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.

[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.

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.

[Discrimination of varieties of brake fluid using visual-near infrared spectra].

PubMed

Jiang, Lu-lu; Tan, Li-hong; Qiu, Zheng-jun; Lu, Jiang-feng; He, Yong

2008-06-01

A new method was developed to fast discriminate brands of brake fluid by means of visual-near infrared spectroscopy. Five different brands of brake fluid were analyzed using a handheld near infrared spectrograph, manufactured by ASD Company, and 60 samples were gotten from each brand of brake fluid. The samples data were pretreated using average smoothing and standard normal variable method, and then analyzed using principal component analysis (PCA). A 2-dimensional plot was drawn based on the first and the second principal components, and the plot indicated that the clustering characteristic of different brake fluid is distinct. The foregoing 6 principal components were taken as input variable, and the band of brake fluid as output variable to build the discriminate model by stepwise discriminant analysis method. Two hundred twenty five samples selected randomly were used to create the model, and the rest 75 samples to verify the model. The result showed that the distinguishing rate was 94.67%, indicating that the method proposed in this paper has good performance in classification and discrimination. It provides a new way to fast discriminate different brands of brake fluid.

From measurements to metrics: PCA-based indicators of cyber anomaly

NASA Astrophysics Data System (ADS)

Ahmed, Farid; Johnson, Tommy; Tsui, Sonia

2012-06-01

We present a framework of the application of Principal Component Analysis (PCA) to automatically obtain meaningful metrics from intrusion detection measurements. In particular, we report the progress made in applying PCA to analyze the behavioral measurements of malware and provide some preliminary results in selecting dominant attributes from an arbitrary number of malware attributes. The results will be useful in formulating an optimal detection threshold in the principal component space, which can both validate and augment existing malware classifiers.

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.

Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs.

PubMed

Azevedo, C F; Nascimento, M; Silva, F F; Resende, M D V; Lopes, P S; Guimarães, S E F; Glória, L S

2015-10-09

A significant contribution of molecular genetics is the direct use of DNA information to identify genetically superior individuals. With this approach, genome-wide selection (GWS) can be used for this purpose. GWS consists of analyzing a large number of single nucleotide polymorphism markers widely distributed in the genome; however, because the number of markers is much larger than the number of genotyped individuals, and such markers are highly correlated, special statistical methods are widely required. Among these methods, independent component regression, principal component regression, partial least squares, and partial principal components stand out. Thus, the aim of this study was to propose an application of the methods of dimensionality reduction to GWS of carcass traits in an F2 (Piau x commercial line) pig population. The results show similarities between the principal and the independent component methods and provided the most accurate genomic breeding estimates for most carcass traits in pigs.

Principal Component Analysis in the Spectral Analysis of the Dynamic Laser Speckle Patterns

NASA Astrophysics Data System (ADS)

Ribeiro, K. M.; Braga, R. A., Jr.; Horgan, G. W.; Ferreira, D. D.; Safadi, T.

2014-02-01

Dynamic laser speckle is a phenomenon that interprets an optical patterns formed by illuminating a surface under changes with coherent light. Therefore, the dynamic change of the speckle patterns caused by biological material is known as biospeckle. Usually, these patterns of optical interference evolving in time are analyzed by graphical or numerical methods, and the analysis in frequency domain has also been an option, however involving large computational requirements which demands new approaches to filter the images in time. Principal component analysis (PCA) works with the statistical decorrelation of data and it can be used as a data filtering. In this context, the present work evaluated the PCA technique to filter in time the data from the biospeckle images aiming the reduction of time computer consuming and improving the robustness of the filtering. It was used 64 images of biospeckle in time observed in a maize seed. The images were arranged in a data matrix and statistically uncorrelated by PCA technique, and the reconstructed signals were analyzed using the routine graphical and numerical methods to analyze the biospeckle. Results showed the potential of the PCA tool in filtering the dynamic laser speckle data, with the definition of markers of principal components related to the biological phenomena and with the advantage of fast computational processing.

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.

[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.

Craniometric relationships among medieval Central European populations: implications for Croat migration and expansion.

PubMed

Slaus, Mario; Tomicić, Zeljko; Uglesić, Ante; Jurić, Radomir

2004-08-01

To determine the ethnic composition of the early medieval Croats, the location from which they migrated to the east coast of the Adriatic, and to separate early medieval Croats from Bijelo brdo culture members, using principal components analysis and discriminant function analysis of craniometric data from Central and South-East European medieval archaeological sites. Mean male values for 8 cranial measurements from 39 European and 5 Iranian sites were analyzed by principal components analysis. Raw data for 17 cranial measurements for 103 female and 112 male skulls were used to develop discriminant functions. The scatter-plot of the analyzed sites on the first 2 principal components showed a pattern of intergroup relationships consistent with geographical and archaeological information not included in the data set. The first 2 principal components separated the sites into 4 distinct clusters: Avaroslav sites west of the Danube, Avaroslav sites east of the Danube, Bijelo brdo sites, and Polish sites. All early medieval Croat sites were located in the cluster of Polish sites. Two discriminant functions successfully differentiated between early medieval Croats and Bijelo brdo members. Overall accuracies were high -- 89.3% for males, and 97.1% for females. Early medieval Croats seem to be of Slavic ancestry, and at one time shared a common homeland with medieval Poles. Application of unstandardized discriminant function coefficients to unclassified crania from 18 sites showed an expansion of early medieval Croats into continental Croatia during the 10th to 13th century.

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.

Descriptive Characteristics of Surface Water Quality in Hong Kong by a Self-Organising Map

PubMed Central

An, Yan; Zou, Zhihong; Li, Ranran

2016-01-01

In this study, principal component analysis (PCA) and a self-organising map (SOM) were used to analyse a complex dataset obtained from the river water monitoring stations in the Tolo Harbor and Channel Water Control Zone (Hong Kong), covering the period of 2009–2011. PCA was initially applied to identify the principal components (PCs) among the nonlinear and complex surface water quality parameters. SOM followed PCA, and was implemented to analyze the complex relationships and behaviors of the parameters. The results reveal that PCA reduced the multidimensional parameters to four significant PCs which are combinations of the original ones. The positive and inverse relationships of the parameters were shown explicitly by pattern analysis in the component planes. It was found that PCA and SOM are efficient tools to capture and analyze the behavior of multivariable, complex, and nonlinear related surface water quality data. PMID:26761018

Descriptive Characteristics of Surface Water Quality in Hong Kong by a Self-Organising Map.

PubMed

An, Yan; Zou, Zhihong; Li, Ranran

2016-01-08

In this study, principal component analysis (PCA) and a self-organising map (SOM) were used to analyse a complex dataset obtained from the river water monitoring stations in the Tolo Harbor and Channel Water Control Zone (Hong Kong), covering the period of 2009-2011. PCA was initially applied to identify the principal components (PCs) among the nonlinear and complex surface water quality parameters. SOM followed PCA, and was implemented to analyze the complex relationships and behaviors of the parameters. The results reveal that PCA reduced the multidimensional parameters to four significant PCs which are combinations of the original ones. The positive and inverse relationships of the parameters were shown explicitly by pattern analysis in the component planes. It was found that PCA and SOM are efficient tools to capture and analyze the behavior of multivariable, complex, and nonlinear related surface water quality data.

Measurement of Scenic Spots Sustainable Capacity Based on PCA-Entropy TOPSIS: A Case Study from 30 Provinces, China

PubMed Central

Liang, Xuedong; Liu, Canmian; Li, Zhi

2017-01-01

In connection with the sustainable development of scenic spots, this paper, with consideration of resource conditions, economic benefits, auxiliary industry scale and ecological environment, establishes a comprehensive measurement model of the sustainable capacity of scenic spots; optimizes the index system by principal components analysis to extract principal components; assigns the weight of principal components by entropy method; analyzes the sustainable capacity of scenic spots in each province of China comprehensively in combination with TOPSIS method and finally puts forward suggestions aid decision-making. According to the study, this method provides an effective reference for the study of the sustainable development of scenic spots and is very significant for considering the sustainable development of scenic spots and auxiliary industries to establish specific and scientific countermeasures for improvement. PMID:29271947

Measurement of Scenic Spots Sustainable Capacity Based on PCA-Entropy TOPSIS: A Case Study from 30 Provinces, China.

PubMed

Liang, Xuedong; Liu, Canmian; Li, Zhi

2017-12-22

In connection with the sustainable development of scenic spots, this paper, with consideration of resource conditions, economic benefits, auxiliary industry scale and ecological environment, establishes a comprehensive measurement model of the sustainable capacity of scenic spots; optimizes the index system by principal components analysis to extract principal components; assigns the weight of principal components by entropy method; analyzes the sustainable capacity of scenic spots in each province of China comprehensively in combination with TOPSIS method and finally puts forward suggestions aid decision-making. According to the study, this method provides an effective reference for the study of the sustainable development of scenic spots and is very significant for considering the sustainable development of scenic spots and auxiliary industries to establish specific and scientific countermeasures for improvement.

Self-aggregation in scaled principal component space

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

Ding, Chris H.Q.; He, Xiaofeng; Zha, Hongyuan

2001-10-05

Automatic grouping of voluminous data into meaningful structures is a challenging task frequently encountered in broad areas of science, engineering and information processing. These data clustering tasks are frequently performed in Euclidean space or a subspace chosen from principal component analysis (PCA). Here we describe a space obtained by a nonlinear scaling of PCA in which data objects self-aggregate automatically into clusters. Projection into this space gives sharp distinctions among clusters. Gene expression profiles of cancer tissue subtypes, Web hyperlink structure and Internet newsgroups are analyzed to illustrate interesting properties of the space.

State-Space Estimation of Soil Organic Carbon Stock

NASA Astrophysics Data System (ADS)

Ogunwole, Joshua O.; Timm, Luis C.; Obidike-Ugwu, Evelyn O.; Gabriels, Donald M.

2014-04-01

Understanding soil spatial variability and identifying soil parameters most determinant to soil organic carbon stock is pivotal to precision in ecological modelling, prediction, estimation and management of soil within a landscape. This study investigates and describes field soil variability and its structural pattern for agricultural management decisions. The main aim was to relate variation in soil organic carbon stock to soil properties and to estimate soil organic carbon stock from the soil properties. A transect sampling of 100 points at 3 m intervals was carried out. Soils were sampled and analyzed for soil organic carbon and other selected soil properties along with determination of dry aggregate and water-stable aggregate fractions. Principal component analysis, geostatistics, and state-space analysis were conducted on the analyzed soil properties. The first three principal components explained 53.2% of the total variation; Principal Component 1 was dominated by soil exchange complex and dry sieved macroaggregates clusters. Exponential semivariogram model described the structure of soil organic carbon stock with a strong dependence indicating that soil organic carbon values were correlated up to 10.8m.Neighbouring values of soil organic carbon stock, all waterstable aggregate fractions, and dithionite and pyrophosphate iron gave reliable estimate of soil organic carbon stock by state-space.

[Study on volatile components from flowers of Gymnema sylvestre].

PubMed

Qiu, Qin; Zhen, Han-Shen; Huang, Pei-Qian

2013-04-01

To analyze the volatile components from flowers of Gymnema sylvestre. Volatile components of flowers of Gymnema sylvestre were extracted by water vapor distilling, and the components were separated and identified by GC-MS. 55 components were separated and 33 components were identified, accounting for 88.73% of all quantity. The principal volatile components are Phytol, Pentacosane, 10-Heneicosene (c, t), 3-Eicosene, (E) -and 2-Methyl-Z-2-docosane. The research can pro-vide scientific basis for chemical component research of flowers of Gymnema sylvestre.

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.

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.

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.

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.

Edge Principal Components and Squash Clustering: Using the Special Structure of Phylogenetic Placement Data for Sample Comparison

PubMed Central

Matsen IV, Frederick A.; Evans, Steven N.

2013-01-01

Principal components analysis (PCA) and hierarchical clustering are two of the most heavily used techniques for analyzing the differences between nucleic acid sequence samples taken from a given environment. They have led to many insights regarding the structure of microbial communities. We have developed two new complementary methods that leverage how this microbial community data sits on a phylogenetic tree. Edge principal components analysis enables the detection of important differences between samples that contain closely related taxa. Each principal component axis is a collection of signed weights on the edges of the phylogenetic tree, and these weights are easily visualized by a suitable thickening and coloring of the edges. Squash clustering outputs a (rooted) clustering tree in which each internal node corresponds to an appropriate “average” of the original samples at the leaves below the node. Moreover, the length of an edge is a suitably defined distance between the averaged samples associated with the two incident nodes, rather than the less interpretable average of distances produced by UPGMA, the most widely used hierarchical clustering method in this context. We present these methods and illustrate their use with data from the human microbiome. PMID:23505415

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

USGS Publications Warehouse

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

1998-01-01

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

How Adequate are One- and Two-Dimensional Free Energy Landscapes for Protein Folding Dynamics?

NASA Astrophysics Data System (ADS)

Maisuradze, Gia G.; Liwo, Adam; Scheraga, Harold A.

2009-06-01

The molecular dynamics trajectories of protein folding or unfolding, generated with the coarse-grained united-residue force field for the B domain of staphylococcal protein A, were analyzed by principal component analysis (PCA). The folding or unfolding process was examined by using free-energy landscapes (FELs) in PC space. By introducing a novel multidimensional FEL, it was shown that the low-dimensional FELs are not always sufficient for the description of folding or unfolding processes. Similarities between the topographies of FELs along low- and high-indexed principal components were observed.

Combination of PCA and LORETA for sources analysis of ERP data: an emotional processing study

NASA Astrophysics Data System (ADS)

Hu, Jin; Tian, Jie; Yang, Lei; Pan, Xiaohong; Liu, Jiangang

2006-03-01

The purpose of this paper is to study spatiotemporal patterns of neuronal activity in emotional processing by analysis of ERP data. 108 pictures (categorized as positive, negative and neutral) were presented to 24 healthy, right-handed subjects while 128-channel EEG data were recorded. An analysis of two steps was applied to the ERP data. First, principal component analysis was performed to obtain significant ERP components. Then LORETA was applied to each component to localize their brain sources. The first six principal components were extracted, each of which showed different spatiotemporal patterns of neuronal activity. The results agree with other emotional study by fMRI or PET. The combination of PCA and LORETA can be used to analyze spatiotemporal patterns of ERP data in emotional processing.

A weighted-means ordination of riparian birds in southeastern Wyoming

Treesearch

Deborah M. Finch

1985-01-01

Variation among habitat associations of 31 riparian bird species in southeastern Wyoming was analyzed using a weighted-means ordination. Three principal components explained 86.7% of the variation among habitat associations of bird species. The components showed high positive loadings for variables associated with canopy, shrub size, and vegetation height.

[Analysis on component difference in Citrus reticulata before and after being processed with salt by UPLC-Q-TOF/MS].

PubMed

Zeng, Rui; Fu, Juan; Wu, La-Bin; Huang, Lin-Fang

2013-07-01

To analyze components of Citrus reticulata and salt-processed C. reticulata by ultra-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF/MS), and compared the changes in components before and after being processed with salt. Principal component analysis (PCA) and partial least squares discriminant analysis (OPLS-DA) were adopted to analyze the difference in fingerprint between crude and processed C. reticulata, showing increased content of eriocitrin, limonin, nomilin and obacunone increase in salt-processed C. reticulata. Potential chemical markers were identified as limonin, obacunone and nomilin, which could be used for distinguishing index components of crude and processed C. reticulata.

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

NASA Astrophysics Data System (ADS)

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

2017-08-01

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

Modulated Hebb-Oja learning rule--a method for principal subspace analysis.

PubMed

Jankovic, Marko V; Ogawa, Hidemitsu

2006-03-01

This paper presents analysis of the recently proposed modulated Hebb-Oja (MHO) method that performs linear mapping to a lower-dimensional subspace. Principal component subspace is the method that will be analyzed. Comparing to some other well-known methods for yielding principal component subspace (e.g., Oja's Subspace Learning Algorithm), the proposed method has one feature that could be seen as desirable from the biological point of view--synaptic efficacy learning rule does not need the explicit information about the value of the other efficacies to make individual efficacy modification. Also, the simplicity of the "neural circuits" that perform global computations and a fact that their number does not depend on the number of input and output neurons, could be seen as good features of the proposed method.

Are polychlorinated biphenyl residues adequately describe by aroclor mixture equivalents. Isomer-specific principal components analysis of such residues in fish and turtles

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

Schwartz, T.R.; Stalling, D.L.; Rice, C.L.

1987-01-01

Polychlorinated biphenyl (PCB) residues from fish and turtles were analyzed with SIMCA (Soft Independent Modeling of Class Analogy), a principal components analysis technique. A series of technical Aroclors were also analyzed to provide a reference data set for pattern recognition. Environmental PCB residues are often expressed in terms of relative Aroclor composition. In this work, we assessed the similarity of Aroclors to class models derived for fish and turtles to ascertain if the PCB residues in the samples could be described by an Aroclor or Aroclor mixture. Using PCA, we found that these samples could not be described by anmore » Aroclor or Aroclor mixture and that it would be inappropriate to report these samples as such. 18 references, 3 figures, 3 tables.« less

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.

Machine learning of frustrated classical spin models. I. Principal component analysis

NASA Astrophysics Data System (ADS)

Wang, Ce; Zhai, Hui

2017-10-01

This work aims at determining whether artificial intelligence can recognize a phase transition without prior human knowledge. If this were successful, it could be applied to, for instance, analyzing data from the quantum simulation of unsolved physical models. Toward this goal, we first need to apply the machine learning algorithm to well-understood models and see whether the outputs are consistent with our prior knowledge, which serves as the benchmark for this approach. In this work, we feed the computer data generated by the classical Monte Carlo simulation for the X Y model in frustrated triangular and union jack lattices, which has two order parameters and exhibits two phase transitions. We show that the outputs of the principal component analysis agree very well with our understanding of different orders in different phases, and the temperature dependences of the major components detect the nature and the locations of the phase transitions. Our work offers promise for using machine learning techniques to study sophisticated statistical models, and our results can be further improved by using principal component analysis with kernel tricks and the neural network method.

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.

Relationship between regional population and healthcare delivery in Japan.

PubMed

Niga, Takeo; Mori, Maiko; Kawahara, Kazuo

2016-01-01

In order to address regional inequality in healthcare delivery in Japan, healthcare districts were established in 1985. However, regional healthcare delivery has now become a national issue because of population migration and the aging population. In this study, the state of healthcare delivery at the district level is examined by analyzing population, the number of physicians, and the number of hospital beds. The results indicate a continuing disparity in healthcare delivery among districts. We find that the rate of change in population has a strong positive correlation with that in the number of physicians and a weak positive correlation with that in the number of hospital beds. In addition, principal component analysis is performed on three variables: the rate of change in population, the number of physicians per capita, and the number of hospital beds per capita. This analysis suggests that the two principal components contribute 90.1% of the information. The first principal component is thought to show the effect of the regulations on hospital beds. The second principal component is thought to show the capacity to recruit physicians. This study indicates that an adjustment to the regulations on hospital beds as well as physician allocation by public funds may be key to resolving the impending issue of regionally disproportionate healthcare delivery.

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

Evidence of tampering in watermark identification

NASA Astrophysics Data System (ADS)

McLauchlan, Lifford; Mehrübeoglu, Mehrübe

2009-08-01

In this work, watermarks are embedded in digital images in the discrete wavelet transform (DWT) domain. Principal component analysis (PCA) is performed on the DWT coefficients. Next higher order statistics based on the principal components and the eigenvalues are determined for different sets of images. Feature sets are analyzed for different types of attacks in m dimensional space. The results demonstrate the separability of the features for the tampered digital copies. Different feature sets are studied to determine more effective tamper evident feature sets. The digital forensics, the probable manipulation(s) or modification(s) performed on the digital information can be identified using the described technique.

Identification and classification of upper limb motions using PCA.

PubMed

Veer, Karan; Vig, Renu

2018-03-28

This paper describes the utility of principal component analysis (PCA) in classifying upper limb signals. PCA is a powerful tool for analyzing data of high dimension. Here, two different input strategies were explored. The first method uses upper arm dual-position-based myoelectric signal acquisition and the other solely uses PCA for classifying surface electromyogram (SEMG) signals. SEMG data from the biceps and the triceps brachii muscles and four independent muscle activities of the upper arm were measured in seven subjects (total dataset=56). The datasets used for the analysis are rotated by class-specific principal component matrices to decorrelate the measured data prior to feature extraction.

Principal component analysis on a torus: Theory and application to protein dynamics.

PubMed

Sittel, Florian; Filk, Thomas; Stock, Gerhard

2017-12-28

A dimensionality reduction method for high-dimensional circular data is developed, which is based on a principal component analysis (PCA) of data points on a torus. Adopting a geometrical view of PCA, various distance measures on a torus are introduced and the associated problem of projecting data onto the principal subspaces is discussed. The main idea is that the (periodicity-induced) projection error can be minimized by transforming the data such that the maximal gap of the sampling is shifted to the periodic boundary. In a second step, the covariance matrix and its eigendecomposition can be computed in a standard manner. Adopting molecular dynamics simulations of two well-established biomolecular systems (Aib 9 and villin headpiece), the potential of the method to analyze the dynamics of backbone dihedral angles is demonstrated. The new approach allows for a robust and well-defined construction of metastable states and provides low-dimensional reaction coordinates that accurately describe the free energy landscape. Moreover, it offers a direct interpretation of covariances and principal components in terms of the angular variables. Apart from its application to PCA, the method of maximal gap shifting is general and can be applied to any other dimensionality reduction method for circular data.

Principal component analysis on a torus: Theory and application to protein dynamics

NASA Astrophysics Data System (ADS)

Sittel, Florian; Filk, Thomas; Stock, Gerhard

2017-12-01

A dimensionality reduction method for high-dimensional circular data is developed, which is based on a principal component analysis (PCA) of data points on a torus. Adopting a geometrical view of PCA, various distance measures on a torus are introduced and the associated problem of projecting data onto the principal subspaces is discussed. The main idea is that the (periodicity-induced) projection error can be minimized by transforming the data such that the maximal gap of the sampling is shifted to the periodic boundary. In a second step, the covariance matrix and its eigendecomposition can be computed in a standard manner. Adopting molecular dynamics simulations of two well-established biomolecular systems (Aib9 and villin headpiece), the potential of the method to analyze the dynamics of backbone dihedral angles is demonstrated. The new approach allows for a robust and well-defined construction of metastable states and provides low-dimensional reaction coordinates that accurately describe the free energy landscape. Moreover, it offers a direct interpretation of covariances and principal components in terms of the angular variables. Apart from its application to PCA, the method of maximal gap shifting is general and can be applied to any other dimensionality reduction method for circular data.

Measurement Characteristics of the Quality of Life Index When Used with Adults Who Have Severe Mental Retardation. Brief Report.

ERIC Educational Resources Information Center

Campo, Stephanie F.; And Others

1996-01-01

The Quality of Life Index was completed by 120 residential staff for 60 adults with severe to profound mental retardation residing in group homes. Measurement integrity was analyzed through use of principal components analysis, confirmatory rotation of components, and Cronbach alphas. Results are compared with results obtained from a more…

Principal component analysis of TOF-SIMS spectra, images and depth profiles: an industrial perspective

NASA Astrophysics Data System (ADS)

Pacholski, Michaeleen L.

2004-06-01

Principal component analysis (PCA) has been successfully applied to time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra, images and depth profiles. Although SIMS spectral data sets can be small (in comparison to datasets typically discussed in literature from other analytical techniques such as gas or liquid chromatography), each spectrum has thousands of ions resulting in what can be a difficult comparison of samples. Analysis of industrially-derived samples means the identity of most surface species are unknown a priori and samples must be analyzed rapidly to satisfy customer demands. PCA enables rapid assessment of spectral differences (or lack there of) between samples and identification of chemically different areas on sample surfaces for images. Depth profile analysis helps define interfaces and identify low-level components in the system.

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…

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.

Characterization of Lake Michigan coastal lakes using zooplankton assemblages

USGS Publications Warehouse

Whitman, Richard L.; Nevers, Meredith B.; Goodrich, Maria L.; Murphy, Paul C.; Davis, Bruce M.

2004-01-01

Zooplankton assemblages and water quality were examined bi-weekly from 17 April to 19 October 1998 in 11 northeastern Lake Michigan coastal lakes of similar origin but varied in trophic status and limnological condition. All lakes were within or adjacent to Sleeping Bear Dunes National Lakeshore, Michigan. Zooplankton (principally microcrustaceans and rotifers) from triplicate Wisconsin net (80 I?m) vertical tows taken at each lake's deepest location were analyzed. Oxygen-temperature-pH-specific conductivity profiles and surface water quality were concurrently measured. Bray-Curtis similarity analysis showed small variations among sample replicates but large temporal differences. The potential use of zooplankton communities for environmental lake comparisons was evaluated by means of BIOENV (Primer 5.1) and principal component analyses. Zooplankton analyzed at the lowest identified taxonomic level yielded greatest sensitivity to limnological variation. Taxonomic and ecological aggregations of zooplankton data performed comparably, but less well than the finest taxonomic analysis. Secchi depth, chlorophyll a, and sulfate concentrations combined to give the best correlation with patterns of variation in the zooplankton data set. Principal component analysis of these variables revealed trophic status as the most influential major limnological gradient among the study lakes. Overall, zooplankton abundance was an excellent indicator of variation in trophic status.

An integrtated approach to the use of Landsat TM data for gold exploration in west central Nevada

NASA Technical Reports Server (NTRS)

Mouat, D. A.; Myers, J. S.; Miller, N. L.

1987-01-01

This paper represents an integration of several Landsat TM image processing techniques with other data to discriminate the lithologies and associated areas of hydrothermal alteration in the vicinity of the Paradise Peak gold mine in west central Nevada. A microprocessor-based image processing system and an IDIMS system were used to analyze data from a 512 X 512 window of a Landsat-5 TM scene collected on June 30, 1984. Image processing techniques included simple band composites, band ratio composites, principal components composites, and baseline-based composites. These techniques were chosen based on their ability to discriminate the spectral characteristics of the products of hydrothermal alteration as well as of the associated regional lithologies. The simple band composite, ratio composite, two principal components composites, and the baseline-based composites separately can define the principal areas of alteration. Combined, they provide a very powerful exploration tool.

A new modulated Hebbian learning rule--biologically plausible method for local computation of a principal subspace.

PubMed

Jankovic, Marko; Ogawa, Hidemitsu

2003-08-01

This paper presents one possible implementation of a transformation that performs linear mapping to a lower-dimensional subspace. Principal component subspace will be the one that will be analyzed. Idea implemented in this paper represents generalization of the recently proposed infinity OH neural method for principal component extraction. The calculations in the newly proposed method are performed locally--a feature which is usually considered as desirable from the biological point of view. Comparing to some other wellknown methods, proposed synaptic efficacy learning rule requires less information about the value of the other efficacies to make single efficacy modification. Synaptic efficacies are modified by implementation of Modulated Hebb-type (MH) learning rule. Slightly modified MH algorithm named Modulated Hebb Oja (MHO) algorithm, will be also introduced. Structural similarity of the proposed network with part of the retinal circuit will be presented, too.

A novel genome signature based on inter-nucleotide distances profiles for visualization of metagenomic data

NASA Astrophysics Data System (ADS)

Xie, Xian-Hua; Yu, Zu-Guo; Ma, Yuan-Lin; Han, Guo-Sheng; Anh, Vo

2017-09-01

There has been a growing interest in visualization of metagenomic data. The present study focuses on the visualization of metagenomic data using inter-nucleotide distances profile. We first convert the fragment sequences into inter-nucleotide distances profiles. Then we analyze these profiles by principal component analysis. Finally the principal components are used to obtain the 2-D scattered plot according to their source of species. We name our method as inter-nucleotide distances profiles (INP) method. Our method is evaluated on three benchmark data sets used in previous published papers. Our results demonstrate that the INP method is good, alternative and efficient for visualization of metagenomic data.

Characterization and Discrimination of Gram-Positive Bacteria Using Raman Spectroscopy with the Aid of Principal Component Analysis.

PubMed

Colniță, Alia; Dina, Nicoleta Elena; Leopold, Nicolae; Vodnar, Dan Cristian; Bogdan, Diana; Porav, Sebastian Alin; David, Leontin

2017-09-01

Raman scattering and its particular effect, surface-enhanced Raman scattering (SERS), are whole-organism fingerprinting spectroscopic techniques that gain more and more popularity in bacterial detection. In this work, two relevant Gram-positive bacteria species, Lactobacillus casei ( L. casei ) and Listeria monocytogenes ( L. monocytogenes ) were characterized based on their Raman and SERS spectral fingerprints. The SERS spectra were used to identify the biochemical structures of the bacterial cell wall. Two synthesis methods of the SERS-active nanomaterials were used and the recorded spectra were analyzed. L. casei and L. monocytogenes were successfully discriminated by applying Principal Component Analysis (PCA) to their specific spectral data.

Characterization and Discrimination of Gram-Positive Bacteria Using Raman Spectroscopy with the Aid of Principal Component Analysis

PubMed Central

Leopold, Nicolae; Vodnar, Dan Cristian; Bogdan, Diana; Porav, Sebastian Alin; David, Leontin

2017-01-01

Raman scattering and its particular effect, surface-enhanced Raman scattering (SERS), are whole-organism fingerprinting spectroscopic techniques that gain more and more popularity in bacterial detection. In this work, two relevant Gram-positive bacteria species, Lactobacillus casei (L. casei) and Listeria monocytogenes (L. monocytogenes) were characterized based on their Raman and SERS spectral fingerprints. The SERS spectra were used to identify the biochemical structures of the bacterial cell wall. Two synthesis methods of the SERS-active nanomaterials were used and the recorded spectra were analyzed. L. casei and L. monocytogenes were successfully discriminated by applying Principal Component Analysis (PCA) to their specific spectral data. PMID:28862655

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

NASA Astrophysics Data System (ADS)

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

2008-11-01

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

Effect of Minerals on Intestinal IgA Production Using Deep Sea Water Drinks.

PubMed

Shiraishi, Hisashi; Fujino, Maho; Shirakawa, Naoki; Ishida, Nanao; Funato, Hiroki; Hirata, Ayumu; Abe, Noriaki; Iizuka, Michiro; Jobu, Kohei; Yokota, Junko; Miyamura, Mitsuhiko

2017-01-01

Minerals are essential for life, as they are a vital part of protein constituents, enzyme cofactors, and other components in living organisms. Deep sea water is characterized by its cleanliness and stable low temperature, and its possible health- and medical benefits are being studied. However, no study has yet evaluated the physical properties of the numerous commercially available deep sea water products, which have varying water sources and production methods. We analyzed these products' mineral content and investigated their effect on living organism, focusing on immune functions, and investigated the relation between physiological immunoactivities and mineral intake. We qualitatively analyzed the mineral compositions of the deep sea water drinks and evaluated the drinks' physical properties using principal component analysis, a type of multivariate analysis, of their mineral content. We create an iron and copper-deficient rat model and administered deep sea water drinks for 8 weeks. We then measured their fecal immunoglobulin A (IgA) to evaluate immune function. Principal component analysis suggested that physical properties of deep sea water drinks could be determined by their sources. Administration of deep sea water drinks increased fecal IgA, thus tending to stimulate immune function, but the extent of this effect varied by drink. Of the minerals contained in deep sea water, iron showed positive correlations with the fecal IgA. The principal component analysis used in this study is suitable for evaluating deep sea water containing many minerals, and our results form a useful basis for comparative evaluations of deep sea water's bioactivity.

MORPHOLOGICAL VARIATION IN HATCHLING AMERICAN ALLIGATORS (ALLIGATOR MISSISSIPPIENSIS) FROM THREE FLORIDA LAKES

EPA Science Inventory

Morphological variation of 508 hatchling alligators from three lakes in north central Florida (Lakes Woodruff, Apopka, and Orange) was analyzed using multivariate statistics. Morphological variation was found among clutches as well as among lakes. Principal components analysis wa...

Interpretable functional principal component analysis.

PubMed

Lin, Zhenhua; Wang, Liangliang; Cao, Jiguo

2016-09-01

Functional principal component analysis (FPCA) is a popular approach to explore major sources of variation in a sample of random curves. These major sources of variation are represented by functional principal components (FPCs). The intervals where the values of FPCs are significant are interpreted as where sample curves have major variations. However, these intervals are often hard for naïve users to identify, because of the vague definition of "significant values". In this article, we develop a novel penalty-based method to derive FPCs that are only nonzero precisely in the intervals where the values of FPCs are significant, whence the derived FPCs possess better interpretability than the FPCs derived from existing methods. To compute the proposed FPCs, we devise an efficient algorithm based on projection deflation techniques. We show that the proposed interpretable FPCs are strongly consistent and asymptotically normal under mild conditions. Simulation studies confirm that with a competitive performance in explaining variations of sample curves, the proposed FPCs are more interpretable than the traditional counterparts. This advantage is demonstrated by analyzing two real datasets, namely, electroencephalography data and Canadian weather data. © 2015, The International Biometric Society.

Dimensionality Reduction Through Classifier Ensembles

NASA Technical Reports Server (NTRS)

Oza, Nikunj C.; Tumer, Kagan; Norwig, Peter (Technical Monitor)

1999-01-01

In data mining, one often needs to analyze datasets with a very large number of attributes. Performing machine learning directly on such data sets is often impractical because of extensive run times, excessive complexity of the fitted model (often leading to overfitting), and the well-known "curse of dimensionality." In practice, to avoid such problems, feature selection and/or extraction are often used to reduce data dimensionality prior to the learning step. However, existing feature selection/extraction algorithms either evaluate features by their effectiveness across the entire data set or simply disregard class information altogether (e.g., principal component analysis). Furthermore, feature extraction algorithms such as principal components analysis create new features that are often meaningless to human users. In this article, we present input decimation, a method that provides "feature subsets" that are selected for their ability to discriminate among the classes. These features are subsequently used in ensembles of classifiers, yielding results superior to single classifiers, ensembles that use the full set of features, and ensembles based on principal component analysis on both real and synthetic datasets.

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

Principal component regression analysis with SPSS.

PubMed

Liu, R X; Kuang, J; Gong, Q; Hou, X L

2003-06-01

The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.

Gambling, games of skill and human ecology: a pilot study by a multidimensional analysis approach.

PubMed

Valera, Luca; Giuliani, Alessandro; Gizzi, Alessio; Tartaglia, Francesco; Tambone, Vittoradolfo

2015-01-01

The present pilot study aims at analyzing the human activity of playing in the light of an indicator of human ecology (HE). We highlighted the four essential anthropological dimensions (FEAD), starting from the analysis of questionnaires administered to actual gamers. The coherence between theoretical construct and observational data is a remarkable proof-of-concept of the possibility of establishing an experimentally motivated link between a philosophical construct (coming from Huizinga's Homo ludens definition) and actual gamers' motivation pattern. The starting hypothesis is that the activity of playing becomes ecological (and thus not harmful) when it achieves the harmony between the FEAD, thus realizing HE; conversely, it becomes at risk of creating some form of addiction, when destroying FEAD balance. We analyzed the data by means of variable clustering (oblique principal components) so to experimentally verify the existence of the hypothesized dimensions. The subsequent projection of statistical units (gamers) on the orthogonal space spanned by principal components allowed us to generate a meaningful, albeit preliminary, clusterization of gamer profiles.

A new simple /spl infin/OH neuron model as a biologically plausible principal component analyzer.

PubMed

Jankovic, M V

2003-01-01

A new approach to unsupervised learning in a single-layer neural network is discussed. An algorithm for unsupervised learning based upon the Hebbian learning rule is presented. A simple neuron model is analyzed. A dynamic neural model, which contains both feed-forward and feedback connections between the input and the output, has been adopted. The, proposed learning algorithm could be more correctly named self-supervised rather than unsupervised. The solution proposed here is a modified Hebbian rule, in which the modification of the synaptic strength is proportional not to pre- and postsynaptic activity, but instead to the presynaptic and averaged value of postsynaptic activity. It is shown that the model neuron tends to extract the principal component from a stationary input vector sequence. Usually accepted additional decaying terms for the stabilization of the original Hebbian rule are avoided. Implementation of the basic Hebbian scheme would not lead to unrealistic growth of the synaptic strengths, thanks to the adopted network structure.

Multivariate analysis of remote LIBS spectra using partial least squares, principal component analysis, and related techniques

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

Clegg, Samuel M; Barefield, James E; Wiens, Roger C

2008-01-01

Quantitative analysis with LIBS traditionally employs calibration curves that are complicated by the chemical matrix effects. These chemical matrix effects influence the LIBS plasma and the ratio of elemental composition to elemental emission line intensity. Consequently, LIBS calibration typically requires a priori knowledge of the unknown, in order for a series of calibration standards similar to the unknown to be employed. In this paper, three new Multivariate Analysis (MV A) techniques are employed to analyze the LIBS spectra of 18 disparate igneous and highly-metamorphosed rock samples. Partial Least Squares (PLS) analysis is used to generate a calibration model from whichmore » unknown samples can be analyzed. Principal Components Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA) are employed to generate a model and predict the rock type of the samples. These MV A techniques appear to exploit the matrix effects associated with the chemistries of these 18 samples.« less

Perturbation analyses of intermolecular interactions

NASA Astrophysics Data System (ADS)

Koyama, Yohei M.; Kobayashi, Tetsuya J.; Ueda, Hiroki R.

2011-08-01

Conformational fluctuations of a protein molecule are important to its function, and it is known that environmental molecules, such as water molecules, ions, and ligand molecules, significantly affect the function by changing the conformational fluctuations. However, it is difficult to systematically understand the role of environmental molecules because intermolecular interactions related to the conformational fluctuations are complicated. To identify important intermolecular interactions with regard to the conformational fluctuations, we develop herein (i) distance-independent and (ii) distance-dependent perturbation analyses of the intermolecular interactions. We show that these perturbation analyses can be realized by performing (i) a principal component analysis using conditional expectations of truncated and shifted intermolecular potential energy terms and (ii) a functional principal component analysis using products of intermolecular forces and conditional cumulative densities. We refer to these analyses as intermolecular perturbation analysis (IPA) and distance-dependent intermolecular perturbation analysis (DIPA), respectively. For comparison of the IPA and the DIPA, we apply them to the alanine dipeptide isomerization in explicit water. Although the first IPA principal components discriminate two states (the α state and PPII (polyproline II) + β states) for larger cutoff length, the separation between the PPII state and the β state is unclear in the second IPA principal components. On the other hand, in the large cutoff value, DIPA eigenvalues converge faster than that for IPA and the top two DIPA principal components clearly identify the three states. By using the DIPA biplot, the contributions of the dipeptide-water interactions to each state are analyzed systematically. Since the DIPA improves the state identification and the convergence rate with retaining distance information, we conclude that the DIPA is a more practical method compared with the IPA. To test the feasibility of the DIPA for larger molecules, we apply the DIPA to the ten-residue chignolin folding in explicit water. The top three principal components identify the four states (native state, two misfolded states, and unfolded state) and their corresponding eigenfunctions identify important chignolin-water interactions to each state. Thus, the DIPA provides the practical method to identify conformational states and their corresponding important intermolecular interactions with distance information.

Perturbation analyses of intermolecular interactions.

PubMed

Koyama, Yohei M; Kobayashi, Tetsuya J; Ueda, Hiroki R

2011-08-01

Conformational fluctuations of a protein molecule are important to its function, and it is known that environmental molecules, such as water molecules, ions, and ligand molecules, significantly affect the function by changing the conformational fluctuations. However, it is difficult to systematically understand the role of environmental molecules because intermolecular interactions related to the conformational fluctuations are complicated. To identify important intermolecular interactions with regard to the conformational fluctuations, we develop herein (i) distance-independent and (ii) distance-dependent perturbation analyses of the intermolecular interactions. We show that these perturbation analyses can be realized by performing (i) a principal component analysis using conditional expectations of truncated and shifted intermolecular potential energy terms and (ii) a functional principal component analysis using products of intermolecular forces and conditional cumulative densities. We refer to these analyses as intermolecular perturbation analysis (IPA) and distance-dependent intermolecular perturbation analysis (DIPA), respectively. For comparison of the IPA and the DIPA, we apply them to the alanine dipeptide isomerization in explicit water. Although the first IPA principal components discriminate two states (the α state and PPII (polyproline II) + β states) for larger cutoff length, the separation between the PPII state and the β state is unclear in the second IPA principal components. On the other hand, in the large cutoff value, DIPA eigenvalues converge faster than that for IPA and the top two DIPA principal components clearly identify the three states. By using the DIPA biplot, the contributions of the dipeptide-water interactions to each state are analyzed systematically. Since the DIPA improves the state identification and the convergence rate with retaining distance information, we conclude that the DIPA is a more practical method compared with the IPA. To test the feasibility of the DIPA for larger molecules, we apply the DIPA to the ten-residue chignolin folding in explicit water. The top three principal components identify the four states (native state, two misfolded states, and unfolded state) and their corresponding eigenfunctions identify important chignolin-water interactions to each state. Thus, the DIPA provides the practical method to identify conformational states and their corresponding important intermolecular interactions with distance information.

Addressing the identification problem in age-period-cohort analysis: a tutorial on the use of partial least squares and principal components analysis.

PubMed

Tu, Yu-Kang; Krämer, Nicole; Lee, Wen-Chung

2012-07-01

In the analysis of trends in health outcomes, an ongoing issue is how to separate and estimate the effects of age, period, and cohort. As these 3 variables are perfectly collinear by definition, regression coefficients in a general linear model are not unique. In this tutorial, we review why identification is a problem, and how this problem may be tackled using partial least squares and principal components regression analyses. Both methods produce regression coefficients that fulfill the same collinearity constraint as the variables age, period, and cohort. We show that, because the constraint imposed by partial least squares and principal components regression is inherent in the mathematical relation among the 3 variables, this leads to more interpretable results. We use one dataset from a Taiwanese health-screening program to illustrate how to use partial least squares regression to analyze the trends in body heights with 3 continuous variables for age, period, and cohort. We then use another dataset of hepatocellular carcinoma mortality rates for Taiwanese men to illustrate how to use partial least squares regression to analyze tables with aggregated data. We use the second dataset to show the relation between the intrinsic estimator, a recently proposed method for the age-period-cohort analysis, and partial least squares regression. We also show that the inclusion of all indicator variables provides a more consistent approach. R code for our analyses is provided in the eAppendix.

Multivariate analysis of molecular and morphological diversity in fig (Ficus carica L.)

USDA-ARS?s Scientific Manuscript database

Genetic polymorphism across 15 microsatellite loci among 194 fig accessions including Common, Smyrna, San Pedro, and Caprifig were analyzed using a cluster analysis (CA) and the principal components analysis (PCA). The collection was moderately variable with observed number of alleles per locus rang...

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.

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

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

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

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

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

DOE PAGES

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

2017-12-28

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

A Two-Step Approach to Analyze Satisfaction Data

ERIC Educational Resources Information Center

Ferrari, Pier Alda; Pagani, Laura; Fiorio, Carlo V.

