.

PubMed

López-Rubio, Ezequiel; Ortiz-DE-Lazcano-Lobato, Juan Miguel

2009-04-01

We present a new neural model which extends the classical competitive learning (CL) by performing a Probabilistic Principal Components Analysis (PPCA) at each neuron. The model also has the ability to learn the number of basis vectors required to represent the principal directions of each cluster, so it overcomes a drawback of most local PCA models, where the dimensionality of a cluster must be fixed a priori. Experimental results are presented to show the performance of the network with multispectral image data.

Further validation of the Maudsley Marital Questionnaire (MMQ).

PubMed

Joseph, Orathinkal; Alfons, Vansteenwegen; Rob, Stroobants

2007-05-01

This study examined the Factor structure and internal consistency of the Dutch version of the Maudsley Marital Questionnaire (MMQ) for a sample of 787 respondents in Belgium. Principal components analysis, followed by varimax orthogonal rotation, identified the expected three factors (M - Marital, S - Sexual, GL - General Life), which jointly explained 53% of the variance. The Cronbach alpha for MMQ - M (.90) and MMQ - S (.80) were of high value while for MMQ - GL (.66) was lower. The MMQ scales were found significantly correlated with each other: M - S, .60; M - GL, .46 and S - GL, .33.

The work and social adjustment scale: reliability, sensitivity and value.

PubMed

Zahra, Daniel; Qureshi, Adam; Henley, William; Taylor, Rod; Quinn, Cath; Pooler, Jill; Hardy, Gillian; Newbold, Alexandra; Byng, Richard

2014-06-01

To investigate the psychometric properties of the Work and Social Adjustment Scale (WSAS) as an outcome measure for the Improving Access to Psychological Therapy programme, assessing its value as an addition to the Patient Health (PHQ-9) and Generalised Anxiety Disorder questionnaires (GAD-7). Little research has investigated these properties to date. Reliability and responsiveness to change were assessed using data from 4,835 patients. Principal components analysis was used to determine whether the WSAS measures a factor distinct from the PHQ-9 and GAD-7. The WSAS measures a distinct social functioning factor, has high internal reliability, and is sensitive to treatment effects. The WSAS, PHQ-9 and GAD-7 perform comparably on measures of reliability and sensitivity. The WSAS also measures a distinct social functioning component suggesting it has potential as an additional outcome measure.

A principal components model of soundscape perception.

PubMed

Axelsson, Östen; Nilsson, Mats E; Berglund, Birgitta

2010-11-01

There is a need for a model that identifies underlying dimensions of soundscape perception, and which may guide measurement and improvement of soundscape quality. With the purpose to develop such a model, a listening experiment was conducted. One hundred listeners measured 50 excerpts of binaural recordings of urban outdoor soundscapes on 116 attribute scales. The average attribute scale values were subjected to principal components analysis, resulting in three components: Pleasantness, eventfulness, and familiarity, explaining 50, 18 and 6% of the total variance, respectively. The principal-component scores were correlated with physical soundscape properties, including categories of dominant sounds and acoustic variables. Soundscape excerpts dominated by technological sounds were found to be unpleasant, whereas soundscape excerpts dominated by natural sounds were pleasant, and soundscape excerpts dominated by human sounds were eventful. These relationships remained after controlling for the overall soundscape loudness (Zwicker's N(10)), which shows that 'informational' properties are substantial contributors to the perception of soundscape. The proposed principal components model provides a framework for future soundscape research and practice. In particular, it suggests which basic dimensions are necessary to measure, how to measure them by a defined set of attribute scales, and how to promote high-quality soundscapes.

Components of self-esteem in affective patients and non-psychiatric controls.

PubMed

Serretti, Alessandro; Olgiati, Paolo; Colombo, Cristina

2005-09-01

Decrease in self-esteem (SE) is found in all mood disorders during inter-episode phases. This trait was associated with relapse and suicidality but its genetic basis is still undefined, probably because SE has multiple components. The aim of the current study was to ascertain which of those components were altered in a sample of affective patients. Three hundred and thirty-one outpatients with bipolar (N=199) and major depressive MD (N=132) disorders in remission for at least three months and one hundred controls completed the Rosenberg Self-esteem Scale (RSE; [Rosenberg, M., 1965. The measurement of self-esteem, Society and the Adolescent Self-Image. Princeton University Press, pp.16-36]). Principal component analysis was performed to identify RSE factor structure. Extracted factors were compared across case and control groups in the whole sample (N=431) and in a sub-sample (N=301) with low self-esteem (RSE <20). PCA yielded a two-factor solution with self-confidence (SC) and self-deprecation (SD) that was largely consistent with the existing literature. Such factors were both associated with lower scores in affective patients than controls (SC: F=52, p<0.01; SD: F=43, p<0.01). However in the low RSE group only self-confidence was found to be decreased in subjects with mood disorders (SC: F=13.8, p<0.01; SD: F=0.05, p=0.9). These findings suggest that self-esteem deficit in affective disorders might involve specific components. Implications for research and clinical practice are discussed.

Factor analysis of the Hamilton Depression Rating Scale in Parkinson's disease.

PubMed

Broen, M P G; Moonen, A J H; Kuijf, M L; Dujardin, K; Marsh, L; Richard, I H; Starkstein, S E; Martinez-Martin, P; Leentjens, A F G

2015-02-01

Several studies have validated the Hamilton Depression Rating Scale (HAMD) in patients with Parkinson's disease (PD), and reported adequate reliability and construct validity. However, the factorial validity of the HAMD has not yet been investigated. The aim of our analysis was to explore the factor structure of the HAMD in a large sample of PD patients. A principal component analysis of the 17-item HAMD was performed on data of 341 PD patients, available from a previous cross sectional study on anxiety. An eigenvalue ≥1 was used to determine the number of factors. Factor loadings ≥0.4 in combination with oblique rotations were used to identify which variables made up the factors. Kaiser-Meyer-Olkin measure (KMO), Cronbach's alpha, Bartlett's test, communality, percentage of non-redundant residuals and the component correlation matrix were computed to assess factor validity. KMO verified the sample's adequacy for factor analysis and Cronbach's alpha indicated a good internal consistency of the total scale. Six factors had eigenvalues ≥1 and together explained 59.19% of the variance. The number of items per factor varied from 1 to 6. Inter-item correlations within each component were low. There was a high percentage of non-redundant residuals and low communality. This analysis demonstrates that the factorial validity of the HAMD in PD is unsatisfactory. This implies that the scale is not appropriate for studying specific symptom domains of depression based on factorial structure in a PD population. Copyright © 2014 Elsevier Ltd. All rights reserved.

Application of principal component analysis in protein unfolding: an all-atom molecular dynamics simulation study.

PubMed

Das, Atanu; Mukhopadhyay, Chaitali

2007-10-28

We have performed molecular dynamics (MD) simulation of the thermal denaturation of one protein and one peptide-ubiquitin and melittin. To identify the correlation in dynamics among various secondary structural fragments and also the individual contribution of different residues towards thermal unfolding, principal component analysis method was applied in order to give a new insight to protein dynamics by analyzing the contribution of coefficients of principal components. The cross-correlation matrix obtained from MD simulation trajectory provided important information regarding the anisotropy of backbone dynamics that leads to unfolding. Unfolding of ubiquitin was found to be a three-state process, while that of melittin, though smaller and mostly helical, is more complicated.

