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

PubMed

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

2018-05-31

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

Feature extraction through parallel Probabilistic Principal Component Analysis for heart disease diagnosis

NASA Astrophysics Data System (ADS)

Shah, Syed Muhammad Saqlain; Batool, Safeera; Khan, Imran; Ashraf, Muhammad Usman; Abbas, Syed Hussnain; Hussain, Syed Adnan

2017-09-01

Automatic diagnosis of human diseases are mostly achieved through decision support systems. The performance of these systems is mainly dependent on the selection of the most relevant features. This becomes harder when the dataset contains missing values for the different features. Probabilistic Principal Component Analysis (PPCA) has reputation to deal with the problem of missing values of attributes. This research presents a methodology which uses the results of medical tests as input, extracts a reduced dimensional feature subset and provides diagnosis of heart disease. The proposed methodology extracts high impact features in new projection by using Probabilistic Principal Component Analysis (PPCA). PPCA extracts projection vectors which contribute in highest covariance and these projection vectors are used to reduce feature dimension. The selection of projection vectors is done through Parallel Analysis (PA). The feature subset with the reduced dimension is provided to radial basis function (RBF) kernel based Support Vector Machines (SVM). The RBF based SVM serves the purpose of classification into two categories i.e., Heart Patient (HP) and Normal Subject (NS). The proposed methodology is evaluated through accuracy, specificity and sensitivity over the three datasets of UCI i.e., Cleveland, Switzerland and Hungarian. The statistical results achieved through the proposed technique are presented in comparison to the existing research showing its impact. The proposed technique achieved an accuracy of 82.18%, 85.82% and 91.30% for Cleveland, Hungarian and Switzerland dataset respectively.

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.

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.

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.

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.

Controlling the release of wood extractives into water bodies by selecting suitable eucalyptus species

NASA Astrophysics Data System (ADS)

Kilulya, K. F.; Msagati, T. A. M.; Mamba, B. B.; Ngila, J. C.; Bush, T.

Pulping industries are increasing worldwide as a result of the increase in the demand for pulp for cellulose derivatives and paper manufacturing. Due to the activities involved in pulping processes, different chemicals from raw materials (wood) and bleaching agents are released in pulp-mill effluent streams discharged into the environment and find their way into water bodies. Large quantities of water and chemicals used in pulping result in large amounts of wastewater with high concentrations of extractives such as unsaturated fatty acids, which are known to be toxic, and plant sterols which affect the development, growth and reproduction of aquatic organisms. This study was aimed at assessing the composition of extractives in two eucalyptus species used for pulp production in South Africa, in order to identify the suitable species with regard to extractive content. Samples from two eucalyptus plant species (Eucalyptus grandis and Eucalyptus dunnii) were collected from three sites and analysed for extractives by first extracting with water, followed by Soxhlet extraction using acetone. Compounds were identified and quantified using gas chromatography-mass spectrometry (GC-MS). Major classes of extractives identified were fatty acids (mainly hexadecanoic acid, 9,12-octadecadienoic, 9-octadecenoic and octadecanoic acids) and sterols (mainly β-sitosterol and stigmastanol). E. dunnii was found to contain higher amounts of the compounds compared to those found in E. grandis in all sampled sites. Principal component analysis (PCA) was performed and explained 92.9% of the total variation using three principal components. It was revealed that the percentage of fatty acids, which has a negative influence on both principal components 2 and 3, was responsible for the difference between the species. E. grandis, which was found to contain low amounts of extractives, was therefore found suitable for pulping with regard to minimal water usage and environment pollution.

Influence of Different Drying Treatments and Extraction Solvents on the Metabolite Profile and Nitric Oxide Inhibitory Activity of Ajwa Dates.

PubMed

Abdul-Hamid, Nur Ashikin; Abas, Faridah; Ismail, Intan Safinar; Shaari, Khozirah; Lajis, Nordin H

2015-11-01

This study aimed to examine the variation in the metabolite profiles and nitric oxide (NO) inhibitory activity of Ajwa dates that were subjected to 2 drying treatments and different extraction solvents. (1)H NMR coupled with multivariate data analysis was employed. A Griess assay was used to determine the inhibition of the production of NO in RAW 264.7 cells treated with LPS and interferon-γ. The oven dried (OD) samples demonstrated the absence of asparagine and ascorbic acid as compared to the freeze dried (FD) dates. The principal component analysis showed distinct clusters between the OD and FD dates by the second principal component. In respect of extraction solvents, chloroform extracts can be distinguished by the absence of arginine, glycine and asparagine compared to the methanol and 50% methanol extracts. The chloroform extracts can be clearly distinguished from the methanol and 50% methanol extracts by first principal component. Meanwhile, the loading score plot of partial least squares analysis suggested that beta glucose, alpha glucose, choline, ascorbic acid and glycine were among the metabolites that were contributing to higher biological activity displayed by FD and methanol extracts of Ajwa. The results highlight an alternative method of metabolomics approach for determination of the metabolites that contribute to NO inhibitory activity. The association between metabolite profiles and nitric oxide (NO) inhibitory activity of the various extracts of Ajwa dates was evaluated by utilizing partial least squares (PLS) model. The validated PLS model can be employed to predict the NO inhibitory activity of new samples of date fruits based on their NMR spectra which was important for assessing fruit quality. The information gained might be used as guidance for quality control, nutritional values and as a basis for the preparation of any food supplements for human health that employs date palm fruit as the raw material. © 2015 Institute of Food Technologists®

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

PubMed

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

2013-02-01

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

Assessing prescription drug abuse using functional principal component analysis (FPCA) of wastewater data.

PubMed

Salvatore, Stefania; Røislien, Jo; Baz-Lomba, Jose A; Bramness, Jørgen G

2017-03-01

Wastewater-based epidemiology is an alternative method for estimating the collective drug use in a community. We applied functional data analysis, a statistical framework developed for analysing curve data, to investigate weekly temporal patterns in wastewater measurements of three prescription drugs with known abuse potential: methadone, oxazepam and methylphenidate, comparing them to positive and negative control drugs. Sewage samples were collected in February 2014 from a wastewater treatment plant in Oslo, Norway. The weekly pattern of each drug was extracted by fitting of generalized additive models, using trigonometric functions to model the cyclic behaviour. From the weekly component, the main temporal features were then extracted using functional principal component analysis. Results are presented through the functional principal components (FPCs) and corresponding FPC scores. Clinically, the most important weekly feature of the wastewater-based epidemiology data was the second FPC, representing the difference between average midweek level and a peak during the weekend, representing possible recreational use of a drug in the weekend. Estimated scores on this FPC indicated recreational use of methylphenidate, with a high weekend peak, but not for methadone and oxazepam. The functional principal component analysis uncovered clinically important temporal features of the weekly patterns of the use of prescription drugs detected from wastewater analysis. This may be used as a post-marketing surveillance method to monitor prescription drugs with abuse potential. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Fuel spill identification using solid-phase extraction and solid-phase microextraction. 1. Aviation turbine fuels.

PubMed

Lavine, B K; Brzozowski, D M; Ritter, J; Moores, A J; Mayfield, H T

2001-12-01

The water-soluble fraction of aviation jet fuels is examined using solid-phase extraction and solid-phase microextraction. Gas chromatographic profiles of solid-phase extracts and solid-phase microextracts of the water-soluble fraction of kerosene- and nonkerosene-based jet fuels reveal that each jet fuel possesses a unique profile. Pattern recognition analysis reveals fingerprint patterns within the data characteristic of fuel type. By using a novel genetic algorithm (GA) that emulates human pattern recognition through machine learning, it is possible to identify features characteristic of the chromatographic profile of each fuel class. The pattern recognition GA identifies a set of features that optimize the separation of the fuel classes in a plot of the two largest principal components of the data. Because principal components maximize variance, the bulk of the information encoded by the selected features is primarily about the differences between the fuel classes.

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.

Discernment of lint trash in raw cotton using multivariate analysis of excitation-emission luminescence spectra

USDA-ARS?s Scientific Manuscript database

Excitation-Emission luminescence spectra of basic (pH 12.5) phosphate buffer solution extracts were used to distinguish among botanical components of trash within seed cotton. All components were separated from whole plants removed from a field in southern New Mexico. Unfolded Principal Component An...

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.

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

PubMed Central

2011-01-01

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

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.

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.

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

Modeling of the L-shell copper X-pinch plasma produced by the compact generator of Ecole polytechnique using pattern recognition

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

Larour, Jean; Aranchuk, Leonid E.; Danisman, Yusuf

2016-03-15

Principal component analysis is applied and compared with the line ratios of special Ne-like transitions for investigating the electron beam effects on the L-shell Cu synthetic spectra. The database for the principal component extraction is created over a non Local Thermodynamic Equilibrium (non-LTE) collisional radiative L-shell Copper model. The extracted principal components are used as a database for Artificial Neural Network in order to estimate the plasma electron temperature, density, and beam fractions from a representative time-integrated spatially resolved L-shell Cu X-pinch plasma spectrum. The spectrum is produced by the explosion of 25-μm Cu wires on a compact LC (40more » kV, 200 kA, and 200 ns) generator. The modeled plasma electron temperatures are about T{sub e} ∼ 150 eV and N{sub e} = 5 × 10{sup 19} cm{sup −3} in the presence of the fraction of the beams with f ∼ 0.05 and a centered energy of ∼10 keV.« less

Binding Isotherms and Time Courses Readily from Magnetic Resonance.

PubMed

Xu, Jia; Van Doren, Steven R

2016-08-16

Evidence is presented that binding isotherms, simple or biphasic, can be extracted directly from noninterpreted, complex 2D NMR spectra using principal component analysis (PCA) to reveal the largest trend(s) across the series. This approach renders peak picking unnecessary for tracking population changes. In 1:1 binding, the first principal component captures the binding isotherm from NMR-detected titrations in fast, slow, and even intermediate and mixed exchange regimes, as illustrated for phospholigand associations with proteins. Although the sigmoidal shifts and line broadening of intermediate exchange distorts binding isotherms constructed conventionally, applying PCA directly to these spectra along with Pareto scaling overcomes the distortion. Applying PCA to time-domain NMR data also yields binding isotherms from titrations in fast or slow exchange. The algorithm readily extracts from magnetic resonance imaging movie time courses such as breathing and heart rate in chest imaging. Similarly, two-step binding processes detected by NMR are easily captured by principal components 1 and 2. PCA obviates the customary focus on specific peaks or regions of images. Applying it directly to a series of complex data will easily delineate binding isotherms, equilibrium shifts, and time courses of reactions or fluctuations.

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®.

Liquid chromatography tandem mass spectrometry determination of chemical markers and principal component analysis of Vitex agnus-castus L. fruits (Verbenaceae) and derived food supplements.

PubMed

Mari, Angela; Montoro, Paola; Pizza, Cosimo; Piacente, Sonia

2012-11-01

A validated analytical method for the quantitative determination of seven chemical markers occurring in a hydroalcoholic extract of Vitex agnus-castus fruits by liquid chromatography electrospray triple quadrupole tandem mass spectrometry (LC/ESI/(QqQ)MSMS) is reported. To carry out a comparative study, five commercial food supplements corresponding to hydroalcoholic extracts of V. agnus-castus fruits were analysed under the same chromatographic conditions of the crude extract. Principal component analysis (PCA), based only on the variation of the amount of the seven chemical markers, was applied in order to find similarities between the hydroalcoholic extract and the food supplements. A second PCA analysis was carried out considering the whole spectroscopic data deriving from liquid chromatography electrospray linear ion trap mass spectrometry (LC/ESI/(LIT)MS) analysis. High similarity between the two PCA was observed, showing the possibility to select one of these two approaches for future applications in the field of comparative analysis of food supplements and quality control procedures. Copyright © 2012 Elsevier B.V. All rights reserved.

Feature extraction across individual time series observations with spikes using wavelet principal component analysis.

PubMed

Røislien, Jo; Winje, Brita

2013-09-20

Clinical studies frequently include repeated measurements of individuals, often for long periods. We present a methodology for extracting common temporal features across a set of individual time series observations. In particular, the methodology explores extreme observations within the time series, such as spikes, as a possible common temporal phenomenon. Wavelet basis functions are attractive in this sense, as they are localized in both time and frequency domains simultaneously, allowing for localized feature extraction from a time-varying signal. We apply wavelet basis function decomposition of individual time series, with corresponding wavelet shrinkage to remove noise. We then extract common temporal features using linear principal component analysis on the wavelet coefficients, before inverse transformation back to the time domain for clinical interpretation. We demonstrate the methodology on a subset of a large fetal activity study aiming to identify temporal patterns in fetal movement (FM) count data in order to explore formal FM counting as a screening tool for identifying fetal compromise and thus preventing adverse birth outcomes. Copyright © 2013 John Wiley & Sons, Ltd.

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.

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.

[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.

The Researches on Damage Detection Method for Truss Structures

NASA Astrophysics Data System (ADS)

Wang, Meng Hong; Cao, Xiao Nan

2018-06-01

This paper presents an effective method to detect damage in truss structures. Numerical simulation and experimental analysis were carried out on a damaged truss structure under instantaneous excitation. The ideal excitation point and appropriate hammering method were determined to extract time domain signals under two working conditions. The frequency response function and principal component analysis were used for data processing, and the angle between the frequency response function vectors was selected as a damage index to ascertain the location of a damaged bar in the truss structure. In the numerical simulation, the time domain signal of all nodes was extracted to determine the location of the damaged bar. In the experimental analysis, the time domain signal of a portion of the nodes was extracted on the basis of an optimal sensor placement method based on the node strain energy coefficient. The results of the numerical simulation and experimental analysis showed that the damage detection method based on the frequency response function and principal component analysis could locate the damaged bar accurately.

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.

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.

A Review of Feature Extraction Software for Microarray Gene Expression Data

PubMed Central

Tan, Ching Siang; Ting, Wai Soon; Mohamad, Mohd Saberi; Chan, Weng Howe; Deris, Safaai; Ali Shah, Zuraini

2014-01-01

When gene expression data are too large to be processed, they are transformed into a reduced representation set of genes. Transforming large-scale gene expression data into a set of genes is called feature extraction. If the genes extracted are carefully chosen, this gene set can extract the relevant information from the large-scale gene expression data, allowing further analysis by using this reduced representation instead of the full size data. In this paper, we review numerous software applications that can be used for feature extraction. The software reviewed is mainly for Principal Component Analysis (PCA), Independent Component Analysis (ICA), Partial Least Squares (PLS), and Local Linear Embedding (LLE). A summary and sources of the software are provided in the last section for each feature extraction method. PMID:25250315

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

ERIC Educational Resources Information Center

Lee, Jongwon; Bednarz, Robert

2012-01-01

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

Analysing Normal and Partial Glossectomee Tongues Using Ultrasound

ERIC Educational Resources Information Center

Bressmann, Tim; Uy, Catherine; Irish, Jonathan C.

2005-01-01

The present study aimed at identifying underlying parameters that govern the shape of the tongue. A functional topography of the tongue surface was developed based on three-dimensional ultrasound scans of sustained speech sounds in ten normal subjects. A principal component analysis extracted three components that explained 89.2% of the variance…

Real time on-chip sequential adaptive principal component analysis for data feature extraction and image compression

NASA Technical Reports Server (NTRS)

Duong, T. A.

2004-01-01

In this paper, we present a new, simple, and optimized hardware architecture sequential learning technique for adaptive Principle Component Analysis (PCA) which will help optimize the hardware implementation in VLSI and to overcome the difficulties of the traditional gradient descent in learning convergence and hardware implementation.

[Determination of the Plant Origin of Licorice Oil Extract, a Natural Food Additive, by Principal Component Analysis Based on Chemical Components].

PubMed

Tada, Atsuko; Ishizuki, Kyoko; Sugimoto, Naoki; Yoshimatsu, Kayo; Kawahara, Nobuo; Suematsu, Takako; Arifuku, Kazunori; Fukai, Toshio; Tamura, Yukiyoshi; Ohtsuki, Takashi; Tahara, Maiko; Yamazaki, Takeshi; Akiyama, Hiroshi

2015-01-01

"Licorice oil extract" (LOE) (antioxidant agent) is described in the notice of Japanese food additive regulations as a material obtained from the roots and/or rhizomes of Glycyrrhiza uralensis, G. inflata or G. glabra. In this study, we aimed to identify the original Glycyrrhiza species of eight food additive products using LC/MS. Glabridin, a characteristic compound in G. glabra, was specifically detected in seven products, and licochalcone A, a characteristic compound in G. inflata, was detected in one product. In addition, Principal Component Analysis (PCA) (a kind of multivariate analysis) using the data of LC/MS or (1)H-NMR analysis was performed. The data of thirty-one samples, including LOE products used as food additives, ethanol extracts of various Glycyrrhiza species and commercially available Glycyrrhiza species-derived products were assessed. Based on the PCA results, the majority of LOE products was confirmed to be derived from G. glabra. This study suggests that PCA using (1)H-NMR analysis data is a simple and useful method to identify the plant species of origin of natural food additive products.

Oral administration of a Spirulina extract enriched for Braun-type lipoproteins protects mice against influenza A(H1N1) virus infection

USDA-ARS?s Scientific Manuscript database

Previous studies indicate that Immulina, a commercial extract of Arthrospira (Spirulina) platensis, is a potent activator of innate immune cells and that Braun-type lipoproteins (a principal toll-like receptor (TLR) 2 ligand) are the main active components within this product. In the present study, ...

Variability search in M 31 using principal component analysis and the Hubble Source Catalogue

NASA Astrophysics Data System (ADS)

Moretti, M. I.; Hatzidimitriou, D.; Karampelas, A.; Sokolovsky, K. V.; Bonanos, A. Z.; Gavras, P.; Yang, M.

2018-06-01

Principal component analysis (PCA) is being extensively used in Astronomy but not yet exhaustively exploited for variability search. The aim of this work is to investigate the effectiveness of using the PCA as a method to search for variable stars in large photometric data sets. We apply PCA to variability indices computed for light curves of 18 152 stars in three fields in M 31 extracted from the Hubble Source Catalogue. The projection of the data into the principal components is used as a stellar variability detection and classification tool, capable of distinguishing between RR Lyrae stars, long-period variables (LPVs) and non-variables. This projection recovered more than 90 per cent of the known variables and revealed 38 previously unknown variable stars (about 30 per cent more), all LPVs except for one object of uncertain variability type. We conclude that this methodology can indeed successfully identify candidate variable stars.

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.

Principal component analysis of indocyanine green fluorescence dynamics for diagnosis of vascular diseases

NASA Astrophysics Data System (ADS)

Seo, Jihye; An, Yuri; Lee, Jungsul; Choi, Chulhee

2015-03-01

Indocyanine green (ICG), a near-infrared fluorophore, has been used in visualization of vascular structure and non-invasive diagnosis of vascular disease. Although many imaging techniques have been developed, there are still limitations in diagnosis of vascular diseases. We have recently developed a minimally invasive diagnostics system based on ICG fluorescence imaging for sensitive detection of vascular insufficiency. In this study, we used principal component analysis (PCA) to examine ICG spatiotemporal profile and to obtain pathophysiological information from ICG dynamics. Here we demonstrated that principal components of ICG dynamics in both feet showed significant differences between normal control and diabetic patients with vascula complications. We extracted the PCA time courses of the first three components and found distinct pattern in diabetic patient. We propose that PCA of ICG dynamics reveal better classification performance compared to fluorescence intensity analysis. We anticipate that specific feature of spatiotemporal ICG dynamics can be useful in diagnosis of various vascular diseases.

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

PubMed

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

2016-06-01

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

[Research on discrimination of cabbage and weeds based on visible and near-infrared spectrum analysis].

PubMed

Zu, Qin; Zhao, Chun-Jiang; Deng, Wei; Wang, Xiu

2013-05-01

The automatic identification of weeds forms the basis for precision spraying of crops infest. The canopy spectral reflectance within the 350-2 500 nm band of two strains of cabbages and five kinds of weeds such as barnyard grass, setaria, crabgrass, goosegrass and pigweed was acquired by ASD spectrometer. According to the spectral curve characteristics, the data in different bands were compressed with different levels to improve the operation efficiency. Firstly, the spectrum was denoised in accordance with the different order of multiple scattering correction (MSC) method and Savitzky-Golay (SG) convolution smoothing method set by different parameters, then the model was built by combining the principal component analysis (PCA) method to extract principal components, finally all kinds of plants were classified by using the soft independent modeling of class analogy (SIMCA) taxonomy and the classification results were compared. The tests results indicate that after the pretreatment of the spectral data with the method of the combination of MSC and SG set with 3rd order, 5th degree polynomial, 21 smoothing points, and the top 10 principal components extraction using PCA as a classification model input variable, 100% correct classification rate was achieved, and it is able to identify cabbage and several kinds of common weeds quickly and nondestructively.

Fractal Dimension Change Point Model for Hydrothermal Alteration Anomalies in Silk Road Economic Belt, the Beishan Area, Gansu, China

NASA Astrophysics Data System (ADS)

Han, H. H.; Wang, Y. L.; Ren, G. L.; LI, J. Q.; Gao, T.; Yang, M.; Yang, J. L.

2016-11-01

Remote sensing plays an important role in mineral exploration of “One Belt One Road” plan. One of its applications is extracting and locating hydrothermal alteration zones that are related to mines. At present, the extracting method for alteration anomalies from principal component image mainly relies on the data's normal distribution, without considering the nonlinear characteristics of geological anomaly. In this study, a Fractal Dimension Change Point Model (FDCPM), calculated by the self-similarity and mutability of alteration anomalies, is employed to quantitatively acquire the critical threshold of alteration anomalies. The realization theory and access mechanism of the model are elaborated by an experiment with ASTER data in Beishan mineralization belt, also the results are compared with traditional method (De-Interfered Anomalous Principal Component Thresholding Technique, DIAPCTT). The results show that the findings produced by FDCPM are agree with well with a mounting body of evidence from different perspectives, with the extracting accuracy over 80%, indicating that FDCPM is an effective extracting method for remote sensing alteration anomalies, and could be used as an useful tool for mineral exploration in similar areas in Silk Road Economic Belt.

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.

Strategies for reducing large fMRI data sets for independent component analysis.

PubMed

Wang, Ze; Wang, Jiongjiong; Calhoun, Vince; Rao, Hengyi; Detre, John A; Childress, Anna R

2006-06-01

In independent component analysis (ICA), principal component analysis (PCA) is generally used to reduce the raw data to a few principal components (PCs) through eigenvector decomposition (EVD) on the data covariance matrix. Although this works for spatial ICA (sICA) on moderately sized fMRI data, it is intractable for temporal ICA (tICA), since typical fMRI data have a high spatial dimension, resulting in an unmanageable data covariance matrix. To solve this problem, two practical data reduction methods are presented in this paper. The first solution is to calculate the PCs of tICA from the PCs of sICA. This approach works well for moderately sized fMRI data; however, it is highly computationally intensive, even intractable, when the number of scans increases. The second solution proposed is to perform PCA decomposition via a cascade recursive least squared (CRLS) network, which provides a uniform data reduction solution for both sICA and tICA. Without the need to calculate the covariance matrix, CRLS extracts PCs directly from the raw data, and the PC extraction can be terminated after computing an arbitrary number of PCs without the need to estimate the whole set of PCs. Moreover, when the whole data set becomes too large to be loaded into the machine memory, CRLS-PCA can save data retrieval time by reading the data once, while the conventional PCA requires numerous data retrieval steps for both covariance matrix calculation and PC extractions. Real fMRI data were used to evaluate the PC extraction precision, computational expense, and memory usage of the presented methods.

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.

[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.

HD 143 418 - An Interacting Binary with a Subsynchronously Rotating Primary

NASA Astrophysics Data System (ADS)

Mikulášek, Z.; Zverko, J.; Žižňovský, J.; Krtička, J.; Iliev, I. Kh.; Kudryavtsev, D. O.; Gráf, T.; Zejda, M.