2011-01-01

In this paper a two-step procedure based on Nonlinear Principal Component Analysis (NLPCA) and Multilevel models (MLM) for the analysis of satisfaction data is proposed. The basic hypothesis is that observed ordinal variables describe different aspects of a latent continuous variable, which depends on covariates connected with individual and…

Counselors and Principals: Collaborating to Improve Instructional Equity

ERIC Educational Resources Information Center

Lashley, Carl A.; Stickl, Jaimie

2016-01-01

Data utilization is a key component of school improvement as data can be used to enhance student achievement both systematically and on an individual student level. Through systemically analyzing multiple sources of data, educators can make informed and knowledgeable school improvement decisions. In order to effectively make data-drive decisions…

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.

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…

Automated Analysis, Classification, and Display of Waveforms

NASA Technical Reports Server (NTRS)

Kwan, Chiman; Xu, Roger; Mayhew, David; Zhang, Frank; Zide, Alan; Bonggren, Jeff

2004-01-01

A computer program partly automates the analysis, classification, and display of waveforms represented by digital samples. In the original application for which the program was developed, the raw waveform data to be analyzed by the program are acquired from space-shuttle auxiliary power units (APUs) at a sampling rate of 100 Hz. The program could also be modified for application to other waveforms -- for example, electrocardiograms. The program begins by performing principal-component analysis (PCA) of 50 normal-mode APU waveforms. Each waveform is segmented. A covariance matrix is formed by use of the segmented waveforms. Three eigenvectors corresponding to three principal components are calculated. To generate features, each waveform is then projected onto the eigenvectors. These features are displayed on a three-dimensional diagram, facilitating the visualization of the trend of APU operations.

Rapid Elemental Analysis and Provenance Study of Blumea balsamifera DC Using Laser-Induced Breakdown Spectroscopy

PubMed Central

Liu, Xiaona; Zhang, Qiao; Wu, Zhisheng; Shi, Xinyuan; Zhao, Na; Qiao, Yanjiang

2015-01-01

Laser-induced breakdown spectroscopy (LIBS) was applied to perform a rapid elemental analysis and provenance study of Blumea balsamifera DC. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were implemented to exploit the multivariate nature of the LIBS data. Scores and loadings of computed principal components visually illustrated the differing spectral data. The PLS-DA algorithm showed good classification performance. The PLS-DA model using complete spectra as input variables had similar discrimination performance to using selected spectral lines as input variables. The down-selection of spectral lines was specifically focused on the major elements of B. balsamifera samples. Results indicated that LIBS could be used to rapidly analyze elements and to perform provenance study of B. balsamifera. PMID:25558999

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.

Noise of High Performance Aircraft at Afterburner

DTIC Science & Technology

2016-02-10

Navy F18E and the Air Force F22 aircraft became available to the principal investigator. The present project is to analyze these data to identify...the end of the first year of this project (2015), we were able to clearly identify two new dominant noise components from the F22 at afterburner...F18E and F22 aircraft. Compare the noise spectra with those of laboratory hot supersonic jets. ii. Identify any new dominant noise components emitted

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.

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.

[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.

A first application of independent component analysis to extracting structure from stock returns.

PubMed

Back, A D; Weigend, A S

1997-08-01

This paper explores the application of a signal processing technique known as independent component analysis (ICA) or blind source separation to multivariate financial time series such as a portfolio of stocks. The key idea of ICA is to linearly map the observed multivariate time series into a new space of statistically independent components (ICs). We apply ICA to three years of daily returns of the 28 largest Japanese stocks and compare the results with those obtained using principal component analysis. The results indicate that the estimated ICs fall into two categories, (i) infrequent large shocks (responsible for the major changes in the stock prices), and (ii) frequent smaller fluctuations (contributing little to the overall level of the stocks). We show that the overall stock price can be reconstructed surprisingly well by using a small number of thresholded weighted ICs. In contrast, when using shocks derived from principal components instead of independent components, the reconstructed price is less similar to the original one. ICA is shown to be a potentially powerful method of analyzing and understanding driving mechanisms in financial time series. The application to portfolio optimization is described in Chin and Weigend (1998).

Principal Component and Linkage Analysis of Cardiovascular Risk Traits in the Norfolk Isolate

PubMed Central

Cox, Hannah C.; Bellis, Claire; Lea, Rod A.; Quinlan, Sharon; Hughes, Roger; Dyer, Thomas; Charlesworth, Jac; Blangero, John; Griffiths, Lyn R.

2009-01-01

Objective(s) An individual's risk of developing cardiovascular disease (CVD) is influenced by genetic factors. This study focussed on mapping genetic loci for CVD-risk traits in a unique population isolate derived from Norfolk Island. Methods This investigation focussed on 377 individuals descended from the population founders. Principal component analysis was used to extract orthogonal components from 11 cardiovascular risk traits. Multipoint variance component methods were used to assess genome-wide linkage using SOLAR to the derived factors. A total of 285 of the 377 related individuals were informative for linkage analysis. Results A total of 4 principal components accounting for 83% of the total variance were derived. Principal component 1 was loaded with body size indicators; principal component 2 with body size, cholesterol and triglyceride levels; principal component 3 with the blood pressures; and principal component 4 with LDL-cholesterol and total cholesterol levels. Suggestive evidence of linkage for principal component 2 (h2 = 0.35) was observed on chromosome 5q35 (LOD = 1.85; p = 0.0008). While peak regions on chromosome 10p11.2 (LOD = 1.27; p = 0.005) and 12q13 (LOD = 1.63; p = 0.003) were observed to segregate with principal components 1 (h2 = 0.33) and 4 (h2 = 0.42), respectively. Conclusion(s): This study investigated a number of CVD risk traits in a unique isolated population. Findings support the clustering of CVD risk traits and provide interesting evidence of a region on chromosome 5q35 segregating with weight, waist circumference, HDL-c and total triglyceride levels. PMID:19339786

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.

Data analysis techniques

NASA Technical Reports Server (NTRS)

Park, Steve

1990-01-01

A large and diverse number of computational techniques are routinely used to process and analyze remotely sensed data. These techniques include: univariate statistics; multivariate statistics; principal component analysis; pattern recognition and classification; other multivariate techniques; geometric correction; registration and resampling; radiometric correction; enhancement; restoration; Fourier analysis; and filtering. Each of these techniques will be considered, in order.

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

PubMed

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

2013-01-01

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

Enhanced Quality Control in Pharmaceutical Applications by Combining Raman Spectroscopy and Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Martinez, J. C.; Guzmán-Sepúlveda, J. R.; Bolañoz Evia, G. R.; Córdova, T.; Guzmán-Cabrera, R.

2018-06-01

In this work, we applied machine learning techniques to Raman spectra for the characterization and classification of manufactured pharmaceutical products. Our measurements were taken with commercial equipment, for accurate assessment of variations with respect to one calibrated control sample. Unlike the typical use of Raman spectroscopy in pharmaceutical applications, in our approach the principal components of the Raman spectrum are used concurrently as attributes in machine learning algorithms. This permits an efficient comparison and classification of the spectra measured from the samples under study. This also allows for accurate quality control as all relevant spectral components are considered simultaneously. We demonstrate our approach with respect to the specific case of acetaminophen, which is one of the most widely used analgesics in the market. In the experiments, commercial samples from thirteen different laboratories were analyzed and compared against a control sample. The raw data were analyzed based on an arithmetic difference between the nominal active substance and the measured values in each commercial sample. The principal component analysis was applied to the data for quantitative verification (i.e., without considering the actual concentration of the active substance) of the difference in the calibrated sample. Our results show that by following this approach adulterations in pharmaceutical compositions can be clearly identified and accurately quantified.

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.

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.

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.

Using dynamic mode decomposition for real-time background/foreground separation in video

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

Kutz, Jose Nathan; Grosek, Jacob; Brunton, Steven

The technique of dynamic mode decomposition (DMD) is disclosed herein for the purpose of robustly separating video frames into background (low-rank) and foreground (sparse) components in real-time. Foreground/background separation is achieved at the computational cost of just one singular value decomposition (SVD) and one linear equation solve, thus producing results orders of magnitude faster than robust principal component analysis (RPCA). Additional techniques, including techniques for analyzing the video for multi-resolution time-scale components, and techniques for reusing computations to allow processing of streaming video in real time, are also described herein.

Multivariate analysis of fatty acid and biochemical constitutes of seaweeds to characterize their potential as bioresource for biofuel and fine chemicals.

PubMed

Verma, Priyanka; Kumar, Manoj; Mishra, Girish; Sahoo, Dinabandhu

2017-02-01

In the present study bio prospecting of thirty seaweeds from Indian coasts was analyzed for their biochemical components including pigments, fatty acid and ash content. Multivariate analysis of biochemical components and fatty acids was done using Principal Component Analysis (PCA) and Agglomerative hierarchical clustering (AHC) to manifest chemotaxonomic relationship among various seaweeds. The overall analysis suggests that these seaweeds have multi-functional properties and can be utilized as promising bioresource for proteins, lipids, pigments and carbohydrates for the food/feed and biofuel industry. Copyright © 2016. Published by Elsevier Ltd.

Anomaly Detection in Gamma-Ray Vehicle Spectra with Principal Components Analysis and Mahalanobis Distances

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

Tardiff, Mark F.; Runkle, Robert C.; Anderson, K. K.

2006-01-23

The goal of primary radiation monitoring in support of routine screening and emergency response is to detect characteristics in vehicle radiation signatures that indicate the presence of potential threats. Two conceptual approaches to analyzing gamma-ray spectra for threat detection are isotope identification and anomaly detection. While isotope identification is the time-honored method, an emerging technique is anomaly detection that uses benign vehicle gamma ray signatures to define an expectation of the radiation signature for vehicles that do not pose a threat. Newly acquired spectra are then compared to this expectation using statistical criteria that reflect acceptable false alarm rates andmore » probabilities of detection. The gamma-ray spectra analyzed here were collected at a U.S. land Port of Entry (POE) using a NaI-based radiation portal monitor (RPM). The raw data were analyzed to develop a benign vehicle expectation by decimating the original pulse-height channels to 35 energy bins, extracting composite variables via principal components analysis (PCA), and estimating statistically weighted distances from the mean vehicle spectrum with the mahalanobis distance (MD) metric. This paper reviews the methods used to establish the anomaly identification criteria and presents a systematic analysis of the response of the combined PCA and MD algorithm to modeled mono-energetic gamma-ray sources.« less

Gas chromatography/principal component similarity system for detection of E. coli and S. aureus contaminating salmon and hamburger.

PubMed

Nakai, S; Wang, Z H; Dou, J; Nakamura, S; Ogawa, M; Nakai, E; Vanderstoep, J

1999-02-01

Coho, Atlantic, Spring, and Sockeye salmon and five commercial samples of hamburger patties were analyzed by processing gas chromatography (GC) data of volatile compounds using the principal component similarity (PCS) technique. PCS scattergrams of the samples inoculated with Escherichia coli and Staphylococcus aureus followed by incubation showed the pattern-shift lines moving away from the data point for uninoculated, unincubated reference samples in different directions with increasing incubation time. When the PCS scattergrams were drawn for samples incubated overnight, the samples inoculated with the two bacterial species and the uninoculated samples appeared as three separated groups. This GC/PCS approach has the potential to ensure quality of samples by discriminating good samples from potentially spoiled samples. The latter may require further microbial assays to identify the bacteria species potentially contaminating foods.

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.

Application of principal component analysis for improvement of X-ray fluorescence images obtained by polycapillary-based micro-XRF technique

NASA Astrophysics Data System (ADS)

Aida, S.; Matsuno, T.; Hasegawa, T.; Tsuji, K.

2017-07-01

Micro X-ray fluorescence (micro-XRF) analysis is repeated as a means of producing elemental maps. In some cases, however, the XRF images of trace elements that are obtained are not clear due to high background intensity. To solve this problem, we applied principal component analysis (PCA) to XRF spectra. We focused on improving the quality of XRF images by applying PCA. XRF images of the dried residue of standard solution on the glass substrate were taken. The XRF intensities for the dried residue were analyzed before and after PCA. Standard deviations of XRF intensities in the PCA-filtered images were improved, leading to clear contrast of the images. This improvement of the XRF images was effective in cases where the XRF intensity was weak.

The Relation between Factor Score Estimates, Image Scores, and Principal Component Scores

ERIC Educational Resources Information Center

Velicer, Wayne F.

1976-01-01

Investigates the relation between factor score estimates, principal component scores, and image scores. The three methods compared are maximum likelihood factor analysis, principal component analysis, and a variant of rescaled image analysis. (RC)

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.

Fast grasping of unknown objects using principal component analysis

NASA Astrophysics Data System (ADS)

Lei, Qujiang; Chen, Guangming; Wisse, Martijn

2017-09-01

Fast grasping of unknown objects has crucial impact on the efficiency of robot manipulation especially subjected to unfamiliar environments. In order to accelerate grasping speed of unknown objects, principal component analysis is utilized to direct the grasping process. In particular, a single-view partial point cloud is constructed and grasp candidates are allocated along the principal axis. Force balance optimization is employed to analyze possible graspable areas. The obtained graspable area with the minimal resultant force is the best zone for the final grasping execution. It is shown that an unknown object can be more quickly grasped provided that the component analysis principle axis is determined using single-view partial point cloud. To cope with the grasp uncertainty, robot motion is assisted to obtain a new viewpoint. Virtual exploration and experimental tests are carried out to verify this fast gasping algorithm. Both simulation and experimental tests demonstrated excellent performances based on the results of grasping a series of unknown objects. To minimize the grasping uncertainty, the merits of the robot hardware with two 3D cameras can be utilized to suffice the partial point cloud. As a result of utilizing the robot hardware, the grasping reliance is highly enhanced. Therefore, this research demonstrates practical significance for increasing grasping speed and thus increasing robot efficiency under unpredictable environments.

[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.

Principal component analysis of three-dimensional face shape: Identifying shape features that change with age.

PubMed

Kurosumi, M; Mizukoshi, K

2018-05-01

The types of shape feature that constitutes a face have not been comprehensively established, and most previous studies of age-related changes in facial shape have focused on individual characteristics, such as wrinkle, sagging skin, etc. In this study, we quantitatively measured differences in face shape between individuals and investigated how shape features changed with age. We analyzed three-dimensionally the faces of 280 Japanese women aged 20-69 years and used principal component analysis to establish the shape features that characterized individual differences. We also evaluated the relationships between each feature and age, clarifying the shape features characteristic of different age groups. Changes in facial shape in middle age were a decreased volume of the upper face and increased volume of the whole cheeks and around the chin. Changes in older people were an increased volume of the lower cheeks and around the chin, sagging skin, and jaw distortion. Principal component analysis was effective for identifying facial shape features that represent individual and age-related differences. This method allowed straightforward measurements, such as the increase or decrease in cheeks caused by soft tissue changes or skeletal-based changes to the forehead or jaw, simply by acquiring three-dimensional facial images. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

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

PubMed

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

2015-03-01

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

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

PubMed

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

2018-01-01

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

Soft-assembled Multilevel Dynamics of Tactical Behaviors in Soccer

PubMed Central

Ric, Angel; Torrents, Carlota; Gonçalves, Bruno; Sampaio, Jaime; Hristovski, Robert

2016-01-01

This study aimed to identify the tactical patterns and the timescales of variables during a soccer match, allowing understanding the multilevel organization of tactical behaviors, and to determine the similarity of patterns performed by different groups of teammates during the first and second halves. Positional data from 20 professional male soccer players from the same team were collected using high frequency global positioning systems (5 Hz). Twenty-nine categories of tactical behaviors were determined from eight positioning-derived variables creating multivariate binary (Boolean) time-series matrices. Hierarchical principal component analysis (PCA) was used to identify the multilevel structure of tactical behaviors. The sequential reduction of each set level of principal components revealed a sole principal component as the slowest collective variable, forming the global basin of attraction of tactical patterns during each half of the match. In addition, the mean dwell time of each positioning-derived variable helped to understand the multilevel organization of collective tactical behavior during a soccer match. This approach warrants further investigations to analyze the influence of task constraints on the emergence of tactical behavior. Furthermore, PCA can help coaches to design representative training tasks according to those tactical patterns captured during match competitions and to compare them depending on situational variables. PMID:27761120

Interpretation of data on the aggregate composition of typical chernozems under different land use by cluster and principal component analyses

NASA Astrophysics Data System (ADS)

Kholodov, V. A.; Yaroslavtseva, N. V.; Lazarev, V. I.; Frid, A. S.

2016-09-01

Cluster analysis and principal component analysis (PCA) have been used for the interpretation of dry sieving data. Chernozems from the treatments of long-term field experiments with different land-use patterns— annually mowed steppe, continuous potato culture, permanent black fallow, and untilled fallow since 1998 after permanent black fallow—have been used. Analysis of dry sieving data by PCA has shown that the treatments of untilled fallow after black fallow and annually mowed steppe differ most in the series considered; the content of dry aggregates of 10-7 mm makes the largest contribution to the distribution of objects along the first principal component. This fraction has been sieved in water and analyzed by PCA. In contrast to dry sieving data, the wet sieving data showed the closest mathematical distance between the treatment of untilled fallow after black fallow and the undisturbed treatment of annually mowed steppe, while the untilled fallow after black fallow and the permanent black fallow were the most distant treatments. Thus, it may be suggested that the water stability of structure is first restored after the removal of destructive anthropogenic load. However, the restoration of the distribution of structural separates to the parameters characteristic of native soils is a significantly longer process.

Modified Attitudes to Psychiatry Scale Created Using Principal-Components Analysis

ERIC Educational Resources Information Center

Shankar, Rohit; Laugharne, Richard; Pritchard, Colin; Joshi, Pallavi; Dhar, Romika

2011-01-01

Objective: The Attitudes to Psychiatry Scale (APS) is a tool used to assess medical students' attitudes toward psychiatry. This study sought to examine the internal validity of the APS in order to identify dimensions within the questionnaire. Method: Using data collected from 549 medical students from India and Ghana, the authors analyzed 28…

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

A Nonlinear Model for Gene-Based Gene-Environment Interaction.

PubMed

Sa, Jian; Liu, Xu; He, Tao; Liu, Guifen; Cui, Yuehua

2016-06-04

A vast amount of literature has confirmed the role of gene-environment (G×E) interaction in the etiology of complex human diseases. Traditional methods are predominantly focused on the analysis of interaction between a single nucleotide polymorphism (SNP) and an environmental variable. Given that genes are the functional units, it is crucial to understand how gene effects (rather than single SNP effects) are influenced by an environmental variable to affect disease risk. Motivated by the increasing awareness of the power of gene-based association analysis over single variant based approach, in this work, we proposed a sparse principle component regression (sPCR) model to understand the gene-based G×E interaction effect on complex disease. We first extracted the sparse principal components for SNPs in a gene, then the effect of each principal component was modeled by a varying-coefficient (VC) model. The model can jointly model variants in a gene in which their effects are nonlinearly influenced by an environmental variable. In addition, the varying-coefficient sPCR (VC-sPCR) model has nice interpretation property since the sparsity on the principal component loadings can tell the relative importance of the corresponding SNPs in each component. We applied our method to a human birth weight dataset in Thai population. We analyzed 12,005 genes across 22 chromosomes and found one significant interaction effect using the Bonferroni correction method and one suggestive interaction. The model performance was further evaluated through simulation studies. Our model provides a system approach to evaluate gene-based G×E interaction.

Principal Milk Components in Buffalo, Holstein Cross, Indigenous Cattle and Red Chittagong Cattle from Bangladesh

PubMed Central

Islam, M. A.; Alam, M. K.; Islam, M. N.; Khan, M. A. S.; Ekeberg, D.; Rukke, E. O.; Vegarud, G. E.

2014-01-01

The aim of the present study was to get a total physical and chemical characterization and comparison of the principal components in Bangladeshi buffalo (B), Holstein cross (HX), Indigenous cattle (IC) and Red Chittagong Cattle (RCC) milk. Protein and casein (CN) composition and type, casein micellar size (CMS), naturally occurring peptides, free amino acids, fat, milk fat globule size (MFGS), fatty acid composition, carbohydrates, total and individual minerals were analyzed. These components are related to technological and nutritional properties of milk. Consequently, they are important for the dairy industry and in the animal feeding and breeding strategies. Considerable variation in most of the principal components of milk were observed among the animals. The milk of RCC and IC contained higher protein, CN, β-CN, whey protein, lactose, total mineral and P. They were more or less similar in most of the all other components. The B milk was found higher in CN number, in the content of αs2-, κ-CN and α-lactalbumin, free amino acids, unsaturated fatty acids, Ca and Ca:P. The B milk was also lower in β-lactoglobulin content and had the largest CMS and MFGS. Proportion of CN to whey protein was lower in HX milk and this milk was found higher in β-lactoglobulin and naturally occuring peptides. Considering the results obtained including the ratio of αs1-, αs2-, β- and κ-CN, B and RCC milk showed best data both from nutritional and technological aspects. PMID:25050028

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

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.

Typification of cider brandy on the basis of cider used in its manufacture.

PubMed

Rodríguez Madrera, Roberto; Mangas Alonso, Juan J

2005-04-20

A study of typification of cider brandies on the basis of the origin of the raw material used in their manufacture was conducted using chemometric techniques (principal component analysis, linear discriminant analysis, and Bayesian analysis) together with their composition in volatile compounds, as analyzed by gas chromatography with flame ionization to detect the major volatiles and by mass spectrometric to detect the minor ones. Significant principal components computed by a double cross-validation procedure allowed the structure of the database to be visualized as a function of the raw material, that is, cider made from fresh apple juice versus cider made from apple juice concentrate. Feasible and robust discriminant rules were computed and validated by a cross-validation procedure that allowed the authors to classify fresh and concentrate cider brandies, obtaining classification hits of >92%. The most discriminating variables for typifying cider brandies according to their raw material were 1-butanol and ethyl hexanoate.

Metabolic fingerprinting of Cannabis sativa L., cannabinoids and terpenoids for chemotaxonomic and drug standardization purposes.

PubMed

Fischedick, Justin Thomas; Hazekamp, Arno; Erkelens, Tjalling; Choi, Young Hae; Verpoorte, Rob

2010-12-01

Cannabis sativa L. is an important medicinal plant. In order to develop cannabis plant material as a medicinal product quality control and clear chemotaxonomic discrimination between varieties is a necessity. Therefore in this study 11 cannabis varieties were grown under the same environmental conditions. Chemical analysis of cannabis plant material used a gas chromatography flame ionization detection method that was validated for quantitative analysis of cannabis monoterpenoids, sesquiterpenoids, and cannabinoids. Quantitative data was analyzed using principal component analysis to determine which compounds are most important in discriminating cannabis varieties. In total 36 compounds were identified and quantified in the 11 varieties. Using principal component analysis each cannabis variety could be chemically discriminated. This methodology is useful for both chemotaxonomic discrimination of cannabis varieties and quality control of plant material. Copyright © 2010 Elsevier Ltd. All rights reserved.

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.

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.

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

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...

Construct Validation of the Self-Efficacy Teaching and Knowledge Instrument for Science Teachers-Revised (SETAKIST-R): Lessons Learned

ERIC Educational Resources Information Center

Pruski, Linda A.; Blanco, Sharon L.; Riggs, Rosemary A.; Grimes, Kandi K.; Fordtran, Chase W.; Barbola, Gina M.; Cornell, John E.; Lichtenstein, Michael J.

2013-01-01

Described herein is the academic lineage and independent validation of the Self-Efficacy Teaching and Knowledge Instrument for Science Teachers-Revised (SETAKIST-R). Data from 334 K-12 science teachers were analyzed using Partial Credit Rasch models. Principal components analysis on the person-item residuals suggest two latent dimensions:…

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

NASA Astrophysics Data System (ADS)

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

2008-04-01

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

Landscape Characteristics of Oriental Honey Buzzards Wintering in Western Part of Flores Island Based on Satellite-Tracking Data

NASA Astrophysics Data System (ADS)

Syartinilia; Farisi, G. H. Al; Higuchi, H.

2017-10-01

Oriental Honey Buzzards (OHBs, Pernis ptilorhynchus) are migratory raptor that has been satellite-tracked since 2003. Some islands in Indonesia which are used for wintering habitat are Flores and Borneo. However, both islands have different characteristics of climate and land cover. The objectives of this research were to analyze the landscape characteristic of the OHBs wintering habitat in western Flores, and to subsequently compare landscape characteristic of the OHBs wintering habitat in Borneo. Landscape habitat characteristics were analyzed using Principal Component Analysis (PCA) combined with GIS and then compared to the previous study in Borneo Island. The result showed that the first of six principal components explained 79.14% and 77.59% of the observed variation in landscape characteristics of both core and edge habitats, subsequently. Habitat selection by OHBs at wintering site was influenced by the availability of thermal wind and food. Savannah was identified as the main landscape characteristic that was different between wintering habitat in Flores and Borneo. Savannah is well-known as a habitat for many species of amphibians, reptiles, and small mammals so that it can be a hunting area that provide alternative feed for OHBs.

Spectroscopic study of honey from Apis mellifera from different regions in Mexico

NASA Astrophysics Data System (ADS)

Frausto-Reyes, C.; Casillas-Peñuelas, R.; Quintanar-Stephano, JL; Macías-López, E.; Bujdud-Pérez, JM; Medina-Ramírez, I.

2017-05-01

The objective of this study was to analyze by Raman and UV-Vis-NIR Spectroscopic techniques, Mexican honey from Apis Mellífera, using representative samples with different botanic origins (unifloral and multifloral) and diverse climates. Using Raman spectroscopy together with principal components analysis, the results obtained represent the possibility to use them for determination of floral origin of honey, independently of the region of sampling. For this, the effect of heat up the honey was analyzed in relation that it was possible to greatly reduce the fluorescence background in Raman spectra, which allowed the visualization of fructose and glucose peaks. Using UV-Vis-NIR, spectroscopy, a characteristic spectrum profile of transmittance was obtained for each honey type. In addition, to have an objective characterization of color, a CIE Yxy and CIE L*a*b* colorimetric register was realized for each honey type. Applying the principal component analysis and their correlation with chromaticity coordinates allowed classifying the honey samples in one plot as: cutoff wavelength, maximum transmittance, tones and lightness. The results show that it is possible to obtain a spectroscopic record of honeys with specific characteristics by reducing the effects of fluorescence.

Correlation between grade of pearlite spheroidization and laser induced spectra

NASA Astrophysics Data System (ADS)

Yao, Shunchun; Dong, Meirong; Lu, Jidong; Li, Jun; Dong, Xuan

2013-12-01

Laser induced breakdown spectroscopy (LIBS) which is used traditionally as a spectrochemical analytical technique was employed to analyze the grade of pearlite spheroidization. Three 12Cr1MoV steel specimens with different grades of pearlite spheroidization were ablated to produce plasma by pulse laser at 266 nm. In order to determine the optimal temporal condition and plasma parameters for correlating the grade of pearlite spheroidization and laser induced spectra, a set of spectra at different delays were analyzed by the principal component analysis method. Then, the relationship between plasma temperature, intensity ratios of ionic to atomic lines and grade of pearlite spheroidization was studied. The analysis results show that the laser induced spectra of different grades of pearlite spheroidization can be readily identifiable by principal component analysis in the range of 271.941-289.672 nm with 1000 ns delay time. It is also found that a good agreement exists between the Fe ionic to atomic line ratios and the tensile strength, whereas there is no obvious difference in the plasma temperature. Therefore, LIBS may be applied not only as a spectrochemical analytical technique but also as a new way to estimate the grade of pearlite spheroidization.

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...

Time-oriented hierarchical method for computation of principal components using subspace learning algorithm.

PubMed

Jankovic, Marko; Ogawa, Hidemitsu

2004-10-01

Principal Component Analysis (PCA) and Principal Subspace Analysis (PSA) are classic techniques in statistical data analysis, feature extraction and data compression. Given a set of multivariate measurements, PCA and PSA provide a smaller set of "basis vectors" with less redundancy, and a subspace spanned by them, respectively. Artificial neurons and neural networks have been shown to perform PSA and PCA when gradient ascent (descent) learning rules are used, which is related to the constrained maximization (minimization) of statistical objective functions. Due to their low complexity, such algorithms and their implementation in neural networks are potentially useful in cases of tracking slow changes of correlations in the input data or in updating eigenvectors with new samples. In this paper we propose PCA learning algorithm that is fully homogeneous with respect to neurons. The algorithm is obtained by modification of one of the most famous PSA learning algorithms--Subspace Learning Algorithm (SLA). Modification of the algorithm is based on Time-Oriented Hierarchical Method (TOHM). The method uses two distinct time scales. On a faster time scale PSA algorithm is responsible for the "behavior" of all output neurons. On a slower scale, output neurons will compete for fulfillment of their "own interests". On this scale, basis vectors in the principal subspace are rotated toward the principal eigenvectors. At the end of the paper it will be briefly analyzed how (or why) time-oriented hierarchical method can be used for transformation of any of the existing neural network PSA method, into PCA method.

Directly Reconstructing Principal Components of Heterogeneous Particles from Cryo-EM Images

PubMed Central

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

2015-01-01

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 inluenza 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. PMID:26049077

Evaluation of 107 legumes for renewable sources of energy

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

Roth, W.B.; Carr, M.E.; Cull, I.M.

One hundred and seven species of randomly-collected Leguminosae were evaluated for their potential as energy-producing crops. Whole plants, excluding roots, were chemically analyzed, and 11 species were identified as the more promising for future considerations based on a numerical rating system developed at this Center. Of the 11 species, one contained principally rubber (polyisoprene) in the hydrocarbon fraction and 7 contained principally wax. Hydrocarbon fractions of 3 species with less than 0.4% were not examined. The oils of species with at least 3.0% oil were examined by thin layer chromatography (TLC) to determine classes of components and were given amore » saponification treatment to determine yields of unsaponifiable matter and fatty acids. The oil of one species was quantitatively analyzed for classes of compounds by TLC-flame ionization detection. Selected species with ratings greater than 10 are briefly discussed. 16 references, 1 figure, 2 tables.« less

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...

Comparison of five Lonicera flowers by simultaneous determination of multi-components with single reference standard method and principal component analysis.

PubMed

Gao, Wen; Wang, Rui; Li, Dan; Liu, Ke; Chen, Jun; Li, Hui-Jun; Xu, Xiaojun; Li, Ping; Yang, Hua

2016-01-05

The flowers of Lonicera japonica Thunb. were extensively used to treat many diseases. As the demands for L. japonica increased, some related Lonicera plants were often confused or misused. Caffeoylquinic acids were always regarded as chemical markers in the quality control of L. japonica, but they could be found in all Lonicera species. Thus, a simple and reliable method for the evaluation of different Lonicera flowers is necessary to be established. In this work a method based on single standard to determine multi-components (SSDMC) combined with principal component analysis (PCA) for control and distinguish of Lonicera species flowers have been developed. Six components including three caffeoylquinic acids and three iridoid glycosides were assayed simultaneously using chlorogenic acid as the reference standard. The credibility and feasibility of the SSDMC method were carefully validated and the results demonstrated that there were no remarkable differences compared with external standard method. Finally, a total of fifty-one batches covering five Lonicera species were analyzed and PCA was successfully applied to distinguish the Lonicera species. This strategy simplifies the processes in the quality control of multiple-componential herbal medicine which effectively adapted for improving the quality control of those herbs belonging to closely related species. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

Source localization of temporal lobe epilepsy using PCA-LORETA analysis on ictal EEG recordings.

PubMed

Stern, Yaki; Neufeld, Miriam Y; Kipervasser, Svetlana; Zilberstein, Amir; Fried, Itzhak; Teicher, Mina; Adi-Japha, Esther

2009-04-01

Localizing the source of an epileptic seizure using noninvasive EEG suffers from inaccuracies produced by other generators not related to the epileptic source. The authors isolated the ictal epileptic activity, and applied a source localization algorithm to identify its estimated location. Ten ictal EEG scalp recordings from five different patients were analyzed. The patients were known to have temporal lobe epilepsy with a single epileptic focus that had a concordant MRI lesion. The patients had become seizure-free following partial temporal lobectomy. A midinterval (approximately 5 seconds) period of ictal activity was used for Principal Component Analysis starting at ictal onset. The level of epileptic activity at each electrode (i.e., the eigenvector of the component that manifest epileptic characteristic), was used as an input for low-resolution tomography analysis for EEG inverse solution (Zilberstain et al., 2004). The algorithm accurately and robustly identified the epileptic focus in these patients. Principal component analysis and source localization methods can be used in the future to monitor the progression of an epileptic seizure and its expansion to other areas.

Establishing ¹H nuclear magnetic resonance based metabonomics fingerprinting profile for spinal cord injury: a pilot study.

PubMed

Jiang, Hua; Peng, Jin; Zhou, Zhi-yuan; Duan, Yu; Chen, Wei; Cai, Bin; Yang, Hao; Zhang, Wei

2010-09-01

Spinal cord injury (SCI) is a complex trauma that consists of multiple pathological mechanisms involving cytotoxic, oxidation stress and immune-endocrine. This study aimed to establish plasma metabonomics fingerprinting atlas for SCI using (1)H nuclear magnetic resonance (NMR) based metabonomics methodology and principal component analysis techniques. Nine Sprague-Dawley (SD) male rats were randomly divided into SCI, normal and sham-operation control groups. Plasma samples were collected for (1)H NMR spectroscopy 3 days after operation. The NMR data were analyzed using principal component analysis technique with Matlab software. Metabonomics analysis was able to distinguish the three groups (SCI, normal control, sham-operation). The fingerprinting atlas indicated that, compared with those without SCI, the SCI group demonstrated the following characteristics with regard to second principal component: it is made up of fatty acids, myc-inositol, arginine, very low-density lipoprotein (VLDL), low-density lipoprotein (LDL), triglyceride (TG), glucose, and 3-methyl-histamine. The data indicated that SCI results in several significant changes in plasma metabolism early on and that a metabonomics approach based on (1)H NMR spectroscopy can provide a metabolic profile comprising several metabolite classes and allow for relative quantification of such changes. The results also provided support for further development and application of metabonomics technologies for studying SCI and for the utilization of multivariate models for classifying the extent of trauma within an individual.

Determination of the Characteristics and Classification of Near-Infrared Spectra of Patchouli Oil (Pogostemon Cablin Benth.) from Different Origin

NASA Astrophysics Data System (ADS)

Diego, M. C. R.; Purwanto, Y. A.; Sutrisno; Budiastra, I. W.

2018-05-01

Research related to the non-destructive method of near-infrared (NIR) spectroscopy in aromatic oil is still in development in Indonesia. The objectives of the study were to determine the characteristics of the near-infrared spectra of patchouli oil and classify it based on its origin. The samples were selected from seven different places in Indonesia (Bogor and Garut from West Java, Aceh, and Jambi from Sumatra and Konawe, Masamba and Kolaka from Sulawesi Island). The spectral data of patchouli oil was obtained by FT-NIR spectrometer at the wavelength of 1000-2500 nm, and after that, the samples were subjected to composition analysis using Gas Chromatography-Mass Spectrometry. The transmittance and absorbance spectra were analyzed and then principal component analysis (PCA) was carried out. Discriminant analysis (DA) of the principal component was developed to classify patchouli oil based on its origin. The result shows that the data of both spectra (transmittance and absorbance spectra) by the PC analysis give a similar result for discriminating the seven types of patchouli oil due to their distribution and behavior. The DA of the three principal component in both data processed spectra could classify patchouli oil accurately. This result exposed that NIR spectroscopy can be successfully used as a correct method to classify patchouli oil based on its origin.

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.

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...

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

Treesearch

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

1993-01-01

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

Characterization of Leaf Extracts of Schinus terebinthifolius Raddi by GC-MS and Chemometric Analysis

PubMed Central

Carneiro, Fabíola B.; Lopes, Pablo Q.; Ramalho, Ricardo C.; Scotti, Marcus T.; Santos, Sócrates G.; Soares, Luiz A. L.

2017-01-01

Background: Schinus terebinthifolius Raddi belongs to Anacardiacea family and is widely known as “aroeira.” This species originates from South America, and its extracts are used in folk medicine due to its therapeutic properties, which include antimicrobial, anti-inflammatory, and antipyretic effects. The complexity and variability of the chemical constitution of the herbal raw material establishes the quality of the respective herbal medicine products. Objective: Thus, the purpose of this study was to investigate the variability of the volatile compounds from leaves of S. terebinthifolius. Materials and Methods: The samples were collected from different states of the Northeast region of Brazil and analyzed with a gas chromatograph coupled to a mass spectrometer (GC-MS). The collected data were analyzed using multivariate data analysis. Results: The samples’ chromatograms, obtained by GC-MS, showed similar chemical profiles in a number of peaks, but some differences were observed in the intensity of these analytical markers. The chromatographic fingerprints obtained by GC-MS were suitable for discrimination of the samples; these results along with a statistical treatment (principal component analysis [PCA]) were used as a tool for comparative analysis between the different samples of S. terebinthifolius. Conclusion: The experimental data show that the PCA used in this study clustered the samples into groups with similar chemical profiles, which builds an appropriate approach to evaluate the similarity in the phytochemical pattern found in the different leaf samples. SUMMARY The leave extracts of Schinus terebinthifolius were obtained by turbo-extractionThe extracts were partitioned with hexane and analyzed by GC-MSThe chromatographic data were analyzed using the principal component analysis (PCA)The PCA plots showed the main compounds (phellandrene, limonene, and carene), which were used to group the samples from a different geographical location in accordance to their chemical similarity. Abbreviations used: AL: Alagoas, BA: Bahia, CE: Ceará, CPETEC: Center for Weather Forecasting and Climate Studies, GC-MS: Gas chromatograph coupled to a mass spectrometer, MA: Maranhão, MVA: Multivariate data analysis, PB: Paraíba, PC1: Direction that describes the maximum variance of the original data, PC2: Maximum direction variance of the data in the subspace orthogonal to PC1, PCA: Principal component analysis, PE: Pernambuco, PI: Piauí, RN: Rio Grande do Norte, SE: Sergipe. PMID:29142431

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.

Chemometric analysis of multisensor hyperspectral images of precipitated atmospheric particulate matter.

PubMed

Ofner, Johannes; Kamilli, Katharina A; Eitenberger, Elisabeth; Friedbacher, Gernot; Lendl, Bernhard; Held, Andreas; Lohninger, Hans

2015-09-15

The chemometric analysis of multisensor hyperspectral data allows a comprehensive image-based analysis of precipitated atmospheric particles. Atmospheric particulate matter was precipitated on aluminum foils and analyzed by Raman microspectroscopy and subsequently by electron microscopy and energy dispersive X-ray spectroscopy. All obtained images were of the same spot of an area of 100 × 100 μm(2). The two hyperspectral data sets and the high-resolution scanning electron microscope images were fused into a combined multisensor hyperspectral data set. This multisensor data cube was analyzed using principal component analysis, hierarchical cluster analysis, k-means clustering, and vertex component analysis. The detailed chemometric analysis of the multisensor data allowed an extensive chemical interpretation of the precipitated particles, and their structure and composition led to a comprehensive understanding of atmospheric particulate matter.

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

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:…

Effects of distillation system and yeast strain on the aroma profile of Albariño (Vitis vinifera L.) grape pomace spirits.

PubMed

Arrieta-Garay, Y; Blanco, P; López-Vázquez, C; Rodríguez-Bencomo, J J; Pérez-Correa, J R; López, F; Orriols, I

2014-10-29

Orujo is a traditional alcoholic beverage produced in Galicia (northwest Spain) from distillation of grape pomace, a byproduct of the winemaking industry. In this study, the effect of the distillation system (copper charentais alembic versus packed column) and the yeast strain (native yeast L1 versus commercial yeast L2) on the chemical and sensory characteristics of orujo obtained from Albariño (Vitis vinifera L.) grape pomace has been analyzed. Principal component analysis, with two components explaining 74% of the variance, is able to clearly differentiate the distillates according to distillation system and yeast strain. Principal component 1, mainly defined by C6-C12 esters, isoamyl octanoate, and methanol, differentiates L1 from L2 distillates. In turn, principal component 2, mainly defined by linear alcohols, linalool, and 1-hexenol, differentiates alembic from packed column distillates. In addition, an aroma descriptive test reveals that the distillate obtained with a packed column from a pomace fermented with L1 presented the highest positive general impression, which is associated with the highest fruity and smallest solvent aroma scores. Moreover, chemical analysis shows that use of a packed column increases average ethanol recovery by 12%, increases the concentration of C6-C12 esters by 25%, and reduces the concentration of higher alcohols by 21%. In turn, L2 yeast obtained lower scores in the alembic distillates aroma profile. In addition, with L1, 9% higher ethanol yields were achieved, and L2 distillates contained 34%-40% more methanol than L1 distillates.

Addressing the selectivity issue of cobalt doped zinc oxide thin film iso-butane sensors: Conductance transients and principal component analyses

NASA Astrophysics Data System (ADS)

Ghosh, A.; Majumder, S. B.

2017-07-01

Iso-butane (i-C4H10) is one of the major components of liquefied petroleum gas which is used as fuel in domestic and industrial applications. Developing chemi-resistive selective i-C4H10 thin film sensors remains a major challenge. Two strategies were undertaken to differentiate carbon monoxide, hydrogen, and iso-butane gases from the measured conductance transients of cobalt doped zinc oxide thin films. Following the first strategy, the response and recovery transients of conductances in these gas environments are fitted using the Langmuir adsorption kinetic model to estimate the heat of adsorption, response time constant, and activation energies for adsorption (response) and desorption (recovery). Although these test gases have seemingly different vapor densities, molecular diameters, and reactivities, analyzing the estimated heat of adsorption and activation energies (for both adsorption and desorption), we could not differentiate these gases unequivocally. However, we have found that the lower the vapor density, the faster the response time irrespective of the test gas concentration. As a second strategy, we demonstrated that feature extraction of conductance transients (using fast Fourier transformation) in conjunction with the pattern recognition algorithm (principal component analysis) is more fruitful to address the cross-sensitivity of Co doped ZnO thin film sensors. We have found that although the dispersion among different concentrations of hydrogen and carbon monoxide could not be avoided, each of these three gases forms distinct clusters in the plot of principal component 2 versus 1 and therefore could easily be differentiated.

Multivariate analysis for scanning tunneling spectroscopy data

NASA Astrophysics Data System (ADS)

Yamanishi, Junsuke; Iwase, Shigeru; Ishida, Nobuyuki; Fujita, Daisuke

2018-01-01

We applied principal component analysis (PCA) to two-dimensional tunneling spectroscopy (2DTS) data obtained on a Si(111)-(7 × 7) surface to explore the effectiveness of multivariate analysis for interpreting 2DTS data. We demonstrated that several components that originated mainly from specific atoms at the Si(111)-(7 × 7) surface can be extracted by PCA. Furthermore, we showed that hidden components in the tunneling spectra can be decomposed (peak separation), which is difficult to achieve with normal 2DTS analysis without the support of theoretical calculations. Our analysis showed that multivariate analysis can be an additional powerful way to analyze 2DTS data and extract hidden information from a large amount of spectroscopic data.

Considering Horn's Parallel Analysis from a Random Matrix Theory Point of View.