Application of principal component analysis in protein unfolding: An all-atom molecular dynamics simulation study

NASA Astrophysics Data System (ADS)

Das, Atanu; Mukhopadhyay, Chaitali

2007-10-01

We have performed molecular dynamics (MD) simulation of the thermal denaturation of one protein and one peptide—ubiquitin and melittin. To identify the correlation in dynamics among various secondary structural fragments and also the individual contribution of different residues towards thermal unfolding, principal component analysis method was applied in order to give a new insight to protein dynamics by analyzing the contribution of coefficients of principal components. The cross-correlation matrix obtained from MD simulation trajectory provided important information regarding the anisotropy of backbone dynamics that leads to unfolding. Unfolding of ubiquitin was found to be a three-state process, while that of melittin, though smaller and mostly helical, is more complicated.

SAS program for quantitative stratigraphic correlation by principal components

USGS Publications Warehouse

Hohn, M.E.

1985-01-01

A SAS program is presented which constructs a composite section of stratigraphic events through principal components analysis. The variables in the analysis are stratigraphic sections and the observational units are range limits of taxa. The program standardizes data in each section, extracts eigenvectors, estimates missing range limits, and computes the composite section from scores of events on the first principal component. Provided is an option of several types of diagnostic plots; these help one to determine conservative range limits or unrealistic estimates of missing values. Inspection of the graphs and eigenvalues allow one to evaluate goodness of fit between the composite and measured data. The program is extended easily to the creation of a rank-order composite. ?? 1985.

A novel principal component analysis for spatially misaligned multivariate air pollution data.

PubMed

Jandarov, Roman A; Sheppard, Lianne A; Sampson, Paul D; Szpiro, Adam A

2017-01-01

We propose novel methods for predictive (sparse) PCA with spatially misaligned data. These methods identify principal component loading vectors that explain as much variability in the observed data as possible, while also ensuring the corresponding principal component scores can be predicted accurately by means of spatial statistics at locations where air pollution measurements are not available. This will make it possible to identify important mixtures of air pollutants and to quantify their health effects in cohort studies, where currently available methods cannot be used. We demonstrate the utility of predictive (sparse) PCA in simulated data and apply the approach to annual averages of particulate matter speciation data from national Environmental Protection Agency (EPA) regulatory monitors.

Principals' Perceptions of Collegial Support as a Component of Administrative Inservice.

ERIC Educational Resources Information Center

Daresh, John C.

To address the problem of increasing professional isolation of building administrators, the Principals' Inservice Project helps establish principals' collegial support groups across the nation. The groups are typically composed of 6 to 10 principals who meet at least once each month over a 2-year period. One collegial support group of seven…

Training the Trainers: Learning to Be a Principal Supervisor

ERIC Educational Resources Information Center

Saltzman, Amy

2017-01-01

While most principal supervisors are former principals themselves, few come to the role with specific training in how to do the job effectively. For this reason, both the Washington, D.C., and Tulsa, Oklahoma, principal supervisor programs include a strong professional development component. In this article, the author takes a look inside these…

Use of Geochemistry Data Collected by the Mars Exploration Rover Spirit in Gusev Crater to Teach Geomorphic Zonation through Principal Components Analysis

ERIC Educational Resources Information Center

Rodrigue, Christine M.

2011-01-01

This paper presents a laboratory exercise used to teach principal components analysis (PCA) as a means of surface zonation. The lab was built around abundance data for 16 oxides and elements collected by the Mars Exploration Rover Spirit in Gusev Crater between Sol 14 and Sol 470. Students used PCA to reduce 15 of these into 3 components, which,…

A Principal Components Analysis and Validation of the Coping with the College Environment Scale (CWCES)

ERIC Educational Resources Information Center

Ackermann, Margot Elise; Morrow, Jennifer Ann

2008-01-01

The present study describes the development and initial validation of the Coping with the College Environment Scale (CWCES). Participants included 433 college students who took an online survey. Principal Components Analysis (PCA) revealed six coping strategies: planning and self-management, seeking support from institutional resources, escaping…

Wavelet based de-noising of breath air absorption spectra profiles for improved classification by principal component analysis

NASA Astrophysics Data System (ADS)

Kistenev, Yu. V.; Shapovalov, A. V.; Borisov, A. V.; Vrazhnov, D. A.; Nikolaev, V. V.; Nikiforova, O. Yu.

2015-11-01

The comparison results of different mother wavelets used for de-noising of model and experimental data which were presented by profiles of absorption spectra of exhaled air are presented. The impact of wavelets de-noising on classification quality made by principal component analysis are also discussed.

Evaluation of skin melanoma in spectral range 450-950 nm using principal component analysis

NASA Astrophysics Data System (ADS)

Jakovels, D.; Lihacova, I.; Kuzmina, I.; Spigulis, J.

2013-06-01

Diagnostic potential of principal component analysis (PCA) of multi-spectral imaging data in the wavelength range 450- 950 nm for distant skin melanoma recognition is discussed. Processing of the measured clinical data by means of PCA resulted in clear separation between malignant melanomas and pigmented nevi.

Stability of Nonlinear Principal Components Analysis: An Empirical Study Using the Balanced Bootstrap

ERIC Educational Resources Information Center

Linting, Marielle; Meulman, Jacqueline J.; Groenen, Patrick J. F.; van der Kooij, Anita J.

2007-01-01

Principal components analysis (PCA) is used to explore the structure of data sets containing linearly related numeric variables. Alternatively, nonlinear PCA can handle possibly nonlinearly related numeric as well as nonnumeric variables. For linear PCA, the stability of its solution can be established under the assumption of multivariate…

40 CFR 60.2998 - What are the principal components of the model rule?

Code of Federal Regulations, 2012 CFR

2012-07-01

... the model rule? 60.2998 Section 60.2998 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines... December 9, 2004 Model Rule-Use of Model Rule § 60.2998 What are the principal components of the model rule...

40 CFR 60.2998 - What are the principal components of the model rule?

Code of Federal Regulations, 2014 CFR

2014-07-01

... the model rule? 60.2998 Section 60.2998 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines... December 9, 2004 Model Rule-Use of Model Rule § 60.2998 What are the principal components of the model rule...

40 CFR 60.2998 - What are the principal components of the model rule?

Code of Federal Regulations, 2011 CFR

2011-07-01

... the model rule? 60.2998 Section 60.2998 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines... December 9, 2004 Model Rule-Use of Model Rule § 60.2998 What are the principal components of the model rule...

40 CFR 60.1580 - What are the principal components of the model rule?

Code of Federal Regulations, 2010 CFR

2010-07-01

... the model rule? 60.1580 Section 60.1580 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines..., 1999 Use of Model Rule § 60.1580 What are the principal components of the model rule? The model rule...

40 CFR 60.2998 - What are the principal components of the model rule?

Code of Federal Regulations, 2013 CFR

2013-07-01

... the model rule? 60.2998 Section 60.2998 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines... December 9, 2004 Model Rule-Use of Model Rule § 60.2998 What are the principal components of the model rule...