2010-12-01

HD 143418 is a non-eclipsing double-lined close binary with orbital period Porb=2.282520 d. The photometrically and spectroscopically dominant primary component is a normal A5V star in the middle of its stay on the main sequence with extremely slow, subsynchronous rotation (Prot being about 14 days!). Its photometric monitoring since 1982 revealed orbitally modulated variations with changing form and amplitude. The advanced principal component analysis (APCA) disentangling extract-ed a steady part of light curves obviously caused by the ellipticity of the primary. Seasonal components of the light curves may be related to an expected incidence of circumstellar matter ejected from the tidally spinning up primary component. A possible scenario of the synchronisation process is also briefly discussed.

Independent component analysis for automatic note extraction from musical trills

NASA Astrophysics Data System (ADS)

Brown, Judith C.; Smaragdis, Paris

2004-05-01

The method of principal component analysis, which is based on second-order statistics (or linear independence), has long been used for redundancy reduction of audio data. The more recent technique of independent component analysis, enforcing much stricter statistical criteria based on higher-order statistical independence, is introduced and shown to be far superior in separating independent musical sources. This theory has been applied to piano trills and a database of trill rates was assembled from experiments with a computer-driven piano, recordings of a professional pianist, and commercially available compact disks. The method of independent component analysis has thus been shown to be an outstanding, effective means of automatically extracting interesting musical information from a sea of redundant data.

Extending the 2 x 2 Achievement Goal Framework: Development of a Measure of Scientific Achievement Goals

ERIC Educational Resources Information Center

Deemer, Eric D.; Carter, Alice P.; Lobrano, Michael T.

2010-01-01

The current research sought to extend the 2 x 2 achievement goal framework by developing and testing the Achievement Goals for Research Scale (AGRS). Participants (N = 317) consisted of graduate students in the life, physical, and behavioral sciences. A principal components analysis (PCA) extracted five components accounting for 72.59% of the…

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.

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.

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.

[Research on spectra recognition method for cabbages and weeds based on PCA and SIMCA].

PubMed

Zu, Qin; Deng, Wei; Wang, Xiu; Zhao, Chun-Jiang

2013-10-01

In order to improve the accuracy and efficiency of weed identification, the difference of spectral reflectance was employed to distinguish between crops and weeds. Firstly, the different combinations of Savitzky-Golay (SG) convolutional derivation and multiplicative scattering correction (MSC) method were applied to preprocess the raw spectral data. Then the clustering analysis of various types of plants was completed by using principal component analysis (PCA) method, and the feature wavelengths which were sensitive for classifying various types of plants were extracted according to the corresponding loading plots of the optimal principal components in PCA results. Finally, setting the feature wavelengths as the input variables, the soft independent modeling of class analogy (SIMCA) classification method was used to identify the various types of plants. The experimental results of classifying cabbages and weeds showed that on the basis of the optimal pretreatment by a synthetic application of MSC and SG convolutional derivation with SG's parameters set as 1rd order derivation, 3th degree polynomial and 51 smoothing points, 23 feature wavelengths were extracted in accordance with the top three principal components in PCA results. When SIMCA method was used for classification while the previously selected 23 feature wavelengths were set as the input variables, the classification rates of the modeling set and the prediction set were respectively up to 98.6% and 100%.

A stock market forecasting model combining two-directional two-dimensional principal component analysis and radial basis function neural network.

PubMed

Guo, Zhiqiang; Wang, Huaiqing; Yang, Jie; Miller, David J

2015-01-01

In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron.

A Stock Market Forecasting Model Combining Two-Directional Two-Dimensional Principal Component Analysis and Radial Basis Function Neural Network

PubMed Central

Guo, Zhiqiang; Wang, Huaiqing; Yang, Jie; Miller, David J.

2015-01-01

In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron. PMID:25849483

Comparison of AIS Versus TMS Data Collected over the Virginia Piedmont

NASA Technical Reports Server (NTRS)

Bell, R.; Evans, C. S.

1985-01-01

The Airborne Imaging Spectrometer (AIS, NS001 Thematic Mapper Simlulator (TMS), and Zeiss camera collected remotely sensed data simultaneously on October 27, 1983, at an altitude of 6860 meters (22,500 feet). AIS data were collected in 32 channels covering 1200 to 1500 nm. A simple atmospheric correction was applied to the AIS data, after which spectra for four cover types were plotted. Spectra for these ground cover classes showed a telescoping effect for the wavelength endpoints. Principal components were extracted from the shortwave region of the AIS (1200 to 1280 nm), full spectrum AIS (1200 to 1500 nm) and TMS (450 to 12,500 nm) to create three separate three-component color image composites. A comparison of the TMS band 5 (1000 to 1300 nm) to the six principal components from the shortwave AIS region (1200 to 1280 nm) showed improved visual discrimination of ground cover types. Contrast of color image composites created from principal components showed the AIS composites to exhibit a clearer demarcation between certain ground cover types but subtle differences within other regions of the imagery were not as readily seen.

Factor Analysis and Counseling Research

ERIC Educational Resources Information Center

Weiss, David J.

1970-01-01

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

Multivariate analysis of the volatile components in tobacco based on infrared-assisted extraction coupled to headspace solid-phase microextraction and gas chromatography-mass spectrometry.

PubMed

Yang, Yanqin; Pan, Yuanjiang; Zhou, Guojun; Chu, Guohai; Jiang, Jian; Yuan, Kailong; Xia, Qian; Cheng, Changhe

2016-11-01

A novel infrared-assisted extraction coupled to headspace solid-phase microextraction followed by gas chromatography with mass spectrometry method has been developed for the rapid determination of the volatile components in tobacco. The optimal extraction conditions for maximizing the extraction efficiency were as follows: 65 μm polydimethylsiloxane-divinylbenzene fiber, extraction time of 20 min, infrared power of 175 W, and distance between the infrared lamp and the headspace vial of 2 cm. Under the optimum conditions, 50 components were found to exist in all ten tobacco samples from different geographical origins. Compared with conventional water-bath heating and nonheating extraction methods, the extraction efficiency of infrared-assisted extraction was greatly improved. Furthermore, multivariate analysis including principal component analysis, hierarchical cluster analysis, and similarity analysis were performed to evaluate the chemical information of these samples and divided them into three classifications, including rich, moderate, and fresh flavors. The above-mentioned classification results were consistent with the sensory evaluation, which was pivotal and meaningful for tobacco discrimination. As a simple, fast, cost-effective, and highly efficient method, the infrared-assisted extraction coupled to headspace solid-phase microextraction technique is powerful and promising for distinguishing the geographical origins of the tobacco samples coupled to suitable chemometrics. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Infrared and visible image fusion based on robust principal component analysis and compressed sensing

NASA Astrophysics Data System (ADS)

Li, Jun; Song, Minghui; Peng, Yuanxi

2018-03-01

Current infrared and visible image fusion methods do not achieve adequate information extraction, i.e., they cannot extract the target information from infrared images while retaining the background information from visible images. Moreover, most of them have high complexity and are time-consuming. This paper proposes an efficient image fusion framework for infrared and visible images on the basis of robust principal component analysis (RPCA) and compressed sensing (CS). The novel framework consists of three phases. First, RPCA decomposition is applied to the infrared and visible images to obtain their sparse and low-rank components, which represent the salient features and background information of the images, respectively. Second, the sparse and low-rank coefficients are fused by different strategies. On the one hand, the measurements of the sparse coefficients are obtained by the random Gaussian matrix, and they are then fused by the standard deviation (SD) based fusion rule. Next, the fused sparse component is obtained by reconstructing the result of the fused measurement using the fast continuous linearized augmented Lagrangian algorithm (FCLALM). On the other hand, the low-rank coefficients are fused using the max-absolute rule. Subsequently, the fused image is superposed by the fused sparse and low-rank components. For comparison, several popular fusion algorithms are tested experimentally. By comparing the fused results subjectively and objectively, we find that the proposed framework can extract the infrared targets while retaining the background information in the visible images. Thus, it exhibits state-of-the-art performance in terms of both fusion effects and timeliness.

Multi-Response Extraction Optimization Based on Anti-Oxidative Activity and Quality Evaluation by Main Indicator Ingredients Coupled with Chemometric Analysis on Thymus quinquecostatus Celak.

PubMed

Chang, Yan-Li; Shen, Meng; Ren, Xue-Yang; He, Ting; Wang, Le; Fan, Shu-Sheng; Wang, Xiu-Huan; Li, Xiao; Wang, Xiao-Ping; Chen, Xiao-Yi; Sui, Hong; She, Gai-Mei

2018-04-19

Thymus quinquecostatus Celak is a species of thyme in China and it used as condiment and herbal medicine for a long time. To set up the quality evaluation of T. quinquecostatus , the response surface methodology (RSM) based on its 2,2-Diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity was introduced to optimize the extraction condition, and the main indicator components were found through an UPLC-LTQ-Orbitrap MS n method. The ethanol concentration, solid-liquid ratio, and extraction time on optimum conditions were 42.32%, 1:17.51, and 1.8 h, respectively. 35 components having 12 phenolic acids and 23 flavonoids were unambiguously or tentatively identified both positive and negative modes to employ for the comprehensive analysis in the optimum anti-oxidative part. A simple, reliable, and sensitive HPLC method was performed for the multi-component quantitative analysis of T. quinquecostatus using six characteristic and principal phenolic acids and flavonoids as reference compounds. Furthermore, the chemometrics methods (principal components analysis (PCA) and hierarchical clustering analysis (HCA)) appraised the growing areas and harvest time of this herb closely relative to the quality-controlled. This study provided full-scale qualitative and quantitative information for the quality evaluation of T. quinquecostatus , which would be a valuable reference for further study and development of this herb and related laid the foundation of further study on its pharmacological efficacy.

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

PubMed

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

2016-09-20

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

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

PubMed Central

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

2016-01-01

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

Performance evaluation of PCA-based spike sorting algorithms.

PubMed

Adamos, Dimitrios A; Kosmidis, Efstratios K; Theophilidis, George

2008-09-01

Deciphering the electrical activity of individual neurons from multi-unit noisy recordings is critical for understanding complex neural systems. A widely used spike sorting algorithm is being evaluated for single-electrode nerve trunk recordings. The algorithm is based on principal component analysis (PCA) for spike feature extraction. In the neuroscience literature it is generally assumed that the use of the first two or most commonly three principal components is sufficient. We estimate the optimum PCA-based feature space by evaluating the algorithm's performance on simulated series of action potentials. A number of modifications are made to the open source nev2lkit software to enable systematic investigation of the parameter space. We introduce a new metric to define clustering error considering over-clustering more favorable than under-clustering as proposed by experimentalists for our data. Both the program patch and the metric are available online. Correlated and white Gaussian noise processes are superimposed to account for biological and artificial jitter in the recordings. We report that the employment of more than three principal components is in general beneficial for all noise cases considered. Finally, we apply our results to experimental data and verify that the sorting process with four principal components is in agreement with a panel of electrophysiology experts.

Application of Hyperspectral Imaging and Chemometric Calibrations for Variety Discrimination of Maize Seeds

PubMed Central

Zhang, Xiaolei; Liu, Fei; He, Yong; Li, Xiaoli

2012-01-01

Hyperspectral imaging in the visible and near infrared (VIS-NIR) region was used to develop a novel method for discriminating different varieties of commodity maize seeds. Firstly, hyperspectral images of 330 samples of six varieties of maize seeds were acquired using a hyperspectral imaging system in the 380–1,030 nm wavelength range. Secondly, principal component analysis (PCA) and kernel principal component analysis (KPCA) were used to explore the internal structure of the spectral data. Thirdly, three optimal wavelengths (523, 579 and 863 nm) were selected by implementing PCA directly on each image. Then four textural variables including contrast, homogeneity, energy and correlation were extracted from gray level co-occurrence matrix (GLCM) of each monochromatic image based on the optimal wavelengths. Finally, several models for maize seeds identification were established by least squares-support vector machine (LS-SVM) and back propagation neural network (BPNN) using four different combinations of principal components (PCs), kernel principal components (KPCs) and textural features as input variables, respectively. The recognition accuracy achieved in the PCA-GLCM-LS-SVM model (98.89%) was the most satisfactory one. We conclude that hyperspectral imaging combined with texture analysis can be implemented for fast classification of different varieties of maize seeds. PMID:23235456

Feature extraction via KPCA for classification of gait patterns.

PubMed

Wu, Jianning; Wang, Jue; Liu, Li

2007-06-01

Automated recognition of gait pattern change is important in medical diagnostics as well as in the early identification of at-risk gait in the elderly. We evaluated the use of Kernel-based Principal Component Analysis (KPCA) to extract more gait features (i.e., to obtain more significant amounts of information about human movement) and thus to improve the classification of gait patterns. 3D gait data of 24 young and 24 elderly participants were acquired using an OPTOTRAK 3020 motion analysis system during normal walking, and a total of 36 gait spatio-temporal and kinematic variables were extracted from the recorded data. KPCA was used first for nonlinear feature extraction to then evaluate its effect on a subsequent classification in combination with learning algorithms such as support vector machines (SVMs). Cross-validation test results indicated that the proposed technique could allow spreading the information about the gait's kinematic structure into more nonlinear principal components, thus providing additional discriminatory information for the improvement of gait classification performance. The feature extraction ability of KPCA was affected slightly with different kernel functions as polynomial and radial basis function. The combination of KPCA and SVM could identify young-elderly gait patterns with 91% accuracy, resulting in a markedly improved performance compared to the combination of PCA and SVM. These results suggest that nonlinear feature extraction by KPCA improves the classification of young-elderly gait patterns, and holds considerable potential for future applications in direct dimensionality reduction and interpretation of multiple gait signals.

Feature Extraction and Selection Strategies for Automated Target Recognition

NASA Technical Reports Server (NTRS)

Greene, W. Nicholas; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2010-01-01

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory region of-interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

Feature extraction and selection strategies for automated target recognition

NASA Astrophysics Data System (ADS)

Greene, W. Nicholas; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2010-04-01

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory regionof- interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

Development of neural network techniques for finger-vein pattern classification

NASA Astrophysics Data System (ADS)

Wu, Jian-Da; Liu, Chiung-Tsiung; Tsai, Yi-Jang; Liu, Jun-Ching; Chang, Ya-Wen

2010-02-01

A personal identification system using finger-vein patterns and neural network techniques is proposed in the present study. In the proposed system, the finger-vein patterns are captured by a device that can transmit near infrared through the finger and record the patterns for signal analysis and classification. The biometric system for verification consists of a combination of feature extraction using principal component analysis and pattern classification using both back-propagation network and adaptive neuro-fuzzy inference systems. Finger-vein features are first extracted by principal component analysis method to reduce the computational burden and removes noise residing in the discarded dimensions. The features are then used in pattern classification and identification. To verify the effect of the proposed adaptive neuro-fuzzy inference system in the pattern classification, the back-propagation network is compared with the proposed system. The experimental results indicated the proposed system using adaptive neuro-fuzzy inference system demonstrated a better performance than the back-propagation network for personal identification using the finger-vein patterns.

Sample-space-based feature extraction and class preserving projection for gene expression data.

PubMed

Wang, Wenjun

2013-01-01

In order to overcome the problems of high computational complexity and serious matrix singularity for feature extraction using Principal Component Analysis (PCA) and Fisher's Linear Discrinimant Analysis (LDA) in high-dimensional data, sample-space-based feature extraction is presented, which transforms the computation procedure of feature extraction from gene space to sample space by representing the optimal transformation vector with the weighted sum of samples. The technique is used in the implementation of PCA, LDA, Class Preserving Projection (CPP) which is a new method for discriminant feature extraction proposed, and the experimental results on gene expression data demonstrate the effectiveness of the method.

Method of Real-Time Principal-Component Analysis

NASA Technical Reports Server (NTRS)

Duong, Tuan; Duong, Vu

2005-01-01

Dominant-element-based gradient descent and dynamic initial learning rate (DOGEDYN) is a method of sequential principal-component analysis (PCA) that is well suited for such applications as data compression and extraction of features from sets of data. In comparison with a prior method of gradient-descent-based sequential PCA, this method offers a greater rate of learning convergence. Like the prior method, DOGEDYN can be implemented in software. However, the main advantage of DOGEDYN over the prior method lies in the facts that it requires less computation and can be implemented in simpler hardware. It should be possible to implement DOGEDYN in compact, low-power, very-large-scale integrated (VLSI) circuitry that could process data in real time.

Iris recognition based on robust principal component analysis

NASA Astrophysics Data System (ADS)

Karn, Pradeep; He, Xiao Hai; Yang, Shuai; Wu, Xiao Hong

2014-11-01

Iris images acquired under different conditions often suffer from blur, occlusion due to eyelids and eyelashes, specular reflection, and other artifacts. Existing iris recognition systems do not perform well on these types of images. To overcome these problems, we propose an iris recognition method based on robust principal component analysis. The proposed method decomposes all training images into a low-rank matrix and a sparse error matrix, where the low-rank matrix is used for feature extraction. The sparsity concentration index approach is then applied to validate the recognition result. Experimental results using CASIA V4 and IIT Delhi V1iris image databases showed that the proposed method achieved competitive performances in both recognition accuracy and computational efficiency.

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.

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.

Exploring the Factor Structure of Neurocognitive Measures in Older Individuals

PubMed Central

Santos, Nadine Correia; Costa, Patrício Soares; Amorim, Liliana; Moreira, Pedro Silva; Cunha, Pedro; Cotter, Jorge; Sousa, Nuno

2015-01-01

Here we focus on factor analysis from a best practices point of view, by investigating the factor structure of neuropsychological tests and using the results obtained to illustrate on choosing a reasonable solution. The sample (n=1051 individuals) was randomly divided into two groups: one for exploratory factor analysis (EFA) and principal component analysis (PCA), to investigate the number of factors underlying the neurocognitive variables; the second to test the “best fit” model via confirmatory factor analysis (CFA). For the exploratory step, three extraction (maximum likelihood, principal axis factoring and principal components) and two rotation (orthogonal and oblique) methods were used. The analysis methodology allowed exploring how different cognitive/psychological tests correlated/discriminated between dimensions, indicating that to capture latent structures in similar sample sizes and measures, with approximately normal data distribution, reflective models with oblimin rotation might prove the most adequate. PMID:25880732

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.

Influence of extractable soil manganese on oxidation capacity of different soils in Korea

NASA Astrophysics Data System (ADS)

Chon, Chul-Min; Kim, Jae Gon; Lee, Gyoo Ho; Kim, Tack Hyun

2008-08-01

We examined the relationship between soil oxidation capacity and extractable soil manganese, iron oxides, and other soil properties. The Korean soils examined in this study exhibited low to medium Cr oxidation capacities, oxidizing 0.00-0.47 mmol/kg, except for TG-4 soils, which had the highest capacity for oxidizing added Cr(III) [>1.01 mmol/kg of oxidized Cr(VI)]. TG and US soils, with high Mn contents, had relatively high oxidation capacities. The Mn amounts extracted by dithionite-citrate-bicarbonate (DCB) (Mnd), NH2OH·HCl (Mnh), and hydroquinone (Mnr) were generally very similar, except for the YS1 soils, and were well correlated. Only small proportions of either total Mn or DCB-extractable Mn were extracted by NH2OH·HCl and hydroquinone in the YS1 soils, suggesting inclusion of NH2OH·HCl and hydroquinone-resistant Mn oxides, because these extractants are weaker reductants than DCB. No Cr oxidation test results were closely related to total Mn concentrations, but Mnd, Mnh, and Mnr showed a relatively high correlation with the Cr tests ( r = 0.655-0.851; P < 0.01). The concentrations of Mnd and Mnh were better correlated with the Cr oxidation tests than was the Mnr concentration, suggesting that the oxidation capacity of our soil samples can be better explained by Mnd and Mnh than by Mnr. The first component in principal components analysis indicated that extractable soil Mn was a main factor controlling net Cr oxidation in the soils. Total soil Mn, Fe oxides, and the clay fraction are crucial for predicting the mobility of pollutants and heavy metals in soils. The second principal component indicated that the presence of Fe oxides in soils had a significant relationship with the clay fraction and total Mn oxide, and was also related to heavy-metal concentrations (Zn, Cd, and Cu, but not Pb).

An Extended Spectral-Spatial Classification Approach for Hyperspectral Data

NASA Astrophysics Data System (ADS)

Akbari, D.

2017-11-01

In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MSF) algorithm. Three different methods of dimension reduction are first used to obtain the subspace of hyperspectral data: (1) unsupervised feature extraction methods including principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF); (2) supervised feature extraction including decision boundary feature extraction (DBFE), discriminate analysis feature extraction (DAFE), and nonparametric weighted feature extraction (NWFE); (3) genetic algorithm (GA). The spectral features obtained are then fed into the enhanced marker-based MSF classification algorithm. In the enhanced MSF algorithm, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm. To evaluate the proposed approach, the Pavia University hyperspectral data is tested. Experimental results show that the proposed approach using GA achieves an approximately 8 % overall accuracy higher than the original MSF-based algorithm.

Facilitated Visual Interpretation of Scores in Principal Component Analysis by Bioactivity-Labeling of 1H-NMR Spectra-Metabolomics Investigation and Identification of a New α-Glucosidase Inhibitor in Radix Astragali.

PubMed

Liu, Yueqiu; Nyberg, Nils T; Jäger, Anna K; Staerk, Dan

2017-03-06

Radix Astragali is a component of several traditional medicines used for the treatment of type 2 diabetes in China. Radix Astragali is known to contain isoflavones, which inhibit α-glucosidase in the small intestines, and thus lowers the blood glucose levels. In this study, 21 samples obtained from different regions of China were extracted with ethyl acetate, then the IC50-values were determined, and the crude extracts were analyzed by 1H-NMR spectroscopy. A principal component analysis of the 1H-NMR spectra labeled with their IC50-values, that is, bioactivity-labeled 1H-NMR spectra, showed a clear correlation between spectral profiles and the α-glucosidase inhibitory activity. The loading plot and LC-HRMS/NMR of microfractions indicated that previously unknown long chain ferulates could be partly responsible for the observed antidiabetic activity of Radix Astragali. Subsequent preparative scale isolation revealed a compound not previously reported, linoleyl ferulate (1), showing α-glucosidase inhibitory activity (IC50 0.5 mM) at a level comparable to the previously studied isoflavones. A closely related analogue, hexadecyl ferulate (2), did not show significant inhibitory activity, and the double bonds in the alcohol part of 1 seem to be important structural features for the α-glucosidase inhibitory activity. This proof of concept study demonstrates that bioactivity-labeling of the 1H-NMR spectral data of crude extracts allows global and nonselective identification of individual constituents contributing to the crude extract's bioactivity.

Fractional, biodegradable and spectral characteristics of extracted and fractionated sludge extracellular polymeric substances.

PubMed

Wei, Liang-Liang; Wang, Kun; Zhao, Qing-Liang; Jiang, Jun-Qiu; Kong, Xiang-Juan; Lee, Duu-Jong

2012-09-15

Correlation between fractional, biodegradable and spectral characteristics of sludge extracellular polymeric substances (EPS) by different protocols has not been well established. This work extracted sludge EPS using alkaline extractants (NH₄OH and formaldehyde + NaOH) and physical protocols (ultrasonication, heating at 80 °C or cation exchange resin (CER)) and then fractionated the extracts using XAD-8/XAD-4 resins. The alkaline extractants yielded more sludge EPS than the physical protocols. However, the physical protocols extracted principally the hydrophilic components which were readily biodegradable by microorganisms. The alkaline extractants dissolved additional humic-like substances from sludge solids which were refractory in nature. Different extraction protocols preferably extracted EPS with distinct fractional, biodegradable and spectral characteristics which could be applied in specific usages. Copyright © 2012 Elsevier Ltd. All rights reserved.