PubMed

Saccenti, Edoardo; Timmerman, Marieke E

2017-03-01

Horn's parallel analysis is a widely used method for assessing the number of principal components and common factors. We discuss the theoretical foundations of parallel analysis for principal components based on a covariance matrix by making use of arguments from random matrix theory. In particular, we show that (i) for the first component, parallel analysis is an inferential method equivalent to the Tracy-Widom test, (ii) its use to test high-order eigenvalues is equivalent to the use of the joint distribution of the eigenvalues, and thus should be discouraged, and (iii) a formal test for higher-order components can be obtained based on a Tracy-Widom approximation. We illustrate the performance of the two testing procedures using simulated data generated under both a principal component model and a common factors model. For the principal component model, the Tracy-Widom test performs consistently in all conditions, while parallel analysis shows unpredictable behavior for higher-order components. For the common factor model, including major and minor factors, both procedures are heuristic approaches, with variable performance. We conclude that the Tracy-Widom procedure is preferred over parallel analysis for statistically testing the number of principal components based on a covariance matrix.

Differential Lipid Profiles of Normal Human Brain Matter and Gliomas by Positive and Negative Mode Desorption Electrospray Ionization – Mass Spectrometry Imaging

PubMed Central

Pirro, Valentina; Hattab, Eyas M.; Cohen-Gadol, Aaron A.; Cooks, R. Graham

2016-01-01

Desorption electrospray ionization—mass spectrometry (DESI-MS) imaging was used to analyze unmodified human brain tissue sections from 39 subjects sequentially in the positive and negative ionization modes. Acquisition of both MS polarities allowed more complete analysis of the human brain tumor lipidome as some phospholipids ionize preferentially in the positive and others in the negative ion mode. Normal brain parenchyma, comprised of grey matter and white matter, was differentiated from glioma using positive and negative ion mode DESI-MS lipid profiles with the aid of principal component analysis along with linear discriminant analysis. Principal component–linear discriminant analyses of the positive mode lipid profiles was able to distinguish grey matter, white matter, and glioma with an average sensitivity of 93.2% and specificity of 96.6%, while the negative mode lipid profiles had an average sensitivity of 94.1% and specificity of 97.4%. The positive and negative mode lipid profiles provided complementary information. Principal component–linear discriminant analysis of the combined positive and negative mode lipid profiles, via data fusion, resulted in approximately the same average sensitivity (94.7%) and specificity (97.6%) of the positive and negative modes when used individually. However, they complemented each other by improving the sensitivity and specificity of all classes (grey matter, white matter, and glioma) beyond 90% when used in combination. Further principal component analysis using the fused data resulted in the subgrouping of glioma into two groups associated with grey and white matter, respectively, a separation not apparent in the principal component analysis scores plots of the separate positive and negative mode data. The interrelationship of tumor cell percentage and the lipid profiles is discussed, and how such a measure could be used to measure residual tumor at surgical margins. PMID:27658243

Medical Gas Analyzer

NASA Technical Reports Server (NTRS)

1983-01-01

The Remote Monitoring System (RMS) is manufactured by Perkin Elmer Corporation. The principal component of the RMS was originally developed for spacecraft use to monitor astronaut's respiratory gases in NASA's Gemini and Apollo program. At Wishard Memorial Hospital in Indianapolis, IN, the RMS is used in operating rooms for analysis of anesthetic gases and measurement of oxygen, carbon dioxide and nitrogen concentrations. It assures that the patient undergoing surgery has the proper breathing environment.

Stress and Coping: A Comparison of Self-Report Measures of Functioning in Families of Young Children with Cerebral Palsy or No Medical Diagnosis

ERIC Educational Resources Information Center

Britner, Preston A.; Morog, Maria C.; Pianta, Robert C.; Marvin, Robert S.

2003-01-01

We analyzed data from 87 mothers of children ages 15 to 44 months with cerebral palsy (CP) or no diagnosis, who completed the Dyadic Adjustment Scale, Parenting Stress Index, Support Functions Scale, and Inventory of Social Support. Principal components analysis of the 15 subscales from the 5 measures revealed few cross-measure loadings. Mothers…

Morphological evidence for discrete stocks of yellow perch in Lake Erie

USGS Publications Warehouse

Kocovsky, Patrick M.; Knight, Carey T.

2012-01-01

Identification and management of unique stocks of exploited fish species are high-priority management goals in the Laurentian Great Lakes. We analyzed whole-body morphometrics of 1430 yellow perch Perca flavescens captured during 2007–2009 from seven known spawning areas in Lake Erie to determine if morphometrics vary among sites and management units to assist in identification of spawning stocks of this heavily exploited species. Truss-based morphometrics (n = 21 measurements) were analyzed using principal component analysis followed by ANOVA of the first three principal components to determine whether yellow perch from the several sampling sites varied morphometrically. Duncan's multiple range test was used to determine which sites differed from one another to test whether morphometrics varied at scales finer than management unit. Morphometrics varied significantly among sites and annually, but differences among sites were much greater. Sites within the same management unit typically differed significantly from one another, indicating morphometric variation at a scale finer than management unit. These results are largely congruent with recently-published studies on genetic variation of yellow perch from many of the same sampling sites. Thus, our results provide additional evidence that there are discrete stocks of yellow perch in Lake Erie and that management units likely comprise multiple stocks.

The Hughes phenomenon in hyperspectral classification based on the ground spectrum of grasslands in the region around Qinghai Lake

NASA Astrophysics Data System (ADS)

Ma, Weiwei; Gong, Cailan; Hu, Yong; Meng, Peng; Xu, Feifei

2013-08-01

Hyperspectral data, consisting of hundreds of spectral bands with a high spectral resolution, enables acquisition of continuous spectral characteristic curves, and therefore have served as a powerful tool for vegetation classification. The difficulty of using hyperspectral data is that they are usually redundant, strongly correlated and subject to Hughes phenomenon where classification accuracy increases gradually in the beginning as the number of spectral bands or dimensions increases, but decreases dramatically when the band number reaches some value. In recent years，some algorithms have been proposed to overcome the Hughes phenomenon in classification, such as selecting several bands from full bands, PCA- and MNF-based feature transformations. Up to date, however, few studies have been conducted to investigate the turning point of Hughes phenomenon (i.e., the point at which the classification accuracy begins to decline). In this paper, we firstly analyze reasons for occurrence of Hughes phenomenon, and then based on the Mahalanobis classifier, classify the ground spectrum of several grasslands which were recorded in September 2012 using FieldSpec3 spectrometer in the regions around Qinghai Lake，a important pasturing area in the north of China. Before classification, we extract features from hyperspectral data by bands selecting and PCA- based feature transformations, and In the process of classification, we analyze how the correlation coefficient between wavebands, the number of waveband channels and the number of principal components affect the classification result. The results show that Hushes phenomenon may occur when the correlation coefficient between wavebands is greater than 94%，the number of wavebands is greater than 6, or the number of principal components is greater than 6. Best classification result can be achieved (overall accuracy of grasslands 90%) if the number of wavebands equals to 3 (the band positions are 370nm, 509nm and 886nm respectively) or the number of principal components ranges from 4 to 6.

EMPCA and Cluster Analysis of Quasar Spectra: Construction and Application to Simulated Spectra

NASA Astrophysics Data System (ADS)

Marrs, Adam; Leighly, Karen; Wagner, Cassidy; Macinnis, Francis

2017-01-01

Quasars have complex spectra with emission lines influenced by many factors. Therefore, to fully describe the spectrum requires specification of a large number of parameters, such as line equivalent width, blueshift, and ratios. Principal Component Analysis (PCA) aims to construct eigenvectors-or principal components-from the data with the goal of finding a few key parameters that can be used to predict the rest of the spectrum fairly well. Analysis of simulated quasar spectra was used to verify and justify our modified application of PCA.We used a variant of PCA called Weighted Expectation Maximization PCA (EMPCA; Bailey 2012) along with k-means cluster analysis to analyze simulated quasar spectra. Our approach combines both analytical methods to address two known problems with classical PCA. EMPCA uses weights to account for uncertainty and missing points in the spectra. K-means groups similar spectra together to address the nonlinearity of quasar spectra, specifically variance in blueshifts and widths of the emission lines.In producing and analyzing simulations, we first tested the effects of varying equivalent widths and blueshifts on the derived principal components, and explored the differences between standard PCA and EMPCA. We also tested the effects of varying signal-to-noise ratio. Next we used the results of fits to composite quasar spectra (see accompanying poster by Wagner et al.) to construct a set of realistic simulated spectra, and subjected those spectra to the EMPCA /k-means analysis. We concluded that our approach was validated when we found that the mean spectra from our k-means clusters derived from PCA projection coefficients reproduced the trends observed in the composite spectra.Furthermore, our method needed only two eigenvectors to identify both sets of correlations used to construct the simulations, as well as indicating the linear and nonlinear segments. Comparing this to regular PCA, which can require a dozen or more components, or to direct spectral analysis that may need measurement of 20 fit parameters, shows why the dual application of these two techniques is such a powerful tool.

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.

Procrustean rotation in concert with principal component analysis of molecular dynamics trajectories: Quantifying global and local differences between conformational samples.

PubMed

Oblinsky, Daniel G; Vanschouwen, Bryan M B; Gordon, Heather L; Rothstein, Stuart M

2009-12-14

Given the principal component analysis (PCA) of a molecular dynamics (MD) conformational trajectory for a model protein, we perform orthogonal Procrustean rotation to "best fit" the PCA squared-loading matrix to that of a target matrix computed for a related but different molecular system. The sum of squared deviations of the elements of the rotated matrix from those of the target, known as the error of fit (EOF), provides a quantitative measure of the dissimilarity between the two conformational samples. To estimate precision of the EOF, we perform bootstrap resampling of the molecular conformations within the trajectories, generating a distribution of EOF values for the system and target. The average EOF per variable is determined and visualized to ascertain where, locally, system and target sample properties differ. We illustrate this approach by analyzing MD trajectories for the wild-type and four selected mutants of the beta1 domain of protein G.

LeuT conformational sampling utilizing accelerated molecular dynamics and principal component analysis.

PubMed

Thomas, James R; Gedeon, Patrick C; Grant, Barry J; Madura, Jeffry D

2012-07-03

Monoamine transporters (MATs) function by coupling ion gradients to the transport of dopamine, norepinephrine, or serotonin. Despite their importance in regulating neurotransmission, the exact conformational mechanism by which MATs function remains elusive. To this end, we have performed seven 250 ns accelerated molecular dynamics simulations of the leucine transporter, a model for neurotransmitter MATs. By varying the presence of binding-pocket leucine substrate and sodium ions, we have sampled plausible conformational states representative of the substrate transport cycle. The resulting trajectories were analyzed using principal component analysis of transmembrane helices 1b and 6a. This analysis revealed seven unique structures: two of the obtained conformations are similar to the currently published crystallographic structures, one conformation is similar to a proposed open inward structure, and four conformations represent novel structures of potential importance to the transport cycle. Further analysis reveals that the presence of binding-pocket sodium ions is necessary to stabilize the locked-occluded and open-inward conformations. Copyright © 2012 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Principal Component Analysis: A Method for Determining the Essential Dynamics of Proteins

PubMed Central

David, Charles C.; Jacobs, Donald J.

2015-01-01

It has become commonplace to employ principal component analysis to reveal the most important motions in proteins. This method is more commonly known by its acronym, PCA. While most popular molecular dynamics packages inevitably provide PCA tools to analyze protein trajectories, researchers often make inferences of their results without having insight into how to make interpretations, and they are often unaware of limitations and generalizations of such analysis. Here we review best practices for applying standard PCA, describe useful variants, discuss why one may wish to make comparison studies, and describe a set of metrics that make comparisons possible. In practice, one will be forced to make inferences about the essential dynamics of a protein without having the desired amount of samples. Therefore, considerable time is spent on describing how to judge the significance of results, highlighting pitfalls. The topic of PCA is reviewed from the perspective of many practical considerations, and useful recipes are provided. PMID:24061923

Principal component analysis: a method for determining the essential dynamics of proteins.

PubMed

David, Charles C; Jacobs, Donald J

2014-01-01

It has become commonplace to employ principal component analysis to reveal the most important motions in proteins. This method is more commonly known by its acronym, PCA. While most popular molecular dynamics packages inevitably provide PCA tools to analyze protein trajectories, researchers often make inferences of their results without having insight into how to make interpretations, and they are often unaware of limitations and generalizations of such analysis. Here we review best practices for applying standard PCA, describe useful variants, discuss why one may wish to make comparison studies, and describe a set of metrics that make comparisons possible. In practice, one will be forced to make inferences about the essential dynamics of a protein without having the desired amount of samples. Therefore, considerable time is spent on describing how to judge the significance of results, highlighting pitfalls. The topic of PCA is reviewed from the perspective of many practical considerations, and useful recipes are provided.

Procrustean rotation in concert with principal component analysis of molecular dynamics trajectories: Quantifying global and local differences between conformational samples

NASA Astrophysics Data System (ADS)

Oblinsky, Daniel G.; VanSchouwen, Bryan M. B.; Gordon, Heather L.; Rothstein, Stuart M.

2009-12-01

Given the principal component analysis (PCA) of a molecular dynamics (MD) conformational trajectory for a model protein, we perform orthogonal Procrustean rotation to "best fit" the PCA squared-loading matrix to that of a target matrix computed for a related but different molecular system. The sum of squared deviations of the elements of the rotated matrix from those of the target, known as the error of fit (EOF), provides a quantitative measure of the dissimilarity between the two conformational samples. To estimate precision of the EOF, we perform bootstrap resampling of the molecular conformations within the trajectories, generating a distribution of EOF values for the system and target. The average EOF per variable is determined and visualized to ascertain where, locally, system and target sample properties differ. We illustrate this approach by analyzing MD trajectories for the wild-type and four selected mutants of the β1 domain of protein G.

Elemental Characterization and Discrimination of Nontoxic Ammunition Using Scanning Electron Microscopy with Energy Dispersive X-Ray Analysis and Principal Components Analysis.

PubMed

Hogg, Seth R; Hunter, Brian C; Waddell Smith, Ruth

2016-01-01

Concerns over the toxic by-products produced by traditional ammunition have led to an increase in popularity of nontoxic ammunition. In this work, the chemical composition of six brands of nontoxic ammunition was investigated and compared to that of a road flare, which served as an environmental source with similar composition. Five rounds of each brand were fired while a further five were disassembled and the primer alone was fired. Particles collected from all samples, including the road flare, were analyzed by scanning electron microscopy with energy dispersive X-ray analysis. Common elements among the different ammunition brands included aluminum, potassium, silicon, calcium, and strontium. Spectra were then subjected to principal components analysis in which association of the primer to the intact ammunition sample was generally possible, with distinction among brands and from the road flare sample. Further, PCA loadings plots indicated the elements responsible for the association and discrimination observed. © 2015 American Academy of Forensic Sciences.

Determination of the geographical origin of green coffee by principal component analysis of carbon, nitrogen and boron stable isotope ratios.

PubMed

Serra, Francesca; Guillou, Claude G; Reniero, Fabiano; Ballarin, Luciano; Cantagallo, Maria I; Wieser, Michael; Iyer, Sundaram S; Héberger, Károly; Vanhaecke, Frank

2005-01-01

In this study we show that the continental origin of coffee can be inferred on the basis of coupling the isotope ratios of several elements determined in green beans. The combination of the isotopic fingerprints of carbon, nitrogen and boron, used as integrated proxies for environmental conditions and agricultural practices, allows discrimination among the three continental areas producing coffee (Africa, Asia and America). In these continents there are countries producing 'specialty coffees', highly rated on the market that are sometimes mislabeled further on along the export-sale chain or mixed with cheaper coffees produced in other regions. By means of principal component analysis we were successful in identifying the continental origin of 88% of the samples analyzed. An intra-continent discrimination has not been possible at this stage of the study, but is planned in future work. Nonetheless, the approach using stable isotope ratios seems quite promising, and future development of this research is also discussed. (c) 2005 John Wiley & Sons, Ltd.

Discrimination of serum Raman spectroscopy between normal and colorectal cancer

NASA Astrophysics Data System (ADS)

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

2011-07-01

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

Statistical Inference for Porous Materials using Persistent Homology.

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

Moon, Chul; Heath, Jason E.; Mitchell, Scott A.

2017-12-01

We propose a porous materials analysis pipeline using persistent homology. We rst compute persistent homology of binarized 3D images of sampled material subvolumes. For each image we compute sets of homology intervals, which are represented as summary graphics called persistence diagrams. We convert persistence diagrams into image vectors in order to analyze the similarity of the homology of the material images using the mature tools for image analysis. Each image is treated as a vector and we compute its principal components to extract features. We t a statistical model using the loadings of principal components to estimate material porosity, permeability,more » anisotropy, and tortuosity. We also propose an adaptive version of the structural similarity index (SSIM), a similarity metric for images, as a measure to determine the statistical representative elementary volumes (sREV) for persistence homology. Thus we provide a capability for making a statistical inference of the uid ow and transport properties of porous materials based on their geometry and connectivity.« less

Principal components technique analysis for vegetation and land use discrimination. [Brazilian cerrados

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Formaggio, A. R.; Dossantos, J. R.; Dias, L. A. V.

1984-01-01

Automatic pre-processing technique called Principal Components (PRINCO) in analyzing LANDSAT digitized data, for land use and vegetation cover, on the Brazilian cerrados was evaluated. The chosen pilot area, 223/67 of MSS/LANDSAT 3, was classified on a GE Image-100 System, through a maximum-likehood algorithm (MAXVER). The same procedure was applied to the PRINCO treated image. PRINCO consists of a linear transformation performed on the original bands, in order to eliminate the information redundancy of the LANDSAT channels. After PRINCO only two channels were used thus reducing computer effort. The original channels and the PRINCO channels grey levels for the five identified classes (grassland, "cerrado", burned areas, anthropic areas, and gallery forest) were obtained through the MAXVER algorithm. This algorithm also presented the average performance for both cases. In order to evaluate the results, the Jeffreys-Matusita distance (JM-distance) between classes was computed. The classification matrix, obtained through MAXVER, after a PRINCO pre-processing, showed approximately the same average performance in the classes separability.

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.

Potential use of hydrocarbons for aging Lucilia sericata blowfly larvae to establish the postmortem interval.

PubMed

Moore, Hannah E; Adam, Craig D; Drijfhout, Falko P

2013-03-01

Previous studies on Diptera have shown the potential for the use of cuticular hydrocarbons' analysis in the determination of larval age and hence the postmortem interval (PMI) for an associated cadaver. In this work, hydrocarbon compounds, extracted daily until pupation from the cuticle of the blowfly Lucilia sericata (Diptera: Calliphoridae), have been analyzed using gas chromatography-mass spectrometry (GC-MS). The results show distinguishing features within the hydrocarbon profile over the period of the larvae life cycle, with significant chemical changes occurring from the younger larvae to the postfeeding larvae. Further interpretation of the chromatograms using principal component analysis revealed a strong correlation between the magnitudes of particular principal components and time. This outcome suggests that, under the conditions of this study, the cuticular hydrocarbons evolve in a systematic fashion with time, thus supporting the potential for GC-MS analysis as a tool for establishing PMI where such a species is present. © 2012 American Academy of Forensic Sciences.

Ionospheric total electron content seismo-perturbation after Japan's March 11, 2011, M=9.0 Tohoku earthquake under a geomagnetic storm; a nonlinear principal component analysis

NASA Astrophysics Data System (ADS)

Lin, Jyh-Woei

2012-10-01

Nonlinear principal component analysis (NLPCA) is implemented to analyze the spatial pattern of total electron content (TEC) anomalies 3 hours after Japan's Tohoku earthquake that occurred at 05:46:23 on 11 March, 2011 (UTC) ( M w =9). A geomagnetic storm was in progress at the time of the earthquake. NLPCA and TEC data processing were conducted on the global ionospheric map (GIM) for the time between 08:30 to 09:30 UTC, about 3 hours after this devastating earthquake and ensuing tsunami. Analysis results show stark earthquake-associated TEC anomalies that are widespread, and appear to have been induced by two acoustic gravity waves due to strong shaking (vertical acoustic wave) and the generation of the tsunami (horizontal Rayleigh mode gravity wave). The TEC anomalies roughly fit the initial mainshock and movement of the tsunami. Observation of the earthquake-associated TEC anomalies does not appear to be affected by a contemporaneous geomagnetic storm.

Documenting mudstone heterogeneity by use of principal component analysis of X-ray diffraction and portable X-ray fluorescence data: A case study in the Triassic Shublik Formation, Alaska North Slope

USGS Publications Warehouse

Boehlke, Adam; Whidden, Katherine J.; Benzel, William M.

2017-01-01

Determining the chemical and mineralogical variability within fine-grained mudrocks poses analytical challenges but is potentially useful for documenting subtle stratigraphic differences in physicochemical environments that may influence petroleum reservoir properties and behavior. In this study, we investigate the utility of combining principal component analysis (PCA) of X-ray diffraction (XRD) data and portable X-ray fluorescence (pXRF) data to identify simplifying relationships within a large number of samples and subsequently evaluate a subset that encompasses the full spectrum or range of mineral and chemical variability within a vertical section. Samples were collected and analyzed from a vertical core of the Shublik Formation, a heterogeneous, phosphate-rich, calcareous mudstone-to-marl unit deposited in the Arctic Alaska Basin (AAB) during the Middle and Late Triassic. The Shublik is a major petroleum source rock in the Alaskan North Slope, and is considered a prime target for continuous self-sourced resource plays.

Anomalous magnetic structure and spin dynamics in magnetoelectric LiFePO 4

DOE PAGES

Toft-Petersen, Rasmus; Reehuis, Manfred; Jensen, Thomas B. S.; ...

2015-07-06

We report significant details of the magnetic structure and spin dynamics of LiFePO 4 obtained by single-crystal neutron scattering. Our results confirm a previously reported collinear rotation of the spins away from the principal b axis, and they determine that the rotation is toward the a axis. In addition, we find a significant spin-canting component along c. Furthermore, the possible causes of these components are discussed, and their significance for the magnetoelectric effect is analyzed. Inelastic neutron scattering along the three principal directions reveals a highly anisotropic hard plane consistent with earlier susceptibility measurements. While using a spin Hamiltonian, wemore » show that the spin dimensionality is intermediate between XY- and Ising-like, with an easy b axis and a hard c axis. As a result, it is shown that both next-nearest neighbor exchange couplings in the bc plane are in competition with the strongest nearest neighbor coupling.« less

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.

Systematic study of anharmonic features in a principal component analysis of gramicidin A.

PubMed

Kurylowicz, Martin; Yu, Ching-Hsing; Pomès, Régis

2010-02-03

We use principal component analysis (PCA) to detect functionally interesting collective motions in molecular-dynamics simulations of membrane-bound gramicidin A. We examine the statistical and structural properties of all PCA eigenvectors and eigenvalues for the backbone and side-chain atoms. All eigenvalue spectra show two distinct power-law scaling regimes, quantitatively separating large from small covariance motions. Time trajectories of the largest PCs converge to Gaussian distributions at long timescales, but groups of small-covariance PCs, which are usually ignored as noise, have subdiffusive distributions. These non-Gaussian distributions imply anharmonic motions on the free-energy surface. We characterize the anharmonic components of motion by analyzing the mean-square displacement for all PCs. The subdiffusive components reveal picosecond-scale oscillations in the mean-square displacement at frequencies consistent with infrared measurements. In this regime, the slowest backbone mode exhibits tilting of the peptide planes, which allows carbonyl oxygen atoms to provide surrogate solvation for water and cation transport in the channel lumen. Higher-frequency modes are also apparent, and we describe their vibrational spectra. Our findings expand the utility of PCA for quantifying the essential features of motion on the anharmonic free-energy surface made accessible by atomistic molecular-dynamics simulations. Copyright (c) 2010 Biophysical Society. Published by Elsevier Inc. All rights reserved.

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.;

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.

Spectral discrimination of serum from liver cancer and liver cirrhosis using Raman spectroscopy

NASA Astrophysics Data System (ADS)

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

2011-07-01

In this paper, Raman spectra of human serum were measured using Raman spectroscopy, then the spectra was analyzed by multivariate statistical methods of principal component analysis (PCA). Then linear discriminant analysis (LDA) was utilized to differentiate the loading score of different diseases as the diagnosing algorithm. Artificial neural network (ANN) was used for cross-validation. The diagnosis sensitivity and specificity by PCA-LDA are 88% and 79%, while that of the PCA-ANN are 89% and 95%. It can be seen that modern analyzing method is a useful tool for the analysis of serum spectra for diagnosing diseases.

Rapidly differentiating grape seeds from different sources based on characteristic fingerprints using direct analysis in real time coupled with time-of-flight mass spectrometry combined with chemometrics.

PubMed

Song, Yuqiao; Liao, Jie; Dong, Junxing; Chen, Li

2015-09-01

The seeds of grapevine (Vitis vinifera) are a byproduct of wine production. To examine the potential value of grape seeds, grape seeds from seven sources were subjected to fingerprinting using direct analysis in real time coupled with time-of-flight mass spectrometry combined with chemometrics. Firstly, we listed all reported components (56 components) from grape seeds and calculated the precise m/z values of the deprotonated ions [M-H](-) . Secondly, the experimental conditions were systematically optimized based on the peak areas of total ion chromatograms of the samples. Thirdly, the seven grape seed samples were examined using the optimized method. Information about 20 grape seed components was utilized to represent characteristic fingerprints. Finally, hierarchical clustering analysis and principal component analysis were performed to analyze the data. Grape seeds from seven different sources were classified into two clusters; hierarchical clustering analysis and principal component analysis yielded similar results. The results of this study lay the foundation for appropriate utilization and exploitation of grape seed samples. Due to the absence of complicated sample preparation methods and chromatographic separation, the method developed in this study represents one of the simplest and least time-consuming methods for grape seed fingerprinting. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

Weighted Components of i-Government Enterprise Architecture

NASA Astrophysics Data System (ADS)

Budiardjo, E. K.; Firmansyah, G.; Hasibuan, Z. A.

2017-01-01

Lack of government performance, among others due to the lack of coordination and communication among government agencies. Whilst, Enterprise Architecture (EA) in the government can be use as a strategic planning tool to improve productivity, efficiency, and effectivity. However, the existence components of Government Enterprise Architecture (GEA) do not show level of importance, that cause difficulty in implementing good e-government for good governance. This study is to explore the weight of GEA components using Principal Component Analysis (PCA) in order to discovered an inherent structure of e-government. The results show that IT governance component of GEA play a major role in the GEA. The rest of components that consist of e-government system, e-government regulation, e-government management, and application key operational, contributed more or less the same. Beside that GEA from other countries analyzes using comparative base on comon enterprise architecture component. These weighted components use to construct i-Government enterprise architecture. and show the relative importance of component in order to established priorities in developing e-government.

[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.

[Discrimination of Rice Syrup Adulterant of Acacia Honey Based Using Near-Infrared Spectroscopy].

PubMed

Zhang, Yan-nan; Chen, Lan-zhen; Xue, Xiao-feng; Wu, Li-ming; Li, Yi; Yang, Juan

2015-09-01

At present, the rice syrup as a low price of the sweeteners was often adulterated into acacia honey and the adulterated honeys were sold in honey markets, while there is no suitable and fast method to identify honey adulterated with rice syrup. In this study, Near infrared spectroscopy (NIR) combined with chemometric methods were used to discriminate authenticity of honey. 20 unprocessed acacia honey samples from the different honey producing areas, mixed? with different proportion of rice syrup, were prepared of seven different concentration gradient? including 121 samples. The near infrared spectrum (NIR) instrument and spectrum processing software have been applied in the? spectrum? scanning and data conversion on adulterant samples, respectively. Then it was analyzed by Principal component analysis (PCA) and canonical discriminant analysis methods in order to discriminating adulterated honey. The results showed that after principal components analysis, the first two principal components accounted for 97.23% of total variation, but the regionalism of the score plot of the first two PCs was not obvious, so the canonical discriminant analysis was used to make the further discrimination, all samples had been discriminated correctly, the first two discriminant functions accounted for 91.6% among the six canonical discriminant functions, Then the different concentration of adulterant samples can be discriminated correctly, it illustrate that canonical discriminant analysis method combined with NIR spectroscopy is not only feasible but also practical for rapid and effective discriminate of the rice syrup adulterant of acacia honey.

Modification of isoflavone profiles in a fermented soy food with almond powder.

PubMed

Park, MinHee; Jeong, Min Kyu; Kim, MiJa; Lee, JaeHwan

2012-01-01

Isoflavone profiles of a fermented soy food, cheonggukjang, were modified using almond powder. Isoflavones were analyzed by high performance liquid chromatography (HPLC) with an ultraviolet detector. Malonyl derivatives of isoflavones decreased and aglycones of isoflavones increased in samples with almond powder for 48 h. As added, almond powder increased from 0%, 5%, and 10% (w/w), amounts of aglycones increased to 21.11%, 26.63%, and 32.45% for 48 h, respectively. β-Glucosidase activity in 5% and 10% almond added samples was significantly higher than samples without addition of almond (P < 0.05). The content of succinyl daidzin and succinyl genistin, new metabolites from isoflavones, in almond-added cheonggukjang was significantly lower than control samples, implying that β-glucosidase activity from almond affected negatively the formation of succinyl derivatives (P < 0.05). Principal component analysis (PCA) for isoflavone distribution showed that first principal component (PC1) and second principal component (PC2) expressed 64.78% and 22.26% of the data variability, respectively. Biotransformation of isoflavones in any fermented soy foods can be achieved using natural products containing high β-glucosidase activity such as almond. The results of this study can help to modify the structural transformation of phytochemicals in any fermented soy foods using natural products. Adjusting the content of almond powder can achieve wanted profiles, for example, high aglycones content. Also, content of metabolites such as succinyl derivatives can be controlled using proper amounts of almond and fermentation time. © 2011 Institute of Food Technologists®

Standardized principal components for vegetation variability monitoring across space and time

NASA Astrophysics Data System (ADS)

Mathew, T. R.; Vohora, V. K.

2016-08-01

Vegetation at any given location changes through time and in space. In what quantity it changes, where and when can help us in identifying sources of ecosystem stress, which is very useful for understanding changes in biodiversity and its effect on climate change. Such changes known for a region are important in prioritizing management. The present study considers the dynamics of savanna vegetation in Kruger National Park (KNP) through the use of temporal satellite remote sensing images. Spatial variability of vegetation is a key characteristic of savanna landscapes and its importance to biodiversity has been demonstrated by field-based studies. The data used for the study were sourced from the U.S. Agency for International Development where AVHRR derived Normalized Difference Vegetation Index (NDVI) images available at spatial resolutions of 8 km and at dekadal scales. The study area was extracted from these images for the time-period 1984-2002. Maximum value composites were derived for individual months resulting in an image dataset of 216 NDVI images. Vegetation dynamics across spatio-temporal domains were analyzed using standardized principal components analysis (SPCA) on the NDVI time-series. Each individual image variability in the time-series is considered. The outcome of this study demonstrated promising results - the variability of vegetation change in the area across space and time, and also indicated changes in landscape on 6 individual principal components (PCs) showing differences not only in magnitude, but also in pattern, of different selected eco-zones with constantly changing and evolving ecosystem.

Source Determination of Red Gel Pen Inks using Raman Spectroscopy and Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy combined with Pearson's Product Moment Correlation Coefficients and Principal Component Analysis.

PubMed

Mohamad Asri, Muhammad Naeim; Mat Desa, Wan Nur Syuhaila; Ismail, Dzulkiflee

2018-01-01

The potential combination of two nondestructive techniques, that is, Raman spectroscopy (RS) and attenuated total reflectance-fourier transform infrared (ATR-FTIR) spectroscopy with Pearson's product moment correlation (PPMC) coefficient (r) and principal component analysis (PCA) to determine the actual source of red gel pen ink used to write a simulated threatening note, was examined. Eighteen (18) red gel pens purchased from Japan and Malaysia from November to December 2014 where one of the pens was used to write a simulated threatening note were analyzed using RS and ATR-FTIR spectroscopy, respectively. The spectra of all the red gel pen inks including the ink deposited on the simulated threatening note gathered from the RS and ATR-FTIR analyses were subjected to PPMC coefficient (r) calculation and principal component analysis (PCA). The coefficients r = 0.9985 and r = 0.9912 for pairwise combination of RS and ATR-FTIR spectra respectively and similarities in terms of PC1 and PC2 scores of one of the inks to the ink deposited on the simulated threatening note substantiated the feasibility of combining RS and ATR-FTIR spectroscopy with PPMC coefficient (r) and PCA for successful source determination of red gel pen inks. The development of pigment spectral library had allowed the ink deposited on the threatening note to be identified as XSL Poppy Red (CI Pigment Red 112). © 2017 American Academy of Forensic Sciences.

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

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...

Independent component analysis-based algorithm for automatic identification of Raman spectra applied to artistic pigments and pigment mixtures.

PubMed

González-Vidal, Juan José; Pérez-Pueyo, Rosanna; Soneira, María José; Ruiz-Moreno, Sergio

2015-03-01

A new method has been developed to automatically identify Raman spectra, whether they correspond to single- or multicomponent spectra. The method requires no user input or judgment. There are thus no parameters to be tweaked. Furthermore, it provides a reliability factor on the resulting identification, with the aim of becoming a useful support tool for the analyst in the decision-making process. The method relies on the multivariate techniques of principal component analysis (PCA) and independent component analysis (ICA), and on some metrics. It has been developed for the application of automated spectral analysis, where the analyzed spectrum is provided by a spectrometer that has no previous knowledge of the analyzed sample, meaning that the number of components in the sample is unknown. We describe the details of this method and demonstrate its efficiency by identifying both simulated spectra and real spectra. The method has been applied to artistic pigment identification. The reliable and consistent results that were obtained make the methodology a helpful tool suitable for the identification of pigments in artwork or in paint in general.

Chemometric characterization of alembic and industrial sugar cane spirits from cape verde and ceará, Brazil.

PubMed

Pereira, Regina F R; Vidal, Carla B; de Lima, Ari C A; Melo, Diego Q; Dantas, Allan N S; Lopes, Gisele S; do Nascimento, Ronaldo F; Gomes, Clerton L; da Silva, Maria Nataniela

2012-01-01

Sugar cane spirits are some of the most popular alcoholic beverages consumed in Cape Verde. The sugar cane spirit industry in Cape Verde is based mainly on archaic practices that operate without supervision and without efficient control of the production process. The objective of this work was to evaluate samples of industrial and alembic sugar cane spirits from Cape Verde and Ceará, Brazil using principal component analysis. Thirty-two samples of spirits were analyzed, twenty from regions of the islands of Cape Verde and twelve from Ceará, Brazil. Of the samples obtained from Ceará, Brazil seven are alembic and five are industrial spirits. The components analyzed in these studies included the following: volatile organic compounds (n-propanol, isobutanol, isoamylic, higher alcohols, alcoholic grade, acetaldehyde, acetic acid, acetate); copper; and sulfates.

Chemometric Characterization of Alembic and Industrial Sugar Cane Spirits from Cape Verde and Ceará, Brazil

PubMed Central

Pereira, Regina F. R.; Vidal, Carla B.; de Lima, Ari C. A.; Melo, Diego Q.; Dantas, Allan N. S.; Lopes, Gisele S.; do Nascimento, Ronaldo F.; Gomes, Clerton L.; da Silva, Maria Nataniela

2012-01-01

Sugar cane spirits are some of the most popular alcoholic beverages consumed in Cape Verde. The sugar cane spirit industry in Cape Verde is based mainly on archaic practices that operate without supervision and without efficient control of the production process. The objective of this work was to evaluate samples of industrial and alembic sugar cane spirits from Cape Verde and Ceará, Brazil using principal component analysis. Thirty-two samples of spirits were analyzed, twenty from regions of the islands of Cape Verde and twelve from Ceará, Brazil. Of the samples obtained from Ceará, Brazil seven are alembic and five are industrial spirits. The components analyzed in these studies included the following: volatile organic compounds (n-propanol, isobutanol, isoamylic, higher alcohols, alcoholic grade, acetaldehyde, acetic acid, acetate); copper; and sulfates. PMID:23227051

Multivariate analyses of crater parameters and the classification of craters

NASA Technical Reports Server (NTRS)

Siegal, B. S.; Griffiths, J. C.

1974-01-01

Multivariate analyses were performed on certain linear dimensions of six genetic types of craters. A total of 320 craters, consisting of laboratory fluidization craters, craters formed by chemical and nuclear explosives, terrestrial maars and other volcanic craters, and terrestrial meteorite impact craters, authenticated and probable, were analyzed in the first data set in terms of their mean rim crest diameter, mean interior relief, rim height, and mean exterior rim width. The second data set contained an additional 91 terrestrial craters of which 19 were of experimental percussive impact and 28 of volcanic collapse origin, and which was analyzed in terms of mean rim crest diameter, mean interior relief, and rim height. Principal component analyses were performed on the six genetic types of craters. Ninety per cent of the variation in the variables can be accounted for by two components. Ninety-nine per cent of the variation in the craters formed by chemical and nuclear explosives is explained by the first component alone.

Chemical Polymorphism of Essential Oils of Artemisia vulgaris Growing Wild in Lithuania.

PubMed

Judzentiene, Asta; Budiene, Jurga

2018-02-01

Compositional variability of mugwort (Artemisia vulgaris L.) essential oils has been investigated in the study. Plant material (over ground parts at full flowering stage) was collected from forty-four wild populations in Lithuania. The oils from aerial parts were obtained by hydrodistillation and analyzed by GC(FID) and GC/MS. In total, up to 111 components were determined in the oils. As the major constituents were found: sabinene, 1,8-cineole, artemisia ketone, both thujone isomers, camphor, cis-chrysanthenyl acetate, davanone and davanone B. The compositional data were subjected to statistical analysis. The application of PCA (Principal Component Analysis) and AHC (Agglomerative Hierarchical Clustering) allowed grouping the oils into six clusters. AHC permitted to distinguish an artemisia ketone chemotype, which, to the best of our knowledge, is very scarce. Additionally, two rare cis-chrysanthenyl acetate and sabinene oil types were determined for the plants growing in Lithuania. Besides, davanone was found for the first time as a principal component in mugwort oils. The performed study revealed significant chemical polymorphism of essential oils in mugwort plants native to Lithuania; it has expanded our chemotaxonomic knowledge both of A. vulgaris species and Artemisia genus. © 2018 Wiley-VHCA AG, Zurich, Switzerland.

Age Effects in Postural Control Analyzed via a Principal Component Analysis of Kinematic Data and Interpreted in Relation to Predictions of the Optimal Feedback Control Theory

PubMed Central

Haid, Thomas H.; Doix, Aude-Clémence M.; Nigg, Benno M.; Federolf, Peter A.

2018-01-01

Optimal feedback control theory suggests that control of movement is focused on movement dimensions that are important for the task's success. The current study tested the hypotheses that age effects would emerge in the control of only specific movement components and that these components would be linked to the task relevance. Fifty healthy volunteers, 25 young and 25 older adults, performed a 80s-tandem stance while their postural movements were recorded using a standard motion capture system. The postural movements were decomposed by a principal component analysis into one-dimensional movement components, PMk, whose control was assessed through two variables, Nk and σk, which characterized the tightness and the regularity of the neuro-muscular control, respectively. The older volunteers showed less tight and more irregular control in PM2 (N2: −9.2%, p = 0.007; σ2: +14.3.0%, p = 0.017) but tighter control in PM8 and PM9 (N8: +4.7%, p = 0.020; N9: +2.5%, p = 0.043; σ9: −8.8%, p = 0.025). These results suggest that aging effects alter the postural control system not as a whole, but emerge in specific, task relevant components. The findings of the current study thus support the hypothesis that the minimal intervention principle, as described in the context of optimal feedback control (OFC), may be relevant when assessing aging effects on postural control. PMID:29459826

Near-infrared Raman spectroscopy to detect anti-Toxoplasma gondii antibodies in blood sera of domestic cats

NASA Astrophysics Data System (ADS)

Duarte, Janaina; Pacheco, Marcos T. T.; Silveira, Landulfo, Jr.; Machado, Rosangela Z.; Martins, Rodrigo A. L.; Zangaro, Renato A.; Villaverde, Antonio G. J. B.

2001-05-01

Near-infrared (NIR) Raman spectroscopy has been studied for the last years for many biomedical applications. It is a powerful tool for biological materials analysis. Toxoplasmosis is an important zoonosis in public health, cats being the principal responsible for the transmission of the disease in Brazil. The objective of this work is to investigate a new method of diagnosis of this disease. NIR Raman spectroscopy was used to detect anti Toxoplasma gondii antibodies in blood sera from domestic cats, without sample preparation. In all, six blood serum samples were used for this study. A previous serological test was done by the Indirect Immunoenzymatic Assay (ELISA) to permit a comparative study between both techniques and it showed that three serum samples were positive and the other three were negative to toxoplasmosis. Raman spectra were taken for all the samples and analyzed by using the principal components analysis (PCA). A diagnosis parameter was defined from the analysis of the second and third principal components of the Raman spectra. It was found that this parameter can detect the infection level of the animal. The results have indicated that NIR Raman spectroscopy, associated to the PCA can be a promising technique for serological analysis, such as toxoplasmosis, allowing a fast and sensitive method of diagnosis.

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…

PumpKin: A tool to find principal pathways in plasma chemical models

NASA Astrophysics Data System (ADS)

Markosyan, A. H.; Luque, A.; Gordillo-Vázquez, F. J.; Ebert, U.

2014-10-01

PumpKin is a software package to find all principal pathways, i.e. the dominant reaction sequences, in chemical reaction systems. Although many tools are available to integrate numerically arbitrarily complex chemical reaction systems, few tools exist in order to analyze the results and interpret them in relatively simple terms. In particular, due to the large disparity in the lifetimes of the interacting components, it is often useful to group reactions into pathways that recycle the fastest species. This allows a researcher to focus on the slow chemical dynamics, eliminating the shortest timescales. Based on the algorithm described by Lehmann (2004), PumpKin automates the process of finding such pathways, allowing the user to analyze complex kinetics and to understand the consumption and production of a certain species of interest. We designed PumpKin with an emphasis on plasma chemical systems but it can also be applied to atmospheric modeling and to industrial applications such as plasma medicine and plasma-assisted combustion.

Principal Component Analysis of Lipid Molecule Conformational Changes in Molecular Dynamics Simulations.

PubMed

Buslaev, Pavel; Gordeliy, Valentin; Grudinin, Sergei; Gushchin, Ivan

2016-03-08

Molecular dynamics simulations of lipid bilayers are ubiquitous nowadays. Usually, either global properties of the bilayer or some particular characteristics of each lipid molecule are evaluated in such simulations, but the structural properties of the molecules as a whole are rarely studied. Here, we show how a comprehensive quantitative description of conformational space and dynamics of a single lipid molecule can be achieved via the principal component analysis (PCA). We illustrate the approach by analyzing and comparing simulations of DOPC bilayers obtained using eight different force fields: all-atom generalized AMBER, CHARMM27, CHARMM36, Lipid14, and Slipids and united-atom Berger, GROMOS43A1-S3, and GROMOS54A7. Similarly to proteins, most of the structural variance of a lipid molecule can be described by only a few principal components. These major components are similar in different simulations, although there are notable distinctions between the older and newer force fields and between the all-atom and united-atom force fields. The DOPC molecules in the simulations generally equilibrate on the time scales of tens to hundreds of nanoseconds. The equilibration is the slowest in the GAFF simulation and the fastest in the Slipids simulation. Somewhat unexpectedly, the equilibration in the united-atom force fields is generally slower than in the all-atom force fields. Overall, there is a clear separation between the more variable previous generation force fields and significantly more similar new generation force fields (CHARMM36, Lipid14, Slipids). We expect that the presented approaches will be useful for quantitative analysis of conformations and dynamics of individual lipid molecules in other simulations of lipid bilayers.