Students' Perceptions of Teaching and Learning Practices: A Principal Component Approach

ERIC Educational Resources Information Center

Mukorera, Sophia; Nyatanga, Phocenah

2017-01-01

Students' attendance and engagement with teaching and learning practices is perceived as a critical element for academic performance. Even with stipulated attendance policies, students still choose not to engage. The study employed a principal component analysis to analyze first- and second-year students' perceptions of the importance of the 12…

Principal Perspectives about Policy Components and Practices for Reducing Cyberbullying in Urban Schools

ERIC Educational Resources Information Center

Hunley-Jenkins, Keisha Janine

2012-01-01

This qualitative study explores large, urban, mid-western principal perspectives about cyberbullying and the policy components and practices that they have found effective and ineffective at reducing its occurrence and/or negative effect on their schools' learning environments. More specifically, the researcher was interested in learning more…

Principal Component Analysis: Resources for an Essential Application of Linear Algebra

ERIC Educational Resources Information Center

Pankavich, Stephen; Swanson, Rebecca

2015-01-01

Principal Component Analysis (PCA) is a highly useful topic within an introductory Linear Algebra course, especially since it can be used to incorporate a number of applied projects. This method represents an essential application and extension of the Spectral Theorem and is commonly used within a variety of fields, including statistics,…

Learning Principal Component Analysis by Using Data from Air Quality Networks

ERIC Educational Resources Information Center

Perez-Arribas, Luis Vicente; Leon-González, María Eugenia; Rosales-Conrado, Noelia

2017-01-01

With the final objective of using computational and chemometrics tools in the chemistry studies, this paper shows the methodology and interpretation of the Principal Component Analysis (PCA) using pollution data from different cities. This paper describes how students can obtain data on air quality and process such data for additional information…

Applications of Nonlinear Principal Components Analysis to Behavioral Data.

ERIC Educational Resources Information Center

Hicks, Marilyn Maginley

1981-01-01

An empirical investigation of the statistical procedure entitled nonlinear principal components analysis was conducted on a known equation and on measurement data in order to demonstrate the procedure and examine its potential usefulness. This method was suggested by R. Gnanadesikan and based on an early paper of Karl Pearson. (Author/AL)

Relationships between Association of Research Libraries (ARL) Statistics and Bibliometric Indicators: A Principal Components Analysis

ERIC Educational Resources Information Center

Hendrix, Dean

2010-01-01

This study analyzed 2005-2006 Web of Science bibliometric data from institutions belonging to the Association of Research Libraries (ARL) and corresponding ARL statistics to find any associations between indicators from the two data sets. Principal components analysis on 36 variables from 103 universities revealed obvious associations between…

A construction and validation of a Freshman Stress Questionnaire: an exploratory study.

PubMed

Boujut, Emilie; Bruchon-Schweitzer, Marilou

2009-04-01

A stress questionnaire for freshmen was developed and administered to 1,102 French students at the beginning of the term (T1). A Principal Component Analysis of responses, followed by varimax and oblique rotations, yielded four factors accounting for 58% of the total variance. Factors were identified as academic stress, university's dysfunctions, feelings of loneliness, and problems with close relations. Associations were observed between scores on these factors and on personal variables measured at the same time (T1), namely, neuroticism, self-esteem, and two coping strategies, as well as variables measured at the end of the term (T2), depressive symptoms, eating disorders, somatic symptoms, and life satisfaction.

Principal component analysis for protein folding dynamics.

PubMed

Maisuradze, Gia G; Liwo, Adam; Scheraga, Harold A

2009-01-09

Protein folding is considered here by studying the dynamics of the folding of the triple beta-strand WW domain from the Formin-binding protein 28. Starting from the unfolded state and ending either in the native or nonnative conformational states, trajectories are generated with the coarse-grained united residue (UNRES) force field. The effectiveness of principal components analysis (PCA), an already established mathematical technique for finding global, correlated motions in atomic simulations of proteins, is evaluated here for coarse-grained trajectories. The problems related to PCA and their solutions are discussed. The folding and nonfolding of proteins are examined with free-energy landscapes. Detailed analyses of many folding and nonfolding trajectories at different temperatures show that PCA is very efficient for characterizing the general folding and nonfolding features of proteins. It is shown that the first principal component captures and describes in detail the dynamics of a system. Anomalous diffusion in the folding/nonfolding dynamics is examined by the mean-square displacement (MSD) and the fractional diffusion and fractional kinetic equations. The collisionless (or ballistic) behavior of a polypeptide undergoing Brownian motion along the first few principal components is accounted for.

Principal Component 2-D Long Short-Term Memory for Font Recognition on Single Chinese Characters.

PubMed

Tao, Dapeng; Lin, Xu; Jin, Lianwen; Li, Xuelong

2016-03-01

Chinese character font recognition (CCFR) has received increasing attention as the intelligent applications based on optical character recognition becomes popular. However, traditional CCFR systems do not handle noisy data effectively. By analyzing in detail the basic strokes of Chinese characters, we propose that font recognition on a single Chinese character is a sequence classification problem, which can be effectively solved by recurrent neural networks. For robust CCFR, we integrate a principal component convolution layer with the 2-D long short-term memory (2DLSTM) and develop principal component 2DLSTM (PC-2DLSTM) algorithm. PC-2DLSTM considers two aspects: 1) the principal component layer convolution operation helps remove the noise and get a rational and complete font information and 2) simultaneously, 2DLSTM deals with the long-range contextual processing along scan directions that can contribute to capture the contrast between character trajectory and background. Experiments using the frequently used CCFR dataset suggest the effectiveness of PC-2DLSTM compared with other state-of-the-art font recognition methods.

Dynamic of consumer groups and response of commodity markets by principal component analysis

NASA Astrophysics Data System (ADS)

Nobi, Ashadun; Alam, Shafiqul; Lee, Jae Woo

2017-09-01

This study investigates financial states and group dynamics by applying principal component analysis to the cross-correlation coefficients of the daily returns of commodity futures. The eigenvalues of the cross-correlation matrix in the 6-month timeframe displays similar values during 2010-2011, but decline following 2012. A sharp drop in eigenvalue implies the significant change of the market state. Three commodity sectors, energy, metals and agriculture, are projected into two dimensional spaces consisting of two principal components (PC). We observe that they form three distinct clusters in relation to various sectors. However, commodities with distinct features have intermingled with one another and scattered during severe crises, such as the European sovereign debt crises. We observe the notable change of the position of two dimensional spaces of groups during financial crises. By considering the first principal component (PC1) within the 6-month moving timeframe, we observe that commodities of the same group change states in a similar pattern, and the change of states of one group can be used as a warning for other group.

[Determination and principal component analysis of mineral elements based on ICP-OES in Nitraria roborowskii fruits from different regions].