Combinations of Ashwagandha Leaf Extracts Protect Brain-Derived Cells against Oxidative Stress and Induce Differentiation

PubMed Central

Shah, Navjot; Singh, Rumani; Sarangi, Upasana; Saxena, Nishant; Chaudhary, Anupama; Kaur, Gurcharan; Kaul, Sunil C.; Wadhwa, Renu

2015-01-01

Background Ashwagandha, a traditional Indian herb, has been known for its variety of therapeutic activities. We earlier demonstrated anticancer activities in the alcoholic and water extracts of the leaves that were mediated by activation of tumor suppressor functions and oxidative stress in cancer cells. Low doses of these extracts were shown to possess neuroprotective activities in vitro and in vivo assays. Methodology/Principal Findings We used cultured glioblastoma and neuroblastoma cells to examine the effect of extracts (alcoholic and water) as well as their bioactive components for neuroprotective activities against oxidative stress. Various biochemical and imaging assays on the marker proteins of glial and neuronal cells were performed along with their survival profiles in control, stressed and recovered conditions. We found that the extracts and one of the purified components, withanone, when used at a low dose, protected the glial and neuronal cells from oxidative as well as glutamate insult, and induced their differentiation per se. Furthermore, the combinations of extracts and active component were highly potent endorsing the therapeutic merit of the combinational approach. Conclusion Ashwagandha leaf derived bioactive compounds have neuroprotective potential and may serve as supplement for brain health. PMID:25789768

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.

Discrimination of healthy and osteoarthritic articular cartilage by Fourier transform infrared imaging and Fisher’s discriminant analysis

PubMed Central

Mao, Zhi-Hua; Yin, Jian-Hua; Zhang, Xue-Xi; Wang, Xiao; Xia, Yang

2016-01-01

Fourier transform infrared spectroscopic imaging (FTIRI) technique can be used to obtain the quantitative information of content and spatial distribution of principal components in cartilage by combining with chemometrics methods. In this study, FTIRI combining with principal component analysis (PCA) and Fisher’s discriminant analysis (FDA) was applied to identify the healthy and osteoarthritic (OA) articular cartilage samples. Ten 10-μm thick sections of canine cartilages were imaged at 6.25μm/pixel in FTIRI. The infrared spectra extracted from the FTIR images were imported into SPSS software for PCA and FDA. Based on the PCA result of 2 principal components, the healthy and OA cartilage samples were effectively discriminated by the FDA with high accuracy of 94% for the initial samples (training set) and cross validation, as well as 86.67% for the prediction group. The study showed that cartilage degeneration became gradually weak with the increase of the depth. FTIRI combined with chemometrics may become an effective method for distinguishing healthy and OA cartilages in future. PMID:26977354

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

PubMed Central

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

2013-01-01

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

Discriminating between the vocalizations of Indo-Pacific humpback and Australian snubfin dolphins in Queensland, Australia.

PubMed

Berg Soto, Alvaro; Marsh, Helene; Everingham, Yvette; Smith, Joshua N; Parra, Guido J; Noad, Michael

2014-08-01

Australian snubfin and Indo-Pacific humpback dolphins co-occur throughout most of their range in coastal waters of tropical Australia. Little is known of their ecology or acoustic repertoires. Vocalizations from humpback and snubfin dolphins were recorded in two locations along the Queensland coast during 2008 and 2010 to describe their vocalizations and evaluate the acoustic differences between these two species. Broad vocalization types were categorized qualitatively. Both species produced click trains burst pulses and whistles. Principal component analysis of the nine acoustic variables extracted from the whistles produced nine principal components that were input into discriminant function analyses to classify 96% of humpback dolphin whistles and about 78% of snubfin dolphin calls correctly. Results indicate clear acoustic differences between the vocal whistle repertoires of these two species. A stepwise routine identified two principal components as significantly distinguishable between whistles of each species: frequency parameters and frequency trend ratio. The capacity to identify these species using acoustic monitoring techniques has the potential to provide information on presence/absence, habitat use and relative abundance for each species.

Principal Component Analysis for pulse-shape discrimination of scintillation radiation detectors

NASA Astrophysics Data System (ADS)

Alharbi, T.

2016-01-01

In this paper, we report on the application of Principal Component analysis (PCA) for pulse-shape discrimination (PSD) of scintillation radiation detectors. The details of the method are described and the performance of the method is experimentally examined by discriminating between neutrons and gamma-rays with a liquid scintillation detector in a mixed radiation field. The performance of the method is also compared against that of the conventional charge-comparison method, demonstrating the superior performance of the method particularly at low light output range. PCA analysis has the important advantage of automatic extraction of the pulse-shape characteristics which makes the PSD method directly applicable to various scintillation detectors without the need for the adjustment of a PSD parameter.

Principal semantic components of language and the measurement of meaning.

PubMed

Samsonovich, Alexei V; Samsonovic, Alexei V; Ascoli, Giorgio A

2010-06-11

Metric systems for semantics, or semantic cognitive maps, are allocations of words or other representations in a metric space based on their meaning. Existing methods for semantic mapping, such as Latent Semantic Analysis and Latent Dirichlet Allocation, are based on paradigms involving dissimilarity metrics. They typically do not take into account relations of antonymy and yield a large number of domain-specific semantic dimensions. Here, using a novel self-organization approach, we construct a low-dimensional, context-independent semantic map of natural language that represents simultaneously synonymy and antonymy. Emergent semantics of the map principal components are clearly identifiable: the first three correspond to the meanings of "good/bad" (valence), "calm/excited" (arousal), and "open/closed" (freedom), respectively. The semantic map is sufficiently robust to allow the automated extraction of synonyms and antonyms not originally in the dictionaries used to construct the map and to predict connotation from their coordinates. The map geometric characteristics include a limited number ( approximately 4) of statistically significant dimensions, a bimodal distribution of the first component, increasing kurtosis of subsequent (unimodal) components, and a U-shaped maximum-spread planar projection. Both the semantic content and the main geometric features of the map are consistent between dictionaries (Microsoft Word and Princeton's WordNet), among Western languages (English, French, German, and Spanish), and with previously established psychometric measures. By defining the semantics of its dimensions, the constructed map provides a foundational metric system for the quantitative analysis of word meaning. Language can be viewed as a cumulative product of human experiences. Therefore, the extracted principal semantic dimensions may be useful to characterize the general semantic dimensions of the content of mental states. This is a fundamental step toward a universal metric system for semantics of human experiences, which is necessary for developing a rigorous science of the mind.

Classification of high-resolution multispectral satellite remote sensing images using extended morphological attribute profiles and independent component analysis

NASA Astrophysics Data System (ADS)

Wu, Yu; Zheng, Lijuan; Xie, Donghai; Zhong, Ruofei

2017-07-01

In this study, the extended morphological attribute profiles (EAPs) and independent component analysis (ICA) were combined for feature extraction of high-resolution multispectral satellite remote sensing images and the regularized least squares (RLS) approach with the radial basis function (RBF) kernel was further applied for the classification. Based on the major two independent components, the geometrical features were extracted using the EAPs method. In this study, three morphological attributes were calculated and extracted for each independent component, including area, standard deviation, and moment of inertia. The extracted geometrical features classified results using RLS approach and the commonly used LIB-SVM library of support vector machines method. The Worldview-3 and Chinese GF-2 multispectral images were tested, and the results showed that the features extracted by EAPs and ICA can effectively improve the accuracy of the high-resolution multispectral image classification, 2% larger than EAPs and principal component analysis (PCA) method, and 6% larger than APs and original high-resolution multispectral data. Moreover, it is also suggested that both the GURLS and LIB-SVM libraries are well suited for the multispectral remote sensing image classification. The GURLS library is easy to be used with automatic parameter selection but its computation time may be larger than the LIB-SVM library. This study would be helpful for the classification application of high-resolution multispectral satellite remote sensing images.

Rotation of EOFs by the Independent Component Analysis: Towards A Solution of the Mixing Problem in the Decomposition of Geophysical Time Series

NASA Technical Reports Server (NTRS)

Aires, Filipe; Rossow, William B.; Chedin, Alain; Hansen, James E. (Technical Monitor)

2001-01-01

The Independent Component Analysis is a recently developed technique for component extraction. This new method requires the statistical independence of the extracted components, a stronger constraint that uses higher-order statistics, instead of the classical decorrelation, a weaker constraint that uses only second-order statistics. This technique has been used recently for the analysis of geophysical time series with the goal of investigating the causes of variability in observed data (i.e. exploratory approach). We demonstrate with a data simulation experiment that, if initialized with a Principal Component Analysis, the Independent Component Analysis performs a rotation of the classical PCA (or EOF) solution. This rotation uses no localization criterion like other Rotation Techniques (RT), only the global generalization of decorrelation by statistical independence is used. This rotation of the PCA solution seems to be able to solve the tendency of PCA to mix several physical phenomena, even when the signal is just their linear sum.

Symbolic dynamic filtering and language measure for behavior identification of mobile robots.

PubMed

Mallapragada, Goutham; Ray, Asok; Jin, Xin

2012-06-01

This paper presents a procedure for behavior identification of mobile robots, which requires limited or no domain knowledge of the underlying process. While the features of robot behavior are extracted by symbolic dynamic filtering of the observed time series, the behavior patterns are classified based on language measure theory. The behavior identification procedure has been experimentally validated on a networked robotic test bed by comparison with commonly used tools, namely, principal component analysis for feature extraction and Bayesian risk analysis for pattern classification.

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

PubMed Central

Butler, Rebecca A.

2014-01-01

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

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

ERIC Educational Resources Information Center

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

2016-01-01

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

An Alternative Way to Model Population Ability Distributions in Large-Scale Educational Surveys

ERIC Educational Resources Information Center

Wetzel, Eunike; Xu, Xueli; von Davier, Matthias

2015-01-01

In large-scale educational surveys, a latent regression model is used to compensate for the shortage of cognitive information. Conventionally, the covariates in the latent regression model are principal components extracted from background data. This operational method has several important disadvantages, such as the handling of missing data and…

Nuclear norm-based 2-DPCA for extracting features from images.

PubMed

Zhang, Fanlong; Yang, Jian; Qian, Jianjun; Xu, Yong

2015-10-01

The 2-D principal component analysis (2-DPCA) is a widely used method for image feature extraction. However, it can be equivalently implemented via image-row-based principal component analysis. This paper presents a structured 2-D method called nuclear norm-based 2-DPCA (N-2-DPCA), which uses a nuclear norm-based reconstruction error criterion. The nuclear norm is a matrix norm, which can provide a structured 2-D characterization for the reconstruction error image. The reconstruction error criterion is minimized by converting the nuclear norm-based optimization problem into a series of F-norm-based optimization problems. In addition, N-2-DPCA is extended to a bilateral projection-based N-2-DPCA (N-B2-DPCA). The virtue of N-B2-DPCA over N-2-DPCA is that an image can be represented with fewer coefficients. N-2-DPCA and N-B2-DPCA are applied to face recognition and reconstruction and evaluated using the Extended Yale B, CMU PIE, FRGC, and AR databases. Experimental results demonstrate the effectiveness of the proposed methods.

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.

A Unified Approach to Functional Principal Component Analysis and Functional Multiple-Set Canonical Correlation.

PubMed

Choi, Ji Yeh; Hwang, Heungsun; Yamamoto, Michio; Jung, Kwanghee; Woodward, Todd S

2017-06-01

Functional principal component analysis (FPCA) and functional multiple-set canonical correlation analysis (FMCCA) are data reduction techniques for functional data that are collected in the form of smooth curves or functions over a continuum such as time or space. In FPCA, low-dimensional components are extracted from a single functional dataset such that they explain the most variance of the dataset, whereas in FMCCA, low-dimensional components are obtained from each of multiple functional datasets in such a way that the associations among the components are maximized across the different sets. In this paper, we propose a unified approach to FPCA and FMCCA. The proposed approach subsumes both techniques as special cases. Furthermore, it permits a compromise between the techniques, such that components are obtained from each set of functional data to maximize their associations across different datasets, while accounting for the variance of the data well. We propose a single optimization criterion for the proposed approach, and develop an alternating regularized least squares algorithm to minimize the criterion in combination with basis function approximations to functions. We conduct a simulation study to investigate the performance of the proposed approach based on synthetic data. We also apply the approach for the analysis of multiple-subject functional magnetic resonance imaging data to obtain low-dimensional components of blood-oxygen level-dependent signal changes of the brain over time, which are highly correlated across the subjects as well as representative of the data. The extracted components are used to identify networks of neural activity that are commonly activated across the subjects while carrying out a working memory task.

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.

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

In-tube extraction and GC-MS analysis of volatile components from wild and cultivated sea buckthorn (Hippophae rhamnoides L. ssp. Carpatica) berry varieties and juice.

PubMed

Socaci, Sonia A; Socaciu, Carmen; Tofană, Maria; Raţi, Ioan V; Pintea, Adela

2013-01-01

The health benefits of sea buckthorn (Hippophae rhamnoides L.) are well documented due to its rich content in bioactive phytochemicals (pigments, phenolics and vitamins) as well as volatiles responsible for specific flavours and bacteriostatic action. The volatile compounds are good biomarkers of berry freshness, quality and authenticity. To develop a fast and efficient GC-MS method including a minimal sample preparation technique (in-tube extraction, ITEX) for the discrimination of sea buckthorn varieties based on their chromatographic volatile fingerprint. Twelve sea buckthorn varieties (wild and cultivated) were collected from forestry departments and experimental fields, respectively. The extraction of volatile compounds was performed using the ITEX technique whereas separation and identification was performed using a GC-MS QP-2010. Principal component analysis (PCA) was applied to discriminate the differences among sample composition. Using GC-MS analysis, from the headspace of sea buckthorn samples, 46 volatile compounds were separated with 43 being identified. The most abundant derivatives were ethyl esters of 2-methylbutanoic acid, 3-methylbutanoic acid, hexanoic acid, octanoic acid and butanoic acid, as well as 3-methylbutyl 3-methylbutanoate, 3-methylbutyl 2-methylbutanoate and benzoic acid ethyl ester (over 80% of all volatile compounds). Principal component analysis showed that the first two components explain 79% of data variance, demonstrating a good discrimination between samples. A reliable, fast and eco-friendly ITEX/GC-MS method was applied to fingerprint the volatile profile and to discriminate between wild and cultivated sea buckthorn berries originating from the Carpathians, with relevance to food science and technology. Copyright © 2013 John Wiley & Sons, Ltd.

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.

Quality Aware Compression of Electrocardiogram Using Principal Component Analysis.

PubMed

Gupta, Rajarshi

2016-05-01

Electrocardiogram (ECG) compression finds wide application in various patient monitoring purposes. Quality control in ECG compression ensures reconstruction quality and its clinical acceptance for diagnostic decision making. In this paper, a quality aware compression method of single lead ECG is described using principal component analysis (PCA). After pre-processing, beat extraction and PCA decomposition, two independent quality criteria, namely, bit rate control (BRC) or error control (EC) criteria were set to select optimal principal components, eigenvectors and their quantization level to achieve desired bit rate or error measure. The selected principal components and eigenvectors were finally compressed using a modified delta and Huffman encoder. The algorithms were validated with 32 sets of MIT Arrhythmia data and 60 normal and 30 sets of diagnostic ECG data from PTB Diagnostic ECG data ptbdb, all at 1 kHz sampling. For BRC with a CR threshold of 40, an average Compression Ratio (CR), percentage root mean squared difference normalized (PRDN) and maximum absolute error (MAE) of 50.74, 16.22 and 0.243 mV respectively were obtained. For EC with an upper limit of 5 % PRDN and 0.1 mV MAE, the average CR, PRDN and MAE of 9.48, 4.13 and 0.049 mV respectively were obtained. For mitdb data 117, the reconstruction quality could be preserved up to CR of 68.96 by extending the BRC threshold. The proposed method yields better results than recently published works on quality controlled ECG compression.

Fault Detection of Bearing Systems through EEMD and Optimization Algorithm

PubMed Central

Lee, Dong-Han; Ahn, Jong-Hyo; Koh, Bong-Hwan

2017-01-01

This study proposes a fault detection and diagnosis method for bearing systems using ensemble empirical mode decomposition (EEMD) based feature extraction, in conjunction with particle swarm optimization (PSO), principal component analysis (PCA), and Isomap. First, a mathematical model is assumed to generate vibration signals from damaged bearing components, such as the inner-race, outer-race, and rolling elements. The process of decomposing vibration signals into intrinsic mode functions (IMFs) and extracting statistical features is introduced to develop a damage-sensitive parameter vector. Finally, PCA and Isomap algorithm are used to classify and visualize this parameter vector, to separate damage characteristics from healthy bearing components. Moreover, the PSO-based optimization algorithm improves the classification performance by selecting proper weightings for the parameter vector, to maximize the visualization effect of separating and grouping of parameter vectors in three-dimensional space. PMID:29143772

Proteome comparison for discrimination between honeydew and floral honeys from botanical species Mimosa scabrella Bentham by principal component analysis.

PubMed

Azevedo, Mônia Stremel; Valentim-Neto, Pedro Alexandre; Seraglio, Siluana Katia Tischer; da Luz, Cynthia Fernandes Pinto; Arisi, Ana Carolina Maisonnave; Costa, Ana Carolina Oliveira

2017-10-01

Due to the increasing valuation and appreciation of honeydew honey in many European countries and also to existing contamination among different types of honeys, authentication is an important aspect of quality control with regard to guaranteeing the origin in terms of source (honeydew or floral) and needs to be determined. Furthermore, proteins are minor components of the honey, despite the importance of their physiological effects, and can differ according to the source of the honey. In this context, the aims of this study were to carry out protein extraction from honeydew and floral honeys and to discriminate these honeys from the same botanical species, Mimosa scabrella Bentham, through proteome comparison using two-dimensional gel electrophoresis and principal component analysis. The results showed that the proteome profile and principal component analysis can be a useful tool for discrimination between these types of honey using matched proteins (45 matched spots). Also, the proteome profile showed 160 protein spots in honeydew honey and 84 spots in the floral honey. The protein profile can be a differential characteristic of this type of honey, in view of the importance of proteins as bioactive compounds in honey. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

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

USGS Publications Warehouse

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

2002-01-01

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

Chemical affinities between the solvent extractable and the bulk organic matter of fossil resin associated with an extinct podocarpaceae

USGS Publications Warehouse

Grimalt, J.O.; Simoneit, B.R.T.; Hatcher, P.G.

1989-01-01

Analyses by GC-MS and GC-IR of resin associated to Dacridiumites mawsonii deposits, an extinct species of Podocarpaceae occurring on the South Island of New Zealand during the Bortonian (Middle Eocene), have revealed that dehydroabietic acid is the predominant component of the solvent soluble fraction. Accordingly, this diterpenoid has been selected as the principal component material for spectroscopic comparison with the bulk resin using IR and CP/MAS 13C NMR. ?? 1989.

High-pressure processing as emergent technology for the extraction of bioactive ingredients from plant materials.

PubMed

Jun, Xi

2013-01-01

High-pressure processing is a food processing technique that has shown great potentials in the food industry. Recently, it was developed to extract bioactive ingredients from plant materials, known as ultrahigh pressure extraction (UPE), taking advantages of time saving, higher extraction yields, fewer impurities in the extraction solution, minimal heat and can avoid thermal degradation on the activity and structure of bioactive components, and so on. This review provides an overview of the developments in the UPE of bioactive ingredients from plant material. Apart from a brief presentation of the theories of UPE and extraction equipment systems, the principal parameters that influence the extraction efficiency to be optimized in the UPE (e.g., solvent, pressure, temperature, extraction time, and the number of cycle) were discussed in detail, and finally the more recent applications of UPE for the extraction of active compounds from plant materials were summarized.

On the Factor Structure of a Reading Comprehension Test

ERIC Educational Resources Information Center

Salehi, Mohammad

2011-01-01

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

The use of multidate multichannel radiance data in urban feature analysis

NASA Technical Reports Server (NTRS)

Duggin, M. J.; Rowntree, R.; Emmons, M.; Hubbard, N.; Odell, A. W.

1986-01-01

Two images were obtained from thematic mappers on Landsats 4 and 5 over the Washington, DC area during November 1982 and March 1984. Selected training areas containing different types of urban land use were examined,one area consisting entirely of forest. Mean digital radiance values for each bandpass in each image were examined, and variances, standard deviations, and covariances between bandpasses were calculated. It has been found that two bandpasses caused forested areas to stand out from other land use types, especially for the November 1982 image. In order to evaluate quantitatively the possible utility of the principal components analysis in selected feature extraction, the eigenvectors were evaluated for principal axes rotations which rendered each selected land use type most separable from all other land use types. The evaluated eigenvectors were plotted as a function of land use type, whose order was decided by considering anticipated shadow component and by examining the relative loadings indicative of vegetation for each of the principal components for the different features considered. The analysis was performed for each seven-band image separately and for the two combined images. It was found that by combining the two images, more dramatic land use type separation could be obtained.

Capillary electrophoresis fingerprinting and spectrophotometric determination of antioxidant potential for classification of Mentha products.

PubMed

Roblová, Vendula; Bittová, Miroslava; Kubáň, Petr; Kubáň, Vlastimil

2016-07-01

In this work aqueous infusions from ten Mentha herbal samples (four different Mentha species and six hybrids of Mentha x piperita) and 20 different peppermint teas were screened by capillary electrophoresis with UV detection. The fingerprint separation was accomplished in a 25 mM borate background electrolyte with 10% methanol at pH 9.3. The total polyphenolic content in the extracts was determined spectrophotometrically at 765 nm by a Folin-Ciocalteu phenol assay. Total antioxidant activity was determined by scavenging of 2,2-diphenyl-1-picrylhydrazyl radical at 515 nm. The peak areas of 12 dominant peaks from CE analysis, present in all samples, and the value of total polyphenolic content and total antioxidant activity obtained by spectrophotometry was combined into a single data matrix and principal component analysis was applied. The obtained principal component analysis model resulted in distinct clusters of Mentha and peppermint tea samples distinguishing the samples according to their potential protective antioxidant effect. Principal component analysis, using a non-targeted approach with no need for compound identification, was found as a new promising tool for the screening of herbal tea products. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Chemical and Antimicrobial Profiling of Propolis from Different Regions within Libya.

PubMed

Siheri, Weam; Zhang, Tong; Ebiloma, Godwin Unekwuojo; Biddau, Marco; Woods, Nicola; Hussain, Muattaz Yassein; Clements, Carol J; Fearnley, James; Ebel, RuAngelie Edrada; Paget, Timothy; Muller, Sylke; Carter, Katharine C; Ferro, Valerie A; De Koning, Harry P; Watson, David G

2016-01-01

Extracts from twelve samples of propolis collected from different regions of Libya were tested for their activity against Trypanosoma brucei, Leishmania donovani, Plasmodium falciparum, Crithidia fasciculata and Mycobacterium marinum and the cytotoxicity of the extracts was tested against mammalian cells. All the extracts were active to some degree against all of the protozoa and the mycobacterium, exhibiting a range of EC50 values between 1.65 and 53.6 μg/ml. The toxicity against mammalian cell lines was only moderate; the most active extract against the protozoan species, P2, displayed an IC50 value of 53.2 μg/ml. The extracts were profiled by using liquid chromatography coupled to high resolution mass spectrometry. The data sets were extracted using m/z Mine and the accurate masses of the features extracted were searched against the Dictionary of Natural Products (DNP). A principal component analysis (PCA) model was constructed which, in combination with hierarchical cluster analysis (HCA), divided the samples into five groups. The outlying groups had different sets of dominant compounds in the extracts, which could be characterised by their elemental composition. Orthogonal partial least squares (OPLS) analysis was used to link the activity of each extract against the different micro-organisms to particular components in the extracts.

Chemical and Antimicrobial Profiling of Propolis from Different Regions within Libya

PubMed Central

Siheri, Weam; Zhang, Tong; Ebiloma, Godwin Unekwuojo; Biddau, Marco; Woods, Nicola; Hussain, Muattaz Yassein; Clements, Carol J.; Fearnley, James; Ebel, RuAngelie Edrada; Paget, Timothy; Muller, Sylke; Carter, Katharine C.; Ferro, Valerie A.; De Koning, Harry P.; Watson, David G.