Advanced Treatment Monitoring for Olympic-Level Athletes Using Unsupervised Modeling Techniques

PubMed Central

Siedlik, Jacob A.; Bergeron, Charles; Cooper, Michael; Emmons, Russell; Moreau, William; Nabhan, Dustin; Gallagher, Philip; Vardiman, John P.

2016-01-01

Context Analysis of injury and illness data collected at large international competitions provides the US Olympic Committee and the national governing bodies for each sport with information to best prepare for future competitions. Research in which authors have evaluated medical contacts to provide the expected level of medical care and sports medicine services at international competitions is limited. Objective To analyze the medical-contact data for athletes, staff, and coaches who participated in the 2011 Pan American Games in Guadalajara, Mexico, using unsupervised modeling techniques to identify underlying treatment patterns. Design Descriptive epidemiology study. Setting Pan American Games. Patients or Other Participants A total of 618 US athletes (337 males, 281 females) participated in the 2011 Pan American Games. Main Outcome Measure(s) Medical data were recorded from the injury-evaluation and injury-treatment forms used by clinicians assigned to the central US Olympic Committee Sport Medicine Clinic and satellite locations during the operational 17-day period of the 2011 Pan American Games. We used principal components analysis and agglomerative clustering algorithms to identify and define grouped modalities. Lift statistics were calculated for within-cluster subgroups. Results Principal component analyses identified 3 components, accounting for 72.3% of the variability in datasets. Plots of the principal components showed that individual contacts focused on 4 treatment clusters: massage, paired manipulation and mobilization, soft tissue therapy, and general medical. Conclusions Unsupervised modeling techniques were useful for visualizing complex treatment data and provided insights for improved treatment modeling in athletes. Given its ability to detect clinically relevant treatment pairings in large datasets, unsupervised modeling should be considered a feasible option for future analyses of medical-contact data from international competitions. PMID:26794628

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

Genetic Evaluation of Dual-Purpose Buffaloes (Bubalus bubalis) in Colombia Using Principal Component Analysis

PubMed Central

Agudelo-Gómez, Divier; Pineda-Sierra, Sebastian; Cerón-Muñoz, Mario Fernando

2015-01-01

Genealogy and productive information of 48621 dual-purpose buffaloes born in Colombia between years 1996 and 2014 was used. The following traits were assessed using one-trait models: milk yield at 270 days (MY270), age at first calving (AFC), weaning weight (WW), and weights at the following ages: first year (W12), 18 months (W18), and 2 years (W24). Direct additive genetic and residual random effects were included in all the traits. Maternal permanent environmental and maternal additive genetic effects were included for WW and W12. The fixed effects were: contemporary group (for all traits), sex (for WW, W12, W18, and W24), parity (for WW, W12, and MY270). Age was included as covariate for WW, W12, W18 and W24. Principal component analysis (PCA) was conducted using the genetic values of 133 breeding males whose breeding-value reliability was higher than 50% for all the traits in order to define the number of principal components (PC) which would explain most of the variation. The highest heritabilities were for W18 and MY270, and the lowest for AFC; with 0.53, 0.23, and 0.17, respectively. The first three PCs represented 66% of the total variance. Correlation of the first PC with meat production traits was higher than 0.73, and it was -0.38 with AFC. Correlations of the second PC with maternal genetic component traits for WW and W12 were above 0.75. The third PC had 0.84 correlation with MY270. PCA is an alternative approach for analyzing traits in dual-purpose buffaloes and reduces the dimension of the traits. PMID:26230093

Quality improvement of diagnosis of the electromyography data based on statistical characteristics of the measured signals

NASA Astrophysics Data System (ADS)

Selivanova, Karina G.; Avrunin, Oleg G.; Zlepko, Sergii M.; Romanyuk, Sergii O.; Zabolotna, Natalia I.; Kotyra, Andrzej; Komada, Paweł; Smailova, Saule

2016-09-01

Research and systematization of motor disorders, taking into account the clinical and neurophysiologic phenomena, are important and actual problem of neurology. The article describes a technique for decomposing surface electromyography (EMG), using Principal Component Analysis. The decomposition is achieved by a set of algorithms that uses a specially developed for analyze EMG. The accuracy was verified by calculation of Mahalanobis distance and Probability error.

Critical Factors Explaining the Leadership Performance of High-Performing Principals

ERIC Educational Resources Information Center

Hutton, Disraeli M.

2018-01-01

The study explored critical factors that explain leadership performance of high-performing principals and examined the relationship between these factors based on the ratings of school constituents in the public school system. The principal component analysis with the use of Varimax Rotation revealed that four components explain 51.1% of the…

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

[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.

Distinguishing dissolved organic matter at its origin: size and optical properties of leaf-litter leachates.

PubMed

Cuss, C W; Guéguen, C

2013-09-01

Dissolved organic matter (DOM) was leached from eight distinct samples of leaves taken from six distinct trees (red maple, bur oak at three times of the year, two sugar maple and two white spruce trees from disparate soil types). Multiple samples were taken over 72-96h of leaching. The size and optical properties of leachates were assessed using asymmetrical flow field-flow fractionation (AF4) coupled to diode-array ultraviolet/visible absorbance and excitation-emission matrix fluorescence detectors (EEM). The fluorescence of unfractionated samples was also analyzed. EEMs were analyzed using parallel factor analysis (PARAFAC) and principal component analysis (PCA) of proportional component loadings. Both the unfractionated and AF4-fractionated leachates had distinct size and optical properties. The 95% confidence ranges for molecular weight distributions were determined as: 210-440Da for spruce, 540-920Da for sugar maple, 630-800Da for spring oak leaves, 930-950Da for senescent oak, 1490-1670 for senescent red maple, and 3430-4270Da for oak leaves that were collected from the ground after spring thaw. In most cases the fluorescence properties of leachates were different for individuals from different soil types and across seasons; however, PCA of PARAFAC loadings revealed that the observed distinctiveness was chiefly species-based. Strong correlations were found between the molecular weight distribution of both unfractionated and fractionated leachates and their principal component loadings (R(2)=0.85 and 0.95, respectively). It is concluded that results support a species-based origin for differences in optical properties. Copyright © 2013 Elsevier Ltd. All rights reserved.

Essential oils and chemical diversity of southeast European populations of Salvia officinalis L.

PubMed

Cvetkovikj, Ivana; Stefkov, Gjoshe; Karapandzova, Marija; Kulevanova, Svetlana; Satović, Zlatko

2015-07-01

The essential oils of 25 populations of Dalmatian sage (Salvia officinalis L.) from nine Balkan countries, including 17 indigenous populations (representing almost the entire native distribution area) and eight non-indigenous (cultivated or naturalized) populations were analyzed. Their essential-oil yield ranged from 0.25 to 3.48%. Within the total of 80 detected compounds, ten (β-pinene, 1,8-cineole, cis-thujone, trans-thujone, camphor, borneol, trans-caryophyllene, α-humulene, viridiflorol, and manool) represented 42.60 to 85.70% of the components in the analyzed essential oils. Strong positive correlations were observed between the contents of trans-caryophyllene and α-humulene, α-humulene and viridiflorol, and viridiflorol and manool. Principal component analysis (PCA) on the basis of the contents of the ten main compounds showed that four principal components had an eigenvalue greater than 1 and explained 79.87% of the total variation. Performing cluster analysis (CA), the sage populations could be grouped into four distinct chemotypes (A-D). The essential oils of 14 out of the 25 populations of Dalmatian sage belonged to Chemotype A and were rich in cis-thujone and camphor, with low contents of trans-thujone. The correlation between the essential-oil composition and geographic variables of the indigenous populations was not significant; hence, the similarities in the essential-oil profile among populations could not be explained by the physical proximity of the populations. Additionally, the southeastern populations tended to have higher EO yields than the northwestern ones. Copyright © 2015 Verlag Helvetica Chimica Acta AG, Zürich.

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.

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…

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.

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.

Comparison of Free Total Amino Acid Compositions and Their Functional Classifications in 13 Wild Edible Mushrooms.

PubMed

Sun, Liping; Liu, Qiuming; Bao, Changjun; Fan, Jian

2017-02-24

Thirteen popular wild edible mushroom species in Yunnan Province, Boletus bicolor , Boletus speciosus , Boletus sinicus , Boletus craspedius , Boletus griseus , Boletus ornatipes , Xerocomus , Suillus placidus , Boletinus pinetorus , Tricholoma terreum , Tricholomopsis lividipileata , Termitomyces microcarpus , and Amanita hemibapha , were analyzed for their free amino acid compositions by online pre-column derivazation reversed phase high-performance liquid chromatography (RP-HPLC) analysis. Twenty free amino acids, aspartic acid, glutamic acid, serine, glycine, alanine, praline, cysteine, valine, methionine, phenylalanine, isoleucine, leucine, lysine, histidine, threonine, asparagines, glutamine, arginine, tyrosine, and tryptophan, were determined. The total free amino acid (TAA) contents ranged from 1462.6 mg/100 g in B. craspedius to 13,106.2 mg/100 g in T. microcarpus . The different species showed distinct free amino acid profiles. The ratio of total essential amino acids (EAA) to TAA was 0.13-0.41. All of the analyzed species showed high contents of hydrophobic amino acids, at 33%-54% of TAA. Alanine, cysteine, glutamine, and glutamic acid were among the most abundant amino acids present in all species. The results showed that the analyzed mushrooms possessed significant free amino acid contents, which may be important compounds contributing to the typical mushroom taste, nutritional value, and potent antioxidant properties of these wild edible mushrooms. Furthermore, the principal component analysis (PCA) showed that the accumulative variance contribution rate of the first four principal components reached 94.39%. Cluster analysis revealed EAA composition and content might be an important parameter to separate the mushroom species, and T. microcarpus and A. hemibapha showed remarkable EAA content among the 13 species.

Resonance Raman Spectroscopy of human brain metastasis of lung cancer analyzed by blind source separation

NASA Astrophysics Data System (ADS)

Zhou, Yan; Liu, Cheng-Hui; Pu, Yang; Cheng, Gangge; Yu, Xinguang; Zhou, Lixin; Lin, Dongmei; Zhu, Ke; Alfano, Robert R.

2017-02-01

Resonance Raman (RR) spectroscopy offers a novel Optical Biopsy method in cancer discrimination by a means of enhancement in Raman scattering. It is widely acknowledged that the RR spectrum of tissue is a superposition of spectra of various key building block molecules. In this study, the Resonance Raman (RR) spectra of human metastasis of lung cancerous and normal brain tissues excited by a visible selected wavelength at 532 nm are used to explore spectral changes caused by the tumor evolution. The potential application of RR spectra human brain metastasis of lung cancer was investigated by Blind Source Separation such as Principal Component Analysis (PCA). PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components (PCs). The results show significant RR spectra difference between human metastasis of lung cancerous and normal brain tissues analyzed by PCA. To evaluate the efficacy of for cancer detection, a linear discriminant analysis (LDA) classifier is utilized to calculate the sensitivity, and specificity and the receiver operating characteristic (ROC) curves are used to evaluate the performance of this criterion. Excellent sensitivity of 0.97, specificity (close to 1.00) and the Area Under ROC Curve (AUC) of 0.99 values are achieved under best optimal circumstance. This research demonstrates that RR spectroscopy is effective for detecting changes of tissues due to the development of brain metastasis of lung cancer. RR spectroscopy analyzed by blind source separation may have potential to be a new armamentarium.

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.

Earthquake Information System

NASA Technical Reports Server (NTRS)

1991-01-01

IAEMIS (Integrated Automated Emergency Management Information System) is the principal tool of an earthquake preparedness program developed by Martin Marietta and the Mid-America Remote Sensing Center (MARC). It is a two-component set of software, data and procedures to provide information enabling management personnel to make informed decisions in disaster situations. The NASA-developed program ELAS, originally used to analyze Landsat data, provides MARC with a spatially-oriented information management system. Additional MARC projects include land resources management, and development of socioeconomic data.

Use of NMR Metabolomics to Analyze the Targets of D-cycloserine in Mycobacteria: Role of D-Alanine Racemase

PubMed Central

Halouska, Steven; Chacon, Ofelia; Fenton, Robert J.; Zinniel, Denise K.; Barletta, Raul G.; Powers, Robert

2008-01-01

D-cycloserine (DCS) is only used with multi-drug resistant strains of tuberculosis because of serious side-effects. DCS is known to inhibit cell wall biosynthesis, but the in vivo lethal target is still unknown. We have applied NMR-based metabolomics combined with principal component analysis to monitor the in vivo affect of DCS on M. smegmatis. Our analysis suggests DCS functions by inhibiting multiple protein targets. PMID:17979227

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.

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.

Non-invasive prediction of hemoglobin levels by principal component and back propagation artificial neural network

PubMed Central

Ding, Haiquan; Lu, Qipeng; Gao, Hongzhi; Peng, Zhongqi

2014-01-01

To facilitate non-invasive diagnosis of anemia, specific equipment was developed, and non-invasive hemoglobin (HB) detection method based on back propagation artificial neural network (BP-ANN) was studied. In this paper, we combined a broadband light source composed of 9 LEDs with grating spectrograph and Si photodiode array, and then developed a high-performance spectrophotometric system. By using this equipment, fingertip spectra of 109 volunteers were measured. In order to deduct the interference of redundant data, principal component analysis (PCA) was applied to reduce the dimensionality of collected spectra. Then the principal components of the spectra were taken as input of BP-ANN model. On this basis we obtained the optimal network structure, in which node numbers of input layer, hidden layer, and output layer was 9, 11, and 1. Calibration and correction sample sets were used for analyzing the accuracy of non-invasive hemoglobin measurement, and prediction sample set was used for testing the adaptability of the model. The correlation coefficient of network model established by this method is 0.94, standard error of calibration, correction, and prediction are 11.29g/L, 11.47g/L, and 11.01g/L respectively. The result proves that there exist good correlations between spectra of three sample sets and actual hemoglobin level, and the model has a good robustness. It is indicated that the developed spectrophotometric system has potential for the non-invasive detection of HB levels with the method of BP-ANN combined with PCA. PMID:24761296

[Identification of varieties of cashmere by Vis/NIR spectroscopy technology based on PCA-SVM].

PubMed

Wu, Gui-Fang; He, Yong

2009-06-01

One mixed algorithm was presented to discriminate cashmere varieties with principal component analysis (PCA) and support vector machine (SVM). Cashmere fiber has such characteristics as threadlike, softness, glossiness and high tensile strength. The quality characters and economic value of each breed of cashmere are very different. In order to safeguard the consumer's rights and guarantee the quality of cashmere product, quickly, efficiently and correctly identifying cashmere has significant meaning to the production and transaction of cashmere material. The present research adopts Vis/NIRS spectroscopy diffuse techniques to collect the spectral data of cashmere. The near infrared fingerprint of cashmere was acquired by principal component analysis (PCA), and support vector machine (SVM) methods were used to further identify the cashmere material. The result of PCA indicated that the score map made by the scores of PC1, PC2 and PC3 was used, and 10 principal components (PCs) were selected as the input of support vector machine (SVM) based on the reliabilities of PCs of 99.99%. One hundred cashmere samples were used for calibration and the remaining 75 cashmere samples were used for validation. A one-against-all multi-class SVM model was built, the capabilities of SVM with different kernel function were comparatively analyzed, and the result showed that SVM possessing with the Gaussian kernel function has the best identification capabilities with the accuracy of 100%. This research indicated that the data mining method of PCA-SVM has a good identification effect, and can work as a new method for rapid identification of cashmere material varieties.

Study on pattern recognition of Raman spectrum based on fuzzy neural network

NASA Astrophysics Data System (ADS)

Zheng, Xiangxiang; Lv, Xiaoyi; Mo, Jiaqing

2017-10-01

Hydatid disease is a serious parasitic disease in many regions worldwide, especially in Xinjiang, China. Raman spectrum of the serum of patients with echinococcosis was selected as the research object in this paper. The Raman spectrum of blood samples from healthy people and patients with echinococcosis are measured, of which the spectrum characteristics are analyzed. The fuzzy neural network not only has the ability of fuzzy logic to deal with uncertain information, but also has the ability to store knowledge of neural network, so it is combined with the Raman spectrum on the disease diagnosis problem based on Raman spectrum. Firstly, principal component analysis (PCA) is used to extract the principal components of the Raman spectrum, reducing the network input and accelerating the prediction speed and accuracy of Network based on remaining the original data. Then, the information of the extracted principal component is used as the input of the neural network, the hidden layer of the network is the generation of rules and the inference process, and the output layer of the network is fuzzy classification output. Finally, a part of samples are randomly selected for the use of training network, then the trained network is used for predicting the rest of the samples, and the predicted results are compared with general BP neural network to illustrate the feasibility and advantages of fuzzy neural network. Success in this endeavor would be helpful for the research work of spectroscopic diagnosis of disease and it can be applied in practice in many other spectral analysis technique fields.

Joint variability of global runoff and global sea surface temperatures

USGS Publications Warehouse

McCabe, G.J.; Wolock, D.M.

2008-01-01

Global land surface runoff and sea surface temperatures (SST) are analyzed to identify the primary modes of variability of these hydroclimatic data for the period 1905-2002. A monthly water-balance model first is used with global monthly temperature and precipitation data to compute time series of annual gridded runoff for the analysis period. The annual runoff time series data are combined with gridded annual sea surface temperature data, and the combined dataset is subjected to a principal components analysis (PCA) to identify the primary modes of variability. The first three components from the PCA explain 29% of the total variability in the combined runoff/SST dataset. The first component explains 15% of the total variance and primarily represents long-term trends in the data. The long-term trends in SSTs are evident as warming in all of the oceans. The associated long-term trends in runoff suggest increasing flows for parts of North America, South America, Eurasia, and Australia; decreasing runoff is most notable in western Africa. The second principal component explains 9% of the total variance and reflects variability of the El Ni??o-Southern Oscillation (ENSO) and its associated influence on global annual runoff patterns. The third component explains 5% of the total variance and indicates a response of global annual runoff to variability in North Aflantic SSTs. The association between runoff and North Atlantic SSTs may explain an apparent steplike change in runoff that occurred around 1970 for a number of continental regions.

Differentiation of Aurantii Fructus Immaturus from Poniciri Trifoliatae Fructus Immaturus using Flow- injection Mass spectrometric (FIMS) Metabolic Fingerprinting Method Combined with Chemometrics

PubMed Central

Zhao, Yang; Chang, Yuan-Shiun; Chen, Pei

2015-01-01

A flow-injection mass spectrometric metabolic fingerprinting method in combination with chemometrics was used to differentiate Aurantii Fructus Immaturus from its counterfeit Poniciri Trifoliatae Fructus Immaturus. Flow-injection mass spectrometric (FIMS) fingerprints of 9 Aurantii Fructus Immaturus samples and 12 Poniciri Trifoliatae Fructus Immaturus samples were acquired and analyzed using principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA). The authentic herbs were differentiated from their counterfeits easily. Eight characteristic components which were responsible for the difference between the samples were tentatively identified. Furthermore, three out of the eight components, naringin, hesperidin, and neohesperidin, were quantified. The results are useful to help identify the authenticity of Aurantii Fructus Immaturus. PMID:25622204

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.

Spherical Harmonics Reveal Standing EEG Waves and Long-Range Neural Synchronization during Non-REM Sleep.

PubMed

Sivakumar, Siddharth S; Namath, Amalia G; Galán, Roberto F

2016-01-01

Previous work from our lab has demonstrated how the connectivity of brain circuits constrains the repertoire of activity patterns that those circuits can display. Specifically, we have shown that the principal components of spontaneous neural activity are uniquely determined by the underlying circuit connections, and that although the principal components do not uniquely resolve the circuit structure, they do reveal important features about it. Expanding upon this framework on a larger scale of neural dynamics, we have analyzed EEG data recorded with the standard 10-20 electrode system from 41 neurologically normal children and adolescents during stage 2, non-REM sleep. We show that the principal components of EEG spindles, or sigma waves (10-16 Hz), reveal non-propagating, standing waves in the form of spherical harmonics. We mathematically demonstrate that standing EEG waves exist when the spatial covariance and the Laplacian operator on the head's surface commute. This in turn implies that the covariance between two EEG channels decreases as the inverse of their relative distance; a relationship that we corroborate with empirical data. Using volume conduction theory, we then demonstrate that superficial current sources are more synchronized at larger distances, and determine the characteristic length of large-scale neural synchronization as 1.31 times the head radius, on average. Moreover, consistent with the hypothesis that EEG spindles are driven by thalamo-cortical rather than cortico-cortical loops, we also show that 8 additional patients with hypoplasia or complete agenesis of the corpus callosum, i.e., with deficient or no connectivity between cortical hemispheres, similarly exhibit standing EEG waves in the form of spherical harmonics. We conclude that spherical harmonics are a hallmark of spontaneous, large-scale synchronization of neural activity in the brain, which are associated with unconscious, light sleep. The analogy with spherical harmonics in quantum mechanics suggests that the variances (eigenvalues) of the principal components follow a Boltzmann distribution, or equivalently, that standing waves are in a sort of "thermodynamic" equilibrium during non-REM sleep. By extension, we speculate that consciousness emerges as the brain dynamics deviate from such equilibrium.

Spherical Harmonics Reveal Standing EEG Waves and Long-Range Neural Synchronization during Non-REM Sleep

PubMed Central

Sivakumar, Siddharth S.; Namath, Amalia G.; Galán, Roberto F.

2016-01-01

Previous work from our lab has demonstrated how the connectivity of brain circuits constrains the repertoire of activity patterns that those circuits can display. Specifically, we have shown that the principal components of spontaneous neural activity are uniquely determined by the underlying circuit connections, and that although the principal components do not uniquely resolve the circuit structure, they do reveal important features about it. Expanding upon this framework on a larger scale of neural dynamics, we have analyzed EEG data recorded with the standard 10–20 electrode system from 41 neurologically normal children and adolescents during stage 2, non-REM sleep. We show that the principal components of EEG spindles, or sigma waves (10–16 Hz), reveal non-propagating, standing waves in the form of spherical harmonics. We mathematically demonstrate that standing EEG waves exist when the spatial covariance and the Laplacian operator on the head's surface commute. This in turn implies that the covariance between two EEG channels decreases as the inverse of their relative distance; a relationship that we corroborate with empirical data. Using volume conduction theory, we then demonstrate that superficial current sources are more synchronized at larger distances, and determine the characteristic length of large-scale neural synchronization as 1.31 times the head radius, on average. Moreover, consistent with the hypothesis that EEG spindles are driven by thalamo-cortical rather than cortico-cortical loops, we also show that 8 additional patients with hypoplasia or complete agenesis of the corpus callosum, i.e., with deficient or no connectivity between cortical hemispheres, similarly exhibit standing EEG waves in the form of spherical harmonics. We conclude that spherical harmonics are a hallmark of spontaneous, large-scale synchronization of neural activity in the brain, which are associated with unconscious, light sleep. The analogy with spherical harmonics in quantum mechanics suggests that the variances (eigenvalues) of the principal components follow a Boltzmann distribution, or equivalently, that standing waves are in a sort of “thermodynamic” equilibrium during non-REM sleep. By extension, we speculate that consciousness emerges as the brain dynamics deviate from such equilibrium. PMID:27445777

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®.

[Study on Commercial Specification of Lonicerae Japonicae Flos].

PubMed

Zhou, Jie; Zou, Lin; Liu, Wei; Bian, Li-hua; Wang, Xiao; Zhang, Yong-qing; Dan, Staerk

2015-04-01

To provide the basis data for the institute of commercial specification standard of Lonicerae Japonicae Flos. 39 samples of Lonicerae Japonicae Flos commercial of different grades in market were collected, and vernier caliper and electronic balance were used to measure the numbers of flower bud and blooming rate per 0. 5 g, contamination content, browning degree, milden and rot, length, upside diameter, middle diameter and bottom diameter of Lonicerae Japonicae Flos. The content of neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid, rutin, galuteolin,3,5-icaffeoylquinic acid and 4,5-dicaffeoylquinic acid were detected by HPLC. Correlation analysis, principal component analysis and cluster analysis were used by SPSS to analyze all index data,and the correlation of appearance characteristics and intrinsic active constituents was discussed. The numbers of flower bud and blooming rate per 0. 5 g, contamination content and browning degree were principal component indexes. The length of flower bud showed a significant correlation with galuteolin content, and the browning degree and upside diameter showed a significant correlation with chlorogenic acid content. Lonicerae Japonicae Flos commercial should be divided into four specification grades by sieved indexes.

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.

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.

Wealth and Its Associations with Enteric Parasitic Infections in a Low-Income Community in Peru: Use of Principal Component Analysis

PubMed Central

Nundy, Shantanu; Gilman, Robert H.; Xiao, Lihua; Cabrera, Lilia; Cama, Rosa; Ortega, Ynes R.; Kahn, Geoffrey; Cama, Vitaliano A.

2011-01-01

The association of wealth and infections with Giardia, Cryptosporidium, Cyclospora, and microsporidia were examined in a longitudinal cohort conducted in Peru from 2001 to 2006. Data from 492 participants were daily clinical manifestations, weekly copro-parasitological diagnosis, and housing characteristics and assets owned (48 variables), and these data were used to construct a global wealth index using principal component analysis. Data were analyzed using continuous and categorical (wealth tertiles) models. Participant's mean age was 3.43 years (range = 0–12 years), with average follow-up of 993 days. Univariate and multivariate analyses identified significant associations between wealth and infections with Giardia and microsporidia. Participants with greater wealth indexes were associated with protection against Giardia (P < 0.001) and persistent Giardia infections (> 14 days). For microsporidia, greater wealth was protective (P = 0.066 continuous and P = 0.042 by tertiles). Contrarily, infections with Cryptosporidium and Cyclospora were independent of wealth. Thus, subtle differences in wealth may affect the frequency of specific parasitic infections within low-income communities. PMID:21212198

Researches upon the cavitation erosion behaviour of austenite steels

NASA Astrophysics Data System (ADS)

Bordeasu, I.; Popoviciu, M. O.; Mitelea, I.; Salcianu, L. C.; Bordeasu, D.; Duma, S. T.; Iosif, A.

2016-02-01

Paper analyzes the cavitation erosion behavior of two stainless steels with 100% austenitic structure but differing by the chemical composition and the values of mechanical properties. The research is based on the MDE(t) and MDER(t) characteristic curves. We studied supplementary the aspect of the eroded areas by other to different means: observations with performing optical microscopes and roughness measurements. The tests were done in the T2 vibratory facility in the Cavitation Laboratory of the Timisoara Polytechnic University. The principal purpose of the study is the identification of the elements influencing significantly the cavitation erosion resistance. It was established the effect of the principal chemical components (determining the proportion of the structural components in conformity the Schaffler diagram) upon the cavitation erosion resistance. The results of the researches present the influence of the proportion of unstable austenite upon cavitation erosion resistance. The stainless steel with the great proportion of unstable austenite has the best behavior. The obtained conclusion are important for the metallurgists which realizes the stainless steels used for manufacturing the runners of hydraulic machineries (turbines and pumps) with increased resistance to cavitation attack.

Linear measurements of the neurocranium are better indicators of population differences than those of the facial skeleton: comparative study of 1,961 skulls.

PubMed

Holló, Gábor; Szathmáry, László; Marcsik, Antónia; Barta, Zoltán

2010-02-01

The aim of this study is to individualize potential differences between two cranial regions used to differentiate human populations. We compared the neurocranium and the facial skeleton using skulls from the Great Hungarian Plain. The skulls date to the 1st-11th centuries, a long space of time that encompasses seven archaeological periods. We analyzed six neurocranial and seven facial measurements. The reduction of the number of variables was carried out using principal components analysis. Linear mixed-effects models were fitted to the principal components of each archaeological period, and then the models were compared using multiple pairwise tests. The neurocranium showed significant differences in seven cases between nonsubsequent periods and in one case, between two subsequent populations. For the facial skeleton, no significant results were found. Our results, which are also compared to previous craniofacial heritability estimates, suggest that the neurocranium is a more conservative region and that population differences can be pointed out better in the neurocranium than in the facial skeleton.

Discrimination of Geographical Origin of Asian Garlic Using Isotopic and Chemical Datasets under Stepwise Principal Component Analysis.

PubMed

Liu, Tsang-Sen; Lin, Jhen-Nan; Peng, Tsung-Ren

2018-01-16

Isotopic compositions of δ 2 H, δ 18 O, δ 13 C, and δ 15 N and concentrations of 22 trace elements from garlic samples were analyzed and processed with stepwise principal component analysis (PCA) to discriminate garlic's country of origin among Asian regions including South Korea, Vietnam, Taiwan, and China. Results indicate that there is no single trace-element concentration or isotopic composition that can accomplish the study's purpose and the stepwise PCA approach proposed does allow for discrimination between countries on a regional basis. Sequentially, Step-1 PCA distinguishes garlic's country of origin among Taiwanese, South Korean, and Vietnamese samples; Step-2 PCA discriminates Chinese garlic from South Korean garlic; and Step-3 and Step-4 PCA, Chinese garlic from Vietnamese garlic. In model tests, countries of origin of all audit samples were correctly discriminated by stepwise PCA. Consequently, this study demonstrates that stepwise PCA as applied is a simple and effective approach to discriminating country of origin among Asian garlics. © 2018 American Academy of Forensic Sciences.

[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.…

CLUSFAVOR 5.0: hierarchical cluster and principal-component analysis of microarray-based transcriptional profiles

PubMed Central

Peterson, Leif E

2002-01-01

CLUSFAVOR (CLUSter and Factor Analysis with Varimax Orthogonal Rotation) 5.0 is a Windows-based computer program for hierarchical cluster and principal-component analysis of microarray-based transcriptional profiles. CLUSFAVOR 5.0 standardizes input data; sorts data according to gene-specific coefficient of variation, standard deviation, average and total expression, and Shannon entropy; performs hierarchical cluster analysis using nearest-neighbor, unweighted pair-group method using arithmetic averages (UPGMA), or furthest-neighbor joining methods, and Euclidean, correlation, or jack-knife distances; and performs principal-component analysis. PMID:12184816

Influence of damping on the frequency-dependent polarizabilities of doped quantum dot

NASA Astrophysics Data System (ADS)

Pal, Suvajit; Ghosh, Manas

2014-09-01

We investigate the profiles of diagonal components of frequency-dependent linear (αxx and αyy), and first nonlinear (βxxx and βyyy) optical response of repulsive impurity doped quantum dots. The dopant impurity potential chosen assumes Gaussian form. The study principally focuses on investigating the role of damping on the polarizability components. In view of this the dopant is considered to be propagating under damped condition which is otherwise linear inherently. The frequency-dependent polarizabilities are then analyzed by placing the doped dot to a periodically oscillating external electric field of given intensity. The damping strength, in conjunction with external oscillation frequency and confinement potentials, fabricate the polarizability components in a fascinating manner which is adorned with emergence of maximization, minimization, and saturation. The discrimination in the values of the polarizability components in x and y-directions has also been addressed in the present context.

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

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;

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.

Shape Analysis of 3D Head Scan Data for U.S. Respirator Users

NASA Astrophysics Data System (ADS)

Zhuang, Ziqing; Slice, DennisE; Benson, Stacey; Lynch, Stephanie; Viscusi, DennisJ

2010-12-01

In 2003, the National Institute for Occupational Safety and Health (NIOSH) conducted a head-and-face anthropometric survey of diverse, civilian respirator users. Of the 3,997 subjects measured using traditional anthropometric techniques, surface scans and 26 three-dimensional (3D) landmark locations were collected for 947 subjects. The objective of this study was to report the size and shape variation of the survey participants using the 3D data. Generalized Procrustes Analysis (GPA) was conducted to standardize configurations of landmarks associated with individuals into a common coordinate system. The superimposed coordinates for each individual were used as commensurate variables that describe individual shape and were analyzed using Principal Component Analysis (PCA) to identify population variation. The first four principal components (PC) account for 49% of the total sample variation. The first PC indicates that overall size is an important component of facial variability. The second PC accounts for long and narrow or short and wide faces. Longer narrow orbits versus shorter wider orbits can be described by PC3, and PC4 represents variation in the degree of ortho/prognathism. Geometric Morphometrics provides a detailed and interpretable assessment of morphological variation that may be useful in assessing respirators and devising new test and certification standards.

Analysis of heavy metal sources in soil using kriging interpolation on principal components.

PubMed

Ha, Hoehun; Olson, James R; Bian, Ling; Rogerson, Peter A

2014-05-06

Anniston, Alabama has a long history of operation of foundries and other heavy industry. We assessed the extent of heavy metal contamination in soils by determining the concentrations of 11 heavy metals (Pb, As, Cd, Cr, Co, Cu, Mn, Hg, Ni, V, and Zn) based on 2046 soil samples collected from 595 industrial and residential sites. Principal Component Analysis (PCA) was adopted to characterize the distribution of heavy metals in soil in this region. In addition, a geostatistical technique (kriging) was used to create regional distribution maps for the interpolation of nonpoint sources of heavy metal contamination using geographical information system (GIS) techniques. There were significant differences found between sampling zones in the concentrations of heavy metals, with the exception of the levels of Ni. Three main components explaining the heavy metal variability in soils were identified. The results suggest that Pb, Cd, Cu, and Zn were associated with anthropogenic activities, such as the operations of some foundries and major railroads, which released these heavy metals, whereas the presence of Co, Mn, and V were controlled by natural sources, such as soil texture, pedogenesis, and soil hydrology. In general terms, the soil levels of heavy metals analyzed in this study were higher than those reported in previous studies in other industrial and residential communities.

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

PubMed

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

2016-10-01

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

The application of probabilistic design theory to high temperature low cycle fatigue

NASA Technical Reports Server (NTRS)

Wirsching, P. H.

1981-01-01

Metal fatigue under stress and thermal cycling is a principal mode of failure in gas turbine engine hot section components such as turbine blades and disks and combustor liners. Designing for fatigue is subject to considerable uncertainty, e.g., scatter in cycles to failure, available fatigue test data and operating environment data, uncertainties in the models used to predict stresses, etc. Methods of analyzing fatigue test data for probabilistic design purposes are summarized. The general strain life as well as homo- and hetero-scedastic models are considered. Modern probabilistic design theory is reviewed and examples are presented which illustrate application to reliability analysis of gas turbine engine components.

Colorimetric Sensor Array for White Wine Tasting.

PubMed

Chung, Soo; Park, Tu San; Park, Soo Hyun; Kim, Joon Yong; Park, Seongmin; Son, Daesik; Bae, Young Min; Cho, Seong In

2015-07-24

A colorimetric sensor array was developed to characterize and quantify the taste of white wines. A charge-coupled device (CCD) camera captured images of the sensor array from 23 different white wine samples, and the change in the R, G, B color components from the control were analyzed by principal component analysis. Additionally, high performance liquid chromatography (HPLC) was used to analyze the chemical components of each wine sample responsible for its taste. A two-dimensional score plot was created with 23 data points. It revealed clusters created from the same type of grape, and trends of sweetness, sourness, and astringency were mapped. An artificial neural network model was developed to predict the degree of sweetness, sourness, and astringency of the white wines. The coefficients of determination (R2) for the HPLC results and the sweetness, sourness, and astringency were 0.96, 0.95, and 0.83, respectively. This research could provide a simple and low-cost but sensitive taste prediction system, and, by helping consumer selection, will be able to have a positive effect on the wine industry.

Colorimetric Sensor Array for White Wine Tasting

PubMed Central

Chung, Soo; Park, Tu San; Park, Soo Hyun; Kim, Joon Yong; Park, Seongmin; Son, Daesik; Bae, Young Min; Cho, Seong In

2015-01-01

A colorimetric sensor array was developed to characterize and quantify the taste of white wines. A charge-coupled device (CCD) camera captured images of the sensor array from 23 different white wine samples, and the change in the R, G, B color components from the control were analyzed by principal component analysis. Additionally, high performance liquid chromatography (HPLC) was used to analyze the chemical components of each wine sample responsible for its taste. A two-dimensional score plot was created with 23 data points. It revealed clusters created from the same type of grape, and trends of sweetness, sourness, and astringency were mapped. An artificial neural network model was developed to predict the degree of sweetness, sourness, and astringency of the white wines. The coefficients of determination (R2) for the HPLC results and the sweetness, sourness, and astringency were 0.96, 0.95, and 0.83, respectively. This research could provide a simple and low-cost but sensitive taste prediction system, and, by helping consumer selection, will be able to have a positive effect on the wine industry. PMID:26213946

Quantitative study of flavonoids in leaves of citrus plants.

PubMed

Kawaii, S; Tomono, Y; Katase, E; Ogawa, K; Yano, M; Koizumi, M; Ito, C; Furukawa, H

2000-09-01

Leaf flavonoids were quantitatively determined in 68 representative or economically important Citrus species, cultivars, and near-Citrus relatives. Contents of 23 flavonoids including 6 polymethoxylated flavones were analyzed by means of reversed phase HPLC analysis. Principal component analysis revealed that the 7 associations according to Tanaka's classification were observed, but some do overlap each other. Group VII species could be divided into two different subgroups, namely, the first-10-species class and the last-19-species class according to Tanaka's classification numbers.

Infrared micro-spectroscopic studies of epithelial cells

PubMed Central

Romeo, Melissa; Mohlenhoff, Brian; Jennings, Michael; Diem, Max

2009-01-01

We report results from a study of human and canine mucosal cells, investigated by infrared micro-spectroscopy, and analyzed by methods of multivariate statistics. We demonstrate that the infrared spectra of individual cells are sensitive to the stage of maturation, and that a distinction between healthy and diseased cells will be possible. Since this report is written for an audience not familiar with infrared micro-spectroscopy, a short introduction into this field is presented along with a summary of principal component analysis. PMID:16797481

Stellar tracking attitude reference system

NASA Technical Reports Server (NTRS)

Klestadt, B.

1974-01-01

A satellite precision attitude control system was designed, based on the use of STARS as the principal sensing system. The entire system was analyzed and simulated in detail, considering the nonideal properties of the control and sensing components and realistic spacecraft mass properties. Experimental results were used to improve the star tracker noise model. The results of the simulation indicate that STARS performs in general as predicted in a realistic application and should be a strong contender in most precision earth pointing applications.

Effects of surface chemistry on hot corrosion life: Overview

NASA Technical Reports Server (NTRS)

Merutka, J.

1982-01-01

This program concentrates on analyzing a limited number of hot corroded components from the field and the carrying out of a series of controlled laboratory experiments to establish the effects of oxide scale and coating chemistry on hot corrosion life. This is to be determined principally from the length of the incubation period, the investigation of the mechanisms of hot corrosion attack, and the fitting of the data generated from the test exposure experiments to an empirical life prediction model.

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.

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.

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.

Factors associated with successful transition among children with disabilities in eight European countries

PubMed Central

2017-01-01

Introduction This research paper aims to assess factors reported by parents associated with the successful transition of children with complex additional support requirements that have undergone a transition between school environments from 8 European Union member states. Methods Quantitative data were collected from 306 parents within education systems from 8 EU member states (Bulgaria, Cyprus, Greece, Ireland, the Netherlands, Romania, Spain and the UK). The data were derived from an online questionnaire and consisted of 41 questions. Information was collected on: parental involvement in their child’s transition, child involvement in transition, child autonomy, school ethos, professionals’ involvement in transition and integrated working, such as, joint assessment, cooperation and coordination between agencies. Survey questions that were designed on a Likert-scale were included in the Principal Components Analysis (PCA), additional survey questions, along with the results from the PCA, were used to build a logistic regression model. Results Four principal components were identified accounting for 48.86% of the variability in the data. Principal component 1 (PC1), ‘child inclusive ethos,’ contains 16.17% of the variation. Principal component 2 (PC2), which represents child autonomy and involvement, is responsible for 8.52% of the total variation. Principal component 3 (PC3) contains questions relating to parental involvement and contributed to 12.26% of the overall variation. Principal component 4 (PC4), which involves transition planning and coordination, contributed to 11.91% of the overall variation. Finally, the principal components were included in a logistic regression to evaluate the relationship between inclusion and a successful transition, as well as whether other factors that may have influenced transition. All four principal components were significantly associated with a successful transition, with PC1 being having the most effect (OR: 4.04, CI: 2.43–7.18, p<0.0001). Discussion To support a child with complex additional support requirements through transition from special school to mainstream, governments and professionals need to ensure children with additional support requirements and their parents are at the centre of all decisions that affect them. It is important that professionals recognise the educational, psychological, social and cultural contexts of a child with additional support requirements and their families which will provide a holistic approach and remove barriers for learning. PMID:28636649

Factors associated with successful transition among children with disabilities in eight European countries.

PubMed

Ravenscroft, John; Wazny, Kerri; Davis, John M

2017-01-01

This research paper aims to assess factors reported by parents associated with the successful transition of children with complex additional support requirements that have undergone a transition between school environments from 8 European Union member states. Quantitative data were collected from 306 parents within education systems from 8 EU member states (Bulgaria, Cyprus, Greece, Ireland, the Netherlands, Romania, Spain and the UK). The data were derived from an online questionnaire and consisted of 41 questions. Information was collected on: parental involvement in their child's transition, child involvement in transition, child autonomy, school ethos, professionals' involvement in transition and integrated working, such as, joint assessment, cooperation and coordination between agencies. Survey questions that were designed on a Likert-scale were included in the Principal Components Analysis (PCA), additional survey questions, along with the results from the PCA, were used to build a logistic regression model. Four principal components were identified accounting for 48.86% of the variability in the data. Principal component 1 (PC1), 'child inclusive ethos,' contains 16.17% of the variation. Principal component 2 (PC2), which represents child autonomy and involvement, is responsible for 8.52% of the total variation. Principal component 3 (PC3) contains questions relating to parental involvement and contributed to 12.26% of the overall variation. Principal component 4 (PC4), which involves transition planning and coordination, contributed to 11.91% of the overall variation. Finally, the principal components were included in a logistic regression to evaluate the relationship between inclusion and a successful transition, as well as whether other factors that may have influenced transition. All four principal components were significantly associated with a successful transition, with PC1 being having the most effect (OR: 4.04, CI: 2.43-7.18, p<0.0001). To support a child with complex additional support requirements through transition from special school to mainstream, governments and professionals need to ensure children with additional support requirements and their parents are at the centre of all decisions that affect them. It is important that professionals recognise the educational, psychological, social and cultural contexts of a child with additional support requirements and their families which will provide a holistic approach and remove barriers for learning.

Selection Process of School Principals in Turkey and Some Other Countries: A Comparative Study

ERIC Educational Resources Information Center

Akbasli, Sait; Sahin, Mehmet; Gül, Burak

2017-01-01

The purpose of this study is to analyze the process of school principal selection and appointment in Turkey and some other developed countries in a comparative way. The specific purpose is to make suggestions in order to improve the school principal selection process in Turkey by comparatively analyzing school principal selection process in Turkey…

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.