PubMed

Yuan, Yuan-Yuan; Zhou, Yu-Bi; Sun, Jing; Deng, Juan; Bai, Ying; Wang, Jie; Lu, Xue-Feng

2017-06-01

The content of elements in fifteen different regions of Nitraria roborowskii samples were determined by inductively coupled plasma-atomic emission spectrometry(ICP-OES), and its elemental characteristics were analyzed by principal component analysis. The results indicated that 18 mineral elements were detected in N. roborowskii of which V cannot be detected. In addition, contents of Na, K and Ca showed high concentration. Ti showed maximum content variance, while K is minimum. Four principal components were gained from the original data. The cumulative variance contribution rate is 81.542% and the variance contribution of the first principal component was 44.997%, indicating that Cr, Fe, P and Ca were the characteristic elements of N. roborowskii.Thus, the established method was simple, precise and can be used for determination of mineral elements in N.roborowskii Kom. fruits. The elemental distribution characteristics among N.roborowskii fruits are related to geographical origins which were clearly revealed by PCA. All the results will provide good basis for comprehensive utilization of N.roborowskii. Copyright© by the Chinese Pharmaceutical Association.

[Applications of three-dimensional fluorescence spectrum of dissolved organic matter to identification of red tide algae].

PubMed

Lü, Gui-Cai; Zhao, Wei-Hong; Wang, Jiang-Tao

2011-01-01

The identification techniques for 10 species of red tide algae often found in the coastal areas of China were developed by combining the three-dimensional fluorescence spectra of fluorescence dissolved organic matter (FDOM) from the cultured red tide algae with principal component analysis. Based on the results of principal component analysis, the first principal component loading spectrum of three-dimensional fluorescence spectrum was chosen as the identification characteristic spectrum for red tide algae, and the phytoplankton fluorescence characteristic spectrum band was established. Then the 10 algae species were tested using Bayesian discriminant analysis with a correct identification rate of more than 92% for Pyrrophyta on the level of species, and that of more than 75% for Bacillariophyta on the level of genus in which the correct identification rates were more than 90% for the phaeodactylum and chaetoceros. The results showed that the identification techniques for 10 species of red tide algae based on the three-dimensional fluorescence spectra of FDOM from the cultured red tide algae and principal component analysis could work well.

Stationary Wavelet-based Two-directional Two-dimensional Principal Component Analysis for EMG Signal Classification

NASA Astrophysics Data System (ADS)

Ji, Yi; Sun, Shanlin; Xie, Hong-Bo

2017-06-01

Discrete wavelet transform (WT) followed by principal component analysis (PCA) has been a powerful approach for the analysis of biomedical signals. Wavelet coefficients at various scales and channels were usually transformed into a one-dimensional array, causing issues such as the curse of dimensionality dilemma and small sample size problem. In addition, lack of time-shift invariance of WT coefficients can be modeled as noise and degrades the classifier performance. In this study, we present a stationary wavelet-based two-directional two-dimensional principal component analysis (SW2D2PCA) method for the efficient and effective extraction of essential feature information from signals. Time-invariant multi-scale matrices are constructed in the first step. The two-directional two-dimensional principal component analysis then operates on the multi-scale matrices to reduce the dimension, rather than vectors in conventional PCA. Results are presented from an experiment to classify eight hand motions using 4-channel electromyographic (EMG) signals recorded in healthy subjects and amputees, which illustrates the efficiency and effectiveness of the proposed method for biomedical signal analysis.

Hyperspectral optical imaging of human iris in vivo: characteristics of reflectance spectra

NASA Astrophysics Data System (ADS)

Medina, José M.; Pereira, Luís M.; Correia, Hélder T.; Nascimento, Sérgio M. C.

2011-07-01

We report a hyperspectral imaging system to measure the reflectance spectra of real human irises with high spatial resolution. A set of ocular prosthesis was used as the control condition. Reflectance data were decorrelated by the principal-component analysis. The main conclusion is that spectral complexity of the human iris is considerable: between 9 and 11 principal components are necessary to account for 99% of the cumulative variance in human irises. Correcting image misalignments associated with spontaneous ocular movements did not influence this result. The data also suggests a correlation between the first principal component and different levels of melanin present in the irises. It was also found that although the spectral characteristics of the first five principal components were not affected by the radial and angular position of the selected iridal areas, they affect the higher-order ones, suggesting a possible influence of the iris texture. The results show that hyperspectral imaging in the iris, together with adequate spectroscopic analyses provide more information than conventional colorimetric methods, making hyperspectral imaging suitable for the characterization of melanin and the noninvasive diagnosis of ocular diseases and iris color.

Seeing wholes: The concept of systems thinking and its implementation in school leadership

NASA Astrophysics Data System (ADS)

Shaked, Haim; Schechter, Chen

2013-12-01

Systems thinking (ST) is an approach advocating thinking about any given issue as a whole, emphasising the interrelationships between its components rather than the components themselves. This article aims to link ST and school leadership, claiming that ST may enable school principals to develop highly performing schools that can cope successfully with current challenges, which are more complex than ever before in today's era of accountability and high expectations. The article presents the concept of ST - its definition, components, history and applications. Thereafter, its connection to education and its contribution to school management are described. The article concludes by discussing practical processes including screening for ST-skilled principal candidates and developing ST skills among prospective and currently performing school principals, pinpointing three opportunities for skills acquisition: during preparatory programmes; during their first years on the job, supported by veteran school principals as mentors; and throughout their entire career. Such opportunities may not only provide school principals with ST skills but also improve their functioning throughout the aforementioned stages of professional development.

A modified procedure for mixture-model clustering of regional geochemical data

USGS Publications Warehouse

Ellefsen, Karl J.; Smith, David B.; Horton, John D.

2014-01-01

A modified procedure is proposed for mixture-model clustering of regional-scale geochemical data. The key modification is the robust principal component transformation of the isometric log-ratio transforms of the element concentrations. This principal component transformation and the associated dimension reduction are applied before the data are clustered. The principal advantage of this modification is that it significantly improves the stability of the clustering. The principal disadvantage is that it requires subjective selection of the number of clusters and the number of principal components. To evaluate the efficacy of this modified procedure, it is applied to soil geochemical data that comprise 959 samples from the state of Colorado (USA) for which the concentrations of 44 elements are measured. The distributions of element concentrations that are derived from the mixture model and from the field samples are similar, indicating that the mixture model is a suitable representation of the transformed geochemical data. Each cluster and the associated distributions of the element concentrations are related to specific geologic and anthropogenic features. In this way, mixture model clustering facilitates interpretation of the regional geochemical data.

Phases of female sexual response cycle among Malaysian women with infertility: a factor analysis study.

PubMed

Seen Heng, Yeoh; Sidi, Hatta; Nik Jaafar, Nik Ruzyanei; Razali, Rosdinom; Ram, Hari

2013-04-01

This cross-sectional study aimed to determine the construct of the phases of the female sexual response cycle (SRC) among women attending an infertility clinic in a Malaysian tertiary center. The sexual response phases were measured with a validated Malay version of the Female Sexual Function Index (FSFI). The correlation structure of the items of the SRC phases (i.e. desire, arousal, orgasm, satisfaction and pain) was determined using principal component analysis (PCA), with varimax rotation method. The number of factors obtained was decided using Kaiser's criteria. A total of 150 married women with a mean age of 32 years participated in this study. Factor loadings using PCA with varimax rotation divided the sexual domains into three components. The first construct comprised sexual arousal, lubrication and pain (suggesting a mechanical component). The second construct were orgasm and sexual satisfaction (suggesting a physical achievement). Sexual desire, suggesting a psychological component, stood on its own as the third. The findings suggest that three constructs could be identified and in favor of the Basson model (a non-linear concept of SRC) for Malaysian women's sexual functioning. Understanding this would help clinicians to strategize the treatment approach of sexual dysfunction in women with infertility. Copyright © 2013 Wiley Publishing Asia Pty Ltd.