2016-01-01

Extracts from twelve samples of propolis collected from different regions of Libya were tested for their activity against Trypanosoma brucei, Leishmania donovani, Plasmodium falciparum, Crithidia fasciculata and Mycobacterium marinum and the cytotoxicity of the extracts was tested against mammalian cells. All the extracts were active to some degree against all of the protozoa and the mycobacterium, exhibiting a range of EC50 values between 1.65 and 53.6 μg/ml. The toxicity against mammalian cell lines was only moderate; the most active extract against the protozoan species, P2, displayed an IC50 value of 53.2 μg/ml. The extracts were profiled by using liquid chromatography coupled to high resolution mass spectrometry. The data sets were extracted using m/z Mine and the accurate masses of the features extracted were searched against the Dictionary of Natural Products (DNP). A principal component analysis (PCA) model was constructed which, in combination with hierarchical cluster analysis (HCA), divided the samples into five groups. The outlying groups had different sets of dominant compounds in the extracts, which could be characterised by their elemental composition. Orthogonal partial least squares (OPLS) analysis was used to link the activity of each extract against the different micro-organisms to particular components in the extracts. PMID:27195790

Classification of stevia sweeteners in soft drinks using liquid chromatography and time-of-flight mass spectrometry.

PubMed

Kakigi, Y; Suzuki, T; Icho, T; Uyama, A; Mochizuki, N

2013-01-01

The aim of this study was to develop a comprehensive analytical method for the characterisation of stevia sweeteners in soft drinks. By using LC and time-of-flight MS, we detected 30 steviol glycosides from nine stevia sweeteners. The mass spectral data of these compounds were applied to the analysis to determine steviol glycosides in nine soft drinks. On the basis of chromatographic data and principal-component analysis, these soft drinks were classified into three groups, and the soft drinks of each group, respectively, contained high-rebaudioside A extract, normal stevia extract or alfa-glucosyltransferase-treated stevia extract.

Towards Solving the Mixing Problem in the Decomposition of Geophysical Time Series by Independent Component Analysis

NASA Technical Reports Server (NTRS)

Aires, Filipe; Rossow, William B.; Chedin, Alain; Hansen, James E. (Technical Monitor)

2000-01-01

The use of the Principal Component Analysis technique for the analysis of geophysical time series has been questioned in particular for its tendency to extract components that mix several physical phenomena even when the signal is just their linear sum. We demonstrate with a data simulation experiment that the Independent Component Analysis, a recently developed technique, is able to solve this problem. This new technique requires the statistical independence of components, a stronger constraint, that uses higher-order statistics, instead of the classical decorrelation a weaker constraint, that uses only second-order statistics. Furthermore, ICA does not require additional a priori information such as the localization constraint used in Rotational Techniques.

Facial Expression Recognition using Multiclass Ensemble Least-Square Support Vector Machine

NASA Astrophysics Data System (ADS)

Lawi, Armin; Sya'Rani Machrizzandi, M.

2018-03-01

Facial expression is one of behavior characteristics of human-being. The use of biometrics technology system with facial expression characteristics makes it possible to recognize a person’s mood or emotion. The basic components of facial expression analysis system are face detection, face image extraction, facial classification and facial expressions recognition. This paper uses Principal Component Analysis (PCA) algorithm to extract facial features with expression parameters, i.e., happy, sad, neutral, angry, fear, and disgusted. Then Multiclass Ensemble Least-Squares Support Vector Machine (MELS-SVM) is used for the classification process of facial expression. The result of MELS-SVM model obtained from our 185 different expression images of 10 persons showed high accuracy level of 99.998% using RBF kernel.

Characterization of Aroma-Active Components and Antioxidant Activity Analysis of E-jiao (Colla Corii Asini) from Different Geographical Origins.

PubMed

Zhang, Shan; Xu, Lu; Liu, Yang-Xi; Fu, Hai-Yan; Xiao, Zuo-Bing; She, Yuan-Bin

2018-04-01

E-jiao (Colla Corii Asini, CCA) has been widely used as a healthy food and Chinese medicine. Although authentic CCA is characterized by its typical sweet and neutral fragrance, its aroma components have been rarely investigated. This work investigated the aroma-active components and antioxidant activity of 19 CCAs from different geographical origins. CCA extracts obtained by simultaneous distillation and extraction were analyzed by gas chromatography-mass spectrometry (GC-MS), gas chromatography-olfactometry (GC-O) and sensory analysis. The antioxidant activity of CCAs was determined by ABTS and DPPH assays. A total of 65 volatile compounds were identified and quantified by GC-MS and 23 aroma-active compounds were identified by GC-O and aroma extract dilution analysis. The most powerful aroma-active compounds were identified based on the flavor dilution factor and their contents were compared among the 19 CCAs. Principal component analysis of the 23 aroma-active components showed 3 significant clusters. Canonical correlation analysis between antioxidant assays and the 23 aroma-active compounds indicates strong correlation (r = 0.9776, p = 0.0281). Analysis of aroma-active components shows potential for quality evaluation and discrimination of CCAs from different geographical origins.

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).

Image preprocessing study on KPCA-based face recognition

NASA Astrophysics Data System (ADS)

Li, Xuan; Li, Dehua

2015-12-01

Face recognition as an important biometric identification method, with its friendly, natural, convenient advantages, has obtained more and more attention. This paper intends to research a face recognition system including face detection, feature extraction and face recognition, mainly through researching on related theory and the key technology of various preprocessing methods in face detection process, using KPCA method, focuses on the different recognition results in different preprocessing methods. In this paper, we choose YCbCr color space for skin segmentation and choose integral projection for face location. We use erosion and dilation of the opening and closing operation and illumination compensation method to preprocess face images, and then use the face recognition method based on kernel principal component analysis method for analysis and research, and the experiments were carried out using the typical face database. The algorithms experiment on MATLAB platform. Experimental results show that integration of the kernel method based on PCA algorithm under certain conditions make the extracted features represent the original image information better for using nonlinear feature extraction method, which can obtain higher recognition rate. In the image preprocessing stage, we found that images under various operations may appear different results, so as to obtain different recognition rate in recognition stage. At the same time, in the process of the kernel principal component analysis, the value of the power of the polynomial function can affect the recognition result.

Statistical evaluation of fatty acid profile and cholesterol content in fish (common carp) lipids obtained by different sample preparation procedures.

PubMed

Spiric, Aurelija; Trbovic, Dejana; Vranic, Danijela; Djinovic, Jasna; Petronijevic, Radivoj; Matekalo-Sverak, Vesna

2010-07-05

Studies performed on lipid extraction from animal and fish tissues do not provide information on its influence on fatty acid composition of the extracted lipids as well as on cholesterol content. Data presented in this paper indicate the impact of extraction procedures on fatty acid profile of fish lipids extracted by the modified Soxhlet and ASE (accelerated solvent extraction) procedure. Cholesterol was also determined by direct saponification method, too. Student's paired t-test used for comparison of the total fat content in carp fish population obtained by two extraction methods shows that differences between values of the total fat content determined by ASE and modified Soxhlet method are not statistically significant. Values obtained by three different methods (direct saponification, ASE and modified Soxhlet method), used for determination of cholesterol content in carp, were compared by one-way analysis of variance (ANOVA). The obtained results show that modified Soxhlet method gives results which differ significantly from the results obtained by direct saponification and ASE method. However the results obtained by direct saponification and ASE method do not differ significantly from each other. The highest quantities for cholesterol (37.65 to 65.44 mg/100 g) in the analyzed fish muscle were obtained by applying direct saponification method, as less destructive one, followed by ASE (34.16 to 52.60 mg/100 g) and modified Soxhlet extraction method (10.73 to 30.83 mg/100 g). Modified Soxhlet method for extraction of fish lipids gives higher values for n-6 fatty acids than ASE method (t(paired)=3.22 t(c)=2.36), while there is no statistically significant difference in the n-3 content levels between the methods (t(paired)=1.31). The UNSFA/SFA ratio obtained by using modified Soxhlet method is also higher than the ratio obtained using ASE method (t(paired)=4.88 t(c)=2.36). Results of Principal Component Analysis (PCA) showed that the highest positive impact to the second principal component (PC2) is recorded by C18:3 n-3, and C20:3 n-6, being present in a higher amount in the samples treated by the modified Soxhlet extraction, while C22:5 n-3, C20:3 n-3, C22:1 and C20:4, C16 and C18 negatively influence the score values of the PC2, showing significantly increased level in the samples treated by ASE method. Hotelling's paired T-square test used on the first three principal components for confirmation of differences in individual fatty acid content obtained by ASE and Soxhlet method in carp muscle showed statistically significant difference between these two data sets (T(2)=161.308, p<0.001). Copyright 2010 Elsevier B.V. All rights reserved.

[Research on optimal modeling strategy for licorice extraction process based on near-infrared spectroscopy technology].

PubMed

Wang, Hai-Xia; Suo, Tong-Chuan; Yu, He-Shui; Li, Zheng

2016-10-01

The manufacture of traditional Chinese medicine (TCM) products is always accompanied by processing complex raw materials and real-time monitoring of the manufacturing process. In this study, we investigated different modeling strategies for the extraction process of licorice. Near-infrared spectra associate with the extraction time was used to detemine the states of the extraction processes. Three modeling approaches, i.e., principal component analysis (PCA), partial least squares regression (PLSR) and parallel factor analysis-PLSR (PARAFAC-PLSR), were adopted for the prediction of the real-time status of the process. The overall results indicated that PCA, PLSR and PARAFAC-PLSR can effectively detect the errors in the extraction procedure and predict the process trajectories, which has important significance for the monitoring and controlling of the extraction processes. Copyright© by the Chinese Pharmaceutical Association.

Effects of physiotherapy treatment on knee osteoarthritis gait data using principal component analysis.

PubMed

Gaudreault, Nathaly; Mezghani, Neila; Turcot, Katia; Hagemeister, Nicola; Boivin, Karine; de Guise, Jacques A

2011-03-01

Interpreting gait data is challenging due to intersubject variability observed in the gait pattern of both normal and pathological populations. The objective of this study was to investigate the impact of using principal component analysis for grouping knee osteoarthritis (OA) patients' gait data in more homogeneous groups when studying the effect of a physiotherapy treatment. Three-dimensional (3D) knee kinematic and kinetic data were recorded during the gait of 29 participants diagnosed with knee OA before and after they received 12 weeks of physiotherapy treatment. Principal component analysis was applied to extract groups of knee flexion/extension, adduction/abduction and internal/external rotation angle and moment data. The treatment's effect on parameters of interest was assessed using paired t-tests performed before and after grouping the knee kinematic data. Increased quadriceps and hamstring strength was observed following treatment (P<0.05). Except for the knee flexion/extension angle, two different groups (G(1) and G(2)) were extracted from the angle and moment data. When pre- and post-treatment analyses were performed considering the groups, participants exhibiting a G(2) knee moment pattern demonstrated a greater first peak flexion moment, lower adduction moment impulse and smaller rotation angle range post-treatment (P<0.05). When pre- and post-treatment comparisons were performed without grouping, the data showed no treatment effect. The results of the present study suggest that the effect of physiotherapy on gait mechanics of knee osteoarthritis patients may be masked or underestimated if kinematic data are not separated into more homogeneous groups when performing pre- and post-treatment comparisons. Copyright © 2010 Elsevier Ltd. All rights reserved.

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

PubMed

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

2015-02-01

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

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

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

Wang, J; Zhao, K; Peng, J

2014-06-15

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

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

NASA Astrophysics Data System (ADS)

Khosla, Deepak; Singh, Manbir; Don, Manuel

1997-05-01

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

Patient feature based dosimetric Pareto front prediction in esophageal cancer radiotherapy

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

Wang, Jiazhou; Zhao, Kuaike; Peng, Jiayuan

2015-02-15

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

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.

Fast noise level estimation algorithm based on principal component analysis transform and nonlinear rectification

NASA Astrophysics Data System (ADS)

Xu, Shaoping; Zeng, Xiaoxia; Jiang, Yinnan; Tang, Yiling

2018-01-01

We proposed a noniterative principal component analysis (PCA)-based noise level estimation (NLE) algorithm that addresses the problem of estimating the noise level with a two-step scheme. First, we randomly extracted a number of raw patches from a given noisy image and took the smallest eigenvalue of the covariance matrix of the raw patches as the preliminary estimation of the noise level. Next, the final estimation was directly obtained with a nonlinear mapping (rectification) function that was trained on some representative noisy images corrupted with different known noise levels. Compared with the state-of-art NLE algorithms, the experiment results show that the proposed NLE algorithm can reliably infer the noise level and has robust performance over a wide range of image contents and noise levels, showing a good compromise between speed and accuracy in general.

Structural aspects of face recognition and the other-race effect.

PubMed

O'Toole, A J; Deffenbacher, K A; Valentin, D; Abdi, H

1994-03-01

The other-race effect was examined in a series of experiments and simulations that looked at the relationships among observer ratings of typicality, familiarity, attractiveness, memorability, and the performance variables of d' and criterion. Experiment 1 replicated the other-race effect with our Caucasian and Japanese stimuli for both Caucasian and Asian observers. In Experiment 2, we collected ratings from Caucasian observers on the faces used in the recognition task. A Varimax-rotated principal components analysis on the rating and performance data for the Caucasian faces replicated Vokey and Read's (1992) finding that typicality is composed of two orthogonal components, dissociable via their independent relationships to: (1) attractiveness and familiarity ratings and (2) memorability ratings. For Japanese faces, however, we found that typicality was related only to memorability. Where performance measures were concerned, two additional principal components dominated by criterion and by d' emerged for Caucasian faces. For the Japanese faces, however, the performance measures of d' and criterion merged into a single component that represented a second component of typicality, one orthogonal to the memorability-dominated component. A measure of face representation quality extracted from an autoassociative neural network trained with a majority of Caucasian faces and a minority of Japanese faces was incorporated into the principal components analysis. For both Caucasian and Japanese faces, the neural network measure related both to memorability ratings and to human accuracy measures. Combined, the human data and simulation results indicate that the memorability component of typicality may be related to small, local, distinctive features, whereas the attractiveness/familiarity component may be more related to the global, shape-based properties of the face.

Comprehensive analysis of commercial willow bark extracts by new technology platform: combined use of metabolomics, high-performance liquid chromatography-solid-phase extraction-nuclear magnetic resonance spectroscopy and high-resolution radical scavenging assay.

PubMed

Agnolet, Sara; Wiese, Stefanie; Verpoorte, Robert; Staerk, Dan

2012-11-02

Here, proof-of-concept of a new analytical platform used for the comprehensive analysis of a small set of commercial willow bark products is presented, and compared with a traditional standardization solely based on analysis of salicin and salicin derivatives. The platform combines principal component analysis (PCA) of two chemical fingerprints, i.e., HPLC and (1)H NMR data, and a pharmacological fingerprint, i.e., high-resolution 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonate) radical cation (ABTS(+)) reduction profile, with targeted identification of constituents of interest by hyphenated HPLC-solid-phase extraction-tube transfer NMR, i.e., HPLC-SPE-ttNMR. Score plots from PCA of HPLC and (1)H NMR fingerprints showed the same distinct grouping of preparations formulated as capsules of Salix alba bark and separation of S. alba cortex. Loading plots revealed this to be due to high amount of salicin in capsules and ampelopsin, taxifolin, 7-O-methyltaxifolin-3'-O-glucoside, and 7-O-methyltaxifolin in S. alba cortex, respectively. PCA of high-resolution radical scavenging profiles revealed clear separation of preparations along principal component 1 due to the major radical scavengers (+)-catechin and ampelopsin. The new analytical platform allowed identification of 16 compounds in commercial willow bark extracts, and identification of ampelopsin, taxifolin, 7-O-methyltaxifolin-3'-O-glucoside, and 7-O-methyltaxifolin in S. alba bark extract is reported for the first time. The detection of the novel compound, ethyl 1-hydroxy-6-oxocyclohex-2-enecarboxylate, is also described. Copyright © 2012 Elsevier B.V. All rights reserved.

Principal component analysis of biometric traits to reveal body confirmation in local hill cattle of Himalayan state of Himachal Pradesh, India.

PubMed

Verma, Deepak; Sankhyan, Varun; Katoch, Sanjeet; Thakur, Yash Pal

2015-12-01

In the present study, biometric traits (body length [BL], heart girth [HG], paunch girth (PG), forelimb length (FLL), hind limb length (HLL), face length, forehead width, forehead length, height at hump, hump length (HL), hook to hook distance, pin to pin distance, tail length (TL), TL up to switch, horn length, horn circumference, and ear length were studied in 218 adult hill cattle of Himachal Pradesh for phenotypic characterization. Morphological and biometrical observations were recorded on 218 hill cattle randomly selected from different districts within the breeding tract. Multivariate statistics and principal component analysis are used to account for the maximum portion of variation present in the original set of variables with a minimum number of composite variables through Statistical software, SAS 9.2. Five components were extracted which accounted for 65.9% of variance. The first component explained general body confirmation and explained 34.7% variation. It was represented by significant loading for BL, HG, PG, FLL, and HLL. Communality estimate ranged from 0.41 (HL) to 0.88 (TL). Second, third, fourth, and fifth component had a high loading for tail characteristics, horn characteristics, facial biometrics, and rear body, respectively. The result of component analysis of biometric traits suggested that indigenous hill cattle of Himachal Pradesh are small and compact size cattle with a medium hump, horizontally placed short ears, and a long tail. The study also revealed that factors extracted from the present investigation could be used in breeding programs with sufficient reduction in the number of biometric traits to be recorded to explain the body confirmation.

Fluorescence Intrinsic Characterization of Excitation-Emission Matrix Using Multi-Dimensional Ensemble Empirical Mode Decomposition

PubMed Central

Chang, Chi-Ying; Chang, Chia-Chi; Hsiao, Tzu-Chien

2013-01-01

Excitation-emission matrix (EEM) fluorescence spectroscopy is a noninvasive method for tissue diagnosis and has become important in clinical use. However, the intrinsic characterization of EEM fluorescence remains unclear. Photobleaching and the complexity of the chemical compounds make it difficult to distinguish individual compounds due to overlapping features. Conventional studies use principal component analysis (PCA) for EEM fluorescence analysis, and the relationship between the EEM features extracted by PCA and diseases has been examined. The spectral features of different tissue constituents are not fully separable or clearly defined. Recently, a non-stationary method called multi-dimensional ensemble empirical mode decomposition (MEEMD) was introduced; this method can extract the intrinsic oscillations on multiple spatial scales without loss of information. The aim of this study was to propose a fluorescence spectroscopy system for EEM measurements and to describe a method for extracting the intrinsic characteristics of EEM by MEEMD. The results indicate that, although PCA provides the principal factor for the spectral features associated with chemical compounds, MEEMD can provide additional intrinsic features with more reliable mapping of the chemical compounds. MEEMD has the potential to extract intrinsic fluorescence features and improve the detection of biochemical changes. PMID:24240806

Comparative multivariate analysis of biometric traits of West African Dwarf and Red Sokoto goats.

PubMed

Yakubu, Abdulmojeed; Salako, Adebowale E; Imumorin, Ikhide G

2011-03-01

The population structure of 302 randomly selected West African Dwarf (WAD) and Red Sokoto (RS) goats was examined using multivariate morphometric analyses. This was to make the case for conservation, rational management and genetic improvement of these two most important Nigerian goat breeds. Fifteen morphometric measurements were made on each individual animal. RS goats were superior (P<0.05) to the WAD for the body size and skeletal proportions investigated. The phenotypic variability between the two breeds was revealed by their mutual responses in the principal components. While four principal components were extracted for WAD goats, three components were obtained for their RS counterparts with variation in the loading traits of each component for each breed. The Mahalanobis distance of 72.28 indicated a high degree of spatial racial separation in morphology between the genotypes. The Ward's option of the cluster analysis consolidated the morphometric distinctness of the two breeds. Application of selective breeding to genetic improvement would benefit from the detected phenotypic differentiation. Other implications for management and conservation of the goats are highlighted.

An algorithm for extraction of periodic signals from sparse, irregularly sampled data

NASA Technical Reports Server (NTRS)

Wilcox, J. Z.

1994-01-01

Temporal gaps in discrete sampling sequences produce spurious Fourier components at the intermodulation frequencies of an oscillatory signal and the temporal gaps, thus significantly complicating spectral analysis of such sparsely sampled data. A new fast Fourier transform (FFT)-based algorithm has been developed, suitable for spectral analysis of sparsely sampled data with a relatively small number of oscillatory components buried in background noise. The algorithm's principal idea has its origin in the so-called 'clean' algorithm used to sharpen images of scenes corrupted by atmospheric and sensor aperture effects. It identifies as the signal's 'true' frequency that oscillatory component which, when passed through the same sampling sequence as the original data, produces a Fourier image that is the best match to the original Fourier space. The algorithm has generally met with succession trials with simulated data with a low signal-to-noise ratio, including those of a type similar to hourly residuals for Earth orientation parameters extracted from VLBI data. For eight oscillatory components in the diurnal and semidiurnal bands, all components with an amplitude-noise ratio greater than 0.2 were successfully extracted for all sequences and duty cycles (greater than 0.1) tested; the amplitude-noise ratios of the extracted signals were as low as 0.05 for high duty cycles and long sampling sequences. When, in addition to these high frequencies, strong low-frequency components are present in the data, the low-frequency components are generally eliminated first, by employing a version of the algorithm that searches for non-integer multiples of the discrete FET minimum frequency.

Feature extraction for ultrasonic sensor based defect detection in ceramic components

NASA Astrophysics Data System (ADS)

Kesharaju, Manasa; Nagarajah, Romesh

2014-02-01

High density silicon carbide materials are commonly used as the ceramic element of hard armour inserts used in traditional body armour systems to reduce their weight, while providing improved hardness, strength and elastic response to stress. Currently, armour ceramic tiles are inspected visually offline using an X-ray technique that is time consuming and very expensive. In addition, from X-rays multiple defects are also misinterpreted as single defects. Therefore, to address these problems the ultrasonic non-destructive approach is being investigated. Ultrasound based inspection would be far more cost effective and reliable as the methodology is applicable for on-line quality control including implementation of accept/reject criteria. This paper describes a recently developed methodology to detect, locate and classify various manufacturing defects in ceramic tiles using sub band coding of ultrasonic test signals. The wavelet transform is applied to the ultrasonic signal and wavelet coefficients in the different frequency bands are extracted and used as input features to an artificial neural network (ANN) for purposes of signal classification. Two different classifiers, using artificial neural networks (supervised) and clustering (un-supervised) are supplied with features selected using Principal Component Analysis(PCA) and their classification performance compared. This investigation establishes experimentally that Principal Component Analysis(PCA) can be effectively used as a feature selection method that provides superior results for classifying various defects in the context of ultrasonic inspection in comparison with the X-ray technique.

Impact of Measurement Uncertainties on Receptor Modeling of Speciated Atmospheric Mercury.

PubMed

Cheng, I; Zhang, L; Xu, X

2016-02-09

Gaseous oxidized mercury (GOM) and particle-bound mercury (PBM) measurement uncertainties could potentially affect the analysis and modeling of atmospheric mercury. This study investigated the impact of GOM measurement uncertainties on Principal Components Analysis (PCA), Absolute Principal Component Scores (APCS), and Concentration-Weighted Trajectory (CWT) receptor modeling results. The atmospheric mercury data input into these receptor models were modified by combining GOM and PBM into a single reactive mercury (RM) parameter and excluding low GOM measurements to improve the data quality. PCA and APCS results derived from RM or excluding low GOM measurements were similar to those in previous studies, except for a non-unique component and an additional component extracted from the RM dataset. The percent variance explained by the major components from a previous study differed slightly compared to RM and excluding low GOM measurements. CWT results were more sensitive to the input of RM than GOM excluding low measurements. Larger discrepancies were found between RM and GOM source regions than those between RM and PBM. Depending on the season, CWT source regions of RM differed by 40-61% compared to GOM from a previous study. No improvement in correlations between CWT results and anthropogenic mercury emissions were found.