Analysis of indoor air pollutants checklist using environmetric technique for health risk assessment of sick building complaint in nonindustrial workplace

PubMed Central

Syazwan, AI; Rafee, B Mohd; Juahir, Hafizan; Azman, AZF; Nizar, AM; Izwyn, Z; Syahidatussyakirah, K; Muhaimin, AA; Yunos, MA Syafiq; Anita, AR; Hanafiah, J Muhamad; Shaharuddin, MS; Ibthisham, A Mohd; Hasmadi, I Mohd; Azhar, MN Mohamad; Azizan, HS; Zulfadhli, I; Othman, J; Rozalini, M; Kamarul, FT

2012-01-01

Purpose To analyze and characterize a multidisciplinary, integrated indoor air quality checklist for evaluating the health risk of building occupants in a nonindustrial workplace setting. Design A cross-sectional study based on a participatory occupational health program conducted by the National Institute of Occupational Safety and Health (Malaysia) and Universiti Putra Malaysia. Method A modified version of the indoor environmental checklist published by the Department of Occupational Health and Safety, based on the literature and discussion with occupational health and safety professionals, was used in the evaluation process. Summated scores were given according to the cluster analysis and principal component analysis in the characterization of risk. Environmetric techniques was used to classify the risk of variables in the checklist. Identification of the possible source of item pollutants was also evaluated from a semiquantitative approach. Result Hierarchical agglomerative cluster analysis resulted in the grouping of factorial components into three clusters (high complaint, moderate-high complaint, moderate complaint), which were further analyzed by discriminant analysis. From this, 15 major variables that influence indoor air quality were determined. Principal component analysis of each cluster revealed that the main factors influencing the high complaint group were fungal-related problems, chemical indoor dispersion, detergent, renovation, thermal comfort, and location of fresh air intake. The moderate-high complaint group showed significant high loading on ventilation, air filters, and smoking-related activities. The moderate complaint group showed high loading on dampness, odor, and thermal comfort. Conclusion This semiquantitative assessment, which graded risk from low to high based on the intensity of the problem, shows promising and reliable results. It should be used as an important tool in the preliminary assessment of indoor air quality and as a categorizing method for further IAQ investigations and complaints procedures. PMID:23055779

Analysis of indoor air pollutants checklist using environmetric technique for health risk assessment of sick building complaint in nonindustrial workplace.

PubMed

Syazwan, Ai; Rafee, B Mohd; Juahir, Hafizan; Azman, Azf; Nizar, Am; Izwyn, Z; Syahidatussyakirah, K; Muhaimin, Aa; Yunos, Ma Syafiq; Anita, Ar; Hanafiah, J Muhamad; Shaharuddin, Ms; Ibthisham, A Mohd; Hasmadi, I Mohd; Azhar, Mn Mohamad; Azizan, Hs; Zulfadhli, I; Othman, J; Rozalini, M; Kamarul, Ft

2012-01-01

To analyze and characterize a multidisciplinary, integrated indoor air quality checklist for evaluating the health risk of building occupants in a nonindustrial workplace setting. A cross-sectional study based on a participatory occupational health program conducted by the National Institute of Occupational Safety and Health (Malaysia) and Universiti Putra Malaysia. A modified version of the indoor environmental checklist published by the Department of Occupational Health and Safety, based on the literature and discussion with occupational health and safety professionals, was used in the evaluation process. Summated scores were given according to the cluster analysis and principal component analysis in the characterization of risk. Environmetric techniques was used to classify the risk of variables in the checklist. Identification of the possible source of item pollutants was also evaluated from a semiquantitative approach. Hierarchical agglomerative cluster analysis resulted in the grouping of factorial components into three clusters (high complaint, moderate-high complaint, moderate complaint), which were further analyzed by discriminant analysis. From this, 15 major variables that influence indoor air quality were determined. Principal component analysis of each cluster revealed that the main factors influencing the high complaint group were fungal-related problems, chemical indoor dispersion, detergent, renovation, thermal comfort, and location of fresh air intake. The moderate-high complaint group showed significant high loading on ventilation, air filters, and smoking-related activities. The moderate complaint group showed high loading on dampness, odor, and thermal comfort. This semiquantitative assessment, which graded risk from low to high based on the intensity of the problem, shows promising and reliable results. It should be used as an important tool in the preliminary assessment of indoor air quality and as a categorizing method for further IAQ investigations and complaints procedures.

Behavior of ambient concentrations of natural radionuclides (7)Be, (210)Pb, (40)K in the Mediterranean coastal city of Málaga (Spain).

PubMed

Gordo, E; Dueñas, C; Fernández, M C; Liger, E; Cañete, S

2015-05-01

During a 4-year period (January 2009-December 2012), the (7)Be, (210)Pb, and (40)K activity concentrations in airborne particulate matter were weekly determined at the Málaga (Spain) located in the southern Iberian Peninsula. Totally 209 polypropylene filters were analyzed in the mentioned period. In 100% of the filters, (7)Be and (40)K activity concentrations were detected while (210)Pb activity concentration was detected in 96% of the filters. The results from individual measurements of (7)Be, (210)Pb, and (40)K concentrations were analyzed to derive the statistical estimates characterizing the distributions. Principal components analysis (PCA) was applied to the datasets and the results of the study reveal that aerosol behavior is represented by two principal components which explain 73.2% of total variance. Components PC1 and PC2 respectively explain 46.0 and 27.2% of total variance. PC1 was related positively to dust content, (7)Be and (40)K concentrations and negatively to sunspot numbers. In contrast, PC2 was related positively to temperature and (210)Pb activity and negatively to precipitation and relative humidity. The (7)Be levels showed a significant correlation with sunspot numbers due to the cosmogenic origin. (40)K activities showed a good correlation with dust deposition in filters mainly because it was transported to the air as resuspended particle from the soil. An inverse relationship was observed between the (210)Pb concentrations and monthly rainfall, indicating washout of atmospheric aerosols carrying these radionuclides and a pronounced positive correlation with the average monthly temperature of air.

Evaluation of the temporal variations of air quality in Taipei City, Taiwan, from 1994 to 2003.

PubMed

Chang, Shuenn-Chin; Lee, Chung-Te

2008-03-01

Data collected from the five air-quality monitoring stations established by the Taiwan Environmental Protection Administration in Taipei City from 1994 to 2003 are analyzed to assess the temporal variations of air quality. Principal component analysis (PCA) is adopted to convert the original measuring pollutants into fewer independent components through linear combinations while still retaining the majority of the variance of the original data set. Two principal components (PCs) are retained together explaining 82.73% of the total variance. PC1, which represents primary pollutants such as CO, NO(x), and SO(2), shows an obvious decrease over the last 10 years. PC2, which represents secondary pollutants such as ozone, displays a yearly increase over the time period when a reduction of primary pollutants is obvious. In order to track down the control measures put forth by the authorities, 47 days of high PM(10) concentrations caused by transboundary transport have been eliminated in analyzing the long-term trend of PM(10) in Taipei City. The temporal variations over the past 10 years show that the moderate peak in O(3) demonstrates a significant upward trend even when the local primary pollutants have been well under control. Monthly variations of PC scores demonstrate that primary pollution is significant from January to April, while ozone increases from April to August. The results of the yearly variations of PC scores show that PM(10) has gradually shifted from a strong correlation with PC1 during the early years to become more related to PC2 in recent years. This implies that after a reduction of primary pollutants, the proportion of secondary aerosols in PM(10) may increase. Thus, reducing the precursor concentrations of secondary aerosols will be an effective way to lower PM(10) concentrations.

Principal Component Analysis for Enhancement of Infrared Spectra Monitoring

NASA Astrophysics Data System (ADS)

Haney, Ricky Lance

The issue of air quality within the aircraft cabin is receiving increasing attention from both pilot and flight attendant unions. This is due to exposure events caused by poor air quality that in some cases may have contained toxic oil components due to bleed air that flows from outside the aircraft and then through the engines into the aircraft cabin. Significant short and long-term medical issues for aircraft crew have been attributed to exposure. The need for air quality monitoring is especially evident in the fact that currently within an aircraft there are no sensors to monitor the air quality and potentially harmful gas levels (detect-to-warn sensors), much less systems to monitor and purify the air (detect-to-treat sensors) within the aircraft cabin. The specific purpose of this research is to utilize a mathematical technique called principal component analysis (PCA) in conjunction with principal component regression (PCR) and proportionality constant calculations (PCC) to simplify complex, multi-component infrared (IR) spectra data sets into a reduced data set used for determination of the concentrations of the individual components. Use of PCA can significantly simplify data analysis as well as improve the ability to determine concentrations of individual target species in gas mixtures where significant band overlap occurs in the IR spectrum region. Application of this analytical numerical technique to IR spectrum analysis is important in improving performance of commercial sensors that airlines and aircraft manufacturers could potentially use in an aircraft cabin environment for multi-gas component monitoring. The approach of this research is two-fold, consisting of a PCA application to compare simulation and experimental results with the corresponding PCR and PCC to determine quantitatively the component concentrations within a mixture. The experimental data sets consist of both two and three component systems that could potentially be present as air contaminants in an aircraft cabin. In addition, experimental data sets are analyzed for a hydrogen peroxide (H2O2) aqueous solution mixture to determine H2O2 concentrations at various levels that could be produced during use of a vapor phase hydrogen peroxide (VPHP) decontamination system. After the PCA application to two and three component systems, the analysis technique is further expanded to include the monitoring of potential bleed air contaminants from engine oil combustion. Simulation data sets created from database spectra were utilized to predict gas components and concentrations in unknown engine oil samples at high temperatures as well as time-evolved gases from the heating of engine oils.

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.

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)

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.

Principal component and normal mode analysis of proteins; a quantitative comparison using the GroEL subunit.

PubMed

Skjaerven, Lars; Martinez, Aurora; Reuter, Nathalie

2011-01-01

Principal component analysis (PCA) and normal mode analysis (NMA) have emerged as two invaluable tools for studying conformational changes in proteins. To compare these approaches for studying protein dynamics, we have used a subunit of the GroEL chaperone, whose dynamics is well characterized. We first show that both PCA on trajectories from molecular dynamics (MD) simulations and NMA reveal a general dynamical behavior in agreement with what has previously been described for GroEL. We thus compare the reproducibility of PCA on independent MD runs and subsequently investigate the influence of the length of the MD simulations. We show that there is a relatively poor one-to-one correspondence between eigenvectors obtained from two independent runs and conclude that caution should be taken when analyzing principal components individually. We also observe that increasing the simulation length does not improve the agreement with the experimental structural difference. In fact, relatively short MD simulations are sufficient for this purpose. We observe a rapid convergence of the eigenvectors (after ca. 6 ns). Although there is not always a clear one-to-one correspondence, there is a qualitatively good agreement between the movements described by the first five modes obtained with the three different approaches; PCA, all-atoms NMA, and coarse-grained NMA. It is particularly interesting to relate this to the computational cost of the three methods. The results we obtain on the GroEL subunit contribute to the generalization of robust and reproducible strategies for the study of protein dynamics, using either NMA or PCA of trajectories from MD simulations. © 2010 Wiley-Liss, Inc.

[Identification of varieties of textile fibers by using Vis/NIR infrared spectroscopy technique].

PubMed

Wu, Gui-Fang; He, Yong

2010-02-01

The aim of the present paper was to provide new insight into Vis/NIR spectroscopic analysis of textile fibers. In order to achieve rapid identification of the varieties of fibers, the authors selected 5 kinds of fibers of cotton, flax, wool, silk and tencel to do a study with Vis/NIR spectroscopy. Firstly, the spectra of each kind of fiber were scanned by spectrometer, and principal component analysis (PCA) method was used to analyze the characteristics of the pattern of Vis/NIR spectra. Principal component scores scatter plot (PC1 x PC2 x PC3) of fiber indicated the classification effect of five varieties of fibers. The former 6 principal components (PCs) were selected according to the quantity and size of PCs. The PCA classification model was optimized by using the least-squares support vector machines (LS-SVM) method. The authors used the 6 PCs extracted by PCA as the inputs of LS-SVM, and PCA-LS-SVM model was built to achieve varieties validation as well as mathematical model building and optimization analysis. Two hundred samples (40 samples for each variety of fibers) of five varieties of fibers were used for calibration of PCA-LS-SVM model, and the other 50 samples (10 samples for each variety of fibers) were used for validation. The result of validation showed that Vis/NIR spectroscopy technique based on PCA-LS-SVM had a powerful classification capability. It provides a new method for identifying varieties of fibers rapidly and real time, so it has important significance for protecting the rights of consumers, ensuring the quality of textiles, and implementing rationalization production and transaction of textile materials and its production.

DOWN-STREAM SPATIAL DISTRIBUTION OF ANTIBIOTIC RESISTANCE TRAITS ALONG METAL CONTAMINATED STREAM REACHES

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

Tuckfield, C; J V Mcarthur

2007-04-16

Sediment bacteria samples were collected from three streams in South Carolina, two contaminated with multiple metals (Four Mile Creek and Castor Creek), one uncontaminated (Meyers Branch), and another metal contaminated stream (Lampert Creek) in northern Washington State. Growth plates inoculated with Four Mile Creek sample extracts show bacteria colony growth after incubation on plates containing either one of two aminoglycosides (kanamycin or streptomycin), tetracycline or chloramphenocol. This study analyzes the spatial pattern of antibiotic resistance in culturable sediment bacteria in all four streams that may be due to metal contamination. We summarize the two aminoglycoside resistance measures and the 10more » metals concentrations by Principal Components Analysis. Respectively, 63% and 58% of the variability was explained in the 1st principal component of each variable set. We used the respective multivariate summary metrics (i.e. 1st principal component scores) as input measures for exploring the spatial correlation between antibiotic resistance and metal concentration for each stream reach sampled. Results show a significant and negative correlation between metals scores versus aminoglycoside resistance scores and suggest that selection for metal tolerance among sediment bacteria may influence selection for antibiotic resistance differently than previously supposed.. In addition, we borrow a method from geostatistics (variography) wherein a spatial cross-correlation analysis shows that decreasing metal concentrations scores are associated with increasing aminoglycoside resistance scores as the separation distance between sediment samples decreases, but for contaminated streams only. Since these results were counter to our initial expectation and to other experimental evidence for water column bacteria, we suspect our field results are influenced by metal bioavailability in the sediments and by a contaminant promoted interaction or ''cocktail effect'' from complex combinations of pollution mediated selection agents.« less

In-line near-infrared (NIR) and Raman spectroscopy coupled with principal component analysis (PCA) for in situ evaluation of the transesterification reaction.

PubMed

Fontalvo-Gómez, Miriam; Colucci, José A; Velez, Natasha; Romañach, Rodolfo J

2013-10-01

Biodiesel was synthesized from different commercially available oils while in-line Raman and near-infrared (NIR) spectra were obtained simultaneously, and the spectral changes that occurred during the reaction were evaluated with principal component analysis (PCA). Raman and NIR spectra were acquired every 30 s with fiber optic probes inserted into the reaction vessel. The reaction was performed at 60-70 °C using magnetic stirring. The time of reaction was 90 min, and during this time, 180 Raman and NIR spectra were collected. NIR spectra were collected using a transflectance probe and an optical path length of 1 mm at 8 cm(-1) spectral resolution and averaging 32 scans; for Raman spectra a 3 s exposure time and three accumulations were adequate for the analysis. Raman spectroscopy showed the ester conversion as evidenced by the displacement of the C=O band from 1747 to 1744 cm(-1) and the decrease in the intensity of the 1000-1050 cm(-1) band and the 1405 cm(-1) band as methanol was consumed in the reaction. NIR spectra also showed the decrease in methanol concentration with the band in the 4750-5000 cm(-1) region; this signal is present in the spectra of the transesterification reaction but not in the neat oils. The variations in the intensity of the methanol band were a main factor in the in-line monitoring of the transesterification reaction using Raman and NIR spectroscopy. The score plot of the first principal component showed the progress of the reaction. The final product was analyzed using (1)H nuclear magnetic resonance ((1)H NMR) spectroscopy and using mid-infrared spectroscopy, confirming the conversion of the oils to biodiesel.

Principal components of hand kinematics and neurophysiological signals in motor cortex during reach to grasp movements

PubMed Central

Aggarwal, Vikram; Thakor, Nitish V.; Schieber, Marc H.

2014-01-01

A few kinematic synergies identified by principal component analysis (PCA) account for most of the variance in the coordinated joint rotations of the fingers and wrist used for a wide variety of hand movements. To examine the possibility that motor cortex might control the hand through such synergies, we collected simultaneous kinematic and neurophysiological data from monkeys performing a reach-to-grasp task. We used PCA, jPCA and isomap to extract kinematic synergies from 18 joint angles in the fingers and wrist and analyzed the relationships of both single-unit and multiunit spike recordings, as well as local field potentials (LFPs), to these synergies. For most spike recordings, the maximal absolute cross-correlations of firing rates were somewhat stronger with an individual joint angle than with any principal component (PC), any jPC or any isomap dimension. In decoding analyses, where spikes and LFP power in the 100- to 170-Hz band each provided better decoding than other LFP-based signals, the first PC was decoded as well as the best decoded joint angle. But the remaining PCs and jPCs were predicted with lower accuracy than individual joint angles. Although PCs, jPCs or isomap dimensions might provide a more parsimonious description of kinematics, our findings indicate that the kinematic synergies identified with these techniques are not represented in motor cortex more strongly than the original joint angles. We suggest that the motor cortex might act to sculpt the synergies generated by subcortical centers, superimposing an ability to individuate finger movements and adapt the hand to grasp a wide variety of objects. PMID:24990564

Combination of 1H nuclear magnetic resonance spectroscopy and principal component analysis to evaluate the lipid fluidity of flutamide-encapsulated lipid nanoemulsions.

PubMed

Takegami, Shigehiko; Ueyama, Keita; Konishi, Atsuko; Kitade, Tatsuya

2018-06-06

The lipid fluidity of various lipid nanoemulsions (LNEs) without and with flutamide (FT) and containing one of two neutral lipids, one of four phosphatidylcholines as a surfactant, and sodium palmitate as a cosurfactant was investigated by the combination of 1 H nuclear magnetic resonance (NMR) spectroscopy and principal component analysis (PCA). In the 1 H NMR spectra, the peaks from the methylene groups of the neutral lipids and surfactants for all LNE preparations showed downfield shifts with increasing temperature from 20 to 60 °C. PCA was applied to the 1 H NMR spectral data obtained for the LNEs. The PCA resulted in a model in which the first two principal components (PCs) extracted 88% of the total spectral variation; the first PC (PC-1) axis and second PC (PC-2) axis accounted for 73 and 15%, respectively, of the total spectral variation. The Score-1 values for PC-1 plotted against temperature revealed the existence of two clusters, which were defined by the neutral lipid of the LNE preparations. Meanwhile, the Score-2 values decreased with rising temperature and reflected the increase in lipid fluidity of each LNE preparation, consistent with fluorescence anisotropy measurements. In addition, the changes of Score-2 values with temperature for LNE preparations with FT were smaller than those for LNE preparations without FT. This indicates that FT encapsulated in LNE particles markedly suppressed the increase in lipid fluidity of LNE particles with rising temperature. Thus, PCA of 1 H NMR spectra will become a powerful tool to analyze the lipid fluidity of lipid nanoparticles. Graphical abstract ᅟ.

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...

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.

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.

[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.

Evaluation of 107 legumes for renewable source of energy

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

Roth, W.B.; Carr, M.E.; Cull, I.M.

One hundred and seven species of randomly-collected Leguminosae were evaluated for their potential as energy-producing crops. Whole plants, excluding roots, were chemically analyzed, and 11 species were identified as the more promising for future considerations based on a numerical rating system developed at this Center. Botanical, fiber, and protein characteristics of the more promising species that had rating of less than 11 were considered excellent. Other characteristics, including contents of oil (1.7-3.2%; dry, ash-free, sample basis), polyphenol (5.4-16.5%), and hydrocarbon (0.3-0.6% for 10 species and 2.6% for one), were generally lower than those of promising species in other families previouslymore » analyzed. Of the 11 species, one contained principally rubber (polyisoprene) in the hydrocarbon fraction and 7 contained principally wax. Hydrocarbon fractions of 3 species with less than 0.4% were not examined. The oils of species with at least 3.0% oil were examined by thin layer chromatography (TLC) to determine classes of components and were given a saponification treatment to determine yields of unsaponifiable matter and fatty acids. The oil of one species was quantitatively analyzed for classes of compounds by TLC-flame ionization detection. Selected species with ratings greater than 10 are briefly discussed.« less

Essential Oil Composition of Pinus peuce Griseb. Needles and Twigs from Two National Parks of Kosovo.

PubMed

Hajdari, Avni; Mustafa, Behxhet; Nebija, Dashnor; Selimi, Hyrmete; Veselaj, Zeqir; Breznica, Pranvera; Quave, Cassandra Leah; Novak, Johannes

The principal aim of this study was to analyze the chemical composition and qualitative and quantitative variability of essential oils obtained from seven naturally grown populations of the Pinus peuce Grisebach, Pinaceae in Kosovo. Plant materials were collected from three populations in the Sharri National Park and from four other populations in the Bjeshkët e Nemuna National Park, in Kosovo. Essential oils were obtained by steam distillation and analyzed by GC-FID (Gas Chromatography-Flame Ionization Detection) and GC-MS (Gas Chromatography-Mass Spectrometry). The results showed that the yield of essential oils (v/w dry weight) varied depending on the origin of population and the plant organs and ranged from 0.7 to 3.3%. In total, 51 compounds were identified. The main compounds were α-pinene (needles: 21.6-34.9%; twigs: 11.0-24%), β-phellandrene (needles: 4.1-27.7; twigs: 29.0-49.8%), and β-pinene (needles: 10.0-16.1; twigs: 6.9-20.7%). HCA (Hierarchical Cluster Analysis) and PCA (Principal Component Analyses) were used to assess geographical variations in essential oil composition. Statistical analysis showed that the analyzed populations are grouped in three main clusters which seem to reflect microclimatic conditions on the chemical composition of the essential oils.

Palate dimensions in six-year-old children with unilateral cleft lip and palate: a six-center study on dental casts.

PubMed

Koželj, Vesna; Vegnuti, Miljana; Drevenšek, Martina; Hortis-Dzierzbicka, Maria; Gonzalez-Landa, Gonzalo; Hanstein, Siiri; Klimova, Irena; Kobus, Kazimierz; Kobus-Zaleśna, Katarzyna; Semb, Gunvor; Shaw, Bill

2012-11-01

To compare palatal dimensions in 6-year-old children with unilateral cleft lip and palate (UCLP) treated by different protocols with those of noncleft children. Retrospective intercenter outcome study. Patients : Upper dental casts from 129 children with repaired UCLP and 30 controls were analyzed by the trigonometric method. Six European cleft centers. Main outcome measures : Sagittal, transverse, and vertical dimensions of the palate were observed. Palate variables were analyzed with descriptive methods and nonparametric tests. Regarding several various characteristics measured on a relatively small number of subjects, hierarchical, k-means clustering, and principal component analyses were used. Mean values of the observed dimensions for five cleft groups differed significantly from the control (p < .05). The group with one-stage closure of the cleft differed significantly from all other cleft groups in most variables (p < .05). Principal component analysis of all 159 cases identified three clusters with specific morphologic characteristics of the palate. A similar number of treated children were classified into each cluster, while all children without clefts were classified in the same cluster. The percentage of treated children from a particular group that fit this cluster ranged from 0% to 70% and increased with age at palatal closure and number of primary surgical procedures. At 6 years of age, children with stepwise repair and hard palate closure after the age of two more frequently result in palatal dimensions of noncleft control than children with earlier palatal closure and one-stage cleft repair.

Analyzing the flavor compounds in Chinese traditional fermented shrimp pastes by HS-SPME-GC/MS and electronic nose

NASA Astrophysics Data System (ADS)

Fan, Yan; Yin, Li'ang; Xue, Yong; Li, Zhaojie; Hou, Hu; Xue, Changhu

2017-04-01

Shrimp paste is a type of condiments with high nutritional value. However, the flavors of shrimp paste, particularly the non-uniformity flavors, have limited its application in food processing. In order to identify the characteristic flavor compounds in Chinese traditional shrimp pastes, five kinds of typical commercial products were evaluated in this study. The differences in the volatile composition of the five products were investigated. Solid phase micro-extraction method was employed to extract the volatile compounds. GC-MS and electronic nose were applied to identify the compounds, and the data were analyzed using principal component analysis (PCA). A total of 62 volatile compounds were identified, including 8 alcohols, 7 aldehydes, 3 ketones, 7 ethers, 7 acids, 3 esters, 6 hydrocarbons, 12 pyrazines, 2 phenols, and 7 other compounds. The typical volatile compounds contributing to the flavor of shrimp paste were found as follows: dimethyl disulfide, dimethyl tetrasulfide, dimethyl trisulfide, 2, 3, 5-trimethyl-6-ethyl pyrazine, ethyl-2, 5-dimethyl-pyrazine, phenol and indole. Propanoic acid, butanoic acid, furans, and 2-hydroxy-3-pentanone caused unpleasant odors, such as pungent and rancid odors. Principal component analysis showed that the content of volatile compounds varied depending on the processing conditions and shrimp species. These results indicated that the combinations of multiple analysis and identification methods could make up the limitations of a single method, enhance the accuracy of identification, and provide useful information for sensory research and product development.

Determination and fingerprint analysis of steroidal saponins in roots of Liriope muscari (Decne.) L. H. Bailey by ultra high performance liquid chromatography coupled with ion trap time-of-flight mass spectrometry.

PubMed

Li, Yong-Wei; Qi, Jin; Wen-Zhang; Zhou, Shui-Ping; Yan-Wu; Yu, Bo-Yang

2014-07-01

Liriope muscari (Decne.) L. H. Bailey is a well-known traditional Chinese medicine used for treating cough and insomnia. There are few reports on the quality evaluation of this herb partly because the major steroid saponins are not readily identified by UV detectors and are not easily isolated due to the existence of many similar isomers. In this study, a qualitative and quantitative method was developed to analyze the major components in L. muscari (Decne.) L. H. Bailey roots. Sixteen components were deduced and identified primarily by the information obtained from ultra high performance liquid chromatography with ion-trap time-of-flight mass spectrometry. The method demonstrated the desired specificity, linearity, stability, precision, and accuracy for simultaneous determination of 15 constituents (13 steroidal glycosides, 25(R)-ruscogenin, and pentylbenzoate) in 26 samples from different origins. The fingerprint was established, and the evaluation was achieved using similarity analysis and principal component analysis of 15 fingerprint peaks from 26 samples by ultra high performance liquid chromatography. The results from similarity analysis were consistent with those of principal component analysis. All results suggest that the established method could be applied effectively to the determination of multi-ingredients and fingerprint analysis of steroid saponins for quality assessment and control of L. muscari (Decne.) L. H. Bailey. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Application of principal component analysis to ecodiversity assessment of postglacial landscape (on the example of Debnica Kaszubska commune, Middle Pomerania)

NASA Astrophysics Data System (ADS)

Wojciechowski, Adam

2017-04-01

In order to assess ecodiversity understood as a comprehensive natural landscape factor (Jedicke 2001), it is necessary to apply research methods which recognize the environment in a holistic way. Principal component analysis may be considered as one of such methods as it allows to distinguish the main factors determining landscape diversity on the one hand, and enables to discover regularities shaping the relationships between various elements of the environment under study on the other hand. The procedure adopted to assess ecodiversity with the use of principal component analysis involves: a) determining and selecting appropriate factors of the assessed environment qualities (hypsometric, geological, hydrographic, plant, and others); b) calculating the absolute value of individual qualities for the basic areas under analysis (e.g. river length, forest area, altitude differences, etc.); c) principal components analysis and obtaining factor maps (maps of selected components); d) generating a resultant, detailed map and isolating several classes of ecodiversity. An assessment of ecodiversity with the use of principal component analysis was conducted in the test area of 299,67 km2 in Debnica Kaszubska commune. The whole commune is situated in the Weichselian glaciation area of high hypsometric and morphological diversity as well as high geo- and biodiversity. The analysis was based on topographical maps of the commune area in scale 1:25000 and maps of forest habitats. Consequently, nine factors reflecting basic environment elements were calculated: maximum height (m), minimum height (m), average height (m), the length of watercourses (km), the area of water reservoirs (m2), total forest area (ha), coniferous forests habitats area (ha), deciduous forest habitats area (ha), alder habitats area (ha). The values for individual factors were analysed for 358 grid cells of 1 km2. Based on the principal components analysis, four major factors affecting commune ecodiversity were distinguished: hypsometric component (PC1), deciduous forest habitats component (PC2), river valleys and alder habitats component (PC3), and lakes component (PC4). The distinguished factors characterise natural qualities of postglacial area and reflect well the role of the four most important groups of environment components in shaping ecodiversity of the area under study. The map of ecodiversity of Debnica Kaszubska commune was created on the basis of the first four principal component scores and then five classes of diversity were isolated: very low, low, average, high and very high. As a result of the assessment, five commune regions of very high ecodiversity were separated. These regions are also very attractive for tourists and valuable in terms of their rich nature which include protected areas such as Slupia Valley Landscape Park. The suggested method of ecodiversity assessment with the use of principal component analysis may constitute an alternative methodological proposition to other research methods used so far. Literature Jedicke E., 2001. Biodiversität, Geodiversität, Ökodiversität. Kriterien zur Analyse der Landschaftsstruktur - ein konzeptioneller Diskussionsbeitrag. Naturschutz und Landschaftsplanung, 33(2/3), 59-68.

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 ...

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.

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

The factorial reliability of the Middlesex Hospital Questionnaire in normal subjects.

PubMed

Bagley, C

1980-03-01

The internal reliability of the Middlesex Hospital Questionnaire and its component subscales has been checked by means of principal components analyses of data on 256 normal subjects. The subscales (with the possible exception of Hysteria) were found to contribute to the general underlying factor of psychoneurosis. In general, the principal components analysis points to the reliability of the subscales, despite some item overlap.

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…

Engineering design of a high-temperature superconductor current lead

NASA Astrophysics Data System (ADS)

Niemann, R. C.; Cha, Y. S.; Hull, J. R.; Daugherty, M. A.; Buckles, W. E.

As part of the US Department of Energy's Superconductivity Pilot Center Program, Argonne National Laboratory and Superconductivity, Inc., are developing high-temperature superconductor (HTS) current leads suitable for application to superconducting magnetic energy storage systems. The principal objective of the development program is to design, construct, and evaluate the performance of HTS current leads suitable for near-term applications. Supporting objectives are to (1) develop performance criteria; (2) develop a detailed design; (3) analyze performance; (4) gain manufacturing experience in the areas of materials and components procurement, fabrication and assembly, quality assurance, and cost; (5) measure performance of critical components and the overall assembly; (6) identify design uncertainties and develop a program for their study; and (7) develop application-acceptance criteria.

2D/3D facial feature extraction

NASA Astrophysics Data System (ADS)

Çinar Akakin, Hatice; Ali Salah, Albert; Akarun, Lale; Sankur, Bülent

2006-02-01

We propose and compare three different automatic landmarking methods for near-frontal faces. The face information is provided as 480x640 gray-level images in addition to the corresponding 3D scene depth information. All three methods follow a coarse-to-fine suite and use the 3D information in an assist role. The first method employs a combination of principal component analysis (PCA) and independent component analysis (ICA) features to analyze the Gabor feature set. The second method uses a subset of DCT coefficients for template-based matching. These two methods employ SVM classifiers with polynomial kernel functions. The third method uses a mixture of factor analyzers to learn Gabor filter outputs. We contrast the localization performance separately with 2D texture and 3D depth information. Although the 3D depth information per se does not perform as well as texture images in landmark localization, the 3D information has still a beneficial role in eliminating the background and the false alarms.

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.

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 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.

Understanding software faults and their role in software reliability modeling

NASA Technical Reports Server (NTRS)

Munson, John C.

1994-01-01

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

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.

Delusions in first-episode psychosis: Principal component analysis of twelve types of delusions and demographic and clinical correlates of resulting domains.

PubMed

Paolini, Enrico; Moretti, Patrizia; Compton, Michael T

2016-09-30

Although delusions represent one of the core symptoms of psychotic disorders, it is remarkable that few studies have investigated distinct delusional themes. We analyzed data from a large sample of first-episode psychosis patients (n=245) to understand relations between delusion types and demographic and clinical correlates. First, we conducted a principal component analysis (PCA) of the 12 delusion items within the Scale for the Assessment of Positive Symptoms (SAPS). Then, using the domains derived via PCA, we tested a priori hypotheses and answered exploratory research questions related to delusional content. PCA revealed five distinct components: Delusions of Influence, Grandiose/Religious Delusions, Paranoid Delusions, Negative Affect Delusions (jealousy, and sin or guilt), and Somatic Delusions. The most prevalent type of delusion was Paranoid Delusions, and such delusions were more common at older ages at onset of psychosis. The level of Delusions of Influence was correlated with the severity of hallucinations and negative symptoms. We ascertained a general relationship between different childhood adversities and delusional themes, and a specific relationship between Somatic Delusions and childhood neglect. Moreover, we found higher scores on Delusions of Influence and Negative Affect Delusions among cannabis and stimulant users. Our results support considering delusions as varied experiences with varying prevalences and correlates. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Modified Inverse First Order Reliability Method (I-FORM) for Predicting Extreme Sea States.

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

Eckert-Gallup, Aubrey Celia; Sallaberry, Cedric Jean-Marie; Dallman, Ann Renee

Environmental contours describing extreme sea states are generated as the input for numerical or physical model simulation s as a part of the stand ard current practice for designing marine structure s to survive extreme sea states. Such environmental contours are characterized by combinations of significant wave height ( ) and energy period ( ) values calculated for a given recurrence interval using a set of data based on hindcast simulations or buoy observations over a sufficient period of record. The use of the inverse first - order reliability method (IFORM) i s standard design practice for generating environmental contours.more » In this paper, the traditional appli cation of the IFORM to generating environmental contours representing extreme sea states is described in detail and its merits and drawbacks are assessed. The application of additional methods for analyzing sea state data including the use of principal component analysis (PCA) to create an uncorrelated representation of the data under consideration is proposed. A reexamination of the components of the IFORM application to the problem at hand including the use of new distribution fitting techniques are shown to contribute to the development of more accurate a nd reasonable representations of extreme sea states for use in survivability analysis for marine struc tures. Keywords: In verse FORM, Principal Component Analysis , Environmental Contours, Extreme Sea State Characteri zation, Wave Energy Converters« less

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 Label-Free Fluorescent Array Sensor Utilizing Liposome Encapsulating Calcein for Discriminating Target Proteins by Principal Component Analysis

PubMed Central

Imamura, Ryota; Murata, Naoki; Shimanouchi, Toshinori; Yamashita, Kaoru; Fukuzawa, Masayuki; Noda, Minoru

2017-01-01

A new fluorescent arrayed biosensor has been developed to discriminate species and concentrations of target proteins by using plural different phospholipid liposome species encapsulating fluorescent molecules, utilizing differences in permeation of the fluorescent molecules through the membrane to modulate liposome-target protein interactions. This approach proposes a basically new label-free fluorescent sensor, compared with the common technique of developed fluorescent array sensors with labeling. We have confirmed a high output intensity of fluorescence emission related to characteristics of the fluorescent molecules dependent on their concentrations when they leak from inside the liposomes through the perturbed lipid membrane. After taking an array image of the fluorescence emission from the sensor using a CMOS imager, the output intensities of the fluorescence were analyzed by a principal component analysis (PCA) statistical method. It is found from PCA plots that different protein species with several concentrations were successfully discriminated by using the different lipid membranes with high cumulative contribution ratio. We also confirmed that the accuracy of the discrimination by the array sensor with a single shot is higher than that of a single sensor with multiple shots. PMID:28714873

A Label-Free Fluorescent Array Sensor Utilizing Liposome Encapsulating Calcein for Discriminating Target Proteins by Principal Component Analysis.

PubMed

Imamura, Ryota; Murata, Naoki; Shimanouchi, Toshinori; Yamashita, Kaoru; Fukuzawa, Masayuki; Noda, Minoru

2017-07-15

A new fluorescent arrayed biosensor has been developed to discriminate species and concentrations of target proteins by using plural different phospholipid liposome species encapsulating fluorescent molecules, utilizing differences in permeation of the fluorescent molecules through the membrane to modulate liposome-target protein interactions. This approach proposes a basically new label-free fluorescent sensor, compared with the common technique of developed fluorescent array sensors with labeling. We have confirmed a high output intensity of fluorescence emission related to characteristics of the fluorescent molecules dependent on their concentrations when they leak from inside the liposomes through the perturbed lipid membrane. After taking an array image of the fluorescence emission from the sensor using a CMOS imager, the output intensities of the fluorescence were analyzed by a principal component analysis (PCA) statistical method. It is found from PCA plots that different protein species with several concentrations were successfully discriminated by using the different lipid membranes with high cumulative contribution ratio. We also confirmed that the accuracy of the discrimination by the array sensor with a single shot is higher than that of a single sensor with multiple shots.

Spatial and spectral analysis of corneal epithelium injury using hyperspectral images

NASA Astrophysics Data System (ADS)

Md Noor, Siti Salwa; Michael, Kaleena; Marshall, Stephen; Ren, Jinchang

2017-12-01

Eye assessment is essential in preventing blindness. Currently, the existing methods to assess corneal epithelium injury are complex and require expert knowledge. Hence, we have introduced a non-invasive technique using hyperspectral imaging (HSI) and an image analysis algorithm of corneal epithelium injury. Three groups of images were compared and analyzed, namely healthy eyes, injured eyes, and injured eyes with stain. Dimensionality reduction using principal component analysis (PCA) was applied to reduce massive data and redundancies. The first 10 principal components (PCs) were selected for further processing. The mean vector of 10 PCs with 45 pairs of all combinations was computed and sent to two classifiers. A quadratic Bayes normal classifier (QDC) and a support vector classifier (SVC) were used in this study to discriminate the eleven eyes into three groups. As a result, the combined classifier of QDC and SVC showed optimal performance with 2D PCA features (2DPCA-QDSVC) and was utilized to classify normal and abnormal tissues, using color image segmentation. The result was compared with human segmentation. The outcome showed that the proposed algorithm produced extremely promising results to assist the clinician in quantifying a cornea injury.

Screening of oil sources by using comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry and multivariate statistical analysis.

PubMed

Zhang, Wanfeng; Zhu, Shukui; He, Sheng; Wang, Yanxin

2015-02-06

Using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC/TOFMS), volatile and semi-volatile organic compounds in crude oil samples from different reservoirs or regions were analyzed for the development of a molecular fingerprint database. Based on the GC×GC/TOFMS fingerprints of crude oils, principal component analysis (PCA) and cluster analysis were used to distinguish the oil sources and find biomarkers. As a supervised technique, the geological characteristics of crude oils, including thermal maturity, sedimentary environment etc., are assigned to the principal components. The results show that tri-aromatic steroid (TAS) series are the suitable marker compounds in crude oils for the oil screening, and the relative abundances of individual TAS compounds have excellent correlation with oil sources. In order to correct the effects of some other external factors except oil sources, the variables were defined as the content ratio of some target compounds and 13 parameters were proposed for the screening of oil sources. With the developed model, the crude oils were easily discriminated, and the result is in good agreement with the practical geological setting. Copyright © 2014 Elsevier B.V. All rights reserved.

Clinical study of noninvasive in vivo melanoma and nonmelanoma skin cancers using multimodal spectral diagnosis

PubMed Central

Lim, Liang; Nichols, Brandon; Migden, Michael R.; Rajaram, Narasimhan; Reichenberg, Jason S.; Markey, Mia K.; Ross, Merrick I.; Tunnell, James W.

2014-01-01

Abstract. The goal of this study was to determine the diagnostic capability of a multimodal spectral diagnosis (SD) for in vivo noninvasive disease diagnosis of melanoma and nonmelanoma skin cancers. We acquired reflectance, fluorescence, and Raman spectra from 137 lesions in 76 patients using custom-built optical fiber-based clinical systems. Biopsies of lesions were classified using standard histopathology as malignant melanoma (MM), nonmelanoma pigmented lesion (PL), basal cell carcinoma (BCC), actinic keratosis (AK), and squamous cell carcinoma (SCC). Spectral data were analyzed using principal component analysis. Using multiple diagnostically relevant principal components, we built leave-one-out logistic regression classifiers. Classification results were compared with histopathology of the lesion. Sensitivity/specificity for classifying MM versus PL (12 versus 17 lesions) was 100%/100%, for SCC and BCC versus AK (57 versus 14 lesions) was 95%/71%, and for AK and SCC and BCC versus normal skin (71 versus 71 lesions) was 90%/85%. The best classification for nonmelanoma skin cancers required multiple modalities; however, the best melanoma classification occurred with Raman spectroscopy alone. The high diagnostic accuracy for classifying both melanoma and nonmelanoma skin cancer lesions demonstrates the potential for SD as a clinical diagnostic device. PMID:25375350

Clinical study of noninvasive in vivo melanoma and nonmelanoma skin cancers using multimodal spectral diagnosis

NASA Astrophysics Data System (ADS)

Lim, Liang; Nichols, Brandon; Migden, Michael R.; Rajaram, Narasimhan; Reichenberg, Jason S.; Markey, Mia K.; Ross, Merrick I.; Tunnell, James W.

2014-11-01

The goal of this study was to determine the diagnostic capability of a multimodal spectral diagnosis (SD) for in vivo noninvasive disease diagnosis of melanoma and nonmelanoma skin cancers. We acquired reflectance, fluorescence, and Raman spectra from 137 lesions in 76 patients using custom-built optical fiber-based clinical systems. Biopsies of lesions were classified using standard histopathology as malignant melanoma (MM), nonmelanoma pigmented lesion (PL), basal cell carcinoma (BCC), actinic keratosis (AK), and squamous cell carcinoma (SCC). Spectral data were analyzed using principal component analysis. Using multiple diagnostically relevant principal components, we built leave-one-out logistic regression classifiers. Classification results were compared with histopathology of the lesion. Sensitivity/specificity for classifying MM versus PL (12 versus 17 lesions) was 100%;/100%;, for SCC and BCC versus AK (57 versus 14 lesions) was 95%;/71%, and for AK and SCC and BCC versus normal skin (71 versus 71 lesions) was 90%/85%. The best classification for nonmelanoma skin cancers required multiple modalities; however, the best melanoma classification occurred with Raman spectroscopy alone. The high diagnostic accuracy for classifying both melanoma and nonmelanoma skin cancer lesions demonstrates the potential for SD as a clinical diagnostic device.

Selective Detection of Target Volatile Organic Compounds in Contaminated Humid Air Using a Sensor Array with Principal Component Analysis

PubMed Central

Itoh, Toshio; Akamatsu, Takafumi; Tsuruta, Akihiro; Shin, Woosuck

2017-01-01

We investigated selective detection of the target volatile organic compounds (VOCs) nonanal, n-decane, and acetoin for lung cancer-related VOCs, and acetone and methyl i-butyl ketone for diabetes-related VOCs, in humid air with simulated VOC contamination (total concentration: 300 μg/m3). We used six “grain boundary-response type” sensors, including four commercially available sensors (TGS 2600, 2610, 2610, and 2620) and two Pt, Pd, and Au-loaded SnO2 sensors (Pt, Pd, Au/SnO2), and two “bulk-response type” sensors, including Zr-doped CeO2 (CeZr10), i.e., eight sensors in total. We then analyzed their sensor signals using principal component analysis (PCA). Although the six “grain boundary-response type” sensors were found to be insufficient for selective detection of the target gases in humid air, the addition of two “bulk-response type” sensors improved the selectivity, even with simulated VOC contamination. To further improve the discrimination, we selected appropriate sensors from the eight sensors based on the PCA results. The selectivity to each target gas was maintained and was not affected by contamination. PMID:28753948

Traceability of Opuntia ficus-indica L. Miller by ICP-MS multi-element profile and chemometric approach.