Temporal evolution of financial-market correlations.

PubMed

Fenn, Daniel J; Porter, Mason A; Williams, Stacy; McDonald, Mark; Johnson, Neil F; Jones, Nick S

2011-08-01

We investigate financial market correlations using random matrix theory and principal component analysis. We use random matrix theory to demonstrate that correlation matrices of asset price changes contain structure that is incompatible with uncorrelated random price changes. We then identify the principal components of these correlation matrices and demonstrate that a small number of components accounts for a large proportion of the variability of the markets that we consider. We characterize the time-evolving relationships between the different assets by investigating the correlations between the asset price time series and principal components. Using this approach, we uncover notable changes that occurred in financial markets and identify the assets that were significantly affected by these changes. We show in particular that there was an increase in the strength of the relationships between several different markets following the 2007-2008 credit and liquidity crisis.

Temporal evolution of financial-market correlations

NASA Astrophysics Data System (ADS)

Fenn, Daniel J.; Porter, Mason A.; Williams, Stacy; McDonald, Mark; Johnson, Neil F.; Jones, Nick S.

2011-08-01

We investigate financial market correlations using random matrix theory and principal component analysis. We use random matrix theory to demonstrate that correlation matrices of asset price changes contain structure that is incompatible with uncorrelated random price changes. We then identify the principal components of these correlation matrices and demonstrate that a small number of components accounts for a large proportion of the variability of the markets that we consider. We characterize the time-evolving relationships between the different assets by investigating the correlations between the asset price time series and principal components. Using this approach, we uncover notable changes that occurred in financial markets and identify the assets that were significantly affected by these changes. We show in particular that there was an increase in the strength of the relationships between several different markets following the 2007-2008 credit and liquidity crisis.

Factor analysis of Wechsler Adult Intelligence Scale-Revised in developmentally disabled persons.

PubMed

Di Nuovo, Santo F; Buono, Serafino

2006-12-01

The results of previous studies on the factorial structure of Wechsler Intelligence Scales are somewhat inconsistent across normal and pathological samples. To study specific clinical groups, such as developmentally disabled persons, it is useful to examine the factor structure in appropriate samples. A factor analysis was carried out using the principal component method and the Varimax orthogonal rotation on the Wechsler Adult Intelligence Scale (WAIS-R) in a sample of 203 developmentally disabled persons, with a mean age of 25 years 4 months. Developmental disability ranged from mild to moderate. Partially contrasting with previous studies on normal samples, results found a two-factor solution. Wechsler's traditional Verbal and Performance scales seems to be more appropriate for this sample than the alternative three-factor solution.

The role of water chemistry in the distribution of Austropotamobius pallipes (Crustacea Decapoda Astacidae) in Piedmont (Italy).

PubMed

Favaro, Livio; Tirelli, Tina; Pessani, Daniela

2010-01-01

Over the last decades, the populations of Austropotamobius pallipes have decreased markedly all over Europe. If we evaluate the ecological factors that determine its presence, we will have information that could guide conservation decisions. This study aims to investigate the chemical-physical demands of A. pallipes in NW Italy. To this end, we investigated 98 sites. We performed Principal Component Analysis using chemical-physical parameters, collected in both presence and absence sites. We then used principal components with eigenvalue > 1 to run Discriminant Function Analysis and Logistic Regression. The statistics on the concentration of Ca(2+), water hardness, pH and BOD(5) were significantly different in the presence and in the absence sites. pH and BOD(5) played the most important role in separating the presence from the absence locations. These findings are further evidence that we should reduce dissolved organic matter and fine particles in order to contribute to species management and conservation. Copyright 2009 Académie des sciences. Published by Elsevier SAS. All rights reserved.

Dynamics and spatio-temporal variability of environmental factors in Eastern Australia using functional principal component analysis

USGS Publications Warehouse

Szabo, J.K.; Fedriani, E.M.; Segovia-Gonzalez, M. M.; Astheimer, L.B.; Hooper, M.J.

2010-01-01

This paper introduces a new technique in ecology to analyze spatial and temporal variability in environmental variables. By using simple statistics, we explore the relations between abiotic and biotic variables that influence animal distributions. However, spatial and temporal variability in rainfall, a key variable in ecological studies, can cause difficulties to any basic model including time evolution. The study was of a landscape scale (three million square kilometers in eastern Australia), mainly over the period of 19982004. We simultaneously considered qualitative spatial (soil and habitat types) and quantitative temporal (rainfall) variables in a Geographical Information System environment. In addition to some techniques commonly used in ecology, we applied a new method, Functional Principal Component Analysis, which proved to be very suitable for this case, as it explained more than 97% of the total variance of the rainfall data, providing us with substitute variables that are easier to manage and are even able to explain rainfall patterns. The main variable came from a habitat classification that showed strong correlations with rainfall values and soil types. ?? 2010 World Scientific Publishing Company.

Non-linear principal component analysis applied to Lorenz models and to North Atlantic SLP

NASA Astrophysics Data System (ADS)

Russo, A.; Trigo, R. M.

2003-04-01

A non-linear generalisation of Principal Component Analysis (PCA), denoted Non-Linear Principal Component Analysis (NLPCA), is introduced and applied to the analysis of three data sets. Non-Linear Principal Component Analysis allows for the detection and characterisation of low-dimensional non-linear structure in multivariate data sets. This method is implemented using a 5-layer feed-forward neural network introduced originally in the chemical engineering literature (Kramer, 1991). The method is described and details of its implementation are addressed. Non-Linear Principal Component Analysis is first applied to a data set sampled from the Lorenz attractor (1963). It is found that the NLPCA approximations are more representative of the data than are the corresponding PCA approximations. The same methodology was applied to the less known Lorenz attractor (1984). However, the results obtained weren't as good as those attained with the famous 'Butterfly' attractor. Further work with this model is underway in order to assess if NLPCA techniques can be more representative of the data characteristics than are the corresponding PCA approximations. The application of NLPCA to relatively 'simple' dynamical systems, such as those proposed by Lorenz, is well understood. However, the application of NLPCA to a large climatic data set is much more challenging. Here, we have applied NLPCA to the sea level pressure (SLP) field for the entire North Atlantic area and the results show a slight imcrement of explained variance associated. Finally, directions for future work are presented.%}

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.

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.

The care dependency scale for measuring basic human needs: an international comparison.