Impact of Measurement Uncertainties on Receptor Modeling of Speciated Atmospheric Mercury

PubMed Central

Cheng, I.; Zhang, L.; Xu, X.

2016-01-01

Gaseous oxidized mercury (GOM) and particle-bound mercury (PBM) measurement uncertainties could potentially affect the analysis and modeling of atmospheric mercury. This study investigated the impact of GOM measurement uncertainties on Principal Components Analysis (PCA), Absolute Principal Component Scores (APCS), and Concentration-Weighted Trajectory (CWT) receptor modeling results. The atmospheric mercury data input into these receptor models were modified by combining GOM and PBM into a single reactive mercury (RM) parameter and excluding low GOM measurements to improve the data quality. PCA and APCS results derived from RM or excluding low GOM measurements were similar to those in previous studies, except for a non-unique component and an additional component extracted from the RM dataset. The percent variance explained by the major components from a previous study differed slightly compared to RM and excluding low GOM measurements. CWT results were more sensitive to the input of RM than GOM excluding low measurements. Larger discrepancies were found between RM and GOM source regions than those between RM and PBM. Depending on the season, CWT source regions of RM differed by 40–61% compared to GOM from a previous study. No improvement in correlations between CWT results and anthropogenic mercury emissions were found. PMID:26857835

Histogram of gradient and binarized statistical image features of wavelet subband-based palmprint features extraction

NASA Astrophysics Data System (ADS)

Attallah, Bilal; Serir, Amina; Chahir, Youssef; Boudjelal, Abdelwahhab

2017-11-01

Palmprint recognition systems are dependent on feature extraction. A method of feature extraction using higher discrimination information was developed to characterize palmprint images. In this method, two individual feature extraction techniques are applied to a discrete wavelet transform of a palmprint image, and their outputs are fused. The two techniques used in the fusion are the histogram of gradient and the binarized statistical image features. They are then evaluated using an extreme learning machine classifier before selecting a feature based on principal component analysis. Three palmprint databases, the Hong Kong Polytechnic University (PolyU) Multispectral Palmprint Database, Hong Kong PolyU Palmprint Database II, and the Delhi Touchless (IIDT) Palmprint Database, are used in this study. The study shows that our method effectively identifies and verifies palmprints and outperforms other methods based on feature extraction.

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…

Comparison of multivariate analysis methods for extracting the paraffin component from the paraffin-embedded cancer tissue spectra for Raman imaging

NASA Astrophysics Data System (ADS)

Meksiarun, Phiranuphon; Ishigaki, Mika; Huck-Pezzei, Verena A. C.; Huck, Christian W.; Wongravee, Kanet; Sato, Hidetoshi; Ozaki, Yukihiro

2017-03-01

This study aimed to extract the paraffin component from paraffin-embedded oral cancer tissue spectra using three multivariate analysis (MVA) methods; Independent Component Analysis (ICA), Partial Least Squares (PLS) and Independent Component - Partial Least Square (IC-PLS). The estimated paraffin components were used for removing the contribution of paraffin from the tissue spectra. These three methods were compared in terms of the efficiency of paraffin removal and the ability to retain the tissue information. It was found that ICA, PLS and IC-PLS could remove the paraffin component from the spectra at almost the same level while Principal Component Analysis (PCA) was incapable. In terms of retaining cancer tissue spectral integrity, effects of PLS and IC-PLS on the non-paraffin region were significantly less than that of ICA where cancer tissue spectral areas were deteriorated. The paraffin-removed spectra were used for constructing Raman images of oral cancer tissue and compared with Hematoxylin and Eosin (H&E) stained tissues for verification. This study has demonstrated the capability of Raman spectroscopy together with multivariate analysis methods as a diagnostic tool for the paraffin-embedded tissue section.

Strain Transient Detection Techniques: A Comparison of Source Parameter Inversions of Signals Isolated through Principal Component Analysis (PCA), Non-Linear PCA, and Rotated PCA

NASA Astrophysics Data System (ADS)

Lipovsky, B.; Funning, G. J.

2009-12-01

We compare several techniques for the analysis of geodetic time series with the ultimate aim to characterize the physical processes which are represented therein. We compare three methods for the analysis of these data: Principal Component Analysis (PCA), Non-Linear PCA (NLPCA), and Rotated PCA (RPCA). We evaluate each method by its ability to isolate signals which may be any combination of low amplitude (near noise level), temporally transient, unaccompanied by seismic emissions, and small scale with respect to the spatial domain. PCA is a powerful tool for extracting structure from large datasets which is traditionally realized through either the solution of an eigenvalue problem or through iterative methods. PCA is an transformation of the coordinate system of our data such that the new "principal" data axes retain maximal variance and minimal reconstruction error (Pearson, 1901; Hotelling, 1933). RPCA is achieved by an orthogonal transformation of the principal axes determined in PCA. In the analysis of meteorological data sets, RPCA has been seen to overcome domain shape dependencies, correct for sampling errors, and to determine principal axes which more closely represent physical processes (e.g., Richman, 1986). NLPCA generalizes PCA such that principal axes are replaced by principal curves (e.g., Hsieh 2004). We achieve NLPCA through an auto-associative feed-forward neural network (Scholz, 2005). We show the geophysical relevance of these techniques by application of each to a synthetic data set. Results are compared by inverting principal axes to determine deformation source parameters. Temporal variability in source parameters, estimated by each method, are also compared.

Hot spot of structural ambivalence in prion protein revealed by secondary structure principal component analysis.

PubMed

Yamamoto, Norifumi

2014-08-21

The conformational conversion of proteins into an aggregation-prone form is a common feature of various neurodegenerative disorders including Alzheimer's, Huntington's, Parkinson's, and prion diseases. In the early stage of prion diseases, secondary structure conversion in prion protein (PrP) causing β-sheet expansion facilitates the formation of a pathogenic isoform with a high content of β-sheets and strong aggregation tendency to form amyloid fibrils. Herein, we propose a straightforward method to extract essential information regarding the secondary structure conversion of proteins from molecular simulations, named secondary structure principal component analysis (SSPCA). The definite existence of a PrP isoform with an increased β-sheet structure was confirmed in a free-energy landscape constructed by mapping protein structural data into a reduced space according to the principal components determined by the SSPCA. We suggest a "spot" of structural ambivalence in PrP-the C-terminal part of helix 2-that lacks a strong intrinsic secondary structure, thus promoting a partial α-helix-to-β-sheet conversion. This result is important to understand how the pathogenic conformational conversion of PrP is initiated in prion diseases. The SSPCA has great potential to solve various challenges in studying highly flexible molecular systems, such as intrinsically disordered proteins, structurally ambivalent peptides, and chameleon sequences.

Dimensionality reduction for the quantitative evaluation of a smartphone-based Timed Up and Go test.

PubMed

Palmerini, Luca; Mellone, Sabato; Rocchi, Laura; Chiari, Lorenzo

2011-01-01

The Timed Up and Go is a clinical test to assess mobility in the elderly and in Parkinson's disease. Lately instrumented versions of the test are being considered, where inertial sensors assess motion. To improve the pervasiveness, ease of use, and cost, we consider a smartphone's accelerometer as the measurement system. Several parameters (usually highly correlated) can be computed from the signals recorded during the test. To avoid redundancy and obtain the features that are most sensitive to the locomotor performance, a dimensionality reduction was performed through principal component analysis (PCA). Forty-nine healthy subjects of different ages were tested. PCA was performed to extract new features (principal components) which are not redundant combinations of the original parameters and account for most of the data variability. They can be useful for exploratory analysis and outlier detection. Then, a reduced set of the original parameters was selected through correlation analysis with the principal components. This set could be recommended for studies based on healthy adults. The proposed procedure could be used as a first-level feature selection in classification studies (i.e. healthy-Parkinson's disease, fallers-non fallers) and could allow, in the future, a complete system for movement analysis to be incorporated in a smartphone.

Distributions of experimental protein structures on coarse-grained free energy landscapes

PubMed Central

Liu, Jie; Jernigan, Robert L.

2015-01-01

Predicting conformational changes of proteins is needed in order to fully comprehend functional mechanisms. With the large number of available structures in sets of related proteins, it is now possible to directly visualize the clusters of conformations and their conformational transitions through the use of principal component analysis. The most striking observation about the distributions of the structures along the principal components is their highly non-uniform distributions. In this work, we use principal component analysis of experimental structures of 50 diverse proteins to extract the most important directions of their motions, sample structures along these directions, and estimate their free energy landscapes by combining knowledge-based potentials and entropy computed from elastic network models. When these resulting motions are visualized upon their coarse-grained free energy landscapes, the basis for conformational pathways becomes readily apparent. Using three well-studied proteins, T4 lysozyme, serum albumin, and sarco-endoplasmic reticular Ca2+ adenosine triphosphatase (SERCA), as examples, we show that such free energy landscapes of conformational changes provide meaningful insights into the functional dynamics and suggest transition pathways between different conformational states. As a further example, we also show that Monte Carlo simulations on the coarse-grained landscape of HIV-1 protease can directly yield pathways for force-driven conformational changes. PMID:26723638

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.

[Principal component analysis and cluster analysis of inorganic elements in sea cucumber Apostichopus japonicus].

PubMed

Liu, Xiao-Fang; Xue, Chang-Hu; Wang, Yu-Ming; Li, Zhao-Jie; Xue, Yong; Xu, Jie

2011-11-01

The present study is to investigate the feasibility of multi-elements analysis in determination of the geographical origin of sea cucumber Apostichopus japonicus, and to make choice of the effective tracers in sea cucumber Apostichopus japonicus geographical origin assessment. The content of the elements such as Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Mo, Cd, Hg and Pb in sea cucumber Apostichopus japonicus samples from seven places of geographical origin were determined by means of ICP-MS. The results were used for the development of elements database. Cluster analysis(CA) and principal component analysis (PCA) were applied to differentiate the sea cucumber Apostichopus japonicus geographical origin. Three principal components which accounted for over 89% of the total variance were extracted from the standardized data. The results of Q-type cluster analysis showed that the 26 samples could be clustered reasonably into five groups, the classification results were significantly associated with the marine distribution of the sea cucumber Apostichopus japonicus samples. The CA and PCA were the effective methods for elements analysis of sea cucumber Apostichopus japonicus samples. The content of the mineral elements in sea cucumber Apostichopus japonicus samples was good chemical descriptors for differentiating their geographical origins.

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

PubMed

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

2011-12-01

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

Removal of BCG artefact from concurrent fMRI-EEG recordings based on EMD and PCA.

PubMed

Javed, Ehtasham; Faye, Ibrahima; Malik, Aamir Saeed; Abdullah, Jafri Malin

2017-11-01

Simultaneous electroencephalography (EEG) and functional magnetic resonance image (fMRI) acquisitions provide better insight into brain dynamics. Some artefacts due to simultaneous acquisition pose a threat to the quality of the data. One such problematic artefact is the ballistocardiogram (BCG) artefact. We developed a hybrid algorithm that combines features of empirical mode decomposition (EMD) with principal component analysis (PCA) to reduce the BCG artefact. The algorithm does not require extra electrocardiogram (ECG) or electrooculogram (EOG) recordings to extract the BCG artefact. The method was tested with both simulated and real EEG data of 11 participants. From the simulated data, the similarity index between the extracted BCG and the simulated BCG showed the effectiveness of the proposed method in BCG removal. On the other hand, real data were recorded with two conditions, i.e. resting state (eyes closed dataset) and task influenced (event-related potentials (ERPs) dataset). Using qualitative (visual inspection) and quantitative (similarity index, improved normalized power spectrum (INPS) ratio, power spectrum, sample entropy (SE)) evaluation parameters, the assessment results showed that the proposed method can efficiently reduce the BCG artefact while preserving the neuronal signals. Compared with conventional methods, namely, average artefact subtraction (AAS), optimal basis set (OBS) and combined independent component analysis and principal component analysis (ICA-PCA), the statistical analyses of the results showed that the proposed method has better performance, and the differences were significant for all quantitative parameters except for the power and sample entropy. The proposed method does not require any reference signal, prior information or assumption to extract the BCG artefact. It will be very useful in circumstances where the reference signal is not available. Copyright © 2017 Elsevier B.V. All rights reserved.

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

ERIC Educational Resources Information Center

John, Lindsay Herbert

2004-01-01

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

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

ERIC Educational Resources Information Center

Ruch, Libby O.; Wang, Chang-Hwai

2006-01-01

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

Geochemical fractionation of metals and metalloids in tailings and appraisal of environmental pollution in the abandoned Musina Copper Mine, South Africa.

PubMed

Gitari, M W; Akinyemi, S A; Ramugondo, L; Matidza, M; Mhlongo, S E

2018-04-30

The economic benefits of mining industry have often overshadowed the serious challenges posed to the environments through huge volume of tailings generated and disposed in tailings dumps. Some of these challenges include the surface and groundwater contamination, dust, and inability to utilize the land for developmental purposes. The abandoned copper mine tailings in Musina (Limpopo province, South Africa) was investigated for particle size distribution, mineralogy, physicochemical properties using arrays of granulometric, X-ray diffraction, and X-ray fluorescence analyses. A modified Community Bureau of Reference (BCR) sequential chemical extraction method followed by inductively coupled plasma mass spectrometry/atomic emission spectrometry (ICP-MS/AES) technique was employed to assess bioavailability of metals. Principal component analysis was performed on the sequential extraction data to reveal different loadings and mobilities of metals in samples collected at various depths. The pH ranged between 7.5 and 8.5 (average ≈ 8.0) indicating alkaline medium. Samples composed mostly of poorly grated sands (i.e. 50% fine sand) with an average permeability of about 387.6 m/s. Samples have SiO 2 /Al 2 O 3 and Na 2 O/(Al 2 O 3  + SiO 2 ) ratios and low plastic index (i.e. PI ≈ 2.79) suggesting non-plastic and very low dry strength. Major minerals were comprised of quartz, epidote, and chlorite while the order of relative abundance of minerals in minor quantities is plagioclase > muscovite > hornblende > calcite > haematite. The largest percentage of elements such as As, Cd and Cr was strongly bound to less extractable fractions. Results showed high concentration and easily extractable Cu in the Musina Copper Mine tailings, which indicates bioavailability and poses environmental risk and potential health risk of human exposure. Principal component analysis revealed Fe-oxide/hydroxides, carbonate and clay components, and copper ore process are controlling the elements distribution.

Protein-RNA specificity by high-throughput principal component analysis of NMR spectra.

PubMed

Collins, Katherine M; Oregioni, Alain; Robertson, Laura E; Kelly, Geoff; Ramos, Andres

2015-03-31

Defining the RNA target selectivity of the proteins regulating mRNA metabolism is a key issue in RNA biology. Here we present a novel use of principal component analysis (PCA) to extract the RNA sequence preference of RNA binding proteins. We show that PCA can be used to compare the changes in the nuclear magnetic resonance (NMR) spectrum of a protein upon binding a set of quasi-degenerate RNAs and define the nucleobase specificity. We couple this application of PCA to an automated NMR spectra recording and processing protocol and obtain an unbiased and high-throughput NMR method for the analysis of nucleobase preference in protein-RNA interactions. We test the method on the RNA binding domains of three important regulators of RNA metabolism. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Learning representative features for facial images based on a modified principal component analysis

NASA Astrophysics Data System (ADS)

Averkin, Anton; Potapov, Alexey

2013-05-01

The paper is devoted to facial image analysis and particularly deals with the problem of automatic evaluation of the attractiveness of human faces. We propose a new approach for automatic construction of feature space based on a modified principal component analysis. Input data sets for the algorithm are the learning data sets of facial images, which are rated by one person. The proposed approach allows one to extract features of the individual subjective face beauty perception and to predict attractiveness values for new facial images, which were not included into a learning data set. The Pearson correlation coefficient between values predicted by our method for new facial images and personal attractiveness estimation values equals to 0.89. This means that the new approach proposed is promising and can be used for predicting subjective face attractiveness values in real systems of the facial images analysis.

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

PubMed

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

2014-12-15

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

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

NASA Astrophysics Data System (ADS)

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

2017-05-01

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

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

PubMed

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

2018-02-01

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

An analytics of electricity consumption characteristics based on principal component analysis

NASA Astrophysics Data System (ADS)

Feng, Junshu

2018-02-01

Abstract . More detailed analysis of the electricity consumption characteristics can make demand side management (DSM) much more targeted. In this paper, an analytics of electricity consumption characteristics based on principal component analysis (PCA) is given, which the PCA method can be used in to extract the main typical characteristics of electricity consumers. Then, electricity consumption characteristics matrix is designed, which can make a comparison of different typical electricity consumption characteristics between different types of consumers, such as industrial consumers, commercial consumers and residents. In our case study, the electricity consumption has been mainly divided into four characteristics: extreme peak using, peak using, peak-shifting using and others. Moreover, it has been found that industrial consumers shift their peak load often, meanwhile commercial and residential consumers have more peak-time consumption. The conclusions can provide decision support of DSM for the government and power providers.

Research of facial feature extraction based on MMC

NASA Astrophysics Data System (ADS)

Xue, Donglin; Zhao, Jiufen; Tang, Qinhong; Shi, Shaokun

2017-07-01

Based on the maximum margin criterion (MMC), a new algorithm of statistically uncorrelated optimal discriminant vectors and a new algorithm of orthogonal optimal discriminant vectors for feature extraction were proposed. The purpose of the maximum margin criterion is to maximize the inter-class scatter while simultaneously minimizing the intra-class scatter after the projection. Compared with original MMC method and principal component analysis (PCA) method, the proposed methods are better in terms of reducing or eliminating the statistically correlation between features and improving recognition rate. The experiment results on Olivetti Research Laboratory (ORL) face database shows that the new feature extraction method of statistically uncorrelated maximum margin criterion (SUMMC) are better in terms of recognition rate and stability. Besides, the relations between maximum margin criterion and Fisher criterion for feature extraction were revealed.

Classification and pose estimation of objects using nonlinear features

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1998-03-01

A new nonlinear feature extraction method called the maximum representation and discrimination feature (MRDF) method is presented for extraction of features from input image data. It implements transformations similar to the Sigma-Pi neural network. However, the weights of the MRDF are obtained in closed form, and offer advantages compared to nonlinear neural network implementations. The features extracted are useful for both object discrimination (classification) and object representation (pose estimation). We show its use in estimating the class and pose of images of real objects and rendered solid CAD models of machine parts from single views using a feature-space trajectory (FST) neural network classifier. We show more accurate classification and pose estimation results than are achieved by standard principal component analysis (PCA) and Fukunaga-Koontz (FK) feature extraction methods.

A graph-Laplacian-based feature extraction algorithm for neural spike sorting.

PubMed

Ghanbari, Yasser; Spence, Larry; Papamichalis, Panos

2009-01-01

Analysis of extracellular neural spike recordings is highly dependent upon the accuracy of neural waveform classification, commonly referred to as spike sorting. Feature extraction is an important stage of this process because it can limit the quality of clustering which is performed in the feature space. This paper proposes a new feature extraction method (which we call Graph Laplacian Features, GLF) based on minimizing the graph Laplacian and maximizing the weighted variance. The algorithm is compared with Principal Components Analysis (PCA, the most commonly-used feature extraction method) using simulated neural data. The results show that the proposed algorithm produces more compact and well-separated clusters compared to PCA. As an added benefit, tentative cluster centers are output which can be used to initialize a subsequent clustering stage.

Statistical mixture design selective extraction of compounds with antioxidant activity and total polyphenol content from Trichilia catigua.

PubMed

Lonni, Audrey Alesandra Stinghen Garcia; Longhini, Renata; Lopes, Gisely Cristiny; de Mello, João Carlos Palazzo; Scarminio, Ieda Spacino

2012-03-16

Statistical design mixtures of water, methanol, acetone and ethanol were used to extract material from Trichilia catigua (Meliaceae) barks to study the effects of different solvents and their mixtures on its yield, total polyphenol content and antioxidant activity. The experimental results and their response surface models showed that quaternary mixtures with approximately equal proportions of all four solvents provided the highest yields, total polyphenol contents and antioxidant activities of the crude extracts followed by ternary design mixtures. Principal component and hierarchical clustering analysis of the HPLC-DAD spectra of the chromatographic peaks of 1:1:1:1 water-methanol-acetone-ethanol mixture extracts indicate the presence of cinchonains, gallic acid derivatives, natural polyphenols, flavanoids, catechins, and epicatechins. Copyright © 2011 Elsevier B.V. All rights reserved.

[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.

PCA feature extraction for change detection in multidimensional unlabeled data.

PubMed

Kuncheva, Ludmila I; Faithfull, William J

2014-01-01

When classifiers are deployed in real-world applications, it is assumed that the distribution of the incoming data matches the distribution of the data used to train the classifier. This assumption is often incorrect, which necessitates some form of change detection or adaptive classification. While there has been a lot of work on change detection based on the classification error monitored over the course of the operation of the classifier, finding changes in multidimensional unlabeled data is still a challenge. Here, we propose to apply principal component analysis (PCA) for feature extraction prior to the change detection. Supported by a theoretical example, we argue that the components with the lowest variance should be retained as the extracted features because they are more likely to be affected by a change. We chose a recently proposed semiparametric log-likelihood change detection criterion that is sensitive to changes in both mean and variance of the multidimensional distribution. An experiment with 35 datasets and an illustration with a simple video segmentation demonstrate the advantage of using extracted features compared to raw data. Further analysis shows that feature extraction through PCA is beneficial, specifically for data with multiple balanced classes.

Regionalization of precipitation characteristics in Iran's Lake Urmia basin

NASA Astrophysics Data System (ADS)

Fazel, Nasim; Berndtsson, Ronny; Uvo, Cintia Bertacchi; Madani, Kaveh; Kløve, Bjørn

2018-04-01

Lake Urmia in northwest Iran, once one of the largest hypersaline lakes in the world, has shrunk by almost 90% in area and 80% in volume during the last four decades. To improve the understanding of regional differences in water availability throughout the region and to refine the existing information on precipitation variability, this study investigated the spatial pattern of precipitation for the Lake Urmia basin. Daily rainfall time series from 122 precipitation stations with different record lengths were used to extract 15 statistical descriptors comprising 25th percentile, 75th percentile, and coefficient of variation for annual and seasonal total precipitation. Principal component analysis in association with cluster analysis identified three main homogeneous precipitation groups in the lake basin. The first sub-region (group 1) includes stations located in the center and southeast; the second sub-region (group 2) covers mostly northern and northeastern part of the basin, and the third sub-region (group 3) covers the western and southern edges of the basin. Results of principal component (PC) and clustering analyses showed that seasonal precipitation variation is the most important feature controlling the spatial pattern of precipitation in the lake basin. The 25th and 75th percentiles of winter and autumn are the most important variables controlling the spatial pattern of the first rotated principal component explaining about 32% of the total variance. Summer and spring precipitation variations are the most important variables in the second and third rotated principal components, respectively. Seasonal variation in precipitation amount and seasonality are explained by topography and influenced by the lake and westerly winds that are related to the strength of the North Atlantic Oscillation. Despite using incomplete time series with different lengths, the identified sub-regions are physically meaningful.