PubMed

Mottese, Antonio Francesco; Naccari, Clara; Vadalà, Rossella; Bua, Giuseppe Daniel; Bartolomeo, Giovanni; Rando, Rossana; Cicero, Nicola; Dugo, Giacomo

2018-01-01

Opuntia ficus-indica L. Miller fruits, particularly 'Ficodindia dell'Etna' of Biancavilla (POD), 'Fico d'india tradizionale di Roccapalumba' with protected brand and samples from an experimental field in Pezzolo (Sicily) were analyzed by inductively coupled plasma mass spectrometry in order to determine the multi-element profile. A multivariate chemometric approach, specifically principal component analysis (PCA), was applied to individuate how mineral elements may represent a marker of geographic origin, which would be useful for traceability. PCA has allowed us to verify that the geographical origin of prickly pear fruits is significantly influenced by trace element content, and the results found in Biancavilla PDO samples were linked to the geological composition of this volcanic areas. It was observed that two principal components accounted for 72.03% of the total variance in the data and, in more detail, PC1 explains 45.51% and PC2 26.52%, respectively. This study demonstrated that PCA is an integrated tool for the traceability of food products and, at the same time, a useful method of authentication of typical local fruits such as prickly pear. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

The fine-scale genetic structure and evolution of the Japanese population.

PubMed

Takeuchi, Fumihiko; Katsuya, Tomohiro; Kimura, Ryosuke; Nabika, Toru; Isomura, Minoru; Ohkubo, Takayoshi; Tabara, Yasuharu; Yamamoto, Ken; Yokota, Mitsuhiro; Liu, Xuanyao; Saw, Woei-Yuh; Mamatyusupu, Dolikun; Yang, Wenjun; Xu, Shuhua; Teo, Yik-Ying; Kato, Norihiro

2017-01-01

The contemporary Japanese populations largely consist of three genetically distinct groups-Hondo, Ryukyu and Ainu. By principal-component analysis, while the three groups can be clearly separated, the Hondo people, comprising 99% of the Japanese, form one almost indistinguishable cluster. To understand fine-scale genetic structure, we applied powerful haplotype-based statistical methods to genome-wide single nucleotide polymorphism data from 1600 Japanese individuals, sampled from eight distinct regions in Japan. We then combined the Japanese data with 26 other Asian populations data to analyze the shared ancestry and genetic differentiation. We found that the Japanese could be separated into nine genetic clusters in our dataset, showing a marked concordance with geography; and that major components of ancestry profile of Japanese were from the Korean and Han Chinese clusters. We also detected and dated admixture in the Japanese. While genetic differentiation between Ryukyu and Hondo was suggested to be caused in part by positive selection, genetic differentiation among the Hondo clusters appeared to result principally from genetic drift. Notably, in Asians, we found the possibility that positive selection accentuated genetic differentiation among distant populations but attenuated genetic differentiation among close populations. These findings are significant for studies of human evolution and medical genetics.

Evaluation of solvent effect on the extraction of phenolic compounds and antioxidant capacities from the berries: application of principal component analysis.

PubMed

Boeing, Joana Schuelter; Barizão, Erica Oliveira; E Silva, Beatriz Costa; Montanher, Paula Fernandes; de Cinque Almeida, Vitor; Visentainer, Jesuí Vergilio

2014-01-01

This study evaluated the effect of the solvent on the extraction of antioxidant compounds from black mulberry (Morus nigra), blackberry (Rubus ulmifolius) and strawberry (Fragaria x ananassa). Different extracts of each berry were evaluated from the determination of total phenolic content, anthocyanin content and antioxidant capacity, and data were applied to the principal component analysis (PCA) to gain an overview of the effect of the solvent in extraction method. For all the berries analyzed, acetone/water (70/30, v/v) solvent mixture was more efficient solvent in the extracting of phenolic compounds, and methanol/water/acetic acid (70/29.5/0.5, v/v/v) showed the best values for anthocyanin content. Mixtures of ethanol/water (50/50, v/v), acetone water/acetic acid (70/29.5/0.5, v/v/v) and acetone/water (50/50, v/v) presented the highest antioxidant capacities for black mulberries, blackberries and strawberries, respectively. Antioxidants extractions are extremely affected by the solvent combination used. In addition, the obtained extracts with the organic solvent-water mixtures were distinguished from the extracts obtained with pure organic solvents, through the PCA analysis.

Application of Principal Component Analysis to NIR Spectra of Phyllosilicates: A Technique for Identifying Phyllosilicates on Mars

NASA Technical Reports Server (NTRS)

Rampe, E. B.; Lanza, N. L.

2012-01-01

Orbital near-infrared (NIR) reflectance spectra of the martian surface from the OMEGA and CRISM instruments have identified a variety of phyllosilicates in Noachian terrains. The types of phyllosilicates present on Mars have important implications for the aqueous environments in which they formed, and, thus, for recognizing locales that may have been habitable. Current identifications of phyllosilicates from martian NIR data are based on the positions of spectral absorptions relative to laboratory data of well-characterized samples and from spectral ratios; however, some phyllosilicates can be difficult to distinguish from one another with these methods (i.e. illite vs. muscovite). Here we employ a multivariate statistical technique, principal component analysis (PCA), to differentiate between spectrally similar phyllosilicate minerals. PCA is commonly used in a variety of industries (pharmaceutical, agricultural, viticultural) to discriminate between samples. Previous work using PCA to analyze raw NIR reflectance data from mineral mixtures has shown that this is a viable technique for identifying mineral types, abundances, and particle sizes. Here, we evaluate PCA of second-derivative NIR reflectance data as a method for classifying phyllosilicates and test whether this method can be used to identify phyllosilicates on Mars.

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

Integrative sparse principal component analysis of gene expression data.

PubMed

Liu, Mengque; Fan, Xinyan; Fang, Kuangnan; Zhang, Qingzhao; Ma, Shuangge

2017-12-01

In the analysis of gene expression data, dimension reduction techniques have been extensively adopted. The most popular one is perhaps the PCA (principal component analysis). To generate more reliable and more interpretable results, the SPCA (sparse PCA) technique has been developed. With the "small sample size, high dimensionality" characteristic of gene expression data, the analysis results generated from a single dataset are often unsatisfactory. Under contexts other than dimension reduction, integrative analysis techniques, which jointly analyze the raw data of multiple independent datasets, have been developed and shown to outperform "classic" meta-analysis and other multidatasets techniques and single-dataset analysis. In this study, we conduct integrative analysis by developing the iSPCA (integrative SPCA) method. iSPCA achieves the selection and estimation of sparse loadings using a group penalty. To take advantage of the similarity across datasets and generate more accurate results, we further impose contrasted penalties. Different penalties are proposed to accommodate different data conditions. Extensive simulations show that iSPCA outperforms the alternatives under a wide spectrum of settings. The analysis of breast cancer and pancreatic cancer data further shows iSPCA's satisfactory performance. © 2017 WILEY PERIODICALS, INC.

In vivo quantitative evaluation of vascular parameters for angiogenesis based on sparse principal component analysis and aggregated boosted trees

NASA Astrophysics Data System (ADS)

Zhao, Fengjun; Liu, Junting; Qu, Xiaochao; Xu, Xianhui; Chen, Xueli; Yang, Xiang; Cao, Feng; Liang, Jimin; Tian, Jie

2014-12-01

To solve the multicollinearity issue and unequal contribution of vascular parameters for the quantification of angiogenesis, we developed a quantification evaluation method of vascular parameters for angiogenesis based on in vivo micro-CT imaging of hindlimb ischemic model mice. Taking vascular volume as the ground truth parameter, nine vascular parameters were first assembled into sparse principal components (PCs) to reduce the multicolinearity issue. Aggregated boosted trees (ABTs) were then employed to analyze the importance of vascular parameters for the quantification of angiogenesis via the loadings of sparse PCs. The results demonstrated that vascular volume was mainly characterized by vascular area, vascular junction, connectivity density, segment number and vascular length, which indicated they were the key vascular parameters for the quantification of angiogenesis. The proposed quantitative evaluation method was compared with both the ABTs directly using the nine vascular parameters and Pearson correlation, which were consistent. In contrast to the ABTs directly using the vascular parameters, the proposed method can select all the key vascular parameters simultaneously, because all the key vascular parameters were assembled into the sparse PCs with the highest relative importance.

Macro policy responses to oil booms and busts in the United Arab Emirates

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

Al-Mutawa, A.K.

1991-01-01

The effects of oil shocks and macro policy changes in the United Arab Emirates are analyzed. A theoretical model is developed within the framework of the Dutch Disease literature. It contains four unique features that are applicable to the United Arab Emirates' economy. There are: (1) the presence of a large foreign labor force; (2) OPEC's oil export quotas; (3) the division of oil profits; and (4) the important role of government expenditures. The model is then used to examine the welfare effects of the above-mentioned shocks. An econometric model is then specified that conforms to the analytical model. Inmore » the econometric model the method of principal components' is applied owing to the undersized sample data. The principal components methodology is used in both the identification testing and the estimation of the structural equations. The oil and macro policy shocks are then simulated. The simulation results show that an oil-quantity boom leads to a higher welfare gain than an oil-price boom. Under certain circumstances, this finding is also confirmed by the comparative statistics that follow from the analytical model.« less

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.

Foot anthropometry and morphology phenomena.

PubMed

Agić, Ante; Nikolić, Vasilije; Mijović, Budimir

2006-12-01

Foot structure description is important for many reasons. The foot anthropometric morphology phenomena are analyzed together with hidden biomechanical functionality in order to fully characterize foot structure and function. For younger Croatian population the scatter data of the individual foot variables were interpolated by multivariate statistics. Foot structure descriptors are influenced by many factors, as a style of life, race, climate, and things of the great importance in human society. Dominant descriptors are determined by principal component analysis. Some practical recommendation and conclusion for medical, sportswear and footwear practice are highlighted.

[Research Progress of Multi-Model Medical Image Fusion at Feature Level].

PubMed

Zhang, Junjie; Zhou, Tao; Lu, Huiling; Wang, Huiqun

2016-04-01

Medical image fusion realizes advantage integration of functional images and anatomical images.This article discusses the research progress of multi-model medical image fusion at feature level.We firstly describe the principle of medical image fusion at feature level.Then we analyze and summarize fuzzy sets,rough sets,D-S evidence theory,artificial neural network,principal component analysis and other fusion methods’ applications in medical image fusion and get summery.Lastly,we in this article indicate present problems and the research direction of multi-model medical images in the future.

Differentiation of aflatoxigenic and non-aflatoxigenic strains of Aspergilli by FT-IR spectroscopy.

PubMed

Atkinson, Curtis; Pechanova, Olga; Sparks, Darrell L; Brown, Ashli; Rodriguez, Jose M

2014-01-01

Fourier transform infrared spectroscopy (FT-IR) is a well-established and widely accepted methodology to identify and differentiate diverse microbial species. In this study, FT-IR was used to differentiate 20 strains of ubiquitous and agronomically important phytopathogens of Aspergillus flavus and Aspergillus parasiticus. By analyzing their spectral profiles via principal component and cluster analysis, differentiation was achieved between the aflatoxin-producing and nonproducing strains of both fungal species. This study thus indicates that FT-IR coupled to multivariate statistics can rapidly differentiate strains of Aspergilli based on their toxigenicity.

Absorption spectroscopy and multi-angle scattering measurements in the visible spectral range for the geographic classification of Italian exravirgin olive oils

NASA Astrophysics Data System (ADS)

Mignani, Anna G.; Ciaccheri, Leonardo; Cimato, Antonio; Sani, Graziano; Smith, Peter R.

2004-03-01

Absorption spectroscopy and multi-angle scattering measurements in the visible spectral range are innovately used to analyze samples of extra virgin olive oils coming from selected areas of Tuscany, a famous Italian region for the production of extra virgin olive oil. The measured spectra are processed by means of the Principal Component Analysis method, so as to create a 3D map capable of clustering the Tuscan oils within the wider area of Italian extra virgin olive oils.

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.

Chemotypes of Pistacia atlantica leaf essential oils from Algeria.

PubMed

Gourine, Nadhir; Bombarda, Isabelle; Yousfi, Mohamed; Gaydou, Emile M

2010-01-01

The essential oils obtained by hydrodistillation of Pistacia atlantica Desf. leaves collected from different regions of Algeria were analyzed by GC and GC-MS. The essential oil was rich in monoterpenes and oxygenated sesquiterpenes. The major components were alpha-pinene (0.0-67%), delta-3-carene (0.0-56%), spathulenol (0.5-22%), camphene (0.0-21%), terpinen-4-ol (0.0-16%) and beta-pinene (0.0-13%). Among the various components identified, twenty were used for statistical analyses. The result of principal component analysis (PCA) showed the occurrence of three chemotypes: a delta-3-carene chemotype (16.4-56.2%), a terpinen-4-ol chemotype (10.8-16.0%) and an alpha-pinene/camphene chemotype (10.9-66.6%/3.8-20.9%). It was found that the essential oil from female plants (delta-3-carene chemotype) could be easily differentiated from the two other chemotypes corresponding to male trees.

Determination of Key Flavor Components in Methylene Chloride Extracts from Processed Grapefruit Juice.

PubMed

Jella; Rouseff; Goodner; Widmer

1998-01-19

The relative correlation of 52 aroma and 5 taste components in commercial not-from-concentrate grapefruit juices with flavor panel preference was determined. Methylene chloride extracts of juice were analyzed using GC/MS with a DB-5 column. Nonvolatiles determined included limonin and naringin by HPLC, degrees Brix, total acids, and degrees Brix/acid ratio. Juice samples were classified into low, medium, or high categories, based on average taste panel preference scores (nine-point hedonic scale). Principal component analysis demonstrated that highest quality juices were tightly clustered. Discriminant analysis indicated that 82% of the samples could be identified in the correct preference category using only myrcene, beta-caryophyllene, linalool, nootkatone, and degrees Brix. Nootkatone alone was not strongly associated with preference scores. The most preferred juices were strongly associated with low myrcene, low linalool, and intermediate levels of beta-caryophyllene.

Association of Cardiometabolic Genes with Arsenic Metabolism Biomarkers in American Indian Communities: The Strong Heart Family Study (SHFS)

PubMed Central

Balakrishnan, Poojitha; Vaidya, Dhananjay; Franceschini, Nora; Voruganti, V. Saroja; Gribble, Matthew O.; Haack, Karin; Laston, Sandra; Umans, Jason G.; Francesconi, Kevin A.; Goessler, Walter; North, Kari E.; Lee, Elisa; Yracheta, Joseph; Best, Lyle G.; MacCluer, Jean W.; Kent, Jack; Cole, Shelley A.; Navas-Acien, Ana

2016-01-01

Background: Metabolism of inorganic arsenic (iAs) is subject to inter-individual variability, which is explained partly by genetic determinants. Objectives: We investigated the association of genetic variants with arsenic species and principal components of arsenic species in the Strong Heart Family Study (SHFS). Methods: We examined variants previously associated with cardiometabolic traits (~ 200,000 from Illumina Cardio MetaboChip) or arsenic metabolism and toxicity (670) among 2,428 American Indian participants in the SHFS. Urine arsenic species were measured by high performance liquid chromatography–inductively coupled plasma mass spectrometry (HPLC-ICP-MS), and percent arsenic species [iAs, monomethylarsonate (MMA), and dimethylarsinate (DMA), divided by their sum × 100] were logit transformed. We created two orthogonal principal components that summarized iAs, MMA, and DMA and were also phenotypes for genetic analyses. Linear regression was performed for each phenotype, dependent on allele dosage of the variant. Models accounted for familial relatedness and were adjusted for age, sex, total arsenic levels, and population stratification. Single nucleotide polymorphism (SNP) associations were stratified by study site and were meta-analyzed. Bonferroni correction was used to account for multiple testing. Results: Variants at 10q24 were statistically significant for all percent arsenic species and principal components of arsenic species. The index SNP for iAs%, MMA%, and DMA% (rs12768205) and for the principal components (rs3740394, rs3740393) were located near AS3MT, whose gene product catalyzes methylation of iAs to MMA and DMA. Among the candidate arsenic variant associations, functional SNPs in AS3MT and 10q24 were most significant (p < 9.33 × 10–5). Conclusions: This hypothesis-driven association study supports the role of common variants in arsenic metabolism, particularly AS3MT and 10q24. Citation: Balakrishnan P, Vaidya D, Franceschini N, Voruganti VS, Gribble MO, Haack K, Laston S, Umans JG, Francesconi KA, Goessler W, North KE, Lee E, Yracheta J, Best LG, MacCluer JW, Kent J Jr., Cole SA, Navas-Acien A. 2017. Association of cardiometabolic genes with arsenic metabolism biomarkers in American Indian communities: the Strong Heart Family Study (SHFS). Environ Health Perspect 125:15–22; http://dx.doi.org/10.1289/EHP251 PMID:27352405

Association of Cardiometabolic Genes with Arsenic Metabolism Biomarkers in American Indian Communities: The Strong Heart Family Study (SHFS).

PubMed

Balakrishnan, Poojitha; Vaidya, Dhananjay; Franceschini, Nora; Voruganti, V Saroja; Gribble, Matthew O; Haack, Karin; Laston, Sandra; Umans, Jason G; Francesconi, Kevin A; Goessler, Walter; North, Kari E; Lee, Elisa; Yracheta, Joseph; Best, Lyle G; MacCluer, Jean W; Kent, Jack; Cole, Shelley A; Navas-Acien, Ana

2017-01-01

Metabolism of inorganic arsenic (iAs) is subject to inter-individual variability, which is explained partly by genetic determinants. We investigated the association of genetic variants with arsenic species and principal components of arsenic species in the Strong Heart Family Study (SHFS). We examined variants previously associated with cardiometabolic traits (~ 200,000 from Illumina Cardio MetaboChip) or arsenic metabolism and toxicity (670) among 2,428 American Indian participants in the SHFS. Urine arsenic species were measured by high performance liquid chromatography-inductively coupled plasma mass spectrometry (HPLC-ICP-MS), and percent arsenic species [iAs, monomethylarsonate (MMA), and dimethylarsinate (DMA), divided by their sum × 100] were logit transformed. We created two orthogonal principal components that summarized iAs, MMA, and DMA and were also phenotypes for genetic analyses. Linear regression was performed for each phenotype, dependent on allele dosage of the variant. Models accounted for familial relatedness and were adjusted for age, sex, total arsenic levels, and population stratification. Single nucleotide polymorphism (SNP) associations were stratified by study site and were meta-analyzed. Bonferroni correction was used to account for multiple testing. Variants at 10q24 were statistically significant for all percent arsenic species and principal components of arsenic species. The index SNP for iAs%, MMA%, and DMA% (rs12768205) and for the principal components (rs3740394, rs3740393) were located near AS3MT, whose gene product catalyzes methylation of iAs to MMA and DMA. Among the candidate arsenic variant associations, functional SNPs in AS3MT and 10q24 were most significant (p < 9.33 × 10-5). This hypothesis-driven association study supports the role of common variants in arsenic metabolism, particularly AS3MT and 10q24. Citation: Balakrishnan P, Vaidya D, Franceschini N, Voruganti VS, Gribble MO, Haack K, Laston S, Umans JG, Francesconi KA, Goessler W, North KE, Lee E, Yracheta J, Best LG, MacCluer JW, Kent J Jr., Cole SA, Navas-Acien A. 2017. Association of cardiometabolic genes with arsenic metabolism biomarkers in American Indian communities: the Strong Heart Family Study (SHFS). Environ Health Perspect 125:15-22; http://dx.doi.org/10.1289/EHP251.

Organic micropollutant removal from wastewater effluent-impacted drinking water sources during bank filtration and artificial recharge.

PubMed

Maeng, Sung Kyu; Ameda, Emmanuel; Sharma, Saroj K; Grützmacher, Gesche; Amy, Gary L

2010-07-01

Natural treatment systems such as bank filtration (BF) and artificial recharge (via an infiltration basin) are a robust barrier for many organic micropollutants (OMPs) and may represent a low-cost alternative compared to advanced drinking water treatment systems. This study analyzes a comprehensive database of OMPs at BF and artificial recharge (AR) sites located near Lake Tegel in Berlin (Germany). The focus of the study was on the derivation of correlations between the removal efficiencies of OMPs and key factors influencing the performance of BF and AR. At the BF site, shallow monitoring wells located close to the Lake Tegel source exhibited oxic conditions followed by prolonged anoxic conditions in deep monitoring wells and a production well. At the AR site, oxic conditions prevailed from the recharge pond along monitoring wells to the production well. Long residence times of up to 4.5 months at the BF site reduced the temperature variation during soil passage between summer and winter. The temperature variations were greater at the AR site as a consequence of shorter residence times. Deep monitoring wells and the production well located at the BF site were under the influence of ambient groundwater and old bank filtrate (up to several years of age). Thus, it is important to account for mixing with native groundwater and other sources (e.g., old bank filtrate) when estimating the performance of BF with respect to removal of OMPs. Principal component analysis (PCA) was used to investigate correlations between OMP removals and hydrogeochemical conditions with spatial and temporal parameters (e.g., well distance, residence time and depth) from both sites. Principal component-1 (PC1) embodied redox conditions (oxidation-reduction potential and dissolved oxygen), and principal component-2 (PC2) embodied degradation potential (e.g., total organic carbon and dissolved organic carbon) with the calcium carbonate dissolution potential (Ca(2+) and HCO(3)(-)) for the BF site. These two PCs explained a total variance of 55% at the BF site. At the AR site, PCA revealed redox conditions (PC1) and degradation potential with temperature (PC2) as principal components, which explained a total variance of 56%. Copyright 2010 Elsevier Ltd. All rights reserved.

A Content Analysis of Themes That Emerge from School Principals' Web2.0 Conversations

ERIC Educational Resources Information Center

Manning, Rory

2011-01-01

The purpose of this qualitative study was to analyze the self initiated conversations held by school principals on web2.o platforms, such as blogs, through the lens of current leadership standards. The online writings of thirteen school principals were analyzed using grounded theory techniques (Strauss and Corbin, 1998) to elucidate emerging…

Two-Dimensional Micro-/Nanoradian Angle Generator with High Resolution and Repeatability Based on Piezo-Driven Double-Axis Flexure Hinge and Three Capacitive Sensors.

PubMed

Tan, Xinran; Zhu, Fan; Wang, Chao; Yu, Yang; Shi, Jian; Qi, Xue; Yuan, Feng; Tan, Jiubin

2017-11-19

This study presents a two-dimensional micro-/nanoradian angle generator (2D-MNAG) that achieves high angular displacement resolution and repeatability using a piezo-driven flexure hinge for two-dimensional deflections and three capacitive sensors for output angle monitoring and feedback control. The principal error of the capacitive sensor for precision microangle measurement is analyzed and compensated for; so as to achieve a high angle output resolution of 10 nrad (0.002 arcsec) and positioning repeatability of 120 nrad (0.024 arcsec) over a large angular range of ±4363 μrad (±900 arcsec) for the 2D-MNAG. The impact of each error component, together with the synthetic error of the 2D-MNAG after principal error compensation are determined using Monte Carlo simulation for further improvement of the 2D-MNAG.

Two-Dimensional Micro-/Nanoradian Angle Generator with High Resolution and Repeatability Based on Piezo-Driven Double-Axis Flexure Hinge and Three Capacitive Sensors

PubMed Central

Tan, Xinran; Zhu, Fan; Wang, Chao; Yu, Yang; Shi, Jian; Qi, Xue; Yuan, Feng; Tan, Jiubin

2017-01-01

This study presents a two-dimensional micro-/nanoradian angle generator (2D-MNAG) that achieves high angular displacement resolution and repeatability using a piezo-driven flexure hinge for two-dimensional deflections and three capacitive sensors for output angle monitoring and feedback control. The principal error of the capacitive sensor for precision microangle measurement is analyzed and compensated for; so as to achieve a high angle output resolution of 10 nrad (0.002 arcsec) and positioning repeatability of 120 nrad (0.024 arcsec) over a large angular range of ±4363 μrad (±900 arcsec) for the 2D-MNAG. The impact of each error component, together with the synthetic error of the 2D-MNAG after principal error compensation are determined using Monte Carlo simulation for further improvement of the 2D-MNAG. PMID:29156595

Thrust Force Analysis of Tripod Constant Velocity Joint Using Multibody Model

NASA Astrophysics Data System (ADS)

Sugiura, Hideki; Matsunaga, Tsugiharu; Mizutani, Yoshiteru; Ando, Yosei; Kashiwagi, Isashi

A tripod constant velocity joint is used in the driveshaft of front wheel drive vehicles. Thrust force generated by this joint causes lateral vibration in these vehicles. To analyze the thrust force, a detailed model is constructed based on a multibody dynamics approach. This model includes all principal parts of the joint defined as rigid bodies and all force elements of contact and friction acting among these parts. This model utilizes a new contact modeling method of needle roller bearings for more precise and faster computation. By comparing computational and experimental results, the appropriateness of this model is verified and the principal factors inducing the second and third rotating order components of the thrust force are clarified. This paper also describes the influence of skewed needle rollers on the thrust force and evaluates the contribution of friction forces at each contact region to the thrust force.

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.

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.

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...

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)

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.

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.

[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.

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.

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.

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.

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.

[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.

Wavelet principal component analysis of fetal movement counting data preceding hospital examinations due to decreased fetal movement: a prospective cohort study

PubMed Central

2013-01-01

Background Fetal movement (FM) counting is a simple and widely used method of assessing fetal well-being. However, little is known about what women perceive as decreased fetal movement (DFM) and how maternally perceived DFM is reflected in FM charts. Methods We analyzed FM counting data from 148 DFM events occurring in 137 pregnancies. The women counted FM daily from pregnancy week 24 until birth using a modified count-to-ten procedure. Common temporal patterns for the two weeks preceding hospital examination due to DFM were extracted from the FM charts using wavelet principal component analysis; a statistical methodology particularly developed for modeling temporal data with sudden changes, i.e. spikes that are frequently found in FM data. The association of the extracted temporal patterns with fetal complications was assessed by including the individuals’ scores on the wavelet principal components as explanatory variables in multivariable logistic regression analyses for two outcome measures: (i) complications identified during DFM-related consultations (n = 148) and (ii) fetal compromise at the time of consultation (including relevant information about birth outcome and placental pathology). The latter outcome variable was restricted to the DFM events occurring within 21 days before birth (n = 76). Results Analyzing the 148 and 76 DFM events, the first three main temporal FM counting patterns explained 87.2% and 87.4%, respectively, of all temporal variation in the FM charts. These three temporal patterns represented overall counting times, sudden spikes around the time of DFM events, and an inverted U-shaped pattern, explaining 75.3%, 8.6%, and 3.3% and 72.5%, 9.6%, and 5.3% of variation in the total cohort and subsample, respectively. Neither of the temporal patterns was significantly associated with the two outcome measures. Conclusions Acknowledging that sudden, large changes in fetal activity may be underreported in FM charts, our study showed that the temporal FM counting patterns in the two weeks preceding DFM-related consultation contributed little to identify clinically important changes in perceived FM. It thus provides insufficient information for giving detailed advice to women on when to contact health care providers. The importance of qualitative features of maternally perceived DFM should be further explored. PMID:24007565

Wavelet principal component analysis of fetal movement counting data preceding hospital examinations due to decreased fetal movement: a prospective cohort study.

PubMed

Winje, Brita Askeland; Røislien, Jo; Saastad, Eli; Eide, Jorid; Riley, Christopher Finne; Stray-Pedersen, Babill; Frøen, J Frederik

2013-09-05

Fetal movement (FM) counting is a simple and widely used method of assessing fetal well-being. However, little is known about what women perceive as decreased fetal movement (DFM) and how maternally perceived DFM is reflected in FM charts. We analyzed FM counting data from 148 DFM events occurring in 137 pregnancies. The women counted FM daily from pregnancy week 24 until birth using a modified count-to-ten procedure. Common temporal patterns for the two weeks preceding hospital examination due to DFM were extracted from the FM charts using wavelet principal component analysis; a statistical methodology particularly developed for modeling temporal data with sudden changes, i.e. spikes that are frequently found in FM data. The association of the extracted temporal patterns with fetal complications was assessed by including the individuals' scores on the wavelet principal components as explanatory variables in multivariable logistic regression analyses for two outcome measures: (i) complications identified during DFM-related consultations (n = 148) and (ii) fetal compromise at the time of consultation (including relevant information about birth outcome and placental pathology). The latter outcome variable was restricted to the DFM events occurring within 21 days before birth (n = 76). Analyzing the 148 and 76 DFM events, the first three main temporal FM counting patterns explained 87.2% and 87.4%, respectively, of all temporal variation in the FM charts. These three temporal patterns represented overall counting times, sudden spikes around the time of DFM events, and an inverted U-shaped pattern, explaining 75.3%, 8.6%, and 3.3% and 72.5%, 9.6%, and 5.3% of variation in the total cohort and subsample, respectively. Neither of the temporal patterns was significantly associated with the two outcome measures. Acknowledging that sudden, large changes in fetal activity may be underreported in FM charts, our study showed that the temporal FM counting patterns in the two weeks preceding DFM-related consultation contributed little to identify clinically important changes in perceived FM. It thus provides insufficient information for giving detailed advice to women on when to contact health care providers. The importance of qualitative features of maternally perceived DFM should be further explored.

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:…

[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.

Predicting Visual Disability in Glaucoma With Combinations of Vision Measures.

PubMed

Lin, Stephanie; Mihailovic, Aleksandra; West, Sheila K; Johnson, Chris A; Friedman, David S; Kong, Xiangrong; Ramulu, Pradeep Y

2018-04-01

We characterized vision in glaucoma using seven visual measures, with the goals of determining the dimensionality of vision, and how many and which visual measures best model activity limitation. We analyzed cross-sectional data from 150 older adults with glaucoma, collecting seven visual measures: integrated visual field (VF) sensitivity, visual acuity, contrast sensitivity (CS), area under the log CS function, color vision, stereoacuity, and visual acuity with noise. Principal component analysis was used to examine the dimensionality of vision. Multivariable regression models using one, two, or three vision tests (and nonvisual predictors) were compared to determine which was best associated with Rasch-analyzed Glaucoma Quality of Life-15 (GQL-15) person measure scores. The participants had a mean age of 70.2 and IVF sensitivity of 26.6 dB, suggesting mild-to-moderate glaucoma. All seven vision measures loaded similarly onto the first principal component (eigenvectors, 0.220-0.442), which explained 56.9% of the variance in vision scores. In models for GQL scores, the maximum adjusted- R 2 values obtained were 0.263, 0.296, and 0.301 when using one, two, and three vision tests in the models, respectively, though several models in each category had similar adjusted- R 2 values. All three of the best-performing models contained CS. Vision in glaucoma is a multidimensional construct that can be described by several variably-correlated vision measures. Measuring more than two vision tests does not substantially improve models for activity limitation. A sufficient description of disability in glaucoma can be obtained using one to two vision tests, especially VF and CS.

Missing-orbital analysis on hyperpolarizability β of organic molecules. Enhancement of the off-diagonal component βzxx using multiple substitutions

NASA Astrophysics Data System (ADS)

Tomonari, Mutsumi; Ookubo, Norio; Takada, Toshikazu

1995-04-01

The first-order hyperpolarizability components, βzzz and βzxx, for C 2v molecules (the z axis being the principal axis) are analyzed after simplified sum-over-states calculations. Compared with p-nitroaniline (PNA), βzxx is three times enhanced by x-extended π conjugation realized by a bulky substrate in 9-amino-10-nitroanthracene (ANA) and by multiple substitutions in 1,5-diamino-2,4-dinitrobenzene (DDB). While ANA shows βzzz unchanged because its z-directed charge transfer (CT) is similar to PNA, DDB has a β zzz1/3 of PNA, which is reduced by two weak CTs introduced by two ortho-positioned donor-acceptor pairs on both sides of the z axis.

Essential Oil Composition of Pinus peuce Griseb. Needles and Twigs from Two National Parks of Kosovo

PubMed Central

Hajdari, Avni; Mustafa, Behxhet; Selimi, Hyrmete; Veselaj, Zeqir; Breznica, Pranvera; Novak, Johannes

2016-01-01

The principal aim of this study was to analyze the chemical composition and qualitative and quantitative variability of essential oils obtained from seven naturally grown populations of the Pinus peuce Grisebach, Pinaceae in Kosovo. Plant materials were collected from three populations in the Sharri National Park and from four other populations in the Bjeshkët e Nemuna National Park, in Kosovo. Essential oils were obtained by steam distillation and analyzed by GC-FID (Gas Chromatography-Flame Ionization Detection) and GC-MS (Gas Chromatography-Mass Spectrometry). The results showed that the yield of essential oils (v/w dry weight) varied depending on the origin of population and the plant organs and ranged from 0.7 to 3.3%. In total, 51 compounds were identified. The main compounds were α-pinene (needles: 21.6–34.9%; twigs: 11.0–24%), β-phellandrene (needles: 4.1–27.7; twigs: 29.0–49.8%), and β-pinene (needles: 10.0–16.1; twigs: 6.9–20.7%). HCA (Hierarchical Cluster Analysis) and PCA (Principal Component Analyses) were used to assess geographical variations in essential oil composition. Statistical analysis showed that the analyzed populations are grouped in three main clusters which seem to reflect microclimatic conditions on the chemical composition of the essential oils. PMID:27579344

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…

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.

Multivariate analyses of salt stress and metabolite sensing in auto- and heterotroph Chenopodium cell suspensions.

PubMed

Wongchai, C; Chaidee, A; Pfeiffer, W

2012-01-01

Global warming increases plant salt stress via evaporation after irrigation, but how plant cells sense salt stress remains unknown. Here, we searched for correlation-based targets of salt stress sensing in Chenopodium rubrum cell suspension cultures. We proposed a linkage between the sensing of salt stress and the sensing of distinct metabolites. Consequently, we analysed various extracellular pH signals in autotroph and heterotroph cell suspensions. Our search included signals after 52 treatments: salt and osmotic stress, ion channel inhibitors (amiloride, quinidine), salt-sensing modulators (proline), amino acids, carboxylic acids and regulators (salicylic acid, 2,4-dichlorphenoxyacetic acid). Multivariate analyses revealed hirarchical clusters of signals and five principal components of extracellular proton flux. The principal component correlated with salt stress was an antagonism of γ-aminobutyric and salicylic acid, confirming involvement of acid-sensing ion channels (ASICs) in salt stress sensing. Proline, short non-substituted mono-carboxylic acids (C2-C6), lactic acid and amiloride characterised the four uncorrelated principal components of proton flux. The proline-associated principal component included an antagonism of 2,4-dichlorphenoxyacetic acid and a set of amino acids (hydrophobic, polar, acidic, basic). The five principal components captured 100% of variance of extracellular proton flux. Thus, a bias-free, functional high-throughput screening was established to extract new clusters of response elements and potential signalling pathways, and to serve as a core for quantitative meta-analysis in plant biology. The eigenvectors reorient research, associating proline with development instead of salt stress, and the proof of existence of multiple components of proton flux can help to resolve controversy about the acid growth theory. © 2011 German Botanical Society and The Royal Botanical Society of the Netherlands.

[The application of the multidimensional statistical methods in the evaluation of the influence of atmospheric pollution on the population's health].

PubMed

Surzhikov, V D; Surzhikov, D V

2014-01-01

The search and measurement of causal relationships between exposure to air pollution and health state of the population is based on the system analysis and risk assessment to improve the quality of research. With this purpose there is applied the modern statistical analysis with the use of criteria of independence, principal component analysis and discriminate function analysis. As a result of analysis out of all atmospheric pollutants there were separated four main components: for diseases of the circulatory system main principal component is implied with concentrations of suspended solids, nitrogen dioxide, carbon monoxide, hydrogen fluoride, for the respiratory diseases the main c principal component is closely associated with suspended solids, sulfur dioxide and nitrogen dioxide, charcoal black. The discriminant function was shown to be used as a measure of the level of air pollution.

Priority of VHS Development Based in Potential Area using Principal Component Analysis

NASA Astrophysics Data System (ADS)

Meirawan, D.; Ana, A.; Saripudin, S.

2018-02-01

The current condition of VHS is still inadequate in quality, quantity and relevance. The purpose of this research is to analyse the development of VHS based on the development of regional potential by using principal component analysis (PCA) in Bandung, Indonesia. This study used descriptive qualitative data analysis using the principle of secondary data reduction component. The method used is Principal Component Analysis (PCA) analysis with Minitab Statistics Software tool. The results of this study indicate the value of the lowest requirement is a priority of the construction of development VHS with a program of majors in accordance with the development of regional potential. Based on the PCA score found that the main priority in the development of VHS in Bandung is in Saguling, which has the lowest PCA value of 416.92 in area 1, Cihampelas with the lowest PCA value in region 2 and Padalarang with the lowest PCA value.

Performance-Based Preparation of Principals: A Framework for Improvement. A Special Report of the NASSP Consortium for the Performance-Based Preparation of Principals.

ERIC Educational Resources Information Center

National Association of Secondary School Principals, Reston, VA.

Preparation programs for principals should have excellent academic and performance based components. In examining the nature of performance based principal preparation this report finds that school administration programs must bridge the gap between conceptual learning in the classroom and the requirements of professional practice. A number of…

Principal component greenness transformation in multitemporal agricultural Landsat data

NASA Technical Reports Server (NTRS)

Abotteen, R. A.

1978-01-01

A data compression technique for multitemporal Landsat imagery which extracts phenological growth pattern information for agricultural crops is described. The principal component greenness transformation was applied to multitemporal agricultural Landsat data for information retrieval. The transformation was favorable for applications in agricultural Landsat data analysis because of its physical interpretability and its relation to the phenological growth of crops. It was also found that the first and second greenness eigenvector components define a temporal small-grain trajectory and nonsmall-grain trajectory, respectively.

Particulate matter in the rural settlement during winter time

NASA Astrophysics Data System (ADS)

Olszowski, Tomasz

2017-10-01

The objective of this study was to analyzed the variability of the ambient particulates mass concentration in an area occupied by rural development. The analysis applied daily and hourly PM2.5 and PM10 levels. Data were derived on the basis of measurement results with the application of stationary gravimetric samplers and optical dust meter. The obtained data were compared with the results from the urban air quality monitoring network in Opole. Principal Component Analysis was used for data analysis. Research hypotheses were checked using U Mann-Whitney. It was indicated that during the smog episodes, the ratio of the inhalable dust fraction in the rural aerosol is greater than for the case of the urban aerosol. It was established that the principal meteorological factors affecting the local air quality. Air temperature, atmospheric pressure, movement of air masses and occurrence of precipitation are the most important. It was demonstrated that the during the temperature inversion phenomenon, the values of the hourly and daily mass concentration of PM2.5 and PM10 are very improper. The decrease of the PM's concentration to a safe level is principally relative to the occurrence of wind and precipitation.

Elemental profiling and geographical differentiation of Ethiopian coffee samples through inductively coupled plasma-optical emission spectroscopy (ICP-OES), ICP-mass spectrometry (ICP-MS) and direct mercury analyzer (DMA).

PubMed

Habte, Girum; Hwang, In Min; Kim, Jae Sung; Hong, Joon Ho; Hong, Young Sin; Choi, Ji Yeon; Nho, Eun Yeong; Jamila, Nargis; Khan, Naeem; Kim, Kyong Su

2016-12-01

This study was aimed to establish the elemental profiling and provenance of coffee samples collected from eleven major coffee producing regions of Ethiopia. A total of 129 samples were analyzed for forty-five elements using inductively coupled plasma (ICP)-optical emission spectroscopy (OES), ICP-mass spectrometry (MS) and direct mercury analyzer (DMA). Among the macro elements, K showed the highest levels whereas Fe was found to have the lowest concentration values. In all the samples, Ca, K, Mg, P and S contents were statistically significant (p<0.05). Micro elements showed the concentrations order of: Mn>Cu>Sr>Zn>Rb>Ni>B. Contents of the trace elements were lower than the permissible standard values. Inter-regions differentiation by cluster analysis (CA), linear discriminant analysis (LDA) and principal component analysis (PCA) showed that micro and trace elements are the best chemical descriptors of the analyzed coffee samples. Copyright © 2016 Elsevier Ltd. All rights reserved.

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)…

Prediction of genomic breeding values for dairy traits in Italian Brown and Simmental bulls using a principal component approach.

PubMed

Pintus, M A; Gaspa, G; Nicolazzi, E L; Vicario, D; Rossoni, A; Ajmone-Marsan, P; Nardone, A; Dimauro, C; Macciotta, N P P

2012-06-01

The large number of markers available compared with phenotypes represents one of the main issues in genomic selection. In this work, principal component analysis was used to reduce the number of predictors for calculating genomic breeding values (GEBV). Bulls of 2 cattle breeds farmed in Italy (634 Brown and 469 Simmental) were genotyped with the 54K Illumina beadchip (Illumina Inc., San Diego, CA). After data editing, 37,254 and 40,179 single nucleotide polymorphisms (SNP) were retained for Brown and Simmental, respectively. Principal component analysis carried out on the SNP genotype matrix extracted 2,257 and 3,596 new variables in the 2 breeds, respectively. Bulls were sorted by birth year to create reference and prediction populations. The effect of principal components on deregressed proofs in reference animals was estimated with a BLUP model. Results were compared with those obtained by using SNP genotypes as predictors with either the BLUP or Bayes_A method. Traits considered were milk, fat, and protein yields, fat and protein percentages, and somatic cell score. The GEBV were obtained for prediction population by blending direct genomic prediction and pedigree indexes. No substantial differences were observed in squared correlations between GEBV and EBV in prediction animals between the 3 methods in the 2 breeds. The principal component analysis method allowed for a reduction of about 90% in the number of independent variables when predicting direct genomic values, with a substantial decrease in calculation time and without loss of accuracy. Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Identifying sources of emerging organic contaminants in a mixed use watershed using principal components analysis.

PubMed

Karpuzcu, M Ekrem; Fairbairn, David; Arnold, William A; Barber, Brian L; Kaufenberg, Elizabeth; Koskinen, William C; Novak, Paige J; Rice, Pamela J; Swackhamer, Deborah L

2014-01-01

Principal components analysis (PCA) was used to identify sources of emerging organic contaminants in the Zumbro River watershed in Southeastern Minnesota. Two main principal components (PCs) were identified, which together explained more than 50% of the variance in the data. Principal Component 1 (PC1) was attributed to urban wastewater-derived sources, including municipal wastewater and residential septic tank effluents, while Principal Component 2 (PC2) was attributed to agricultural sources. The variances of the concentrations of cotinine, DEET and the prescription drugs carbamazepine, erythromycin and sulfamethoxazole were best explained by PC1, while the variances of the concentrations of the agricultural pesticides atrazine, metolachlor and acetochlor were best explained by PC2. Mixed use compounds carbaryl, iprodione and daidzein did not specifically group with either PC1 or PC2. Furthermore, despite the fact that caffeine and acetaminophen have been historically associated with human use, they could not be attributed to a single dominant land use category (e.g., urban/residential or agricultural). Contributions from septic systems did not clarify the source for these two compounds, suggesting that additional sources, such as runoff from biosolid-amended soils, may exist. Based on these results, PCA may be a useful way to broadly categorize the sources of new and previously uncharacterized emerging contaminants or may help to clarify transport pathways in a given area. Acetaminophen and caffeine were not ideal markers for urban/residential contamination sources in the study area and may need to be reconsidered as such in other areas as well.