PubMed

Dijkstra, Ate; Yönt, Gülendam Hakverdioğlu; Korhan, Esra Akin; Muszalik, Marta; Kędziora-Kornatowska, Kornelia; Suzuki, Mizue

2012-10-01

To report a study conducted to compare the utility of the care dependency scale across four countries. The care dependency scale provides a framework for assessing the needs of institutionalized patients for nursing care. Henderson's components of nursing care have been used to specify the variable aspects of the concept of care dependency and to develop the care dependency scale items. The study used a cross-cultural survey design. Patients were recruited from four different countries: Japan, The Netherlands, Poland and Turkey. In each of the participating countries, basic human needs were assessed by nurses using a translated version of the original Dutch care dependency scale. Psychometric properties in terms of reliability and validity of the care dependency scale have been assessed using Cronbach's alpha, Guttman's lambda-2, inter-item correlation and principal components analysis. Data were collected in 2008 and 2009. High internal consistency values were demonstrated. Principal component analysis confirmed the one-factor model reported in earlier studies. Outcomes confirm Henderson's idea that human needs are fundamental appearing in every patient-nurse relationship, independent of the patient's age, the type of care setting and/or cultural background. The psychometric characteristics of the care dependency scale make this instrument very useful for comparative research across countries. © 2012 Blackwell Publishing Ltd.

Pattern Analysis of Dynamic Susceptibility Contrast-enhanced MR Imaging Demonstrates Peritumoral Tissue Heterogeneity

PubMed Central

Akbari, Hamed; Macyszyn, Luke; Da, Xiao; Wolf, Ronald L.; Bilello, Michel; Verma, Ragini; O’Rourke, Donald M.

2014-01-01

Purpose To augment the analysis of dynamic susceptibility contrast material–enhanced magnetic resonance (MR) images to uncover unique tissue characteristics that could potentially facilitate treatment planning through a better understanding of the peritumoral region in patients with glioblastoma. Materials and Methods Institutional review board approval was obtained for this study, with waiver of informed consent for retrospective review of medical records. Dynamic susceptibility contrast-enhanced MR imaging data were obtained for 79 patients, and principal component analysis was applied to the perfusion signal intensity. The first six principal components were sufficient to characterize more than 99% of variance in the temporal dynamics of blood perfusion in all regions of interest. The principal components were subsequently used in conjunction with a support vector machine classifier to create a map of heterogeneity within the peritumoral region, and the variance of this map served as the heterogeneity score. Results The calculated principal components allowed near-perfect separability of tissue that was likely highly infiltrated with tumor and tissue that was unlikely infiltrated with tumor. The heterogeneity map created by using the principal components showed a clear relationship between voxels judged by the support vector machine to be highly infiltrated and subsequent recurrence. The results demonstrated a significant correlation (r = 0.46, P < .0001) between the heterogeneity score and patient survival. The hazard ratio was 2.23 (95% confidence interval: 1.4, 3.6; P < .01) between patients with high and low heterogeneity scores on the basis of the median heterogeneity score. Conclusion Analysis of dynamic susceptibility contrast-enhanced MR imaging data by using principal component analysis can help identify imaging variables that can be subsequently used to evaluate the peritumoral region in glioblastoma. These variables are potentially indicative of tumor infiltration and may become useful tools in guiding therapy, as well as individualized prognostication. © RSNA, 2014 PMID:24955928

Signal-to-noise contribution of principal component loads in reconstructed near-infrared Raman tissue spectra.

PubMed

Grimbergen, M C M; van Swol, C F P; Kendall, C; Verdaasdonk, R M; Stone, N; Bosch, J L H R

2010-01-01

The overall quality of Raman spectra in the near-infrared region, where biological samples are often studied, has benefited from various improvements to optical instrumentation over the past decade. However, obtaining ample spectral quality for analysis is still challenging due to device requirements and short integration times required for (in vivo) clinical applications of Raman spectroscopy. Multivariate analytical methods, such as principal component analysis (PCA) and linear discriminant analysis (LDA), are routinely applied to Raman spectral datasets to develop classification models. Data compression is necessary prior to discriminant analysis to prevent or decrease the degree of over-fitting. The logical threshold for the selection of principal components (PCs) to be used in discriminant analysis is likely to be at a point before the PCs begin to introduce equivalent signal and noise and, hence, include no additional value. Assessment of the signal-to-noise ratio (SNR) at a certain peak or over a specific spectral region will depend on the sample measured. Therefore, the mean SNR over the whole spectral region (SNR(msr)) is determined in the original spectrum as well as for spectra reconstructed from an increasing number of principal components. This paper introduces a method of assessing the influence of signal and noise from individual PC loads and indicates a method of selection of PCs for LDA. To evaluate this method, two data sets with different SNRs were used. The sets were obtained with the same Raman system and the same measurement parameters on bladder tissue collected during white light cystoscopy (set A) and fluorescence-guided cystoscopy (set B). This method shows that the mean SNR over the spectral range in the original Raman spectra of these two data sets is related to the signal and noise contribution of principal component loads. The difference in mean SNR over the spectral range can also be appreciated since fewer principal components can reliably be used in the low SNR data set (set B) compared to the high SNR data set (set A). Despite the fact that no definitive threshold could be found, this method may help to determine the cutoff for the number of principal components used in discriminant analysis. Future analysis of a selection of spectral databases using this technique will allow optimum thresholds to be selected for different applications and spectral data quality levels.

Principals and School Factors that Impact Elementary School Student Achievement

ERIC Educational Resources Information Center

Gieselmann, Sharon

2009-01-01

This study examined principals and school factors associated with elementary school student achievement. Nine predictor variables were analyzed to determine their impact on student state assessment scores: (a) years of principal experience, (b) years of teaching experience by the principal, (c) years of principal experience at present site, (d)…

Item-Level Psychometrics of the Glasgow Outcome Scale: Extended Structured Interviews.

PubMed

Hong, Ickpyo; Li, Chih-Ying; Velozo, Craig A

2016-04-01

The Glasgow Outcome Scale-Extended (GOSE) structured interview captures critical components of activities and participation, including home, shopping, work, leisure, and family/friend relationships. Eighty-nine community dwelling adults with mild-moderate traumatic brain injury (TBI) were recruited (average = 2.7 year post injury). Nine items of the 19 items were used for the psychometrics analysis purpose. Factor analysis and item-level psychometrics were investigated using the Rasch partial-credit model. Although the principal components analysis of residuals suggests that a single measurement factor dominates the measure, the instrument did not meet the factor analysis criteria. Five items met the rating scale criteria. Eight items fit the Rasch model. The instrument demonstrated low person reliability (0.63), low person strata (2.07), and a slight ceiling effect. The GOSE demonstrated limitations in precisely measuring activities/participation for individuals after TBI. Future studies should examine the impact of the low precision of the GOSE on effect size. © The Author(s) 2016.

Delirium in elderly people: a study of a psychiatric liaison service in north India.