Relaxation mode analysis and Markov state relaxation mode analysis for chignolin in aqueous solution near a transition temperature

NASA Astrophysics Data System (ADS)

Mitsutake, Ayori; Takano, Hiroshi

2015-09-01

It is important to extract reaction coordinates or order parameters from protein simulations in order to investigate the local minimum-energy states and the transitions between them. The most popular method to obtain such data is principal component analysis, which extracts modes of large conformational fluctuations around an average structure. We recently applied relaxation mode analysis for protein systems, which approximately estimates the slow relaxation modes and times from a simulation and enables investigations of the dynamic properties underlying the structural fluctuations of proteins. In this study, we apply this relaxation mode analysis to extract reaction coordinates for a system in which there are large conformational changes such as those commonly observed in protein folding/unfolding. We performed a 750-ns simulation of chignolin protein near its folding transition temperature and observed many transitions between the most stable, misfolded, intermediate, and unfolded states. We then applied principal component analysis and relaxation mode analysis to the system. In the relaxation mode analysis, we could automatically extract good reaction coordinates. The free-energy surfaces provide a clearer understanding of the transitions not only between local minimum-energy states but also between the folded and unfolded states, even though the simulation involved large conformational changes. Moreover, we propose a new analysis method called Markov state relaxation mode analysis. We applied the new method to states with slow relaxation, which are defined by the free-energy surface obtained in the relaxation mode analysis. Finally, the relaxation times of the states obtained with a simple Markov state model and the proposed Markov state relaxation mode analysis are compared and discussed.

Assessing Footwear Effects from Principal Features of Plantar Loading during Running.

PubMed

Trudeau, Matthieu B; von Tscharner, Vinzenz; Vienneau, Jordyn; Hoerzer, Stefan; Nigg, Benno M

2015-09-01

The effects of footwear on the musculoskeletal system are commonly assessed by interpreting the resultant force at the foot during the stance phase of running. However, this approach overlooks loading patterns across the entire foot. An alternative technique for assessing foot loading across different footwear conditions is possible using comprehensive analysis tools that extract different foot loading features, thus enhancing the functional interpretation of the differences across different interventions. The purpose of this article was to use pattern recognition techniques to develop and use a novel comprehensive method for assessing the effects of different footwear interventions on plantar loading. A principal component analysis was used to extract different loading features from the stance phase of running, and a support vector machine (SVM) was used to determine whether and how these loading features were different across three shoe conditions. The results revealed distinct loading features at the foot during the stance phase of running. The loading features determined from the principal component analysis allowed successful classification of all three shoe conditions using the SVM. Several differences were found in the location and timing of the loading across each pairwise shoe comparison using the output from the SVM. The analysis approach proposed can successfully be used to compare different loading patterns with a much greater resolution than has been reported previously. This study has several important applications. One such application is that it would not be relevant for a user to select a shoe or for a manufacturer to alter a shoe's construction if the classification across shoe conditions would not have been significant.

Metabolite profiling of Clinacanthus nutans leaves extracts obtained from different drying methods by 1H NMR-based metabolomics

NASA Astrophysics Data System (ADS)

Hashim, Noor Haslinda Noor; Latip, Jalifah; Khatib, Alfi

2016-11-01

The metabolites of Clinacanthus nutans leaves extracts and their dependence on drying process were systematically characterized using 1H nuclear magnetic resonance spectroscopy (NMR) multivariate data analysis. Principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) were able to distinguish the leaves extracts obtained from different drying methods. The identified metabolites were carbohydrates, amino acid, flavonoids and sulfur glucoside compounds. The major metabolites responsible for the separation in PLS-DA loading plots were lupeol, cycloclinacosides, betulin, cerebrosides and choline. The results showed that the combination of 1H NMR spectroscopy and multivariate data analyses could act as an efficient technique to understand the C. nutans composition and its variation.

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.

From Principal Component to Direct Coupling Analysis of Coevolution in Proteins: Low-Eigenvalue Modes are Needed for Structure Prediction

PubMed Central

Cocco, Simona; Monasson, Remi; Weigt, Martin

2013-01-01

Various approaches have explored the covariation of residues in multiple-sequence alignments of homologous proteins to extract functional and structural information. Among those are principal component analysis (PCA), which identifies the most correlated groups of residues, and direct coupling analysis (DCA), a global inference method based on the maximum entropy principle, which aims at predicting residue-residue contacts. In this paper, inspired by the statistical physics of disordered systems, we introduce the Hopfield-Potts model to naturally interpolate between these two approaches. The Hopfield-Potts model allows us to identify relevant ‘patterns’ of residues from the knowledge of the eigenmodes and eigenvalues of the residue-residue correlation matrix. We show how the computation of such statistical patterns makes it possible to accurately predict residue-residue contacts with a much smaller number of parameters than DCA. This dimensional reduction allows us to avoid overfitting and to extract contact information from multiple-sequence alignments of reduced size. In addition, we show that low-eigenvalue correlation modes, discarded by PCA, are important to recover structural information: the corresponding patterns are highly localized, that is, they are concentrated in few sites, which we find to be in close contact in the three-dimensional protein fold. PMID:23990764

In-vitro effects of Thymus munbyanus essential oil and thymol on human sperm motility and function.

PubMed

Chikhoune, Amirouche; Stouvenel, Laurence; Iguer-Ouada, Mokrane; Hazzit, Mohamed; Schmitt, Alain; Lorès, Patrick; Wolf, Jean Philippe; Aissat, Kamel; Auger, Jacques; Vaiman, Daniel; Touré, Aminata

2015-09-01

Traditional medicine has been used worldwide for centuries to cure or prevent disease and for male or female contraception. Only a few studies have directly investigated the effects of herbal compounds on spermatozoa. In this study, essential oil from Thymus munbyanus was extracted and its effect on human spermatozoa in vitro was analysed. Gas chromatography and Gas chromatography-mass spectrometry analyses identified 64 components, accounting for 98.9% of the composition of the oil. The principal components were thymol (52.0%), γ-terpinene (11.0%), ρ-cymene (8.5%) and carvacrol (5.2%). Freshly ejaculated spermatozoa was exposed from control individuals to various doses of the essential oil for different time periods, and recorded the vitality, the mean motility, the movement characteristics (computer-aided sperm analysis), the morphology and the ability to undergo protein hyperphosphorylation and acrosomal reaction, which constitute two markers of sperm capacitation and fertilizing ability. In vitro, both the essential oil extracted from T. munbyanus and thymol, the principal compound present in this oil, impaired human sperm motility and its capacity to undergo hyperphosphorylation and acrosome reaction. These compounds may, therefore, be of interest in the field of reproductive biology, as potential anti-spermatic agents. Copyright © 2015 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.

Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition.

PubMed

Caggiano, Alessandra

2018-03-09

Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA) is proposed. PCA allowed to identify a smaller number of features ( k = 2 features), the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear ( VB max ) was achieved, with predicted values very close to the measured tool wear values.

Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition

PubMed Central

2018-01-01

Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA) is proposed. PCA allowed to identify a smaller number of features (k = 2 features), the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear (VBmax) was achieved, with predicted values very close to the measured tool wear values. PMID:29522443

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

An expert system based on principal component analysis, artificial immune system and fuzzy k-NN for diagnosis of valvular heart diseases.

PubMed

Sengur, Abdulkadir

2008-03-01

In the last two decades, the use of artificial intelligence methods in medical analysis is increasing. This is mainly because the effectiveness of classification and detection systems have improved a great deal to help the medical experts in diagnosing. In this work, we investigate the use of principal component analysis (PCA), artificial immune system (AIS) and fuzzy k-NN to determine the normal and abnormal heart valves from the Doppler heart sounds. The proposed heart valve disorder detection system is composed of three stages. The first stage is the pre-processing stage. Filtering, normalization and white de-noising are the processes that were used in this stage. The feature extraction is the second stage. During feature extraction stage, wavelet packet decomposition was used. As a next step, wavelet entropy was considered as features. For reducing the complexity of the system, PCA was used for feature reduction. In the classification stage, AIS and fuzzy k-NN were used. To evaluate the performance of the proposed methodology, a comparative study is realized by using a data set containing 215 samples. The validation of the proposed method is measured by using the sensitivity and specificity parameters; 95.9% sensitivity and 96% specificity rate was obtained.

Detection of goal events in soccer videos

NASA Astrophysics Data System (ADS)

Kim, Hyoung-Gook; Roeber, Steffen; Samour, Amjad; Sikora, Thomas

2005-01-01

In this paper, we present an automatic extraction of goal events in soccer videos by using audio track features alone without relying on expensive-to-compute video track features. The extracted goal events can be used for high-level indexing and selective browsing of soccer videos. The detection of soccer video highlights using audio contents comprises three steps: 1) extraction of audio features from a video sequence, 2) event candidate detection of highlight events based on the information provided by the feature extraction Methods and the Hidden Markov Model (HMM), 3) goal event selection to finally determine the video intervals to be included in the summary. For this purpose we compared the performance of the well known Mel-scale Frequency Cepstral Coefficients (MFCC) feature extraction method vs. MPEG-7 Audio Spectrum Projection feature (ASP) extraction method based on three different decomposition methods namely Principal Component Analysis( PCA), Independent Component Analysis (ICA) and Non-Negative Matrix Factorization (NMF). To evaluate our system we collected five soccer game videos from various sources. In total we have seven hours of soccer games consisting of eight gigabytes of data. One of five soccer games is used as the training data (e.g., announcers' excited speech, audience ambient speech noise, audience clapping, environmental sounds). Our goal event detection results are encouraging.

Comparison of water extraction methods in Tibet based on GF-1 data

NASA Astrophysics Data System (ADS)

Jia, Lingjun; Shang, Kun; Liu, Jing; Sun, Zhongqing

2018-03-01

In this study, we compared four different water extraction methods with GF-1 data according to different water types in Tibet, including Support Vector Machine (SVM), Principal Component Analysis (PCA), Decision Tree Classifier based on False Normalized Difference Water Index (FNDWI-DTC), and PCA-SVM. The results show that all of the four methods can extract large area water body, but only SVM and PCA-SVM can obtain satisfying extraction results for small size water body. The methods were evaluated by both overall accuracy (OAA) and Kappa coefficient (KC). The OAA of PCA-SVM, SVM, FNDWI-DTC, PCA are 96.68%, 94.23%, 93.99%, 93.01%, and the KCs are 0.9308, 0.8995, 0.8962, 0.8842, respectively, in consistent with visual inspection. In summary, SVM is better for narrow rivers extraction and PCA-SVM is suitable for water extraction of various types. As for dark blue lakes, the methods using PCA can extract more quickly and accurately.

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

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.

Heavy metal contamination of agricultural soils affected by mining activities around the Ganxi River in Chenzhou, Southern China.

PubMed

Ma, Li; Sun, Jing; Yang, Zhaoguang; Wang, Lin

2015-12-01

Heavy metal contamination attracted a wide spread attention due to their strong toxicity and persistence. The Ganxi River, located in Chenzhou City, Southern China, has been severely polluted by lead/zinc ore mining activities. This work investigated the heavy metal pollution in agricultural soils around the Ganxi River. The total concentrations of heavy metals were determined by inductively coupled plasma-mass spectrometry. The potential risk associated with the heavy metals in soil was assessed by Nemerow comprehensive index and potential ecological risk index. In both methods, the study area was rated as very high risk. Multivariate statistical methods including Pearson's correlation analysis, hierarchical cluster analysis, and principal component analysis were employed to evaluate the relationships between heavy metals, as well as the correlation between heavy metals and pH, to identify the metal sources. Three distinct clusters have been observed by hierarchical cluster analysis. In principal component analysis, a total of two components were extracted to explain over 90% of the total variance, both of which were associated with anthropogenic sources.

Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis

USGS Publications Warehouse

Chavez, P.S.; Kwarteng, A.Y.

1989-01-01

A challenge encountered with Landsat Thematic Mapper (TM) data, which includes data from size reflective spectral bands, is displaying as much information as possible in a three-image set for color compositing or digital analysis. Principal component analysis (PCA) applied to the six TM bands simultaneously is often used to address this problem. However, two problems that can be encountered using the PCA method are that information of interest might be mathematically mapped to one of the unused components and that a color composite can be difficult to interpret. "Selective' PCA can be used to minimize both of these problems. The spectral contrast among several spectral regions was mapped for a northern Arizona site using Landsat TM data. Field investigations determined that most of the spectral contrast seen in this area was due to one of the following: the amount of iron and hematite in the soils and rocks, vegetation differences, standing and running water, or the presence of gypsum, which has a higher moisture retention capability than do the surrounding soils and rocks. -from Authors

Extracting grid cell characteristics from place cell inputs using non-negative principal component analysis

PubMed Central

Dordek, Yedidyah; Soudry, Daniel; Meir, Ron; Derdikman, Dori

2016-01-01

Many recent models study the downstream projection from grid cells to place cells, while recent data have pointed out the importance of the feedback projection. We thus asked how grid cells are affected by the nature of the input from the place cells. We propose a single-layer neural network with feedforward weights connecting place-like input cells to grid cell outputs. Place-to-grid weights are learned via a generalized Hebbian rule. The architecture of this network highly resembles neural networks used to perform Principal Component Analysis (PCA). Both numerical results and analytic considerations indicate that if the components of the feedforward neural network are non-negative, the output converges to a hexagonal lattice. Without the non-negativity constraint, the output converges to a square lattice. Consistent with experiments, grid spacing ratio between the first two consecutive modules is −1.4. Our results express a possible linkage between place cell to grid cell interactions and PCA. DOI: http://dx.doi.org/10.7554/eLife.10094.001 PMID:26952211

How many atoms are required to characterize accurately trajectory fluctuations of a protein?

NASA Astrophysics Data System (ADS)

Cukier, Robert I.

2010-06-01

Large molecules, whose thermal fluctuations sample a complex energy landscape, exhibit motions on an extended range of space and time scales. Principal component analysis (PCA) is often used to extract dominant motions that in proteins are typically domain motions. These motions are captured in the large eigenvalue (leading) principal components. There is also information in the small eigenvalues, arising from approximate linear dependencies among the coordinates. These linear dependencies suggest that instead of using all the atom coordinates to represent a trajectory, it should be possible to use a reduced set of coordinates with little loss in the information captured by the large eigenvalue principal components. In this work, methods that can monitor the correlation (overlap) between a reduced set of atoms and any number of retained principal components are introduced. For application to trajectory data generated by simulations, where the overall translational and rotational motion needs to be eliminated before PCA is carried out, some difficulties with the overlap measures arise and methods are developed to overcome them. The overlap measures are evaluated for a trajectory generated by molecular dynamics for the protein adenylate kinase, which consists of a stable, core domain, and two more mobile domains, referred to as the LID domain and the AMP-binding domain. The use of reduced sets corresponding, for the smallest set, to one-eighth of the alpha carbon (CA) atoms relative to using all the CA atoms is shown to predict the dominant motions of adenylate kinase. The overlap between using all the CA atoms and all the backbone atoms is essentially unity for a sum over PCA modes that effectively capture the exact trajectory. A reduction to a few atoms (three in the LID and three in the AMP-binding domain) shows that at least the first principal component, characterizing a large part of the LID-binding and AMP-binding motion, is well described. Based on these results, the overlap criterion should be applicable as a guide to postulating and validating coarse-grained descriptions of generic biomolecular assemblies.

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.

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

PubMed Central

Gürgen, Fikret; Gürgen, Nurgül

2003-01-01

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

[Identification of Pummelo Cultivars Based on Hyperspectral Imaging Technology].

PubMed

Li, Xun-lan; Yi, Shi-lai; He, Shao-lan; Lü, Qiang; Xie, Rang-jin; Zheng, Yong-qiang; Deng, Lie

2015-09-01

Existing methods for the identification of pummelo cultivars are usually time-consuming and costly, and are therefore inconvenient to be used in cases that a rapid identification is needed. This research was aimed at identifying different pummelo cultivars by hyperspectral imaging technology which can achieve a rapid and highly sensitive measurement. A total of 240 leaf samples, 60 for each of the four cultivars were investigated. Samples were divided into two groups such as calibration set (48 samples of each cultivar) and validation set (12 samples of each cultivar) by a Kennard-Stone-based algorithm. Hyperspectral images of both adaxial and abaxial surfaces of each leaf were obtained, and were segmented into a region of interest (ROI) using a simple threshold. Spectra of leaf samples were extracted from ROI. To remove the absolute noises of the spectra, only the date of spectral range 400~1000 nm was used for analysis. Multiplicative scatter correction (MSC) and standard normal variable (SNV) were utilized for data preprocessing. Principal component analysis (PCA) was used to extract the best principal components, and successive projections algorithm (SPA) was used to extract the effective wavelengths. Least squares support vector machine (LS-SVM) was used to obtain the discrimination model of the four different pummelo cultivars. To find out the optimal values of σ2 and γ which were important parameters in LS-SVM modeling, Grid-search technique and Cross-Validation were applied. The first 10 and 11 principal components were extracted by PCA for the hyperspectral data of adaxial surface and abaxial surface, respectively. There were 31 and 21 effective wavelengths selected by SPA based on the hyperspectral data of adaxial surface and abaxial surface, respectively. The best principal components and the effective wavelengths were used as inputs of LS-SVM models, and then the PCA-LS-SVM model and the SPA-LS-SVM model were built. The results showed that 99.46% and 98.44% of identification accuracy was achieved in the calibration set for the PCA-LS-SVM model and the SPA-LS-SVM model, respectively, and a 95.83% of identification accuracy was achieved in the validation set for both the PCA-LS-SVM and the SPA- LS-SVM models, which were built based on the hyperspectral data of adaxial surface. Comparatively, the results of the PCA-LS-SVM and the SPA-LS-SVM models built based on the hyperspectral data of abaxial surface both achieved identification accuracies of 100% for both calibration set and validation set. The overall results demonstrated that use of hyperspectral data of adaxial and abaxial leaf surfaces coupled with the use of PCA-LS-SVM and the SPA-LS-SVM could achieve an accurate identification of pummelo cultivars. It was feasible to use hyperspectral imaging technology to identify different pummelo cultivars, and hyperspectral imaging technology provided an alternate way of rapid identification of pummelo cultivars. Moreover, the results in this paper demonstrated that the data from the abaxial surface of leaf was more sensitive in identifying pummelo cultivars. This study provided a new method for to the fast discrimination of pummelo cultivars.

Medicinal cannabis: Principal cannabinoids concentration and their stability evaluated by a high performance liquid chromatography coupled to diode array and quadrupole time of flight mass spectrometry method.

PubMed

Citti, Cinzia; Ciccarella, Giuseppe; Braghiroli, Daniela; Parenti, Carlo; Vandelli, Maria Angela; Cannazza, Giuseppe

2016-09-05

In the last few years, there has been a boost in the use of cannabis-based extracts for medicinal purposes, although their preparation procedure has not been standardized but rather decided by the individual pharmacists. The present work describes the development of a simple and rapid high performance liquid chromatography method with UV detection (HPLC-UV) for the qualitative and quantitative determination of the principal cannabinoids (CBD-A, CBD, CBN, THC and THC-A) that could be applied to all cannabis-based medicinal extracts (CMEs) and easily performed by a pharmacist. In order to evaluate the identity and purity of the analytes, a high-resolution mass spectrometry (HPLC-ESI-QTOF) analysis was also carried out. Full method validation has been performed in terms of specificity, selectivity, linearity, recovery, dilution integrity and thermal stability. Moreover, the influence of the solvent (ethyl alcohol and olive oil) was evaluated on cannabinoids degradation rate. An alternative extraction method has then been proposed in order to preserve cannabis monoterpene component in final CMEs. Copyright © 2016 Elsevier B.V. All rights reserved.

Modeling and Prediction of Monthly Total Ozone Concentrations by Use of an Artificial Neural Network Based on Principal Component Analysis

NASA Astrophysics Data System (ADS)

Chattopadhyay, Surajit; Chattopadhyay, Goutami

2012-10-01

In the work discussed in this paper we considered total ozone time series over Kolkata (22°34'10.92″N, 88°22'10.92″E), an urban area in eastern India. Using cloud cover, average temperature, and rainfall as the predictors, we developed an artificial neural network, in the form of a multilayer perceptron with sigmoid non-linearity, for prediction of monthly total ozone concentrations from values of the predictors in previous months. We also estimated total ozone from values of the predictors in the same month. Before development of the neural network model we removed multicollinearity by means of principal component analysis. On the basis of the variables extracted by principal component analysis, we developed three artificial neural network models. By rigorous statistical assessment it was found that cloud cover and rainfall can act as good predictors for monthly total ozone when they are considered as the set of input variables for the neural network model constructed in the form of a multilayer perceptron. In general, the artificial neural network has good potential for predicting and estimating monthly total ozone on the basis of the meteorological predictors. It was further observed that during pre-monsoon and winter seasons, the proposed models perform better than during and after the monsoon.

A network view on psychiatric disorders: network clusters of symptoms as elementary syndromes of psychopathology.

PubMed

Goekoop, Rutger; Goekoop, Jaap G

2014-01-01

The vast number of psychopathological syndromes that can be observed in clinical practice can be described in terms of a limited number of elementary syndromes that are differentially expressed. Previous attempts to identify elementary syndromes have shown limitations that have slowed progress in the taxonomy of psychiatric disorders. To examine the ability of network community detection (NCD) to identify elementary syndromes of psychopathology and move beyond the limitations of current classification methods in psychiatry. 192 patients with unselected mental disorders were tested on the Comprehensive Psychopathological Rating Scale (CPRS). Principal component analysis (PCA) was performed on the bootstrapped correlation matrix of symptom scores to extract the principal component structure (PCS). An undirected and weighted network graph was constructed from the same matrix. Network community structure (NCS) was optimized using a previously published technique. In the optimal network structure, network clusters showed a 89% match with principal components of psychopathology. Some 6 network clusters were found, including "Depression", "Mania", "Anxiety", "Psychosis", "Retardation", and "Behavioral Disorganization". Network metrics were used to quantify the continuities between the elementary syndromes. We present the first comprehensive network graph of psychopathology that is free from the biases of previous classifications: a 'Psychopathology Web'. Clusters within this network represent elementary syndromes that are connected via a limited number of bridge symptoms. Many problems of previous classifications can be overcome by using a network approach to psychopathology.

SU-F-J-199: Predictive Models for Cone Beam CT-Based Online Verification of Pencil Beam Scanning Proton Therapy

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

Yin, L; Lin, A; Ahn, P

Purpose: To utilize online CBCT scans to develop models for predicting DVH metrics in proton therapy of head and neck tumors. Methods: Nine patients with locally advanced oropharyngeal cancer were retrospectively selected in this study. Deformable image registration was applied to the simulation CT, target volumes, and organs at risk (OARs) contours onto each weekly CBCT scan. Intensity modulated proton therapy (IMPT) treatment plans were created on the simulation CT and forward calculated onto each corrected CBCT scan. Thirty six potentially predictive metrics were extracted from each corrected CBCT. These features include minimum/maximum/mean over and under-ranges at the proximal andmore » distal surface of PTV volumes, and geometrical and water equivalent distance between PTV and each OARs. Principal component analysis (PCA) was used to reduce the dimension of the extracted features. Three principal components were found to account for over 90% of variances in those features. Datasets from eight patients were used to train a machine learning model to fit these principal components with DVH metrics (dose to 95% and 5% of PTV, mean dose or max dose to OARs) from the forward calculated dose on each corrected CBCT. The accuracy of this model was verified on the datasets from the 9th patient. Results: The predicted changes of DVH metrics from the model were in good agreement with actual values calculated on corrected CBCT images. Median differences were within 1 Gy for most DVH metrics except for larynx and constrictor mean dose. However, a large spread of the differences was observed, indicating additional training datasets and predictive features are needed to improve the model. Conclusion: Intensity corrected CBCT scans hold the potential to be used for online verification of proton therapy and prediction of delivered dose distributions.« less

Chemical and microstructural characterizations of plasma polymer films by time-of-flight secondary ion mass spectrometry and principal component analysis

NASA Astrophysics Data System (ADS)

Cossement, Damien; Renaux, Fabian; Thiry, Damien; Ligot, Sylvie; Francq, Rémy; Snyders, Rony

2015-11-01

It is accepted that the macroscopic properties of functional plasma polymer films (PPF) are defined by their functional density and their crosslinking degree (χ) which are quantities that most of the time behave in opposite trends. If the PPF chemistry is relatively easy to evaluate, it is much more challenging for χ. This paper reviews the recent work developed in our group on the application of principal component analysis (PCA) to time-of-flight secondary ion mass spectrometric (ToF-SIMS) positive spectra data in order to extract the relative cross-linking degree (χ) of PPF. NH2-, COOR- and SH-containing PPF synthesized in our group by plasma enhanced chemical vapor deposition (PECVD) varying the applied radiofrequency power (PRF), have been used as model surfaces. For the three plasma polymer families, the scores of the first computed principal component (PC1) highlighted significant differences in the chemical composition supported by X-Ray photoelectron spectroscopy (XPS) data. The most important fragments contributing to PC1 (loadings > 90%) were used to compute an average C/H ratio index for samples synthesized at low and high PRF. This ratio being an evaluation of χ, these data, accordingly to the literature, indicates an increase of χ with PRF excepted for the SH-PPF. These results have been cross-checked by the evaluation of functional properties of the plasma polymers namely a linear correlation with the stability of NH2-PPF in ethanol and a correlation with the mechanical properties of the COOR-PPF. For the SH-PPF family, the peculiar evolution of χ is supported by the understanding of the growth mechanism of the PPF from plasma diagnostic. The whole set of data clearly demonstrates the potential of the PCA method for extracting information on the microstructure of plasma polymers from ToF-SIMS measurements.