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.

Turbulent flux variability and energy balance closure in the TERENO prealpine observatory: a hydrometeorological data analysis

NASA Astrophysics Data System (ADS)

Soltani, Mohsen; Mauder, Matthias; Laux, Patrick; Kunstmann, Harald

2017-07-01

The temporal multiscale variability of the surface heat fluxes is assessed by the analysis of the turbulent heat and moisture fluxes using the eddy covariance (EC) technique at the TERrestrial ENvironmental Observatories (TERENO) prealpine region. The fast and slow response variables from three EC sites located at Fendt, Rottenbuch, and Graswang are gathered for the period of 2013 to 2014. Here, the main goals are to characterize the multiscale variations and drivers of the turbulent fluxes, as well as to quantify the energy balance closure (EBC) and analyze the possible reasons for the lack of EBC at the EC sites. To achieve these goals, we conducted a principal component analysis (PCA) and a climatological turbulent flux footprint analysis. The results show significant differences in the mean diurnal variations of the sensible heat (H) and latent heat (LE) fluxes, because of variations in the solar radiation, precipitation patterns, soil moisture, and the vegetation fraction throughout the year. LE was the main consumer of net radiation. Based on the first principal component (PC1), the radiation and temperature components with a total mean contribution of 29.5 and 41.3%, respectively, were found to be the main drivers of the turbulent fluxes at the study EC sites. A general lack of EBC is observed, where the energy imbalance values amount 35, 44, and 35% at the Fendt, Rottenbuch, and Graswang sites, respectively. An average energy balance ratio (EBR) of 0.65 is obtained in the region. The best closure occurred in the afternoon peaking shortly before sunset with a different pattern and intensity between the study sites. The size and shape of the annual mean half-hourly turbulent flux footprint climatology was analyzed. On average, 80% of the flux footprint was emitted from a radius of approximately 250 m around the EC stations. Moreover, the overall shape of the flux footprints was in good agreement with the prevailing wind direction for all three TERENO EC sites.

Experimental Investigation of Principal Residual Stress and Fatigue Performance for Turned Nickel-Based Superalloy Inconel 718.

PubMed

Hua, Yang; Liu, Zhanqiang

2018-05-24

Residual stresses of turned Inconel 718 surface along its axial and circumferential directions affect the fatigue performance of machined components. However, it has not been clear that the axial and circumferential directions are the principle residual stress direction. The direction of the maximum principal residual stress is crucial for the machined component service life. The present work aims to focuses on determining the direction and magnitude of principal residual stress and investigating its influence on fatigue performance of turned Inconel 718. The turning experimental results show that the principal residual stress magnitude is much higher than surface residual stress. In addition, both the principal residual stress and surface residual stress increase significantly as the feed rate increases. The fatigue test results show that the direction of the maximum principal residual stress increased by 7.4%, while the fatigue life decreased by 39.4%. The maximum principal residual stress magnitude diminished by 17.9%, whereas the fatigue life increased by 83.6%. The maximum principal residual stress has a preponderant influence on fatigue performance as compared to the surface residual stress. The maximum principal residual stress can be considered as a prime indicator for evaluation of the residual stress influence on fatigue performance of turned Inconel 718.

Principal component analysis for designed experiments.

PubMed

Konishi, Tomokazu

2015-01-01

Principal component analysis is used to summarize matrix data, such as found in transcriptome, proteome or metabolome and medical examinations, into fewer dimensions by fitting the matrix to orthogonal axes. Although this methodology is frequently used in multivariate analyses, it has disadvantages when applied to experimental data. First, the identified principal components have poor generality; since the size and directions of the components are dependent on the particular data set, the components are valid only within the data set. Second, the method is sensitive to experimental noise and bias between sample groups. It cannot reflect the experimental design that is planned to manage the noise and bias; rather, it estimates the same weight and independence to all the samples in the matrix. Third, the resulting components are often difficult to interpret. To address these issues, several options were introduced to the methodology. First, the principal axes were identified using training data sets and shared across experiments. These training data reflect the design of experiments, and their preparation allows noise to be reduced and group bias to be removed. Second, the center of the rotation was determined in accordance with the experimental design. Third, the resulting components were scaled to unify their size unit. The effects of these options were observed in microarray experiments, and showed an improvement in the separation of groups and robustness to noise. The range of scaled scores was unaffected by the number of items. Additionally, unknown samples were appropriately classified using pre-arranged axes. Furthermore, these axes well reflected the characteristics of groups in the experiments. As was observed, the scaling of the components and sharing of axes enabled comparisons of the components beyond experiments. The use of training data reduced the effects of noise and bias in the data, facilitating the physical interpretation of the principal axes. Together, these introduced options result in improved generality and objectivity of the analytical results. The methodology has thus become more like a set of multiple regression analyses that find independent models that specify each of the axes.

Application of Principal Component Analysis (PCA) to Reduce Multicollinearity Exchange Rate Currency of Some Countries in Asia Period 2004-2014

ERIC Educational Resources Information Center

Rahayu, Sri; Sugiarto, Teguh; Madu, Ludiro; Holiawati; Subagyo, Ahmad

2017-01-01

This study aims to apply the model principal component analysis to reduce multicollinearity on variable currency exchange rate in eight countries in Asia against US Dollar including the Yen (Japan), Won (South Korea), Dollar (Hong Kong), Yuan (China), Bath (Thailand), Rupiah (Indonesia), Ringgit (Malaysia), Dollar (Singapore). It looks at yield…

Radiative Transfer Modeling and Retrievals for Advanced Hyperspectral Sensors

NASA Technical Reports Server (NTRS)

Liu, Xu; Zhou, Daniel K.; Larar, Allen M.; Smith, William L., Sr.; Mango, Stephen A.

2009-01-01

A novel radiative transfer model and a physical inversion algorithm based on principal component analysis will be presented. Instead of dealing with channel radiances, the new approach fits principal component scores of these quantities. Compared to channel-based radiative transfer models, the new approach compresses radiances into a much smaller dimension making both forward modeling and inversion algorithm more efficient.

Enhancement of chest radiographs using eigenimage processing

NASA Astrophysics Data System (ADS)

Bones, Philip J.; Butler, Anthony P. H.; Hurrell, Michael

2006-08-01

Frontal chest radiographs ("chest X-rays") are routinely used by medical personnel to assess patients for a wide range of suspected disorders. Often large numbers of images need to be analyzed. Furthermore, at times the images need to analyzed ("reported") when no radiological expert is available. A system which enhances the images in such a way that abnormalities are more obvious is likely to reduce the chance that an abnormality goes unnoticed. The authors previously reported the use of principal components analysis to derive a basis set of eigenimages from a training set made up of images from normal subjects. The work is here extended to investigate how best to emphasize the abnormalities in chest radiographs. Results are also reported for various forms of image normalizing transformations used in performing the eigenimage processing.

Statistical analysis and machine learning algorithms for optical biopsy

NASA Astrophysics Data System (ADS)

Wu, Binlin; Liu, Cheng-hui; Boydston-White, Susie; Beckman, Hugh; Sriramoju, Vidyasagar; Sordillo, Laura; Zhang, Chunyuan; Zhang, Lin; Shi, Lingyan; Smith, Jason; Bailin, Jacob; Alfano, Robert R.

2018-02-01

Analyzing spectral or imaging data collected with various optical biopsy methods is often times difficult due to the complexity of the biological basis. Robust methods that can utilize the spectral or imaging data and detect the characteristic spectral or spatial signatures for different types of tissue is challenging but highly desired. In this study, we used various machine learning algorithms to analyze a spectral dataset acquired from human skin normal and cancerous tissue samples using resonance Raman spectroscopy with 532nm excitation. The algorithms including principal component analysis, nonnegative matrix factorization, and autoencoder artificial neural network are used to reduce dimension of the dataset and detect features. A support vector machine with a linear kernel is used to classify the normal tissue and cancerous tissue samples. The efficacies of the methods are compared.

Multifrequency dial sensing of the atmospheric gaseous constituents using the first and second harmonics of a tunable CO2 laser radiation

NASA Technical Reports Server (NTRS)

Zuev, V. E.; Andreev, Y. M.; Voevodin, V. G.; Gribenyukov, A. I.; Kapitanov, V. A.; Sosnin, A. V.; Stuchebrov, G. A.; Khmelnitskii, G. S.

1986-01-01

The results of field measurements of concentration of some gaseous components of the atmosphere along the paths, in Sofia, Bulgaria, using a gas analyzer based on the use of a CO2 laser radiation frequency-doubled with ZnGeP2 monocrystals are presented. The gas analyzer is a traditional long path absorption meter. Radiation from the tunable CO2 laser of low pressure and from an additional He-Ne laser is directed to a colliminating hundredfold Gregori telescope with a 300 mm diameter of the principal mirror. The dimensions of the mirrors of a retroreflector 500 x 500 mm and a receiving telescope allow one to totally intercept the beam passed through the atmospheric layer under study and back.

Odor Profile of Different Varieties of Extra-Virgin Olive Oil During Deep Frying Using an Electronic Nose and SPME-GC-FID

NASA Astrophysics Data System (ADS)

Messina, Valeria; Biolatto, Andrea; Sancho, Ana; Descalzo, Adriana; Grigioni, Gabriela; de Reca, Noemí Walsöe

2011-09-01

The aim of the performed work was to evaluate with an electronic nose changes in odor profile of Arauco and Arbequina varieties of extra-virgin olive oil during deep-frying. Changes in odor were analyzed using an electronic nose composed of 16 sensors. Volatile compounds were analyzed by SPME-GC-FID. Principal Component Analysis was applied for electronic results. Arauco variety showed the highest response for sensors. Statistical analysis for volatile compounds indicated a significant (P<0.001) interaction between variety and time of frying processes. Arauco variety showed the highest production of volatile compounds at 60 min of deep frying. The two varieties presented distinct patterns of volatile products, being clearly identified with the electronic nose.

Principal component analysis of Raman spectra for TiO2 nanoparticle characterization

NASA Astrophysics Data System (ADS)

Ilie, Alina Georgiana; Scarisoareanu, Monica; Morjan, Ion; Dutu, Elena; Badiceanu, Maria; Mihailescu, Ion

2017-09-01

The Raman spectra of anatase/rutile mixed phases of Sn doped TiO2 nanoparticles and undoped TiO2 nanoparticles, synthesised by laser pyrolysis, with nanocrystallite dimensions varying from 8 to 28 nm, was simultaneously processed with a self-written software that applies Principal Component Analysis (PCA) on the measured spectrum to verify the possibility of objective auto-characterization of nanoparticles from their vibrational modes. The photo-excited process of Raman scattering is very sensible to the material characteristics, especially in the case of nanomaterials, where more properties become relevant for the vibrational behaviour. We used PCA, a statistical procedure that performs eigenvalue decomposition of descriptive data covariance, to automatically analyse the sample's measured Raman spectrum, and to interfere the correlation between nanoparticle dimensions, tin and carbon concentration, and their Principal Component values (PCs). This type of application can allow an approximation of the crystallite size, or tin concentration, only by measuring the Raman spectrum of the sample. The study of loadings of the principal components provides information of the way the vibrational modes are affected by the nanoparticle features and the spectral area relevant for the classification.

Testing for Non-Random Mating: Evidence for Ancestry-Related Assortative Mating in the Framingham Heart Study

PubMed Central

Sebro, Ronnie; Hoffman, Thomas J.; Lange, Christoph; Rogus, John J.; Risch, Neil J.

2013-01-01

Population stratification leads to a predictable phenomenon—a reduction in the number of heterozygotes compared to that calculated assuming Hardy-Weinberg Equilibrium (HWE). We show that population stratification results in another phenomenon—an excess in the proportion of spouse-pairs with the same genotypes at all ancestrally informative markers, resulting in ancestrally related positive assortative mating. We use principal components analysis to show that there is evidence of population stratification within the Framingham Heart Study, and show that the first principal component correlates with a North-South European cline. We then show that the first principal component is highly correlated between spouses (r=0.58, p=0.0013), demonstrating that there is ancestrally related positive assortative mating among the Framingham Caucasian population. We also show that the single nucleotide polymorphisms loading most heavily on the first principal component show an excess of homozygotes within the spouses, consistent with similar ancestry-related assortative mating in the previous generation. This nonrandom mating likely affects genetic structure seen more generally in the North American population of European descent today, and decreases the rate of decay of linkage disequilibrium for ancestrally informative markers. PMID:20842694

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.

Statistical analysis of major ion and trace element geochemistry of water, 1986-2006, at seven wells transecting the freshwater/saline-water interface of the Edwards Aquifer, San Antonio, Texas

USGS Publications Warehouse

Mahler, Barbara J.

2008-01-01

The statistical analyses taken together indicate that the geochemistry at the freshwater-zone wells is more variable than that at the transition-zone wells. The geochemical variability at the freshwater-zone wells might result from dilution of ground water by meteoric water. This is indicated by relatively constant major ion molar ratios; a preponderance of positive correlations between SC, major ions, and trace elements; and a principal components analysis in which the major ions are strongly loaded on the first principal component. Much of the variability at three of the four transition-zone wells might result from the use of different laboratory analytical methods or reporting procedures during the period of sampling. This is reflected by a lack of correlation between SC and major ion concentrations at the transition-zone wells and by a principal components analysis in which the variability is fairly evenly distributed across several principal components. The statistical analyses further indicate that, although the transition-zone wells are less well connected to surficial hydrologic conditions than the freshwater-zone wells, there is some connection but the response time is longer. 

DCE-MRI defined subvolumes of a brain metastatic lesion by principle component analysis and fuzzy-c-means clustering for response assessment of radiation therapy

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

Farjam, Reza; Tsien, Christina I.; Lawrence, Theodore S.

Purpose: To develop a pharmacokinetic modelfree framework to analyze the dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data for assessment of response of brain metastases to radiation therapy. Methods: Twenty patients with 45 analyzable brain metastases had MRI scans prior to whole brain radiation therapy (WBRT) and at the end of the 2-week therapy. The volumetric DCE images covering the whole brain were acquired on a 3T scanner with approximately 5 s temporal resolution and a total scan time of about 3 min. DCE curves from all voxels of the 45 brain metastases were normalized and then temporally aligned. Amore » DCE matrix that is constructed from the aligned DCE curves of all voxels of the 45 lesions obtained prior to WBRT is processed by principal component analysis to generate the principal components (PCs). Then, the projection coefficient maps prior to and at the end of WBRT are created for each lesion. Next, a pattern recognition technique, based upon fuzzy-c-means clustering, is used to delineate the tumor subvolumes relating to the value of the significant projection coefficients. The relationship between changes in different tumor subvolumes and treatment response was evaluated to differentiate responsive from stable and progressive tumors. Performance of the PC-defined tumor subvolume was also evaluated by receiver operating characteristic (ROC) analysis in prediction of nonresponsive lesions and compared with physiological-defined tumor subvolumes. Results: The projection coefficient maps of the first three PCs contain almost all response-related information in DCE curves of brain metastases. The first projection coefficient, related to the area under DCE curves, is the major component to determine response while the third one has a complimentary role. In ROC analysis, the area under curve of 0.88 ± 0.05 and 0.86 ± 0.06 were achieved for the PC-defined and physiological-defined tumor subvolume in response assessment. Conclusions: The PC-defined subvolume of a brain metastasis could predict tumor response to therapy similar to the physiological-defined one, while the former is determined more rapidly for clinical decision-making support.« less

DCE-MRI defined subvolumes of a brain metastatic lesion by principle component analysis and fuzzy-c-means clustering for response assessment of radiation therapy

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

Farjam, Reza; Tsien, Christina I.; Lawrence, Theodore S.

2014-01-15

Purpose: To develop a pharmacokinetic modelfree framework to analyze the dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data for assessment of response of brain metastases to radiation therapy. Methods: Twenty patients with 45 analyzable brain metastases had MRI scans prior to whole brain radiation therapy (WBRT) and at the end of the 2-week therapy. The volumetric DCE images covering the whole brain were acquired on a 3T scanner with approximately 5 s temporal resolution and a total scan time of about 3 min. DCE curves from all voxels of the 45 brain metastases were normalized and then temporally aligned. Amore » DCE matrix that is constructed from the aligned DCE curves of all voxels of the 45 lesions obtained prior to WBRT is processed by principal component analysis to generate the principal components (PCs). Then, the projection coefficient maps prior to and at the end of WBRT are created for each lesion. Next, a pattern recognition technique, based upon fuzzy-c-means clustering, is used to delineate the tumor subvolumes relating to the value of the significant projection coefficients. The relationship between changes in different tumor subvolumes and treatment response was evaluated to differentiate responsive from stable and progressive tumors. Performance of the PC-defined tumor subvolume was also evaluated by receiver operating characteristic (ROC) analysis in prediction of nonresponsive lesions and compared with physiological-defined tumor subvolumes. Results: The projection coefficient maps of the first three PCs contain almost all response-related information in DCE curves of brain metastases. The first projection coefficient, related to the area under DCE curves, is the major component to determine response while the third one has a complimentary role. In ROC analysis, the area under curve of 0.88 ± 0.05 and 0.86 ± 0.06 were achieved for the PC-defined and physiological-defined tumor subvolume in response assessment. Conclusions: The PC-defined subvolume of a brain metastasis could predict tumor response to therapy similar to the physiological-defined one, while the former is determined more rapidly for clinical decision-making support.« less

Time Management Ideas for Assistant Principals.

ERIC Educational Resources Information Center

Cronk, Jerry

1987-01-01

Prioritizing the use of time, effective communication, delegating authority, having detailed job descriptions, and good secretarial assistance are important components of time management for assistant principals. (MD)

Study of Chemical Intermediates by Means of ATR-IR Spectroscopy and Hybrid Hard- and Soft-Modelling Multivariate Curve Resolution-Alternating Least Squares

PubMed Central

Ma, Junxiu; Qi, Juan; Gao, Xinyu; Yan, Chunhua; Zhang, Tianlong; Tang, Hongsheng

2017-01-01

3,5-Diamino-1,2,4-triazole (DAT) became a significant energetic materials intermediate, and the study of its reaction mechanism has fundamental significance in chemistry. The aim of this study is to investigate the ability of online attenuated total reflection infrared (ATR-IR) spectroscopy combined with the novel approach of hybrid hard- and soft-modelling multivariate curve resolution-alternating least squares (HS-MCR) analysis to monitor and detect changes in structural properties of compound during 3,5-diamino-1,2,4-triazole (DAT) synthesis processes. The subspace comparison method (SCM) was used to obtain the principal components number, and then the pure IR spectra of each substance were obtained by independent component analysis (ICA) and HS-MCR. The extent of rotation ambiguity was estimated from the band boundaries of feasible solutions calculated using the MCR-BANDS procedure. There were five principal components including two intermediates in the process in the results. The reaction rate constants of DAT formation reaction were also obtained by HS-MCR. HS-MCR was used to analyze spectroscopy data in chemical synthesis process, which not only increase the information domain but also reduce the ambiguities of the obtained results. This study provides the theoretical basis for the optimization of synthesis process and technology of energetic materials and provides a strong technical support of research and development of energy material with extraordinary damage effects. PMID:28386512

A data fusion-based drought index

NASA Astrophysics Data System (ADS)

Azmi, Mohammad; Rüdiger, Christoph; Walker, Jeffrey P.

2016-03-01

Drought and water stress monitoring plays an important role in the management of water resources, especially during periods of extreme climate conditions. Here, a data fusion-based drought index (DFDI) has been developed and analyzed for three different locations of varying land use and climate regimes in Australia. The proposed index comprehensively considers all types of drought through a selection of indices and proxies associated with each drought type. In deriving the proposed index, weekly data from three different data sources (OzFlux Network, Asia-Pacific Water Monitor, and MODIS-Terra satellite) were employed to first derive commonly used individual standardized drought indices (SDIs), which were then grouped using an advanced clustering method. Next, three different multivariate methods (principal component analysis, factor analysis, and independent component analysis) were utilized to aggregate the SDIs located within each group. For the two clusters in which the grouped SDIs best reflected the water availability and vegetation conditions, the variables were aggregated based on an averaging between the standardized first principal components of the different multivariate methods. Then, considering those two aggregated indices as well as the classifications of months (dry/wet months and active/non-active months), the proposed DFDI was developed. Finally, the symbolic regression method was used to derive mathematical equations for the proposed DFDI. The results presented here show that the proposed index has revealed new aspects in water stress monitoring which previous indices were not able to, by simultaneously considering both hydrometeorological and ecological concepts to define the real water stress of the study areas.

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.

Nature of Driving Force for Protein Folding: A Result From Analyzing the Statistical Potential

NASA Astrophysics Data System (ADS)

Li, Hao; Tang, Chao; Wingreen, Ned S.

1997-07-01

In a statistical approach to protein structure analysis, Miyazawa and Jernigan derived a 20×20 matrix of inter-residue contact energies between different types of amino acids. Using the method of eigenvalue decomposition, we find that the Miyazawa-Jernigan matrix can be accurately reconstructed from its first two principal component vectors as Mij = C0+C1\\(qi+qj\\)+C2qiqj, with constant C's, and 20 q values associated with the 20 amino acids. This regularity is due to hydrophobic interactions and a force of demixing, the latter obeying Hildebrand's solubility theory of simple liquids.

Patterns of behavior in online homework for introductory physics

NASA Astrophysics Data System (ADS)

Fredericks, Colin

Student activity in online homework was obtained from courses in physics in 2003 and 2005. This data was analyzed through a variety of methods, including principal component analysis, Pearson's r correlation, and comparison to performance measures such as detailed exam scores. Through this analysis it was determined which measured homework behaviors were associated with high exam scores and course grades. It was also determined that homework problems requiring analysis can have an impact on certain types of exam problems where traditional homework does not. Suggestions are given for future research and possible use of these methods in other contexts.

Use of Raman spectroscopy in the analysis of nickel allergy

NASA Astrophysics Data System (ADS)

Alda, Javier; Castillo-Martinez, Claudio; Valdes-Rodriguez, Rodrigo; Hernández-Blanco, Diana; Moncada, Benjamin; González, Francisco J.

2013-06-01

Raman spectra of the skin of subjects with nickel allergy are analyzed and compared to the spectra of healthy subjects to detect possible biochemical differences in the structure of the skin that could help diagnose metal allergies in a noninvasive manner. Results show differences between the two groups of Raman spectra. These spectral differences can be classified using principal component analysis. Based on these findings, a novel computational technique to make a fast evaluation and classification of the Raman spectra of the skin is presented and proposed as a noninvasive technique for the detection of nickel allergy.

Comparison of three chemometrics methods for near-infrared spectra of glucose in the whole blood

NASA Astrophysics Data System (ADS)

Zhang, Hongyan; Ding, Dong; Li, Xin; Chen, Yu; Tang, Yuguo

2005-01-01

Principal Component Regression (PCR), Partial Least Square (PLS) and Artificial Neural Networks (ANN) methods are used in the analysis for the near infrared (NIR) spectra of glucose in the whole blood. The calibration model is built up in the spectrum band where there are the glucose has much more spectral absorption than the water, fat, and protein with these methods and the correlation coefficients of the model are showed in this paper. Comparing these results, a suitable method to analyze the glucose NIR spectrum in the whole blood is found.

Essential Oils and Antifungal Activity

PubMed Central

Coppola, Raffaele; De Feo, Vincenzo

2017-01-01

Since ancient times, folk medicine and agro-food science have benefitted from the use of plant derivatives, such as essential oils, to combat different diseases, as well as to preserve food. In Nature, essential oils play a fundamental role in protecting the plant from biotic and abiotic attacks to which it may be subjected. Many researchers have analyzed in detail the modes of action of essential oils and most of their components. The purpose of this brief review is to describe the properties of essential oils, principally as antifungal agents, and their role in blocking cell communication mechanisms, fungal biofilm formation, and mycotoxin production. PMID:29099084

Increasing the Coverage of Medicinal Chemistry-Relevant Space in Commercial Fragments Screening

PubMed Central

2014-01-01

Analyzing the chemical space coverage in commercial fragment screening collections revealed the overlap between bioactive medicinal chemistry substructures and rule-of-three compliant fragments is only ∼25%. We recommend including these fragments in fragment screening libraries to maximize confidence in discovering hit matter within known bioactive chemical space, while incorporation of nonoverlapping substructures could offer novel hits in screening libraries. Using principal component analysis, polar and three-dimensional substructures display a higher-than-average enrichment of bioactive compounds, indicating increasing representation of these substructures may be beneficial in fragment screening. PMID:24405118

Detection Method of TOXOPLASMA GONDII Tachyzoites

NASA Astrophysics Data System (ADS)

Eassa, Souzan; Bose, Chhanda; Alusta, Pierre; Tarasenko, Olga

2011-06-01

Tachyzoites are considered to be the most important stage of Toxoplasma gondii which causes toxoplasmosis. T. gondii is, an obligate intracellular parasite which infects a wide range of cells. The present study was designed to develop a method for an early detection of T. gondii tachyzoites. The method comprised of a binding assay which was analyzed using principal component and cluster analysis. Our data showed that glycoconjugates GC1, GC2, GC3 and GC10 exhibit a significantly higher binding affinity for T. gondii tachyzoites as compared to controls (T. gondii only, PAA only, GC 1, 2, 3, and 10 only).

Principal component analysis of the nonlinear coupling of harmonic modes in heavy-ion collisions

NASA Astrophysics Data System (ADS)

BoŻek, Piotr

2018-03-01

The principal component analysis of flow correlations in heavy-ion collisions is studied. The correlation matrix of harmonic flow is generalized to correlations involving several different flow vectors. The method can be applied to study the nonlinear coupling between different harmonic modes in a double differential way in transverse momentum or pseudorapidity. The procedure is illustrated with results from the hydrodynamic model applied to Pb + Pb collisions at √{sN N}=2760 GeV. Three examples of generalized correlations matrices in transverse momentum are constructed corresponding to the coupling of v22 and v4, of v2v3 and v5, or of v23,v33 , and v6. The principal component decomposition is applied to the correlation matrices and the dominant modes are calculated.

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.

Polyhedral gamut representation of natural objects based on spectral reflectance database and its application

NASA Astrophysics Data System (ADS)

Haneishi, Hideaki; Sakuda, Yasunori; Honda, Toshio

2002-06-01

Spectral reflectance of most reflective objects such as natural objects and color hardcopy is relatively smooth and can be approximated by several numbers of principal components with high accuracy. Though the subspace spanned by those principal components represents a space in which reflective objects can exist, it dos not provide the bound in which the samples distribute. In this paper we propose to represent the gamut of reflective objects in more distinct form, i.e., as a polyhedron in the subspace spanned by several principal components. Concept of the polyhedral gamut representation and its application to calculation of metamer ensemble are described. Color-mismatch volume caused by different illuminant and/or observer for a metamer ensemble is also calculated and compared with theoretical one.

Evaluation of Low-Voltage Distribution Network Index Based on Improved Principal Component Analysis

NASA Astrophysics Data System (ADS)

Fan, Hanlu; Gao, Suzhou; Fan, Wenjie; Zhong, Yinfeng; Zhu, Lei

2018-01-01

In order to evaluate the development level of the low-voltage distribution network objectively and scientifically, chromatography analysis method is utilized to construct evaluation index model of low-voltage distribution network. Based on the analysis of principal component and the characteristic of logarithmic distribution of the index data, a logarithmic centralization method is adopted to improve the principal component analysis algorithm. The algorithm can decorrelate and reduce the dimensions of the evaluation model and the comprehensive score has a better dispersion degree. The clustering method is adopted to analyse the comprehensive score because the comprehensive score of the courts is concentrated. Then the stratification evaluation of the courts is realized. An example is given to verify the objectivity and scientificity of the evaluation method.

Online signature recognition using principal component analysis and artificial neural network

NASA Astrophysics Data System (ADS)

Hwang, Seung-Jun; Park, Seung-Je; Baek, Joong-Hwan

2016-12-01

In this paper, we propose an algorithm for on-line signature recognition using fingertip point in the air from the depth image acquired by Kinect. We extract 10 statistical features from X, Y, Z axis, which are invariant to changes in shifting and scaling of the signature trajectories in three-dimensional space. Artificial neural network is adopted to solve the complex signature classification problem. 30 dimensional features are converted into 10 principal components using principal component analysis, which is 99.02% of total variances. We implement the proposed algorithm and test to actual on-line signatures. In experiment, we verify the proposed method is successful to classify 15 different on-line signatures. Experimental result shows 98.47% of recognition rate when using only 10 feature vectors.

Use of Principal Components Analysis to Explain Controls on Nutrient Fluxes to the Chesapeake Bay

NASA Astrophysics Data System (ADS)

Rice, K. C.; Mills, A. L.

2017-12-01

The Chesapeake Bay watershed, on the east coast of the United States, encompasses about 166,000-square kilometers (km2) of diverse land use, which includes a mixture of forested, agricultural, and developed land. The watershed is now managed under a Total Daily Maximum Load (TMDL), which requires implementation of management actions by 2025 that are sufficient to reduce nitrogen, phosphorus, and suspended-sediment fluxes to the Chesapeake Bay and restore the bay's water quality. We analyzed nutrient and sediment data along with land-use and climatic variables in nine sub watersheds to better understand the drivers of flux within the watershed and to provide relevant management implications. The nine sub watersheds range in area from 300 to 30,000 km2, and the analysis period was 1985-2014. The 31 variables specific to each sub watershed were highly statistically significantly correlated, so Principal Components Analysis was used to reduce the dimensionality of the dataset. The analysis revealed that about 80% of the variability in the whole dataset can be explained by discharge, flux, and concentration of nutrients and sediment. The first two principal components (PCs) explained about 68% of the total variance. PC1 loaded strongly on discharge and flux, and PC2 loaded on concentration. The PC scores of both PC1 and PC2 varied by season. Subsequent analysis of PC1 scores versus PC2 scores, broken out by sub watershed, revealed management implications. Some of the largest sub watersheds are largely driven by discharge, and consequently large fluxes. In contrast, some of the smaller sub watersheds are more variable in nutrient concentrations than discharge and flux. Our results suggest that, given no change in discharge, a reduction in nutrient flux to the streams in the smaller watersheds could result in a proportionately larger decrease in fluxes of nutrients down the river to the bay, than in the larger watersheds.

Biomarkers of furan exposure by metabolic profiling of rat urine with liquid chromatography-tandem mass spectrometry and principal component analysis.

PubMed

Kellert, Marco; Wagner, Silvia; Lutz, Ursula; Lutz, Werner K

2008-03-01

Furan has been found in a number of heated food items and is carcinogenic in the liver of rats and mice. Estimates of human exposure on the basis of concentrations measured in food are not reliable because of the volatility of furan. A biomarker approach is therefore indicated. We searched for metabolites excreted in the urine of male Fischer 344 rats treated by oral gavage with 40 mg of furan per kg of body weight. A control group received the vehicle oil only. Urine collected over two 24-h periods both before and after treatment was analyzed by a column-switching LC-MS/MS method. Data were acquired by a full scan survey scan in combination with information dependent acquisition of fragmentation spectra by the use of a linear ion trap. Areas of 449 peaks were extracted from the chromatograms and used for principal component analysis (PCA). The first principal component fully separated the samples of treated rats from the controls in the first post-treatment sampling period. Thirteen potential biomarkers selected from the corresponding loadings plot were reanalyzed using specific transitions in the MRM mode. Seven peaks that increased significantly upon treatment were further investigated as biomarkers of exposure. MS/MS information indicated conjugation with glutathione on the basis of the characteristic neutral loss of 129 for mercapturates. Adducts with the side chain amino group of lysine were characterized by a neutral loss of 171 for N-acetyl- l-lysine. Analysis of products of in vitro incubations of the reactive furan metabolite cis-2-butene-1,4-dial with the respective amino acid derivatives supported five structures, including a new 3-methylthio-pyrrole metabolite probably formed by beta-lyase reaction on a glutathione conjugate, followed by methylation of the thiol group. Our results demonstrate the potential of comprehensive mass spectrometric analysis of urine combined with multivariate analyses for metabolic profiling in search of biomarkers of exposure.

Microglia Morphological Categorization in a Rat Model of Neuroinflammation by Hierarchical Cluster and Principal Components Analysis.

PubMed

Fernández-Arjona, María Del Mar; Grondona, Jesús M; Granados-Durán, Pablo; Fernández-Llebrez, Pedro; López-Ávalos, María D

2017-01-01

It is known that microglia morphology and function are closely related, but only few studies have objectively described different morphological subtypes. To address this issue, morphological parameters of microglial cells were analyzed in a rat model of aseptic neuroinflammation. After the injection of a single dose of the enzyme neuraminidase (NA) within the lateral ventricle (LV) an acute inflammatory process occurs. Sections from NA-injected animals and sham controls were immunolabeled with the microglial marker IBA1, which highlights ramifications and features of the cell shape. Using images obtained by section scanning, individual microglial cells were sampled from various regions (septofimbrial nucleus, hippocampus and hypothalamus) at different times post-injection (2, 4 and 12 h). Each cell yielded a set of 15 morphological parameters by means of image analysis software. Five initial parameters (including fractal measures) were statistically different in cells from NA-injected rats (most of them IL-1β positive, i.e., M1-state) compared to those from control animals (none of them IL-1β positive, i.e., surveillant state). However, additional multimodal parameters were revealed more suitable for hierarchical cluster analysis (HCA). This method pointed out the classification of microglia population in four clusters. Furthermore, a linear discriminant analysis (LDA) suggested three specific parameters to objectively classify any microglia by a decision tree. In addition, a principal components analysis (PCA) revealed two extra valuable variables that allowed to further classifying microglia in a total of eight sub-clusters or types. The spatio-temporal distribution of these different morphotypes in our rat inflammation model allowed to relate specific morphotypes with microglial activation status and brain location. An objective method for microglia classification based on morphological parameters is proposed. Main points Microglia undergo a quantifiable morphological change upon neuraminidase induced inflammation.Hierarchical cluster and principal components analysis allow morphological classification of microglia.Brain location of microglia is a relevant factor.

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.

Principal component analysis of MSBAS DInSAR time series from Campi Flegrei, Italy

NASA Astrophysics Data System (ADS)

Tiampo, Kristy F.; González, Pablo J.; Samsonov, Sergey; Fernández, Jose; Camacho, Antonio

2017-09-01

Because of its proximity to the city of Naples and with a population of nearly 1 million people within its caldera, Campi Flegrei is one of the highest risk volcanic areas in the world. Since the last major eruption in 1538, the caldera has undergone frequent episodes of ground subsidence and uplift accompanied by seismic activity that has been interpreted as the result of a stationary, deeper source below the caldera that feeds shallower eruptions. However, the location and depth of the deeper source is not well-characterized and its relationship to current activity is poorly understood. Recently, a significant increase in the uplift rate has occurred, resulting in almost 13 cm of uplift by 2013 (De Martino et al., 2014; Samsonov et al., 2014b; Di Vito et al., 2016). Here we apply a principal component decomposition to high resolution time series from the region produced by the advanced Multidimensional SBAS DInSAR technique in order to better delineate both the deeper source and the recent shallow activity. We analyzed both a period of substantial subsidence (1993-1999) and a second of significant uplift (2007-2013) and inverted the associated vertical surface displacement for the most likely source models. Results suggest that the underlying dynamics of the caldera changed in the late 1990s, from one in which the primary signal arises from a shallow deflating source above a deeper, expanding source to one dominated by a shallow inflating source. In general, the shallow source lies between 2700 and 3400 m below the caldera while the deeper source lies at 7600 m or more in depth. The combination of principal component analysis with high resolution MSBAS time series data allows for these new insights and confirms the applicability of both to areas at risk from dynamic natural hazards.

Microglia Morphological Categorization in a Rat Model of Neuroinflammation by Hierarchical Cluster and Principal Components Analysis

PubMed Central

Fernández-Arjona, María del Mar; Grondona, Jesús M.; Granados-Durán, Pablo; Fernández-Llebrez, Pedro; López-Ávalos, María D.

2017-01-01

It is known that microglia morphology and function are closely related, but only few studies have objectively described different morphological subtypes. To address this issue, morphological parameters of microglial cells were analyzed in a rat model of aseptic neuroinflammation. After the injection of a single dose of the enzyme neuraminidase (NA) within the lateral ventricle (LV) an acute inflammatory process occurs. Sections from NA-injected animals and sham controls were immunolabeled with the microglial marker IBA1, which highlights ramifications and features of the cell shape. Using images obtained by section scanning, individual microglial cells were sampled from various regions (septofimbrial nucleus, hippocampus and hypothalamus) at different times post-injection (2, 4 and 12 h). Each cell yielded a set of 15 morphological parameters by means of image analysis software. Five initial parameters (including fractal measures) were statistically different in cells from NA-injected rats (most of them IL-1β positive, i.e., M1-state) compared to those from control animals (none of them IL-1β positive, i.e., surveillant state). However, additional multimodal parameters were revealed more suitable for hierarchical cluster analysis (HCA). This method pointed out the classification of microglia population in four clusters. Furthermore, a linear discriminant analysis (LDA) suggested three specific parameters to objectively classify any microglia by a decision tree. In addition, a principal components analysis (PCA) revealed two extra valuable variables that allowed to further classifying microglia in a total of eight sub-clusters or types. The spatio-temporal distribution of these different morphotypes in our rat inflammation model allowed to relate specific morphotypes with microglial activation status and brain location. An objective method for microglia classification based on morphological parameters is proposed. Main points Microglia undergo a quantifiable morphological change upon neuraminidase induced inflammation.Hierarchical cluster and principal components analysis allow morphological classification of microglia.Brain location of microglia is a relevant factor. PMID:28848398

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.

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.

Meta-Analyses of Dehalococcoides mccartyi Strain 195 Transcriptomic Profiles Identify a Respiration Rate-Related Gene Expression Transition Point and Interoperon Recruitment of a Key Oxidoreductase Subunit

PubMed Central

Mansfeldt, Cresten B.; Rowe, Annette R.; Heavner, Gretchen L. W.; Zinder, Stephen H.

2014-01-01

A cDNA-microarray was designed and used to monitor the transcriptomic profile of Dehalococcoides mccartyi strain 195 (in a mixed community) respiring various chlorinated organics, including chloroethenes and 2,3-dichlorophenol. The cultures were continuously fed in order to establish steady-state respiration rates and substrate levels. The organization of array data into a clustered heat map revealed two major experimental partitions. This partitioning in the data set was further explored through principal component analysis. The first two principal components separated the experiments into those with slow (1.6 ± 0.6 μM Cl−/h)- and fast (22.9 ± 9.6 μM Cl−/h)-respiring cultures. Additionally, the transcripts with the highest loadings in these principal components were identified, suggesting that those transcripts were responsible for the partitioning of the experiments. By analyzing the transcriptomes (n = 53) across experiments, relationships among transcripts were identified, and hypotheses about the relationships between electron transport chain members were proposed. One hypothesis, that the hydrogenases Hup and Hym and the formate dehydrogenase-like oxidoreductase (DET0186-DET0187) form a complex (as displayed by their tight clustering in the heat map analysis), was explored using a nondenaturing protein separation technique combined with proteomic sequencing. Although these proteins did not migrate as a single complex, DET0112 (an FdhB-like protein encoded in the Hup operon) was found to comigrate with DET0187 rather than with the catalytic Hup subunit DET0110. On closer inspection of the genome annotations of all Dehalococcoides strains, the DET0185-to-DET0187 operon was found to lack a key subunit, an FdhB-like protein. Therefore, on the basis of the transcriptomic, genomic, and proteomic evidence, the place of the missing subunit in the DET0185-to-DET0187 operon is likely filled by recruiting a subunit expressed from the Hup operon (DET0112). PMID:25063656

Direct Analysis in Real Time by Mass Spectrometric Technique for Determining the Variation in Metabolite Profiles of Cinnamomum tamala Nees and Eberm Genotypes

PubMed Central

Singh, Vineeta; Gupta, Atul Kumar; Singh, S. P.; Kumar, Anil

2012-01-01

Cinnamomum tamala Nees & Eberm. is an important traditional medicinal plant, mentioned in various ancient literatures such as Ayurveda. Several of its medicinal properties have recently been proved. To characterize diversity in terms of metabolite profiles of Cinnamomum tamala Nees and Eberm genotypes, a newly emerging mass spectral ionization technique direct time in real time (DART) is very helpful. The DART ion source has been used to analyze an extremely wide range of phytochemicals present in leaves of Cinnamomum tamala. Ten genotypes were assessed for the presence of different phytochemicals. Phytochemical analysis showed the presence of mainly terpenes and phenols. These constituents vary in the different genotypes of Cinnamomum tamala. Principal component analysis has also been employed to analyze the DART data of these Cinnamomum genotypes. The result shows that the genotype of Cinnamomum tamala could be differentiated using DART MS data. The active components present in Cinnamomum tamala may be contributing significantly to high amount of antioxidant property of leaves and, in turn, conditional effects for diabetic patients. PMID:22701361

Characterization of Sugar and Polyphenolic Diversity in Floral Nectar of Different 'Oblačinska' Sour Cherry Clones.

PubMed

Guffa, Basem; Nedić, Nebojša M; Dabić Zagorac, Dragana Č; Tosti, Tomislav B; Gašić, Uroš M; Natić, Maja M; Fotirić Akšić, Milica M

2017-09-01

'Oblačinska' sour cherry, an autochthonous cultivar, is the most planted cultivar in Serbian orchards. Since fruit trees in temperate zone reward insects by producing nectar which 'quality' affects the efficiency of insect pollination, the aim of this study was analyzing of sugars and polyphenolics in floral nectar of 16 'Oblačinska' sour cherry clones with different yielding potential. The contents of sugars and sugar alcohols were analyzed by ion chromatography, while polyphenolic profile was established using liquid chromatography/mass spectrometry technique. Fourteen sugars and six sugar alcohols were detected in nectar samples and the most abundant were fructose, glucose, and sucrose. Eleven polyphenols were quantified using available standards, while another 17 were identified according to their exact masses and characteristic fragmentations. Among quantified polyphenols, rutin, naringenin, and chrysin were the most abundant in nectar. Principal component analysis showed that some polyphenol components (naringin, naringenin, and rutin) together with sugars had high impact of spatial distribution of nectar samples on score plot. © 2017 Wiley-VHCA AG, Zurich, Switzerland.

A theoretical model for the collective motion of proteins by means of principal component analysis

NASA Astrophysics Data System (ADS)

Kamberaj, Hiqmet

2011-02-01

A coarse grained model in the frame work of principal component analysis is presented. We used a bath of harmonic oscillators approach, based on classical mechanics, to derive the generalized Langevin equations of motion for the collective coordinates. The dynamics of the protein collective coordinates derived from molecular dynamics simulations have been studied for the Bovine Pancreatic Trypsin Inhibitor. We analyzed the stability of the method by studying structural fluctuations of the C a atoms obtained from a 20 ns molecular dynamics simulation. Subsequently, the dynamics of the collective coordinates of protein were characterized by calculating the dynamical friction coefficient and diffusion coefficients along with time-dependent correlation functions of collective coordinates. A dual diffusion behavior was observed with a fast relaxation time of short diffusion regime 0.2-0.4 ps and slow relaxation time of long diffusion about 1-2 ps. In addition, we observed a power law decay of dynamical friction coefficient with exponent for the first five collective coordinates varying from -0.746 to -0.938 for the real part and from -0.528 to -0.665 for its magnitude. It was found that only the first ten collective coordinates are responsible for configuration transitions occurring on time scale longer than 50 ps.