PubMed

Grover, Sandeep; Kate, Natasha; Agarwal, Munish; Mattoo, Surendra Kumar; Avasthi, Ajit; Malhotra, Savita; Kulhara, Parmanand; Chakrabarti, Subho; Basu, Debasish

2012-01-01

Very few studies from India have studied the phenomenology of delirium. The aim of the present study was to study the phenomenology as measured using the Delirium Rating Scale-Revised-98 (DRS-R98), the associated etiologies and the outcome of delirium among the elderly participants seen by the consultation-liaison psychiatric service in India. In addition, an attempt was made to study the factor structure of symptoms using principal components analysis. The case notes of 109 elderly patients referred to psychiatry liaison services were reviewed. The mean age of the sample was 73.35 years (SD: 7.44; range 65-95 years) and two-thirds of the sample had hospital emergent delirium. The mean DRS-R98 severity score was 18.77 and the DRS-R98 total score was 24.81. In 15 patients the DRS-R98 scores were in the subsyndromal range. Among the various symptoms present, most patients had sleep-wake cycle disturbance, disturbance in orientation, attention and short-term memory impairments, fluctuation of symptoms, temporal onset of symptoms and a physical disorder. Principal components analysis identified three factors which explained 43.5% of variance of symptomatology and it yielded a three-factor structure. Endocrine/metabolic disturbances were the commonest associated etiological category with delirium. The mean hospital stay after being referred to psychiatry referral services was 8.89 days, after which delirium improved in 58.7% of cases. The mortality rate during the inpatient stay was 16.5%. Results suggest that the symptoms of delirium as assessed by DRS-R98 separate out into a three-factor structure. Delirium is commonly associated with metabolic endocrine disturbances and about one-sixth of the patients die during the short inpatient stay.

The Factor Structure and Psychometric Properties of the Persian Version of the Revised Prenatal Coping Inventory (Nu-PCI).

PubMed

Faramarzi, Mahbobeh; Pasha, Hajar; Khafri, Sorayya; Heidary, Shima

2017-03-01

Familiarity with coping strategies is essential for stress management during pregnancy. The Revised Prenatal Coping Inventory (Nu-PCI) was developed to assess coping strategies during pregnancy. This study aimed to assess the factor structure and psychometric properties of the Persian version of the Nu-PCI. After forward-backward translation, the Nu-PCI was administered to 210 pregnant women who were enrolled in two teaching referral clinics in the North of the Islamic Republic of Iran (Babol). The participants completed the Persian Nu-PCI and Ways of Coping Questionnaire (WCQ), which was used to determine the validity of the Persian Nu-PCI. To test construct validity of the Persian Nu-PCI, a principal components factor analysis was performed. Principal components analysis with varimax rotation showed a best fitting 3-factor structure similar to the original with three coping subscales: planning-preparation, avoidance, and spiritual-positive coping. The Persian Nu-PCI was internally consistent and within the acceptable range (α=0.89-0.97). The alpha coefficients for the Nu-PCI and the subscales of planning-preparation, avoidance, and spiritual-positive coping were high. Test-retest coefficients for the Nu-PCI and subscales were 0.98-0.99. The Nu-PCI and its subscales correlated with the WCQ in the entire sample and within each trimester. The Persian version of the Nu-PCI and the subscales of planning-preparation, avoidance, and spiritual-positive coping represent the first reliable standardized tool for measuring coping strategies during pregnancy in the Islamic Republic of Iran. Therefore, it can be applied as a quick and accurate preliminary screening tool for evaluating coping strategies throughout pregnancy in clinics and other medical and research settings.

Illustrative Examples of Principal Component Analysis using SPSS-X/FACTOR.

DTIC Science & Technology

1987-06-26

south of the Third #rmv §*ctar. Montoomer. al O ordered LTG J. Lawton Collins. VII Corps commander, to be precared to counterattaci south as soon as the...Ike Plan to Blame Montgomery For ’Bulge’ Defeat?" Article form The Dail Progress, Charlottesville. Virginia. May 2 and 3. 1976, p. 16-17. OOJ. Lawton ...Clausewitz. Carl von. On War. Princeton: Princeton University Press. 1184. Collins. J. Lawton . Lightning Joe. Baton Rouae: Louisiana State University

Copernicus spectra of beta Lyrae. [in far UV

NASA Technical Reports Server (NTRS)

Hack, M.; Hutchings, J. B.; Kondo, Y.; Mccluskey, G. E.; Plavec, M.; Polidan, R. S.

1974-01-01

The observations reported were made in August and September 1973. The principal data were scans in the low resolution mode at phases nearly coincident with the two light minima. Shorter scans were obtained at the two quadrature phases. The data show that the secondary component of beta Lyrae is a hotter object than the visible B8 star. The velocity amplitude of the lines suggests that the secondary is the more massive object, by a factor of several times.

Males Have Greater "g": Sex Differences in General Mental Ability from 100,000 17- to 18-Year-Olds on the Scholastic Assessment Test

ERIC Educational Resources Information Center

Jackson, Douglas N.; Rushton, J. Philippe

2006-01-01

In this study we found that 17- to 18-year old males averaged 3.63 IQ points higher than did their female counterparts on the 1991 Scholastic Assessment Test (SAT). We analysed 145 item responses from 46,509 males and 56,007 females (total N = 102,516) using a principal components procedure. We found (1) the "g" factor underlies both the…

Quantitative assessment in thermal image segmentation for artistic objects

NASA Astrophysics Data System (ADS)

Yousefi, Bardia; Sfarra, Stefano; Maldague, Xavier P. V.

2017-07-01

The application of the thermal and infrared technology in different areas of research is considerably increasing. These applications involve Non-destructive Testing (NDT), Medical analysis (Computer Aid Diagnosis/Detection- CAD), Arts and Archaeology among many others. In the arts and archaeology field, infrared technology provides significant contributions in term of finding defects of possible impaired regions. This has been done through a wide range of different thermographic experiments and infrared methods. The proposed approach here focuses on application of some known factor analysis methods such as standard Non-Negative Matrix Factorization (NMF) optimized by gradient-descent-based multiplicative rules (SNMF1) and standard NMF optimized by Non-negative least squares (NNLS) active-set algorithm (SNMF2) and eigen decomposition approaches such as Principal Component Thermography (PCT), Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT) to obtain the thermal features. On one hand, these methods are usually applied as preprocessing before clustering for the purpose of segmentation of possible defects. On the other hand, a wavelet based data fusion combines the data of each method with PCT to increase the accuracy of the algorithm. The quantitative assessment of these approaches indicates considerable segmentation along with the reasonable computational complexity. It shows the promising performance and demonstrated a confirmation for the outlined properties. In particular, a polychromatic wooden statue and a fresco were analyzed using the above mentioned methods and interesting results were obtained.

Cultivating an Environment that Contributes to Teaching and Learning in Schools: High School Principals' Actions

ERIC Educational Resources Information Center

Lin, Mind-Dih

2012-01-01

Improving principal leadership is a vital component to the success of educational reform initiatives that seek to improve whole-school performance, as principal leadership often exercises positive but indirect effects on student learning. Because of the importance of principals within the field of school improvement, this article focuses on…

Measuring Principals' Effectiveness: Results from New Jersey's First Year of Statewide Principal Evaluation. REL 2016-156

ERIC Educational Resources Information Center

Herrmann, Mariesa; Ross, Christine

2016-01-01

States and districts across the country are implementing new principal evaluation systems that include measures of the quality of principals' school leadership practices and measures of student achievement growth. Because these evaluation systems will be used for high-stakes decisions, it is important that the component measures of the evaluation…

The Views of Novice and Late Career Principals Concerning Instructional and Organizational Leadership within Their Evaluation

ERIC Educational Resources Information Center

Hvidston, David J.; Range, Bret G.; McKim, Courtney Ann; Mette, Ian M.