Use of regularized principal component analysis to model anatomical changes during head and neck radiation therapy for treatment adaptation and response assessment

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

Chetvertkov, Mikhail A., E-mail: chetvertkov@wayne

2016-10-15

Purpose: To develop standard (SPCA) and regularized (RPCA) principal component analysis models of anatomical changes from daily cone beam CTs (CBCTs) of head and neck (H&N) patients and assess their potential use in adaptive radiation therapy, and for extracting quantitative information for treatment response assessment. Methods: Planning CT images of ten H&N patients were artificially deformed to create “digital phantom” images, which modeled systematic anatomical changes during radiation therapy. Artificial deformations closely mirrored patients’ actual deformations and were interpolated to generate 35 synthetic CBCTs, representing evolving anatomy over 35 fractions. Deformation vector fields (DVFs) were acquired between pCT and syntheticmore » CBCTs (i.e., digital phantoms) and between pCT and clinical CBCTs. Patient-specific SPCA and RPCA models were built from these synthetic and clinical DVF sets. EigenDVFs (EDVFs) having the largest eigenvalues were hypothesized to capture the major anatomical deformations during treatment. Results: Principal component analysis (PCA) models achieve variable results, depending on the size and location of anatomical change. Random changes prevent or degrade PCA’s ability to detect underlying systematic change. RPCA is able to detect smaller systematic changes against the background of random fraction-to-fraction changes and is therefore more successful than SPCA at capturing systematic changes early in treatment. SPCA models were less successful at modeling systematic changes in clinical patient images, which contain a wider range of random motion than synthetic CBCTs, while the regularized approach was able to extract major modes of motion. Conclusions: Leading EDVFs from the both PCA approaches have the potential to capture systematic anatomical change during H&N radiotherapy when systematic changes are large enough with respect to random fraction-to-fraction changes. In all cases the RPCA approach appears to be more reliable at capturing systematic changes, enabling dosimetric consequences to be projected once trends are established early in a treatment course, or based on population models.« less

Evaluation of the influence of white grape seed extracts as copigment sources on the anthocyanin extraction from grape skins previously classified by near infrared hyperspectral tools.

PubMed

Nogales-Bueno, Julio; Baca-Bocanegra, Berta; Jara-Palacios, María José; Hernández-Hierro, José Miguel; Heredia, Francisco José

2017-04-15

Hyperspectral imaging has been used to classify red grapes (Vitis vinifera L.) according to their predicted extractable total anthocyanin content (i.e. extractable total anthocyanin content determined by a hyperspectral method). Low, medium and high levels of predicted extractable total anthocyanin content were established. Then, grape skins were split into three parts and each part was macerated into a different model wine solution for a three-day period. Wine model solutions were made up with different concentration of copigments coming from white grape seeds. Aqueous supernatants were analyzed by HPLC-DAD and extractable anthocyanin contents were obtained. Principal component analyses and analyses of variance were carried out with the aim of studying trends related to the extractable anthocyanin contents. Significant differences were found among grapes with different levels of predicted extractable anthocyanin contents. Moreover, no significant differences were found on the extractable anthocyanin contents using different copigment concentrations in grape skin macerations. Copyright © 2016 Elsevier Ltd. All rights reserved.

Analysis of Phenolic Compounds and Antioxidant Activity in Wild Blackberry Fruits

PubMed Central

Oszmiański, Jan; Nowicka, Paulina; Teleszko, Mirosława; Wojdyło, Aneta; Cebulak, Tomasz; Oklejewicz, Krzysztof

2015-01-01

Twenty three different wild blackberry fruit samples were assessed regarding their phenolic profiles and contents (by LC/MS quadrupole time-of-flight (QTOF) and antioxidant activity (ferric reducing ability of plasma (FRAP) and 2,2-azinobis (3-ethyl-benzothiazoline-6-sulfonic acid) (ABTS)) by two different extraction methods. Thirty four phenolic compounds were detected (8 anthocyanins, 15 flavonols, 3 hydroxycinnamic acids, 6 ellagic acid derivatives and 2 flavones). In samples, where pressurized liquid extraction (PLE) was used for extraction, a greater increase in yields of phenolic compounds was observed, especially in ellagic acid derivatives (max. 59%), flavonols (max. 44%) and anthocyanins (max. 29%), than after extraction by the ultrasonic technique extraction (UAE) method. The content of phenolic compounds was significantly correlated with the antioxidant activity of the analyzed samples. Principal component analysis (PCA) revealed that the PLE method was more suitable for the quantitative extraction of flavonols, while the UAE method was for hydroxycinnamic acids. PMID:26132562

Crude ethanolic extract from spent coffee grounds: Volatile and functional properties.

PubMed

Page, Julio C; Arruda, Neusa P; Freitas, Suely P

2017-11-01

Espresso capsule consumption and spent coffee ground (SCG) generation have increased, and the present study was undertaken to evaluate the volatile profile (VP), the antioxidant activity (AA) and the sun protection factor (SPF) of the Crude ethanolic extract obtained from the SCG in capsules. The extract yield was superior to the ether yield because a higher unsaponifiable matter (U.M.) amount was recovered by ethanol. The obtained VP (70 compounds) was typical of roasted coffee oil. Furthermore, chemometric analysis using principal components (PCA) discriminated the extracts and grouped the replicates for each sample, which showed the repeatability of the extraction process. The AA ranged from 18.4 to 23.6 (mg extract mg DPPH -1 ) and the SPF from 2.27 to 2.76. The combination of the coffee VP, AA and SPF gave the espresso SCG's crude ethanolicextract, desirable properties that can be used in cosmetic and food industries. Copyright © 2017 Elsevier Ltd. All rights reserved.

Identification of major phenolic compounds from Nephelium lappaceum L. and their antioxidant activities.

PubMed

Thitilertdecha, Nont; Teerawutgulrag, Aphiwat; Kilburn, Jeremy D; Rakariyatham, Nuansri

2010-03-09

Nephelium lappaceum is a tropical fruit whose peel possesses antioxidant properties. Experiments on the isolation and identification of the active constituents were conducted, and on their antioxidant activity using a lipid peroxidation inhibition assay. The methanolic extract of N. lappaceum peels exhibited strong antioxidant properties. Sephadex LH-20 chromatography was utilized in the isolation of each constituent and the antioxidant properties of each was studied. The isolated compounds were identified as ellagic acid (EA) (1), corilagin (2) and geraniin (3). These compounds accounted for 69.3% of methanolic extract, with geraniin (56.8%) as the major component, and exhibited much greater antioxidant activities than BHT in both lipid peroxidation (77-186 fold) and DPPH* (42-87 fold) assays. The results suggest that the isolated ellagitannins, as the principal components of rambutan peels, could be further utilized as both a medicine and in the food industry.

A novel automated spike sorting algorithm with adaptable feature extraction.

PubMed

Bestel, Robert; Daus, Andreas W; Thielemann, Christiane

2012-10-15

To study the electrophysiological properties of neuronal networks, in vitro studies based on microelectrode arrays have become a viable tool for analysis. Although in constant progress, a challenging task still remains in this area: the development of an efficient spike sorting algorithm that allows an accurate signal analysis at the single-cell level. Most sorting algorithms currently available only extract a specific feature type, such as the principal components or Wavelet coefficients of the measured spike signals in order to separate different spike shapes generated by different neurons. However, due to the great variety in the obtained spike shapes, the derivation of an optimal feature set is still a very complex issue that current algorithms struggle with. To address this problem, we propose a novel algorithm that (i) extracts a variety of geometric, Wavelet and principal component-based features and (ii) automatically derives a feature subset, most suitable for sorting an individual set of spike signals. Thus, there is a new approach that evaluates the probability distribution of the obtained spike features and consequently determines the candidates most suitable for the actual spike sorting. These candidates can be formed into an individually adjusted set of spike features, allowing a separation of the various shapes present in the obtained neuronal signal by a subsequent expectation maximisation clustering algorithm. Test results with simulated data files and data obtained from chick embryonic neurons cultured on microelectrode arrays showed an excellent classification result, indicating the superior performance of the described algorithm approach. Copyright © 2012 Elsevier B.V. All rights reserved.

Dynamical analysis of contrastive divergence learning: Restricted Boltzmann machines with Gaussian visible units.

PubMed

Karakida, Ryo; Okada, Masato; Amari, Shun-Ichi

2016-07-01

The restricted Boltzmann machine (RBM) is an essential constituent of deep learning, but it is hard to train by using maximum likelihood (ML) learning, which minimizes the Kullback-Leibler (KL) divergence. Instead, contrastive divergence (CD) learning has been developed as an approximation of ML learning and widely used in practice. To clarify the performance of CD learning, in this paper, we analytically derive the fixed points where ML and CDn learning rules converge in two types of RBMs: one with Gaussian visible and Gaussian hidden units and the other with Gaussian visible and Bernoulli hidden units. In addition, we analyze the stability of the fixed points. As a result, we find that the stable points of CDn learning rule coincide with those of ML learning rule in a Gaussian-Gaussian RBM. We also reveal that larger principal components of the input data are extracted at the stable points. Moreover, in a Gaussian-Bernoulli RBM, we find that both ML and CDn learning can extract independent components at one of stable points. Our analysis demonstrates that the same feature components as those extracted by ML learning are extracted simply by performing CD1 learning. Expanding this study should elucidate the specific solutions obtained by CD learning in other types of RBMs or in deep networks. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

PubMed

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

2016-01-01

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

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

PubMed Central

Edwards, Larissa; Kang, Jeremy

2016-01-01

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

Metabolomics study of Saw palmetto extracts based on 1H NMR spectroscopy.

PubMed

de Combarieu, Eric; Martinelli, Ernesto Marco; Pace, Roberto; Sardone, Nicola

2015-04-01

Preparations containing Saw palmetto extracts are used in traditional medicine to treat benign prostatic hyperplasia. According to the European and the American Pharmacopoeias, the extract is obtained from comminuted Saw palmetto berries by a suitable extracting procedure using ethanol or supercritical carbon dioxide or a mixture of n-hexane and methylpentanes. In the present study an approach to metabolomics profiling using nuclear magnetic resonance (NMR) has been used as a finger-printing tool to assess the overall composition of the extracts. The phytochemical analysis coupled with principal component analysis (PCA) showed the same composition of the Saw palmetto extracts obtained with carbon dioxide and hexane with minor not significant differences for extracts obtained with ethanol. In fact these differences are anyhow lower than the batch-to-batch variability ascribable to the natural-occurring variability in the Saw palmetto fruits' phytochemical composition. The fingerprinting analysis combined with chemometric method, is a technique, which would provide a tool to comprehensively assess the quality control of Saw palmetto extracts. Copyright © 2015 Elsevier B.V. All rights reserved.

Characterizing natural colloidal/particulate-protein interactions using fluorescence-based techniques and principal component analysis.

PubMed

Peiris, Ramila H; Ignagni, Nicholas; Budman, Hector; Moresoli, Christine; Legge, Raymond L

2012-09-15

Characterization of the interactions between natural colloidal/particulate- and protein-like matter is important for understanding their contribution to different physiochemical phenomena like membrane fouling, adsorption of bacteria onto surfaces and various applications of nanoparticles in nanomedicine and nanotoxicology. Precise interpretation of the extent of such interactions is however hindered due to the limitations of most characterization methods to allow rapid, sensitive and accurate measurements. Here we report on a fluorescence-based excitation-emission matrix (EEM) approach in combination with principal component analysis (PCA) to extract information related to the interaction between natural colloidal/particulate- and protein-like matter. Surface plasmon resonance (SPR) analysis and fiber-optic probe based surface fluorescence measurements were used to confirm that the proposed approach can be used to characterize colloidal/particulate-protein interactions at the physical level. This method has potential to be a fundamental measurement of these interactions with the advantage that it can be performed rapidly and with high sensitivity. Copyright © 2012 Elsevier B.V. All rights reserved.

Optimizing protection efforts for amphibian conservation in Mediterranean landscapes

NASA Astrophysics Data System (ADS)

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

2013-05-01

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

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

PubMed

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

2017-01-01

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

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

PubMed

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

2015-02-15

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

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

PubMed Central

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

2014-01-01

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

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

Performance analysis of a Principal Component Analysis ensemble classifier for Emotiv headset P300 spellers.

PubMed

Elsawy, Amr S; Eldawlatly, Seif; Taher, Mohamed; Aly, Gamal M

2014-01-01

The current trend to use Brain-Computer Interfaces (BCIs) with mobile devices mandates the development of efficient EEG data processing methods. In this paper, we demonstrate the performance of a Principal Component Analysis (PCA) ensemble classifier for P300-based spellers. We recorded EEG data from multiple subjects using the Emotiv neuroheadset in the context of a classical oddball P300 speller paradigm. We compare the performance of the proposed ensemble classifier to the performance of traditional feature extraction and classifier methods. Our results demonstrate the capability of the PCA ensemble classifier to classify P300 data recorded using the Emotiv neuroheadset with an average accuracy of 86.29% on cross-validation data. In addition, offline testing of the recorded data reveals an average classification accuracy of 73.3% that is significantly higher than that achieved using traditional methods. Finally, we demonstrate the effect of the parameters of the P300 speller paradigm on the performance of the method.

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.

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.

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.

Principal component analysis for fermionic critical points

NASA Astrophysics Data System (ADS)

Costa, Natanael C.; Hu, Wenjian; Bai, Z. J.; Scalettar, Richard T.; Singh, Rajiv R. P.

2017-11-01

We use determinant quantum Monte Carlo (DQMC), in combination with the principal component analysis (PCA) approach to unsupervised learning, to extract information about phase transitions in several of the most fundamental Hamiltonians describing strongly correlated materials. We first explore the zero-temperature antiferromagnet to singlet transition in the periodic Anderson model, the Mott insulating transition in the Hubbard model on a honeycomb lattice, and the magnetic transition in the 1/6-filled Lieb lattice. We then discuss the prospects for learning finite temperature superconducting transitions in the attractive Hubbard model, for which there is no sign problem. Finally, we investigate finite temperature charge density wave (CDW) transitions in the Holstein model, where the electrons are coupled to phonon degrees of freedom, and carry out a finite size scaling analysis to determine Tc. We examine the different behaviors associated with Hubbard-Stratonovich auxiliary field configurations on both the entire space-time lattice and on a single imaginary time slice, or other quantities, such as equal-time Green's and pair-pair correlation functions.

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.

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

PubMed

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

2015-11-01

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

Free amino acids, biogenic amines, and ammonium profiling in tobacco from different geographical origins using microwave-assisted extraction followed by ultra high performance liquid chromatography.

PubMed

Cai, Kai; Xiang, Zhangmin; Li, Hongqin; Zhao, Huina; Lin, Yechun; Pan, Wenjie; Lei, Bo

2017-12-01

This work describes a rapid, stable, and accurate method for determining the free amino acids, biogenic amines, and ammonium in tobacco. The target analytes were extracted with microwave-assisted extraction and then derivatized with diethyl ethoxymethylenemalonate, followed by ultra high performance liquid chromatography analysis. The experimental design used to optimize the microwave-assisted extraction conditions showed that the optimal extraction time was 10 min with a temperature of 60°C. The stability of aminoenone derivatives was improved by keeping the pH near 9.0, and there was no obvious degradation during the 80°C heating and room temperature storage. Under optimal conditions, this method showed good linearity (R 2 > 0.999) and sensitivity (limits of detection 0.010-0.081 μg/mL). The extraction recoveries were between 88.4 and 106.5%, while the repeatability and reproducibility ranged from 0.48 to 5.12% and from 1.56 to 6.52%, respectively. The newly developed method was employed to analyze the tobacco from different geographical origins. Principal component analysis showed that four geographical origins of tobacco could be clearly distinguished and that each had their characteristic components. The proposed method also showed great potential for further investigations on nitrogen metabolism in plants. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

DTIC Science & Technology

2012-03-01

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

Analysis of wine volatile profile by purge-and-trap-gas chromatography-mass spectrometry. Application to the analysis of red and white wines from different Spanish regions.

PubMed

Aznar, Margarita; Arroyo, Teresa

2007-09-21

The purge-and-trap extraction method, coupled to a gas chromatograph with mass spectrometry detection, has been applied to the determination of 26 aromatic volatiles in wine. The method was optimized, validated and applied to the analyses of 40 red and white wines from 7 different Spanish regions. Principal components analyses of data showed the correlation between wines of similar origin.

Characterization and scaling of anisotropy of fabrics and fractures at laboratory scales: insights from volumetric analysis using computed tomography

NASA Astrophysics Data System (ADS)

Ketcham, Richard A.

2017-04-01

Anisotropy in three-dimensional quantities such as geometric shape and orientation is commonly quantified using principal components analysis, in which a second order tensor determines the orientations of orthogonal components and their relative magnitudes. This approach has many advantages, such as simplicity and ability to accommodate many forms of data, and resilience to data sparsity. However, when data are sufficiently plentiful and precise, they sometimes show that aspects of the principal components approach are oversimplifications that may affect how the data are interpreted or extrapolated for mathematical or physical modeling. High-resolution X-ray computed tomography (CT) can effectively extract thousands of measurements from a single sample, providing a data density sufficient to examine the ways in which anisotropy on the hand-sample scale and smaller can be quantified, and the extent to which the ways the data are simplified are faithful to the underlying distributions. Features within CT data can be considered as discrete objects or continuum fabrics; the latter can be characterized using a variety of metrics, such as the most commonly used mean intercept length, and also the more specialized star length and star volume distributions. Each method posits a different scaling among components that affects the measured degree of anisotropy. The star volume distribution is the most sensitive to anisotropy, and commonly distinguishes strong fabric components that are not orthogonal. Although these data are well-presented using a stereoplot, 3D rose diagrams are another visualization option that can often help identify these components. This talk presents examples from a number of cases, starting with trabecular bone and extending to geological features such as fractures and brittle and ductile fabrics, in which non-orthogonal principal components identified using CT provide some insight into the origin of the underlying structures, and how they should be interpreted and potentially up-scaled.

Bimodal spectroscopic evaluation of ultra violet-irradiated mouse skin inflammatory and precancerous stages: instrumentation, spectral feature extraction/selection and classification (k-NN, LDA and SVM)

NASA Astrophysics Data System (ADS)

Díaz-Ayil, G.; Amouroux, M.; Blondel, W. C. P. M.; Bourg-Heckly, G.; Leroux, A.; Guillemin, F.; Granjon, Y.

2009-07-01

This paper deals with the development and application of in vivo spatially-resolved bimodal spectroscopy (AutoFluorescence AF and Diffuse Reflectance DR), to discriminate various stages of skin precancer in a preclinical model (UV-irradiated mouse): Compensatory Hyperplasia CH, Atypical Hyperplasia AH and Dysplasia D. A programmable instrumentation was developed for acquiring AF emission spectra using 7 excitation wavelengths: 360, 368, 390, 400, 410, 420 and 430 nm, and DR spectra in the 390-720 nm wavelength range. After various steps of intensity spectra preprocessing (filtering, spectral correction and intensity normalization), several sets of spectral characteristics were extracted and selected based on their discrimination power statistically tested for every pair-wise comparison of histological classes. Data reduction with Principal Components Analysis (PCA) was performed and 3 classification methods were implemented (k-NN, LDA and SVM), in order to compare diagnostic performance of each method. Diagnostic performance was studied and assessed in terms of sensitivity (Se) and specificity (Sp) as a function of the selected features, of the combinations of 3 different inter-fibers distances and of the numbers of principal components, such that: Se and Sp ≈ 100% when discriminating CH vs. others; Sp ≈ 100% and Se > 95% when discriminating Healthy vs. AH or D; Sp ≈ 74% and Se ≈ 63%for AH vs. D.

Multivariate analysis of chromatographic retention data as a supplementary means for grouping structurally related compounds.

PubMed

Fasoula, S; Zisi, Ch; Sampsonidis, I; Virgiliou, Ch; Theodoridis, G; Gika, H; Nikitas, P; Pappa-Louisi, A

2015-03-27

In the present study a series of 45 metabolite standards belonging to four chemically similar metabolite classes (sugars, amino acids, nucleosides and nucleobases, and amines) was subjected to LC analysis on three HILIC columns under 21 different gradient conditions with the aim to explore whether the retention properties of these analytes are determined from the chemical group they belong. Two multivariate techniques, principal component analysis (PCA) and discriminant analysis (DA), were used for statistical evaluation of the chromatographic data and extraction similarities between chemically related compounds. The total variance explained by the first two principal components of PCA was found to be about 98%, whereas both statistical analyses indicated that all analytes are successfully grouped in four clusters of chemical structure based on the retention obtained in four or at least three chromatographic runs, which, however should be performed on two different HILIC columns. Moreover, leave-one-out cross-validation of the above retention data set showed that the chemical group in which an analyte belongs can be 95.6% correctly predicted when the analyte is subjected to LC analysis under the same four or three experimental conditions as the all set of analytes was run beforehand. That, in turn, may assist with disambiguation of analyte identification in complex biological extracts. Copyright © 2015 Elsevier B.V. All rights reserved.

Identification of More Feasible MicroRNA-mRNA Interactions within Multiple Cancers Using Principal Component Analysis Based Unsupervised Feature Extraction.

PubMed

Taguchi, Y-H

2016-05-10

MicroRNA(miRNA)-mRNA interactions are important for understanding many biological processes, including development, differentiation and disease progression, but their identification is highly context-dependent. When computationally derived from sequence information alone, the identification should be verified by integrated analyses of mRNA and miRNA expression. The drawback of this strategy is the vast number of identified interactions, which prevents an experimental or detailed investigation of each pair. In this paper, we overcome this difficulty by the recently proposed principal component analysis (PCA)-based unsupervised feature extraction (FE), which reduces the number of identified miRNA-mRNA interactions that properly discriminate between patients and healthy controls without losing biological feasibility. The approach is applied to six cancers: hepatocellular carcinoma, non-small cell lung cancer, esophageal squamous cell carcinoma, prostate cancer, colorectal/colon cancer and breast cancer. In PCA-based unsupervised FE, the significance does not depend on the number of samples (as in the standard case) but on the number of features, which approximates the number of miRNAs/mRNAs. To our knowledge, we have newly identified miRNA-mRNA interactions in multiple cancers based on a single common (universal) criterion. Moreover, the number of identified interactions was sufficiently small to be sequentially curated by literature searches.

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

PubMed

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

2016-11-22

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

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

PubMed Central

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

2016-01-01

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

[Discrimination of types of polyacrylamide based on near infrared spectroscopy coupled with least square support vector machine].