What’s Wrong with the Murals at the Mogao Grottoes: A Near-Infrared Hyperspectral Imaging Method

PubMed Central

Sun, Meijun; Zhang, Dong; Wang, Zheng; Ren, Jinchang; Chai, Bolong; Sun, Jizhou

2015-01-01

Although a significant amount of work has been performed to preserve the ancient murals in the Mogao Grottoes by Dunhuang Cultural Research, non-contact methods need to be developed to effectively evaluate the degree of flaking of the murals. In this study, we propose to evaluate the flaking by automatically analyzing hyperspectral images that were scanned at the site. Murals with various degrees of flaking were scanned in the 126th cave using a near-infrared (NIR) hyperspectral camera with a spectral range of approximately 900 to 1700 nm. The regions of interest (ROIs) of the murals were manually labeled and grouped into four levels: normal, slight, moderate, and severe. The average spectral data from each ROI and its group label were used to train our classification model. To predict the degree of flaking, we adopted four algorithms: deep belief networks (DBNs), partial least squares regression (PLSR), principal component analysis with a support vector machine (PCA + SVM) and principal component analysis with an artificial neural network (PCA + ANN). The experimental results show the effectiveness of our method. In particular, better results are obtained using DBNs when the training data contain a significant amount of striping noise. PMID:26394926

Gut microbiota of liver transplantation recipients.

PubMed

Sun, Li-Ying; Yang, Yun-Sheng; Qu, Wei; Zhu, Zhi-Jun; Wei, Lin; Ye, Zhi-Sheng; Zhang, Jian-Rui; Sun, Xiao-Ye; Zeng, Zhi-Gui

2017-06-19

The characteristics of intestinal microbial communities may be affected by changes in the pathophysiology of patients with end-stage liver disease. Here, we focused on the characteristics of intestinal fecal microbial communities in post-liver transplantation (LT) patients in comparison with those in the same individuals pre-LT and in healthy individuals. The fecal microbial communities were analyzed via MiSeq-PE250 sequencing of the V4 region of 16S ribosomal RNA and were then compared between groups. We found that the gut microbiota of patients with severe liver disease who were awaiting LT was significantly different from that of healthy controls, as represented by the first principal component (p = 0.0066). Additionally, the second principal component represented a significant difference in the gut microbiota of patients between pre-LT and post-LT surgery (p = 0.03125). After LT, there was a significant decrease in the abundance of certain microbial species, such as Actinobacillus, Escherichia, and Shigella, and a significant increase in the abundance of other microbial species, such as Micromonosporaceae, Desulfobacterales, the Sarcina genus of Eubacteriaceae, and Akkermansia. Based on KEGG profiles, 15 functional modules were enriched and 21 functional modules were less represented in the post-LT samples compared with the pre-LT samples. Our study demonstrates that fecal microbial communities were significantly altered by LT.

Effects of highway construction on stream water quality and macroinvertebrate condition in a mid-atlantic highlands watershed, USA.

PubMed

Chen, Yushun; Viadero, Roger C; Wei, Xinchao; Fortney, Ronald; Hedrick, Lara B; Welsh, Stuart A; Anderson, James T; Lin, Lian-Shin

2009-01-01

Refining best management practices (BMPs) for future highway construction depends on a comprehensive understanding of environmental impacts from current construction methods. Based on a before-after-control impact (BACI) experimental design, long-term stream monitoring (1997-2006) was conducted at upstream (as control, n = 3) and downstream (as impact, n = 6) sites in the Lost River watershed of the Mid-Atlantic Highlands region, West Virginia. Monitoring data were analyzed to assess impacts of during and after highway construction on 15 water quality parameters and macroinvertebrate condition using the West Virginia stream condition index (WVSCI). Principal components analysis (PCA) identified regional primary water quality variances, and paired t tests and time series analysis detected seven highway construction-impacted water quality parameters which were mainly associated with the second principal component. In particular, impacts on turbidity, total suspended solids, and total iron during construction, impacts on chloride and sulfate during and after construction, and impacts on acidity and nitrate after construction were observed at the downstream sites. The construction had statistically significant impacts on macroinvertebrate index scores (i.e., WVSCI) after construction, but did not change the overall good biological condition. Implementing BMPs that address those construction-impacted water quality parameters can be an effective mitigation strategy for future highway construction in this highlands region.

[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.

A Model to Explain Plant Growth Promotion Traits: A Multivariate Analysis of 2,211 Bacterial Isolates

PubMed Central

da Costa, Pedro Beschoren; Granada, Camille E.; Ambrosini, Adriana; Moreira, Fernanda; de Souza, Rocheli; dos Passos, João Frederico M.; Arruda, Letícia; Passaglia, Luciane M. P.

2014-01-01

Plant growth-promoting bacteria can greatly assist sustainable farming by improving plant health and biomass while reducing fertilizer use. The plant-microorganism-environment interaction is an open and complex system, and despite the active research in the area, patterns in root ecology are elusive. Here, we simultaneously analyzed the plant growth-promoting bacteria datasets from seven independent studies that shared a methodology for bioprospection and phenotype screening. The soil richness of the isolate's origin was classified by a Principal Component Analysis. A Categorical Principal Component Analysis was used to classify the soil richness according to isolate's indolic compound production, siderophores production and phosphate solubilization abilities, and bacterial genera composition. Multiple patterns and relationships were found and verified with nonparametric hypothesis testing. Including niche colonization in the analysis, we proposed a model to explain the expression of bacterial plant growth-promoting traits according to the soil nutritional status. Our model shows that plants favor interaction with growth hormone producers under rich nutrient conditions but favor nutrient solubilizers under poor conditions. We also performed several comparisons among the different genera, highlighting interesting ecological interactions and limitations. Our model could be used to direct plant growth-promoting bacteria bioprospection and metagenomic sampling. PMID:25542031

Microbial community analysis of field-grown soybeans with different nodulation phenotypes.

PubMed

Ikeda, Seishi; Rallos, Lynn Esther E; Okubo, Takashi; Eda, Shima; Inaba, Shoko; Mitsui, Hisayuki; Minamisawa, Kiwamu

2008-09-01

Microorganisms associated with the stems and roots of nonnodulated (Nod(-)), wild-type nodulated (Nod(+)), and hypernodulated (Nod(++)) soybeans [Glycine max (L.) Merril] were analyzed by ribosomal intergenic transcribed spacer analysis (RISA) and automated RISA (ARISA). RISA of stem samples detected no bands specific to the nodulation phenotype, whereas RISA of root samples revealed differential bands for the nodulation phenotypes. Pseudomonas fluorescens was exclusively associated with Nod(+) soybean roots. Fusarium solani was stably associated with nodulated (Nod(+) and Nod(++)) roots and less abundant in Nod(-) soybeans, whereas the abundance of basidiomycetes was just the opposite. The phylogenetic analyses suggested that these basidiomycetous fungi might represent a root-associated group in the Auriculariales. Principal-component analysis of the ARISA results showed that there was no clear relationship between nodulation phenotype and bacterial community structure in the stem. In contrast, both the bacterial and fungal community structures in the roots were related to nodulation phenotype. The principal-component analysis further suggested that bacterial community structure in roots could be classified into three groups according to the nodulation phenotype (Nod(-), Nod(+), or Nod(++)). The analysis of root samples indicated that the microbial community in Nod(-) soybeans was more similar to that in Nod(++) soybeans than to that in Nod(+) soybeans.

Microbial Community Analysis of Field-Grown Soybeans with Different Nodulation Phenotypes▿

PubMed Central

Ikeda, Seishi; Rallos, Lynn Esther E.; Okubo, Takashi; Eda, Shima; Inaba, Shoko; Mitsui, Hisayuki; Minamisawa, Kiwamu

2008-01-01

Microorganisms associated with the stems and roots of nonnodulated (Nod−), wild-type nodulated (Nod+), and hypernodulated (Nod++) soybeans [Glycine max (L.) Merril] were analyzed by ribosomal intergenic transcribed spacer analysis (RISA) and automated RISA (ARISA). RISA of stem samples detected no bands specific to the nodulation phenotype, whereas RISA of root samples revealed differential bands for the nodulation phenotypes. Pseudomonas fluorescens was exclusively associated with Nod+ soybean roots. Fusarium solani was stably associated with nodulated (Nod+ and Nod++) roots and less abundant in Nod− soybeans, whereas the abundance of basidiomycetes was just the opposite. The phylogenetic analyses suggested that these basidiomycetous fungi might represent a root-associated group in the Auriculariales. Principal-component analysis of the ARISA results showed that there was no clear relationship between nodulation phenotype and bacterial community structure in the stem. In contrast, both the bacterial and fungal community structures in the roots were related to nodulation phenotype. The principal-component analysis further suggested that bacterial community structure in roots could be classified into three groups according to the nodulation phenotype (Nod−, Nod+, or Nod++). The analysis of root samples indicated that the microbial community in Nod− soybeans was more similar to that in Nod++ soybeans than to that in Nod+ soybeans. PMID:18658280

Multivariate approach to quantitative analysis of Aphis gossypii Glover (Hemiptera: Aphididae) and their natural enemy populations at different cotton spacings.

PubMed

Malaquias, José B; Ramalho, Francisco S; Dos S Dias, Carlos T; Brugger, Bruno P; S Lira, Aline Cristina; Wilcken, Carlos F; Pachú, Jéssica K S; Zanuncio, José C

2017-02-09

The relationship between pests and natural enemies using multivariate analysis on cotton in different spacing has not been documented yet. Using multivariate approaches is possible to optimize strategies to control Aphis gossypii at different crop spacings because the possibility of a better use of the aphid sampling strategies as well as the conservation and release of its natural enemies. The aims of the study were (i) to characterize the temporal abundance data of aphids and its natural enemies using principal components, (ii) to analyze the degree of correlation between the insects and between groups of variables (pests and natural enemies), (iii) to identify the main natural enemies responsible for regulating A. gossypii populations, and (iv) to investigate the similarities in arthropod occurrence patterns at different spacings of cotton crops over two seasons. High correlations in the occurrence of Scymnus rubicundus with aphids are shown through principal component analysis and through the important role the species plays in canonical correlation analysis. Clustering the presence of apterous aphids matches the pattern verified for Chrysoperla externa at the three different spacings between rows. Our results indicate that S. rubicundus is the main candidate to regulate the aphid populations in all spacings studied.

Multivariate approach to quantitative analysis of Aphis gossypii Glover (Hemiptera: Aphididae) and their natural enemy populations at different cotton spacings

PubMed Central

Malaquias, José B.; Ramalho, Francisco S.; dos S. Dias, Carlos T.; Brugger, Bruno P.; S. Lira, Aline Cristina; Wilcken, Carlos F.; Pachú, Jéssica K. S.; Zanuncio, José C.

2017-01-01

The relationship between pests and natural enemies using multivariate analysis on cotton in different spacing has not been documented yet. Using multivariate approaches is possible to optimize strategies to control Aphis gossypii at different crop spacings because the possibility of a better use of the aphid sampling strategies as well as the conservation and release of its natural enemies. The aims of the study were (i) to characterize the temporal abundance data of aphids and its natural enemies using principal components, (ii) to analyze the degree of correlation between the insects and between groups of variables (pests and natural enemies), (iii) to identify the main natural enemies responsible for regulating A. gossypii populations, and (iv) to investigate the similarities in arthropod occurrence patterns at different spacings of cotton crops over two seasons. High correlations in the occurrence of Scymnus rubicundus with aphids are shown through principal component analysis and through the important role the species plays in canonical correlation analysis. Clustering the presence of apterous aphids matches the pattern verified for Chrysoperla externa at the three different spacings between rows. Our results indicate that S. rubicundus is the main candidate to regulate the aphid populations in all spacings studied. PMID:28181503

Next Generation Aura-OMI SO2 Retrieval Algorithm: Introduction and Implementation Status

NASA Technical Reports Server (NTRS)

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

2014-01-01

We introduce our next generation algorithm to retrieve SO2 using radiance measurements from the Aura Ozone Monitoring Instrument (OMI). We employ a principal component analysis technique to analyze OMI radiance spectral in 310.5-340 nm acquired over regions with no significant SO2. The resulting principal components (PCs) capture radiance variability caused by both physical processes (e.g., Rayleigh and Raman scattering, and ozone absorption) and measurement artifacts, enabling us to account for these various interferences in SO2 retrievals. By fitting these PCs along with SO2 Jacobians calculated with a radiative transfer model to OMI-measured radiance spectra, we directly estimate SO2 vertical column density in one step. As compared with the previous generation operational OMSO2 PBL (Planetary Boundary Layer) SO2 product, our new algorithm greatly reduces unphysical biases and decreases the noise by a factor of two, 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 long-term, consistent SO2 records for air quality and climate research. We have operationally implemented this new algorithm on OMI SIPS for producing the new generation standard OMI SO2 products.

Multivariate approach to quantitative analysis of Aphis gossypii Glover (Hemiptera: Aphididae) and their natural enemy populations at different cotton spacings

NASA Astrophysics Data System (ADS)

Malaquias, José B.; Ramalho, Francisco S.; Dos S. Dias, Carlos T.; Brugger, Bruno P.; S. Lira, Aline Cristina; Wilcken, Carlos F.; Pachú, Jéssica K. S.; Zanuncio, José C.

2017-02-01

The relationship between pests and natural enemies using multivariate analysis on cotton in different spacing has not been documented yet. Using multivariate approaches is possible to optimize strategies to control Aphis gossypii at different crop spacings because the possibility of a better use of the aphid sampling strategies as well as the conservation and release of its natural enemies. The aims of the study were (i) to characterize the temporal abundance data of aphids and its natural enemies using principal components, (ii) to analyze the degree of correlation between the insects and between groups of variables (pests and natural enemies), (iii) to identify the main natural enemies responsible for regulating A. gossypii populations, and (iv) to investigate the similarities in arthropod occurrence patterns at different spacings of cotton crops over two seasons. High correlations in the occurrence of Scymnus rubicundus with aphids are shown through principal component analysis and through the important role the species plays in canonical correlation analysis. Clustering the presence of apterous aphids matches the pattern verified for Chrysoperla externa at the three different spacings between rows. Our results indicate that S. rubicundus is the main candidate to regulate the aphid populations in all spacings studied.

The fine-scale genetic structure and evolution of the Japanese population

PubMed Central

Katsuya, Tomohiro; Kimura, Ryosuke; Nabika, Toru; Isomura, Minoru; Ohkubo, Takayoshi; Tabara, Yasuharu; Yamamoto, Ken; Yokota, Mitsuhiro; Liu, Xuanyao; Saw, Woei-Yuh; Mamatyusupu, Dolikun; Yang, Wenjun; Xu, Shuhua

2017-01-01

The contemporary Japanese populations largely consist of three genetically distinct groups—Hondo, Ryukyu and Ainu. By principal-component analysis, while the three groups can be clearly separated, the Hondo people, comprising 99% of the Japanese, form one almost indistinguishable cluster. To understand fine-scale genetic structure, we applied powerful haplotype-based statistical methods to genome-wide single nucleotide polymorphism data from 1600 Japanese individuals, sampled from eight distinct regions in Japan. We then combined the Japanese data with 26 other Asian populations data to analyze the shared ancestry and genetic differentiation. We found that the Japanese could be separated into nine genetic clusters in our dataset, showing a marked concordance with geography; and that major components of ancestry profile of Japanese were from the Korean and Han Chinese clusters. We also detected and dated admixture in the Japanese. While genetic differentiation between Ryukyu and Hondo was suggested to be caused in part by positive selection, genetic differentiation among the Hondo clusters appeared to result principally from genetic drift. Notably, in Asians, we found the possibility that positive selection accentuated genetic differentiation among distant populations but attenuated genetic differentiation among close populations. These findings are significant for studies of human evolution and medical genetics. PMID:29091727

The Hierarchical Implications of Internet Gaming Disorder Criteria: Which Indicate more Severe Pathology?

PubMed Central

Lee, Seung-Yup; Lee, Hae Kook; Jeong, Hyunsuk; Yim, Hyeon Woo; Bhang, Soo-Young; Jo, Sun-Jin; Baek, Kyung-Young; Kim, Eunjin; Kim, Min Seob; Choi, Jung-Seok

2017-01-01

Objective To explore the structure of Internet gaming disorder (IGD) criteria and their distribution according to the different severity level of IGD. The associations of psychiatric comorbidities to each IGD symptom and to the IGD severity were also investigated. Methods Consecutively recruited 330 Korean middle school students underwent face-to-face diagnostic interviews to assess their gaming problems by clinicians. The psychiatric comorbidities were also evaluated with a semi-structured instrument. The data was analyzed using principal components analysis and the distribution of criteria among different severity groups was visualized by plotting univariate curves. Results Two principal components of ‘Compulsivity’ and ‘Tolerance’ were extracted. ‘Decrease in other activities’ and ‘Jeopardizing relationship/career’ may indicate a higher severity of IGD. While ‘Craving’ deserved more recognition in clinical utility, ‘Tolerance’ did not demonstrate much difference in distribution by the IGD severity. Internalizing and externalizing psychiatric disorders differed in distribution by the IGD severity. Conclusion A hierarchic presentation of IGD criteria was revealed. ‘Decrease in other activities’ and ‘Jeopardizing relationship/career’ may represent a higher severity, thus indicating more clinical attention to such symptoms. However, ‘Tolerance’ was not found to be a valid diagnostic criterion. PMID:28539943

Applying Fourier Transform Mid Infrared Spectroscopy to Detect the Adulteration of Salmo salar with Oncorhynchus mykiss

PubMed Central

Moreira, Maria João

2018-01-01

The aim of this study was to evaluate the potential of Fourier transform infrared (FTIR) spectroscopy coupled with chemometric methods to detect fish adulteration. Muscles of Atlantic salmon (Salmo salar) (SS) and Salmon trout (Onconrhynchus mykiss) (OM) muscles were mixed in different percentages and transformed into mini-burgers. These were stored at 3 °C, then examined at 0, 72, 160, and 240 h for deteriorative microorganisms. Mini-burgers was submitted to Soxhlet extraction, following which lipid extracts were analyzed by FTIR. The principal component analysis (PCA) described the studied adulteration using four principal components with an explained variance of 95.60%. PCA showed that the absorbance in the spectral region from 721, 1097, 1370, 1464, 1655, 2805, to 2935, 3009 cm−1 may be attributed to biochemical fingerprints related to differences between SS and OM. The partial least squares regression (PLS-R) predicted the presence/absence of adulteration in fish samples of an external set with high accuracy. The proposed methods have the advantage of allowing quick measurements, despite the storage time of the adulterated fish. FTIR combined with chemometrics showed that a methodology to identify the adulteration of SS with OM can be established, even when stored for different periods of time. PMID:29621135

Principal Component Analysis in Construction of 3D Human Knee Joint Models Using a Statistical Shape Model Method

PubMed Central

Tsai, Tsung-Yuan; Li, Jing-Sheng; Wang, Shaobai; Li, Pingyue; Kwon, Young-Min; Li, Guoan

2013-01-01

The statistical shape model (SSM) method that uses 2D images of the knee joint to predict the 3D joint surface model has been reported in literature. In this study, we constructed a SSM database using 152 human CT knee joint models, including the femur, tibia and patella and analyzed the characteristics of each principal component of the SSM. The surface models of two in vivo knees were predicted using the SSM and their 2D bi-plane fluoroscopic images. The predicted models were compared to their CT joint models. The differences between the predicted 3D knee joint surfaces and the CT image-based surfaces were 0.30 ± 0.81 mm, 0.34 ± 0.79 mm and 0.36 ± 0.59 mm for the femur, tibia and patella, respectively (average ± standard deviation). The computational time for each bone of the knee joint was within 30 seconds using a personal computer. The analysis of this study indicated that the SSM method could be a useful tool to construct 3D surface models of the knee with sub-millimeter accuracy in real time. Thus it may have a broad application in computer assisted knee surgeries that require 3D surface models of the knee. PMID:24156375

Study of T-wave morphology parameters based on Principal Components Analysis during acute myocardial ischemia

NASA Astrophysics Data System (ADS)

Baglivo, Fabricio Hugo; Arini, Pedro David

2011-12-01

Electrocardiographic repolarization abnormalities can be detected by Principal Components Analysis of the T-wave. In this work we studied the efect of signal averaging on the mean value and reproducibility of the ratio of the 2nd to the 1st eigenvalue of T-wave (T21W) and the absolute and relative T-wave residuum (TrelWR and TabsWR) in the ECG during ischemia induced by Percutaneous Coronary Intervention. Also, the intra-subject and inter-subject variability of T-wave parameters have been analyzed. Results showed that TrelWR and TabsWR evaluated from the average of 10 complexes had lower values and higher reproducibility than those obtained from 1 complex. On the other hand T21W calculated from 10 complexes did not show statistical diferences versus the T21W calculated on single beats. The results of this study corroborate that, with a signal averaging technique, the 2nd and the 1st eigenvalue are not afected by noise while the 4th to 8th eigenvalues are so much afected by this, suggesting the use of the signal averaged technique before calculation of absolute and relative T-wave residuum. Finally, we have shown that T-wave morphology parameters present high intra-subject stability.

Analyzing coastal environments by means of functional data analysis

NASA Astrophysics Data System (ADS)

Sierra, Carlos; Flor-Blanco, Germán; Ordoñez, Celestino; Flor, Germán; Gallego, José R.

2017-07-01

Here we used Functional Data Analysis (FDA) to examine particle-size distributions (PSDs) in a beach/shallow marine sedimentary environment in Gijón Bay (NW Spain). The work involved both Functional Principal Components Analysis (FPCA) and Functional Cluster Analysis (FCA). The grainsize of the sand samples was characterized by means of laser dispersion spectroscopy. Within this framework, FPCA was used as a dimension reduction technique to explore and uncover patterns in grain-size frequency curves. This procedure proved useful to describe variability in the structure of the data set. Moreover, an alternative approach, FCA, was applied to identify clusters and to interpret their spatial distribution. Results obtained with this latter technique were compared with those obtained by means of two vector approaches that combine PCA with CA (Cluster Analysis). The first method, the point density function (PDF), was employed after adapting a log-normal distribution to each PSD and resuming each of the density functions by its mean, sorting, skewness and kurtosis. The second applied a centered-log-ratio (clr) to the original data. PCA was then applied to the transformed data, and finally CA to the retained principal component scores. The study revealed functional data analysis, specifically FPCA and FCA, as a suitable alternative with considerable advantages over traditional vector analysis techniques in sedimentary geology studies.

Principal component and spatial correlation analysis of spectroscopic-imaging data in scanning probe microscopy.

PubMed

Jesse, Stephen; Kalinin, Sergei V

2009-02-25

An approach for the analysis of multi-dimensional, spectroscopic-imaging data based on principal component analysis (PCA) is explored. PCA selects and ranks relevant response components based on variance within the data. It is shown that for examples with small relative variations between spectra, the first few PCA components closely coincide with results obtained using model fitting, and this is achieved at rates approximately four orders of magnitude faster. For cases with strong response variations, PCA allows an effective approach to rapidly process, de-noise, and compress data. The prospects for PCA combined with correlation function analysis of component maps as a universal tool for data analysis and representation in microscopy are discussed.

The Artistic Nature of the High School Principal.

ERIC Educational Resources Information Center

Ritschel, Robert E.

The role of high school principals can be compared to that of composers of music. For instance, composers put musical components together into a coherent whole; similarly, principals organize high schools by establishing class schedules, assigning roles to subordinates, and maintaining a safe and orderly learning environment. Second, composers…

Collaborative Relationships between Principals and School Counselors: Facilitating a Model for Developing a Working Alliance

ERIC Educational Resources Information Center

Odegard-Koester, Melissa A.; Watkins, Paul

2016-01-01

The working relationship between principals and school counselors have received some attention in the literature, however, little empirical research exists that examines specifically the components that facilitate a collaborative working relationship between the principal and school counselor. This qualitative case study examined the unique…

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…

Teacher Supervision Practices and Principals' Characteristics

ERIC Educational Resources Information Center

April, Daniel; Bouchamma, Yamina

2015-01-01

A questionnaire was used to determine the individual and collective teacher supervision practices of school principals and vice-principals in Québec (n = 39) who participated in a research-action study on pedagogical supervision. These practices were then analyzed in terms of the principals' sociodemographic and socioprofessional characteristics…

Differences in chemical constituents of Artemisia annua L from different geographical regions in China.

PubMed

Zhang, Xiaobo; Zhao, Yuping; Guo, Lanping; Qiu, Zhidong; Huang, Luqi; Qu, Xiaobo

2017-01-01

Daodi-herb is a part of Chinese culture, which has been naturally selected by traditional Chinese medicine clinical practice for many years. Sweet wormwood herb is a kind of Daodi-herb, and comes from Artemisia annua L. Artemisinin is a kind of effective antimalarial drug being extracted from A. annua. Because of artemisinin, Sweet wormwood herb earns a reputation. Based on the Pharmacopoeia of the People's Republic of China (PPRC), Sweet wormwood herb can be used to resolve summerheat-heat, and prevent malaria. Besides, it also has other medical efficacies. A. annua, a medicinal plant that is widely distributed in the world contains many kinds of chemical composition. Research has shown that compatibility of artemisinin, scopoletin, arteannuin B and arteannuic acid has antimalarial effect. Compatibility of scopoletin, arteannuin B and arteannuic acid is conducive to resolving summerheat-heat. Chemical constituents in A. annua vary significantly according to geographical locations. So, distribution of A. annua may play a key role in the characteristics of efficacy and chemical constituents of Sweet wormwood herb. It is of great significance to study this relationship. We mainly analyzed the relationship between the chemical constituents (arteannuin B, artemisinin, artemisinic acid, and scopoletin) with special efficacy in A. annua that come from different provinces in china, and analyzed the relationship between chemical constituents and spatial distribution, in order to find out the relationship between efficacy, chemical constituents and distribution. A field survey was carried out to collect A. annua plant samples. A global positioning system (GPS) was used for obtaining geographical coordinates of sampling sites. Chemical constituents in A. annua were determined by liquid chromatography tandem an atmospheric pressure ionization-electrospray mass spectrometry. Relationship between chemical constituents including proportions, correlation analysis (CoA), principal component analysis (PCA) and cluster analysis (ClA) was displayed through Excel and R software version2.3.2(R), while the one between efficacy, chemical constituents and spatial distribution was presented through ArcGIS10.0, Excel and R software. According to the results of CoA, arteannuin B content presented a strong positive correlation with artemisinic acid content (p = 0), and a strong negative correlation with artemisinin content (p = 0). Scopoletin content presented a strong positive correlation with artemisinin content (p = 0), and a strong negative correlation with artemisinic acid content (p = 0). According to the results of PCA, the first two principal components accounted for 81.57% of the total accumulation contribution rate. The contribution of the first principal component is about 45.12%, manly including arteannuin B and artemisinic acid. The contribution of the second principal component is 36.45% of the total, manly including artemisinin and scopoletin. According to the ClA by using the principal component scores, 19 provinces could be divided into two groups. In terms of provinces in group one, the proportions of artemisinin are all higher than 80%. Based on the results of PCA, ClA, percentages and scatter plot analysis, chemical types are defined as "QHYS type", "INT type" and "QHS type." As a conclusion, this paper shows the relationship between efficacy, chemical constituents and distribution. Sweet wormwood herb with high arteannuin B and artemisinic acid content, mainly distributes in northern China. Sweet wormwood herb with high artemisinin and scopoletin content has the medical function of preventing malaria, which mainly distributes in southern China. In this paper, it is proved that Sweet wormwood Daodi herb growing in particular geographic regions, has more significant therapeutical effect and higher chemical constituents compared with other same kind of CMM. And also, it has proved the old saying in China that Sweet wormwood Daodi herb which has been used to resolve summerheat-heat and prevent malaria, which distributed in central China. But in modern time, Daodi Sweet wormwood herb mainly has been used to extract artemisinin and prevent malaria, so the Daod-region has transferred to the southern China.

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…

Quality Evaluation and Chemical Markers Screening of Salvia miltiorrhiza Bge. (Danshen) Based on HPLC Fingerprints and HPLC-MSn Coupled with Chemometrics.

PubMed

Liang, Wenyi; Chen, Wenjing; Wu, Lingfang; Li, Shi; Qi, Qi; Cui, Yaping; Liang, Linjin; Ye, Ting; Zhang, Lanzhen

2017-03-17

Danshen, the dried root of Salvia miltiorrhiza Bge., is a widely used commercially available herbal drug, and unstable quality of different samples is a current issue. This study focused on a comprehensive and systematic method combining fingerprints and chemical identification with chemometrics for discrimination and quality assessment of Danshen samples. Twenty-five samples were analyzed by HPLC-PAD and HPLC-MS n . Forty-nine components were identified and characteristic fragmentation regularities were summarized for further interpretation of bioactive components. Chemometric analysis was employed to differentiate samples and clarify the quality differences of Danshen including hierarchical cluster analysis, principal component analysis, and partial least squares discriminant analysis. Consistent results were that the samples were divided into three categories which reflected the difference in quality of Danshen samples. By analyzing the reasons for sample classification, it was revealed that the processing method had a more obvious impact on sample classification than the geographical origin, it induced the different content of bioactive compounds and finally lead to different qualities. Cryptotanshinone, trijuganone B, and 15,16-dihydrotanshinone I were screened out as markers to distinguish samples by different processing methods. The developed strategy could provide a reference for evaluation and discrimination of other traditional herbal medicines.

Phenomenology of mixed states: a principal component analysis study.

PubMed

Bertschy, G; Gervasoni, N; Favre, S; Liberek, C; Ragama-Pardos, E; Aubry, J-M; Gex-Fabry, M; Dayer, A

2007-12-01

To contribute to the definition of external and internal limits of mixed states and study the place of dysphoric symptoms in the psychopathology of mixed states. One hundred and sixty-five inpatients with major mood episodes were diagnosed as presenting with either pure depression, mixed depression (depression plus at least three manic symptoms), full mixed state (full depression and full mania), mixed mania (mania plus at least three depressive symptoms) or pure mania, using an adapted version of the Mini International Neuropsychiatric Interview (DSM-IV version). They were evaluated using a 33-item inventory of depressive, manic and mixed affective signs and symptoms. Principal component analysis without rotation yielded three components that together explained 43.6% of the variance. The first component (24.3% of the variance) contrasted typical depressive symptoms with typical euphoric, manic symptoms. The second component, labeled 'dysphoria', (13.8%) had strong positive loadings for irritability, distressing sensitivity to light and noise, impulsivity and inner tension. The third component (5.5%) included symptoms of insomnia. Median scores for the first component significantly decreased from the pure depression group to the pure mania group. For the dysphoria component, scores were highest among patients with full mixed states and decreased towards both patients with pure depression and those with pure mania. Principal component analysis revealed that dysphoria represents an important dimension of mixed states.

Soil Components in Heterogeneous Impact Glass in Martian Meteorite EETA79001

NASA Technical Reports Server (NTRS)

Schrader, C. M.; Cohen, B. A.; Donovan, J. J.; Vicenzi, E. P.

2010-01-01

Martian soil composition can illuminate past and ongoing near-surface processes such as impact gardening [2] and hydrothermal and volcanic activity [3,4]. Though the Mars Exploration Rovers (MER) have analyzed the major-element composition of Martian soils, no soil samples have been returned to Earth for detailed chemical analysis. Rao et al. [1] suggested that Martian meteorite EETA79001 contains melted Martian soil in its impact glass (Lithology C) based on sulfur enrichment of Lithology C relative to the meteorite s basaltic lithologies (A and B) [1,2]. If true, it may be possible to extract detailed soil chemical analyses using this meteoritic sample. We conducted high-resolution (0.3 m/pixel) element mapping of Lithology C in thin section EETA79001,18 by energy dispersive spectrometry (EDS). We use these data for principal component analysis (PCA).

A Principle Component Analysis of Galaxy Properties from a Large, Gas-Selected Sample

DOE PAGES

Chang, Yu-Yen; Chao, Rikon; Wang, Wei-Hao; ...

2012-01-01

Disney emore » t al. (2008) have found a striking correlation among global parameters of H i -selected galaxies and concluded that this is in conflict with the CDM model. Considering the importance of the issue, we reinvestigate the problem using the principal component analysis on a fivefold larger sample and additional near-infrared data. We use databases from the Arecibo Legacy Fast Arecibo L -band Feed Array Survey for the gas properties, the Sloan Digital Sky Survey for the optical properties, and the Two Micron All Sky Survey for the near-infrared properties. We confirm that the parameters are indeed correlated where a single physical parameter can explain 83% of the variations. When color ( g - i ) is included, the first component still dominates but it develops a second principal component. In addition, the near-infrared color ( i - J ) shows an obvious second principal component that might provide evidence of the complex old star formation. Based on our data, we suggest that it is premature to pronounce the failure of the CDM model and it motivates more theoretical work.« less

Principal component analysis of dynamic fluorescence images for diagnosis of diabetic vasculopathy

NASA Astrophysics Data System (ADS)

Seo, Jihye; An, Yuri; Lee, Jungsul; Ku, Taeyun; Kang, Yujung; Ahn, Chulwoo; Choi, Chulhee

2016-04-01

Indocyanine green (ICG) fluorescence imaging has been clinically used for noninvasive visualizations of vascular structures. We have previously developed a diagnostic system based on dynamic ICG fluorescence imaging for sensitive detection of vascular disorders. However, because high-dimensional raw data were used, the analysis of the ICG dynamics proved difficult. We used principal component analysis (PCA) in this study to extract important elements without significant loss of information. We examined ICG spatiotemporal profiles and identified critical features related to vascular disorders. PCA time courses of the first three components showed a distinct pattern in diabetic patients. Among the major components, the second principal component (PC2) represented arterial-like features. The explained variance of PC2 in diabetic patients was significantly lower than in normal controls. To visualize the spatial pattern of PCs, pixels were mapped with red, green, and blue channels. The PC2 score showed an inverse pattern between normal controls and diabetic patients. We propose that PC2 can be used as a representative bioimaging marker for the screening of vascular diseases. It may also be useful in simple extractions of arterial-like features.

Topographical characteristics and principal component structure of the hypnagogic EEG.

PubMed

Tanaka, H; Hayashi, M; Hori, T

1997-07-01

The purpose of the present study was to identify the dominant topographic components of electroencephalographs (EEG) and their behavior during the waking-sleeping transition period. Somnography of nocturnal sleep was recorded on 10 male subjects. Each recording, from "lights-off" to 5 minutes after the appearance of the first sleep spindle, was analyzed. The typical EEG patterns during hypnagogic period were classified into nine EEG stages. Topographic maps demonstrated that the dominant areas of alpha-band activity moved from the posterior areas to anterior areas along the midline of the scalp. In delta-, theta-, and sigma-band activities, the differences of EEG amplitude between the focus areas (the dominant areas) and the surrounding areas increased as a function of EEG stage. To identify the dominant topographic components, a principal component analysis was carried out on a 12-channel EEG data set for each of six frequency bands. The dominant areas of alpha 2- (9.6-11.4 Hz) and alpha 3- (11.6-13.4 Hz) band activities moved from the posterior to anterior areas, respectively. The distribution of alpha 2-band activity on the scalp clearly changed just after EEG stage 3 (alpha intermittent, < 50%). On the other hand, alpha 3-band activity became dominant in anterior areas after the appearance of vertex sharp-wave bursts (EEG stage 7). For the sigma band, the amplitude of extensive areas from the frontal pole to the parietal showed a rapid rise after the onset of stage 7 (the appearance of vertex sharp-wave bursts). Based on the results, sleep onset process probably started before the onset of sleep stage 1 in standard criteria. On the other hand, the basic sleep process may start before the onset of sleep stage 2 or the manually scored spindles.

[HPLC specific chromatogram of Lamiophlomis Herba and its counterfeit and determination of four effective components].

PubMed

Zan, Ke; Jiao, Xing-Ping; Guo, Li-Nong; Zheng, Jian; Ma, Shuang-Cheng

2016-06-01

This study is to establish the HPLC specific chromatogram and determine four main effective components of Lamiophlomis Herba and its counterfeit.Chlorogenic acid, forsythoside B, acteoside and luteoloside were reference substance.HPLC analysis was performed on a Waters XSelect C₁₈ column (4.6 mm×250 mm,5 μm).The mobile phase was acetonitrile-0.5% phosphoric acid solution (18∶82) with isocratic elution.The flow rate was 1.0 mL•min⁻¹, the detection wavelength was 332 nm and the column temperature was 30 ℃.Chemometrics software Chempattern was employed to analyze the research data.HPLC specific chromatogram of Lamiophlomis Herba from different samples were of high similarity, but the similarity of the HPLC specific chromatogram of its counterfeit were less than 0.65.Both of cluster and principal component analysis can distinguish certified products and adulterants.The HPLC specific chromatogram and contents of four effective components can be used for the quality control of Lamiophlomis Herba and its preparations.It provided scientific basis to standardize the use of the crude drug. Copyright© by the Chinese Pharmaceutical Association.

Discriminant analysis of resting-state functional connectivity patterns on the Grassmann manifold

NASA Astrophysics Data System (ADS)

Fan, Yong; Liu, Yong; Jiang, Tianzi; Liu, Zhening; Hao, Yihui; Liu, Haihong

2010-03-01

The functional networks, extracted from fMRI images using independent component analysis, have been demonstrated informative for distinguishing brain states of cognitive functions and neurological diseases. In this paper, we propose a novel algorithm for discriminant analysis of functional networks encoded by spatial independent components. The functional networks of each individual are used as bases for a linear subspace, referred to as a functional connectivity pattern, which facilitates a comprehensive characterization of temporal signals of fMRI data. The functional connectivity patterns of different individuals are analyzed on the Grassmann manifold by adopting a principal angle based subspace distance. In conjunction with a support vector machine classifier, a forward component selection technique is proposed to select independent components for constructing the most discriminative functional connectivity pattern. The discriminant analysis method has been applied to an fMRI based schizophrenia study with 31 schizophrenia patients and 31 healthy individuals. The experimental results demonstrate that the proposed method not only achieves a promising classification performance for distinguishing schizophrenia patients from healthy controls, but also identifies discriminative functional networks that are informative for schizophrenia diagnosis.

SU-G-BRA-03: PCA Based Imaging Angle Optimization for 2D Cine MRI Based Radiotherapy Guidance

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

Chen, T; Yue, N; Jabbour, S

2016-06-15

Purpose: To develop an imaging angle optimization methodology for orthogonal 2D cine MRI based radiotherapy guidance using Principal Component Analysis (PCA) of target motion retrieved from 4DCT. Methods: We retrospectively analyzed 4DCT of 6 patients with lung tumor. A radiation oncologist manually contoured the target volume at the maximal inhalation phase of the respiratory cycle. An object constrained deformable image registration (DIR) method has been developed to track the target motion along the respiration at ten phases. The motion of the center of the target mass has been analyzed using the PCA to find out the principal motion components thatmore » were uncorrelated with each other. Two orthogonal image planes for cineMRI have been determined using this method to minimize the through plane motion during MRI based radiotherapy guidance. Results: 3D target respiratory motion for all 6 patients has been efficiently retrieved from 4DCT. In this process, the object constrained DIR demonstrated satisfactory accuracy and efficiency to enable the automatic motion tracking for clinical application. The average motion amplitude in the AP, lateral, and longitudinal directions were 3.6mm (min: 1.6mm, max: 5.6mm), 1.7mm (min: 0.6mm, max: 2.7mm), and 5.6mm (min: 1.8mm, max: 16.1mm), respectively. Based on PCA, the optimal orthogonal imaging planes were determined for cineMRI. The average angular difference between the PCA determined imaging planes and the traditional AP and lateral imaging planes were 47 and 31 degrees, respectively. After optimization, the average amplitude of through plane motion reduced from 3.6mm in AP images to 2.5mm (min:1.3mm, max:3.9mm); and from 1.7mm in lateral images to 0.6mm (min: 0.2mm, max:1.5mm), while the principal in plane motion amplitude increased from 5.6mm to 6.5mm (min: 2.8mm, max: 17mm). Conclusion: DIR and PCA can be used to optimize the orthogonal image planes of cineMRI to minimize the through plane motion during radiotherapy guidance.« less

Efficient principal component analysis for multivariate 3D voxel-based mapping of brain functional imaging data sets as applied to FDG-PET and normal aging.

PubMed

Zuendorf, Gerhard; Kerrouche, Nacer; Herholz, Karl; Baron, Jean-Claude

2003-01-01

Principal component analysis (PCA) is a well-known technique for reduction of dimensionality of functional imaging data. PCA can be looked at as the projection of the original images onto a new orthogonal coordinate system with lower dimensions. The new axes explain the variance in the images in decreasing order of importance, showing correlations between brain regions. We used an efficient, stable and analytical method to work out the PCA of Positron Emission Tomography (PET) images of 74 normal subjects using [(18)F]fluoro-2-deoxy-D-glucose (FDG) as a tracer. Principal components (PCs) and their relation to age effects were investigated. Correlations between the projections of the images on the new axes and the age of the subjects were carried out. The first two PCs could be identified as being the only PCs significantly correlated to age. The first principal component, which explained 10% of the data set variance, was reduced only in subjects of age 55 or older and was related to loss of signal in and adjacent to ventricles and basal cisterns, reflecting expected age-related brain atrophy with enlarging CSF spaces. The second principal component, which accounted for 8% of the total variance, had high loadings from prefrontal, posterior parietal and posterior cingulate cortices and showed the strongest correlation with age (r = -0.56), entirely consistent with previously documented age-related declines in brain glucose utilization. Thus, our method showed that the effect of aging on brain metabolism has at least two independent dimensions. This method should have widespread applications in multivariate analysis of brain functional images. Copyright 2002 Wiley-Liss, Inc.

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

NASA Astrophysics Data System (ADS)

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

2016-09-01

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

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.

Assessing the impact of fishing in shallow rocky reefs: a multivariate approach to ecosystem management.

PubMed

Sangil, Carlos; Martín-García, Laura; Clemente, Sabrina

2013-11-15

In this paper we develop a tool to assess the impact of fishing on ecosystem functioning in shallow rocky reefs. The relationships between biological parameters (fishes, sea urchins, seaweeds), and fishing activities (fish traps, boats, land-based fishing, spearfishing) were tested in La Palma island (Canary Islands). Data from fishing activities and biological parameters were analyzed using principal component analyses. We produced two models using the first component of these analyses. This component was interpreted as a new variable that described the fishing pressure and the conservation status at each studied site. Subsequently the scores on the first axis were mapped using universal kriging methods and the models obtained were extrapolated across the whole island to display the expected fishing pressure and conservation status more widely. The fishing pressure and conservation status models were spatially related; zones where fishing pressure was high coincided with zones in the unhealthiest ecological state. Copyright © 2013 Elsevier Ltd. All rights reserved.