2015-01-01

This study examined the perspectives of novice and late career principals concerning instructional and organizational leadership within their performance evaluations. An online survey was sent to 251 principals with a return rate of 49%. Instructional leadership components of the evaluation that were most important to all principals were:…

Air Force highly integrated photonics program: development and demonstration of an optically transparent fiber optic network for avionics applications

NASA Astrophysics Data System (ADS)

Whaley, Gregory J.; Karnopp, Roger J.

2010-04-01

The goal of the Air Force Highly Integrated Photonics (HIP) program is to develop and demonstrate single photonic chip components which support a single mode fiber network architecture for use on mobile military platforms. We propose an optically transparent, broadcast and select fiber optic network as the next generation interconnect on avionics platforms. In support of this network, we have developed three principal, single-chip photonic components: a tunable laser transmitter, a 32x32 port star coupler, and a 32 port multi-channel receiver which are all compatible with demanding avionics environmental and size requirements. The performance of the developed components will be presented as well as the results of a demonstration system which integrates the components into a functional network representative of the form factor used in advanced avionics computing and signal processing applications.

Checking Dimensionality in Item Response Models with Principal Component Analysis on Standardized Residuals

ERIC Educational Resources Information Center

Chou, Yeh-Tai; Wang, Wen-Chung

2010-01-01

Dimensionality is an important assumption in item response theory (IRT). Principal component analysis on standardized residuals has been used to check dimensionality, especially under the family of Rasch models. It has been suggested that an eigenvalue greater than 1.5 for the first eigenvalue signifies a violation of unidimensionality when there…

Variable Neighborhood Search Heuristics for Selecting a Subset of Variables in Principal Component Analysis

ERIC Educational Resources Information Center

Brusco, Michael J.; Singh, Renu; Steinley, Douglas

2009-01-01

The selection of a subset of variables from a pool of candidates is an important problem in several areas of multivariate statistics. Within the context of principal component analysis (PCA), a number of authors have argued that subset selection is crucial for identifying those variables that are required for correct interpretation of the…

Relaxation mode analysis of a peptide system: comparison with principal component analysis.

PubMed

Mitsutake, Ayori; Iijima, Hiromitsu; Takano, Hiroshi

2011-10-28

This article reports the first attempt to apply the relaxation mode analysis method to a simulation of a biomolecular system. In biomolecular systems, the principal component analysis is a well-known method for analyzing the static properties of fluctuations of structures obtained by a simulation and classifying the structures into some groups. On the other hand, the relaxation mode analysis has been used to analyze the dynamic properties of homopolymer systems. In this article, a long Monte Carlo simulation of Met-enkephalin in gas phase has been performed. The results are analyzed by the principal component analysis and relaxation mode analysis methods. We compare the results of both methods and show the effectiveness of the relaxation mode analysis.

Matrix partitioning and EOF/principal component analysis of Antarctic Sea ice brightness temperatures

NASA Technical Reports Server (NTRS)

Murray, C. W., Jr.; Mueller, J. L.; Zwally, H. J.

1984-01-01

A field of measured anomalies of some physical variable relative to their time averages, is partitioned in either the space domain or the time domain. Eigenvectors and corresponding principal components of the smaller dimensioned covariance matrices associated with the partitioned data sets are calculated independently, then joined to approximate the eigenstructure of the larger covariance matrix associated with the unpartitioned data set. The accuracy of the approximation (fraction of the total variance in the field) and the magnitudes of the largest eigenvalues from the partitioned covariance matrices together determine the number of local EOF's and principal components to be joined by any particular level. The space-time distribution of Nimbus-5 ESMR sea ice measurement is analyzed.

Fast principal component analysis for stacking seismic data

NASA Astrophysics Data System (ADS)

Wu, Juan; Bai, Min

2018-04-01

Stacking seismic data plays an indispensable role in many steps of the seismic data processing and imaging workflow. Optimal stacking of seismic data can help mitigate seismic noise and enhance the principal components to a great extent. Traditional average-based seismic stacking methods cannot obtain optimal performance when the ambient noise is extremely strong. We propose a principal component analysis (PCA) algorithm for stacking seismic data without being sensitive to noise level. Considering the computational bottleneck of the classic PCA algorithm in processing massive seismic data, we propose an efficient PCA algorithm to make the proposed method readily applicable for industrial applications. Two numerically designed examples and one real seismic data are used to demonstrate the performance of the presented method.

Executive Dysfunction Among Children With Reading Comprehension Deficits

PubMed Central

Locascio, Gianna; Mahone, E. Mark; Eason, Sarah H.; Cutting, Laurie E.

2010-01-01

Emerging research supports the contribution of executive function (EF) to reading comprehension; however, a unique pattern has not been established for children who demonstrate comprehension difficulties despite average word recognition ability (specific reading comprehension deficit; S-RCD). To identify particular EF components on which children with S-RCD struggle, a range of EF skills was compared among 86 children, ages 10 to 14, grouped by word reading and comprehension abilities: 24 average readers, 44 with word recognition deficits (WRD), and 18 S-RCD. An exploratory principal components analysis of EF tests identified three latent factors, used in subsequent group comparisons: Planning/Spatial Working Memory, Verbal Working Memory, and Response Inhibition. The WRD group exhibited deficits (relative to controls) on Verbal Working Memory and Inhibition factors; S-RCD children performed more poorly than controls on the Planning factor. Further analyses suggested the WRD group’s poor performance on EF factors was a by-product of core deficits linked to WRD (after controlling for phonological processing, this group no longer showed EF deficits). In contrast, the S-RCD group’s poor performance on the planning component remained significant after controlling for phonological processing. Findings suggest reading comprehension difficulties are linked to executive dysfunction; in particular, poor strategic planning/organizing may lead to reading comprehension problems. PMID:20375294

Executive dysfunction among children with reading comprehension deficits.

PubMed

Locascio, Gianna; Mahone, E Mark; Eason, Sarah H; Cutting, Laurie E

2010-01-01

Emerging research supports the contribution of executive function (EF) to reading comprehension; however, a unique pattern has not been established for children who demonstrate comprehension difficulties despite average word recognition ability (specific reading comprehension deficit; S-RCD). To identify particular EF components on which children with S-RCD struggle, a range of EF skills was compared among 86 children, ages 10 to 14, grouped by word reading and comprehension abilities: 24 average readers, 44 with word recognition deficits (WRD), and 18 S-RCD. An exploratory principal components analysis of EF tests identified three latent factors, used in subsequent group comparisons: Planning/ Spatial Working Memory, Verbal Working Memory, and Response Inhibition. The WRD group exhibited deficits (relative to controls) on Verbal Working Memory and Inhibition factors; S-RCD children performed more poorly than controls on the Planning factor. Further analyses suggested the WRD group's poor performance on EF factors was a by-product of core deficits linked to WRD (after controlling for phonological processing, this group no longer showed EF deficits). In contrast, the S-RCD group's poor performance on the planning component remained significant after controlling for phonological processing. Findings suggest reading comprehension difficulties are linked to executive dysfunction; in particular, poor strategic planning/organizing may lead to reading comprehension problems.