PubMed

Zhang, Hong-Guang; Yang, Qin-Min; Lu, Jian-Gang

2014-04-01

In this paper, a novel discriminant methodology based on near infrared spectroscopic analysis technique and least square support vector machine was proposed for rapid and nondestructive discrimination of different types of Polyacrylamide. The diffuse reflectance spectra of samples of Non-ionic Polyacrylamide, Anionic Polyacrylamide and Cationic Polyacrylamide were measured. Then principal component analysis method was applied to reduce the dimension of the spectral data and extract of the principal compnents. The first three principal components were used for cluster analysis of the three different types of Polyacrylamide. Then those principal components were also used as inputs of least square support vector machine model. The optimization of the parameters and the number of principal components used as inputs of least square support vector machine model was performed through cross validation based on grid search. 60 samples of each type of Polyacrylamide were collected. Thus a total of 180 samples were obtained. 135 samples, 45 samples for each type of Polyacrylamide, were randomly split into a training set to build calibration model and the rest 45 samples were used as test set to evaluate the performance of the developed model. In addition, 5 Cationic Polyacrylamide samples and 5 Anionic Polyacrylamide samples adulterated with different proportion of Non-ionic Polyacrylamide were also prepared to show the feasibilty of the proposed method to discriminate the adulterated Polyacrylamide samples. The prediction error threshold for each type of Polyacrylamide was determined by F statistical significance test method based on the prediction error of the training set of corresponding type of Polyacrylamide in cross validation. The discrimination accuracy of the built model was 100% for prediction of the test set. The prediction of the model for the 10 mixing samples was also presented, and all mixing samples were accurately discriminated as adulterated samples. The overall results demonstrate that the discrimination method proposed in the present paper can rapidly and nondestructively discriminate the different types of Polyacrylamide and the adulterated Polyacrylamide samples, and offered a new approach to discriminate the types of Polyacrylamide.

Lingzhi or Reishi Medicinal Mushroom, Ganoderma lucidum (Agaricomycetes), Inhibits Candida Biofilms: A Metabolomic Approach.

PubMed

Bhardwaj, Anuja; Gupta, Payal; Kumar, Navin; Mishra, Jigni; Kumar, Ajai; Rakhee, Rajput; Misra, Kshipra

2017-01-01

This article presents a comparative gas chromatography (GC)-mass spectrometry (MS)-based metabolomic analysis of mycelia and fruiting bodies of the medicinal mushroom Ganoderma lucidum. Three aqueous extracts-mycelia, fruiting bodies, and a mixture of them-and their sequential fractions (methanolic and ethyl acetate), prepared using an accelerated solvent extractor, were characterized by GC-MS to determine volatile organic compounds and by high-performance thin-layer chromatography to quantify ascorbic acid, a potent antioxidant. In addition, these extracts and fractions were assessed against Candida albicans and C. glabrata biofilms via the XTT reduction assay, and their antioxidant potential was evaluated. Application of chemometrics (hierarchical cluster analysis and principal component analysis) to GC data revealed variability in volatile organic compound profiles among G. lucidum extracts and fractions. The mycelial aqueous extract demonstrated higher anti-Candida activity and ascorbic acid content among all the extracts and fractions. Thus, this study illustrates the preventive effect of G. lucidum against C. albicans and C. glabrata biofilms along with its nutritional value.

Linear degrees of freedom in speech production: analysis of cineradio- and labio-film data and articulatory-acoustic modeling.

PubMed

Beautemps, D; Badin, P; Bailly, G

2001-05-01

The following contribution addresses several issues concerning speech degrees of freedom in French oral vowels, stop, and fricative consonants based on an analysis of tongue and lip shapes extracted from cineradio- and labio-films. The midsagittal tongue shapes have been submitted to a linear decomposition where some of the loading factors were selected such as jaw and larynx position while four other components were derived from principal component analysis (PCA). For the lips, in addition to the more traditional protrusion and opening components, a supplementary component was extracted to explain the upward movement of both the upper and lower lips in [v] production. A linear articulatory model was developed; the six tongue degrees of freedom were used as the articulatory control parameters of the midsagittal tongue contours and explained 96% of the tongue data variance. These control parameters were also used to specify the frontal lip width dimension derived from the labio-film front views. Finally, this model was complemented by a conversion model going from the midsagittal to the area function, based on a fitting of the midsagittal distances and the formant frequencies for both vowels and consonants.

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.

Physiological and anthropometric determinants of rhythmic gymnastics performance.

PubMed

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

2008-03-01

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

Defining genetic and chemical diversity in wheat grain by 1H‐NMR spectroscopy of polar metabolites

PubMed Central

Corol, Delia I.; Jones, Huw D.; Beale, Michael H.; Ward, Jane L.

2017-01-01

Scope The application of high‐throughput 1H nuclear magnetic resonance (1H‐NMR) of unpurified extracts to determine genetic diversity and the contents of polar components in grain of wheat. Methods and results Milled whole wheat grain was extracted with 80:20 D2O:CD3OD containing 0.05% d4–trimethylsilylpropionate. 1H‐NMR spectra were acquired under automation at 300°K using an Avance Spectrometer operating at 600.0528 MHz. Regions for individual metabolites were identified by comparison to a library of known standards run under identical conditions. The individual 1H‐NMR peaks or levels of known metabolites were then compared by Principal Component Analysis using SIMCA‐P software. Conclusions High‐throughput 1H‐NMR is an excellent tool to compare the extent of genetic diversity within and between wheat species, and to quantify specific components (including glycine betaine, choline, and asparagine) in individual genotypes. It can also be used to monitor changes in composition related to environmental factors and to support comparisons of the substantial equivalence of transgenic lines. PMID:28087883

DREEM on: validation of the Dundee Ready Education Environment Measure in Pakistan.

PubMed

Khan, Junaid Sarfraz; Tabasum, Saima; Yousafzai, Usman Khalil; Fatima, Mehreen

2011-09-01

To validate DREEM in medical education environment of Punjab, Pakistan. The DREEM questionnaire was anonymously collected from Final year Baccalaureate of Medicine; Baccalaureate of Surgery students in the private and public medical colleges affiliated with the University of Health Sciences, Lahore. Data was analyzed using Principal Component Analysis with Varimax Rotation. The response rate was 84.14 %. The average DREEM score was 125. Confirmatory and Exploratory Factor Analysis was applied under the conditions of eigenvalues >1 and loadings > or = 0.3. In CONFIRMATORY FACTOR ANALYSIS, Five components were extracted accounting for 40.10% of variance and in EXPLORATORY FACTOR ANALYSIS, Ten components were extracted accounting for 52.33% of variance. Total 50 items had internal consistency reliability of 0.91 (Cronbach's Alpha). The value of Spearman-Brown was 0.868 showing the reliability of the analysis. In both analyses the subscales produced were sensible but the mismatch from the original was largely due to the English-Pakistan contextual and cultural differences. DREEM is a generic instrument that will do well with regional modifications to suit individual, contextual and cultural settings.

Comparison and evaluation on image fusion methods for GaoFen-1 imagery

NASA Astrophysics Data System (ADS)

Zhang, Ningyu; Zhao, Junqing; Zhang, Ling

2016-10-01

Currently, there are many research works focusing on the best fusion method suitable for satellite images of SPOT, QuickBird, Landsat and so on, but only a few of them discuss the application of GaoFen-1 satellite images. This paper proposes a novel idea by using four fusion methods, such as principal component analysis transform, Brovey transform, hue-saturation-value transform, and Gram-Schmidt transform, from the perspective of keeping the original image spectral information. The experimental results showed that the transformed images by the four fusion methods not only retain high spatial resolution on panchromatic band but also have the abundant spectral information. Through comparison and evaluation, the integration of Brovey transform is better, but the color fidelity is not the premium. The brightness and color distortion in hue saturation-value transformed image is the largest. Principal component analysis transform did a good job in color fidelity, but its clarity still need improvement. Gram-Schmidt transform works best in color fidelity, and the edge of the vegetation is the most obvious, the fused image sharpness is higher than that of principal component analysis. Brovey transform, is suitable for distinguishing the Gram-Schmidt transform, and the most appropriate for GaoFen-1 satellite image in vegetation and non-vegetation area. In brief, different fusion methods have different advantages in image quality and class extraction, and should be used according to the actual application information and image fusion algorithm.

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.

Spatial assessment of water quality using chemometrics in the Pearl River Estuary, China

NASA Astrophysics Data System (ADS)

Wu, Meilin; Wang, Youshao; Dong, Junde; Sun, Fulin; Wang, Yutu; Hong, Yiguo

2017-03-01

A cruise was commissioned in the summer of 2009 to evaluate water quality in the Pearl River Estuary (PRE). Chemometrics such as Principal Component Analysis (PCA), Cluster analysis (CA) and Self-Organizing Map (SOM) were employed to identify anthropogenic and natural influences on estuary water quality. The scores of stations in the surface layer in the first principal component (PC1) were related to NH4-N, PO4-P, NO2-N, NO3-N, TP, and Chlorophyll a while salinity, turbidity, and SiO3-Si in the second principal component (PC2). Similarly, the scores of stations in the bottom layers in PC1 were related to PO4-P, NO2-N, NO3-N, and TP, while salinity, Chlorophyll a, NH4-N, and SiO3-Si in PC2. Results of the PCA identified the spatial distribution of the surface and bottom water quality, namely the Guangzhou urban reach, Middle reach, and Lower reach of the estuary. Both cluster analysis and PCA produced the similar results. Self-organizing map delineated the Guangzhou urban reach of the Pearl River that was mainly influenced by human activities. The middle and lower reaches of the PRE were mainly influenced by the waters in the South China Sea. The information extracted by PCA, CA, and SOM would be very useful to regional agencies in developing a strategy to carry out scientific plans for resource use based on marine system functions.

A Network View on Psychiatric Disorders: Network Clusters of Symptoms as Elementary Syndromes of Psychopathology

PubMed Central

Goekoop, Rutger; Goekoop, Jaap G.

2014-01-01

Introduction The vast number of psychopathological syndromes that can be observed in clinical practice can be described in terms of a limited number of elementary syndromes that are differentially expressed. Previous attempts to identify elementary syndromes have shown limitations that have slowed progress in the taxonomy of psychiatric disorders. Aim To examine the ability of network community detection (NCD) to identify elementary syndromes of psychopathology and move beyond the limitations of current classification methods in psychiatry. Methods 192 patients with unselected mental disorders were tested on the Comprehensive Psychopathological Rating Scale (CPRS). Principal component analysis (PCA) was performed on the bootstrapped correlation matrix of symptom scores to extract the principal component structure (PCS). An undirected and weighted network graph was constructed from the same matrix. Network community structure (NCS) was optimized using a previously published technique. Results In the optimal network structure, network clusters showed a 89% match with principal components of psychopathology. Some 6 network clusters were found, including "DEPRESSION", "MANIA", “ANXIETY”, "PSYCHOSIS", "RETARDATION", and "BEHAVIORAL DISORGANIZATION". Network metrics were used to quantify the continuities between the elementary syndromes. Conclusion We present the first comprehensive network graph of psychopathology that is free from the biases of previous classifications: a ‘Psychopathology Web’. Clusters within this network represent elementary syndromes that are connected via a limited number of bridge symptoms. Many problems of previous classifications can be overcome by using a network approach to psychopathology. PMID:25427156

Visual feature extraction and establishment of visual tags in the intelligent visual internet of things

NASA Astrophysics Data System (ADS)

Zhao, Yiqun; Wang, Zhihui

2015-12-01

The Internet of things (IOT) is a kind of intelligent networks which can be used to locate, track, identify and supervise people and objects. One of important core technologies of intelligent visual internet of things ( IVIOT) is the intelligent visual tag system. In this paper, a research is done into visual feature extraction and establishment of visual tags of the human face based on ORL face database. Firstly, we use the principal component analysis (PCA) algorithm for face feature extraction, then adopt the support vector machine (SVM) for classifying and face recognition, finally establish a visual tag for face which is already classified. We conducted a experiment focused on a group of people face images, the result show that the proposed algorithm have good performance, and can show the visual tag of objects conveniently.

Development of methodology for identification the nature of the polyphenolic extracts by FTIR associated with multivariate analysis

NASA Astrophysics Data System (ADS)

Grasel, Fábio dos Santos; Ferrão, Marco Flôres; Wolf, Carlos Rodolfo

2016-01-01

Tannins are polyphenolic compounds of complex structures formed by secondary metabolism in several plants. These polyphenolic compounds have different applications, such as drugs, anti-corrosion agents, flocculants, and tanning agents. This study analyses six different type of polyphenolic extracts by Fourier transform infrared spectroscopy (FTIR) combined with multivariate analysis. Through both principal component analysis (PCA) and hierarchical cluster analysis (HCA), we observed well-defined separation between condensed (quebracho and black wattle) and hydrolysable (valonea, chestnut, myrobalan, and tara) tannins. For hydrolysable tannins, it was also possible to observe the formation of two different subgroups between samples of chestnut and valonea and between samples of tara and myrobalan. Among all samples analysed, the chestnut and valonea showed the greatest similarity, indicating that these extracts contain equivalent chemical compositions and structure and, therefore, similar properties.

History of 232-F, tritium extraction processing

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

Blackburn, G.W.

1994-08-01

In 1950 the Atomic Energy Commission authorized the Savannah River Project principally for the production of tritium and plutonium-239 for use in thermonuclear weapons. 232-F was built as an interim facility in 1953--1954, at a cost of $3.9M. Tritium extraction operations began in October, 1955, after the reactor and separations startups. In July, 1957 a larger tritium facility began operation in 232-H. In 1958 the capacity of 232-H was doubled. Also, in 1957 a new task was assigned to Savannah River, the loading of tritium into reservoirs that would be actual components of thermonuclear weapons. This report describes the historymore » of 232-F, the process for tritium extraction, and the lessons learned over the years that were eventually incorporated into the new Replacement Tritium Facility.« less

Hybrid Feature Extraction-based Approach for Facial Parts Representation and Recognition

NASA Astrophysics Data System (ADS)

Rouabhia, C.; Tebbikh, H.

2008-06-01

Face recognition is a specialized image processing which has attracted a considerable attention in computer vision. In this article, we develop a new facial recognition system from video sequences images dedicated to person identification whose face is partly occulted. This system is based on a hybrid image feature extraction technique called ACPDL2D (Rouabhia et al. 2007), it combines two-dimensional principal component analysis and two-dimensional linear discriminant analysis with neural network. We performed the feature extraction task on the eyes and the nose images separately then a Multi-Layers Perceptron classifier is used. Compared to the whole face, the results of simulation are in favor of the facial parts in terms of memory capacity and recognition (99.41% for the eyes part, 98.16% for the nose part and 97.25 % for the whole face).

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

PubMed

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

2012-05-01

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

Extracting features from protein sequences to improve deep extreme learning machine for protein fold recognition.

PubMed

Ibrahim, Wisam; Abadeh, Mohammad Saniee

2017-05-21

Protein fold recognition is an important problem in bioinformatics to predict three-dimensional structure of a protein. One of the most challenging tasks in protein fold recognition problem is the extraction of efficient features from the amino-acid sequences to obtain better classifiers. In this paper, we have proposed six descriptors to extract features from protein sequences. These descriptors are applied in the first stage of a three-stage framework PCA-DELM-LDA to extract feature vectors from the amino-acid sequences. Principal Component Analysis PCA has been implemented to reduce the number of extracted features. The extracted feature vectors have been used with original features to improve the performance of the Deep Extreme Learning Machine DELM in the second stage. Four new features have been extracted from the second stage and used in the third stage by Linear Discriminant Analysis LDA to classify the instances into 27 folds. The proposed framework is implemented on the independent and combined feature sets in SCOP datasets. The experimental results show that extracted feature vectors in the first stage could improve the performance of DELM in extracting new useful features in second stage. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

Feature selection for neural network based defect classification of ceramic components using high frequency ultrasound.

PubMed

Kesharaju, Manasa; Nagarajah, Romesh

2015-09-01

The motivation for this research stems from a need for providing a non-destructive testing method capable of detecting and locating any defects and microstructural variations within armour ceramic components before issuing them to the soldiers who rely on them for their survival. The development of an automated ultrasonic inspection based classification system would make possible the checking of each ceramic component and immediately alert the operator about the presence of defects. Generally, in many classification problems a choice of features or dimensionality reduction is significant and simultaneously very difficult, as a substantial computational effort is required to evaluate possible feature subsets. In this research, a combination of artificial neural networks and genetic algorithms are used to optimize the feature subset used in classification of various defects in reaction-sintered silicon carbide ceramic components. Initially wavelet based feature extraction is implemented from the region of interest. An Artificial Neural Network classifier is employed to evaluate the performance of these features. Genetic Algorithm based feature selection is performed. Principal Component Analysis is a popular technique used for feature selection and is compared with the genetic algorithm based technique in terms of classification accuracy and selection of optimal number of features. The experimental results confirm that features identified by Principal Component Analysis lead to improved performance in terms of classification percentage with 96% than Genetic algorithm with 94%. Copyright © 2015 Elsevier B.V. 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.

A novel approach to rapidly explore analytical markers for quality control of Radix Salviae Miltiorrhizae extract granules by robust principal component analysis with ultra-high performance liquid chromatography-ultraviolet-quadrupole time-of-flight mass spectrometry.

PubMed

Song, Jing-Zheng; Li, Song-Lin; Zhou, Yan; Qiao, Chun-Feng; Chen, Shi-Lin; Xu, Hong-Xi

2010-11-02

In a well-controlled experiment, outliers discriminated by robust principal component analysis (RPCA) represent contents in samples which are of particular quality distinguishable from the rest of the others, therefore chemical constituents in a natural product causing discrimination between outliers and the majority of samples could be considered as analytical markers for quality control. Based on this strategy, a novel approach for rapidly exploring characteristic analytical markers was proposed for the quality control of extract granules of Radix Salviae Miltiorrhizae (EGRSM). In this study, large sizes of samples were analyzed via high-throughput ultra-high performance liquid chromatography-ultraviolet-quadrupole time-of-flight mass spectrometry (UHPLC-UV-Q-Tof MS). RPCA was first performed on the three groups of samples: RSM (the raw material), the in-house prepared aqueous extract of Radix Salviae Miltiorrhizae (AERSM) and commercial product of EGRSM, to determine the variation of specific constituents between raw material and the final products as well as the effect of manufacturing process on the overall quality. Then RPCA was performed on the commercial products of EGRSM to explore the applicability of identified characteristic markers for the quality control of EGRSM. Candidate markers were extracted by RPCA, and their molecular formulae were determined by high resolution electrospray ionization-mass spectrometric (ESI-MS) analysis. The suitability of identified markers was then evaluated by determining the relationship between quantities of the identified markers with their antioxidant activities biologically, and further confirmed in a variety of samples. In conclusion, the combination of RPCA with UHPLC-UV-Q-Tof MS is a reliable means to identify chemical markers for evaluating quality of herbal medicines. Copyright (c) 2010 Elsevier B.V. All rights reserved.

A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets.

PubMed

Li, Der-Chiang; Liu, Chiao-Wen; Hu, Susan C

2011-05-01

Medical data sets are usually small and have very high dimensionality. Too many attributes will make the analysis less efficient and will not necessarily increase accuracy, while too few data will decrease the modeling stability. Consequently, the main objective of this study is to extract the optimal subset of features to increase analytical performance when the data set is small. This paper proposes a fuzzy-based non-linear transformation method to extend classification related information from the original data attribute values for a small data set. Based on the new transformed data set, this study applies principal component analysis (PCA) to extract the optimal subset of features. Finally, we use the transformed data with these optimal features as the input data for a learning tool, a support vector machine (SVM). Six medical data sets: Pima Indians' diabetes, Wisconsin diagnostic breast cancer, Parkinson disease, echocardiogram, BUPA liver disorders dataset, and bladder cancer cases in Taiwan, are employed to illustrate the approach presented in this paper. This research uses the t-test to evaluate the classification accuracy for a single data set; and uses the Friedman test to show the proposed method is better than other methods over the multiple data sets. The experiment results indicate that the proposed method has better classification performance than either PCA or kernel principal component analysis (KPCA) when the data set is small, and suggest creating new purpose-related information to improve the analysis performance. This paper has shown that feature extraction is important as a function of feature selection for efficient data analysis. When the data set is small, using the fuzzy-based transformation method presented in this work to increase the information available produces better results than the PCA and KPCA approaches. Copyright © 2011 Elsevier B.V. All rights reserved.

Identification of the traditional Tibetan medicine "Shaji" and their different extracts through tri-step infrared spectroscopy

NASA Astrophysics Data System (ADS)

Liu, Yue; Li, Jingyi; Fan, Gang; Sun, Suqin; Zhang, Yuxin; Zhang, Yi; Tu, Ya

2016-11-01

Hippophae rhamnoides subsp. sinensis Rousi, Hippophae gyantsensis (Rousi) Y. S. Lian, Hippophae neurocarpa S. W. Liu & T. N. He and Hippophae tibetana Schlechtendal are typically used under one name "Shaji", to treat cardiovascular diseases and lung disorders in Tibetan medicine (TM). A complete set of infrared (IR) macro-fingerprints of these four Hippophae species should be characterized and compared simply, accurately, and in detail for identification. In the present study, tri-step IR spectroscopy, which included Fourier transform IR (FT-IR) spectroscopy, second derivative IR (SD-IR) spectroscopy and two-dimensional correlation IR (2D-IR) spectroscopy, was employed to discriminate the four Hippophae species and their corresponding extracts using different solvents. The relevant spectra exhibited the holistic chemical compositions and variations. Flavonoids, fatty acids and sugars were found to be the main chemical components. Characteristic peak positions, intensities and shapes derived from FT-IR, SD-IR and 2D-IR spectra provided valuable information for sample discrimination. Principal component analysis (PCA) of spectral differences was performed to illustrate the objective identification. Results showed that the species and their extracts can be clearly distinguished. Thus, a quick, precise and effective tri-step IR spectroscopy combined with PCA can be applied to identify and discriminate medicinal materials and their extracts in TM research.

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.

[A spatial adaptive algorithm for endmember extraction on multispectral remote sensing image].

PubMed

Zhu, Chang-Ming; Luo, Jian-Cheng; Shen, Zhan-Feng; Li, Jun-Li; Hu, Xiao-Dong

2011-10-01

Due to the problem that the convex cone analysis (CCA) method can only extract limited endmember in multispectral imagery, this paper proposed a new endmember extraction method by spatial adaptive spectral feature analysis in multispectral remote sensing image based on spatial clustering and imagery slice. Firstly, in order to remove spatial and spectral redundancies, the principal component analysis (PCA) algorithm was used for lowering the dimensions of the multispectral data. Secondly, iterative self-organizing data analysis technology algorithm (ISODATA) was used for image cluster through the similarity of the pixel spectral. And then, through clustering post process and litter clusters combination, we divided the whole image data into several blocks (tiles). Lastly, according to the complexity of image blocks' landscape and the feature of the scatter diagrams analysis, the authors can determine the number of endmembers. Then using hourglass algorithm extracts endmembers. Through the endmember extraction experiment on TM multispectral imagery, the experiment result showed that the method can extract endmember spectra form multispectral imagery effectively. What's more, the method resolved the problem of the amount of endmember limitation and improved accuracy of the endmember extraction. The method has provided a new way for multispectral image endmember extraction.

The Effect of Temperature on Pressurised Hot Water Extraction of Pharmacologically Important Metabolites as Analysed by UPLC-qTOF-MS and PCA

PubMed Central

Khoza, B. S.; Chimuka, L.; Mukwevho, E.; Steenkamp, P. A.; Madala, N. E.

2014-01-01

Metabolite extraction methods have been shown to be a critical consideration for pharmacometabolomics studies and, as such, optimization and development of new extraction methods are crucial. In the current study, an organic solvent-free method, namely, pressurised hot water extraction (PHWE), was used to extract pharmacologically important metabolites from dried Moringa oleifera leaves. Here, the temperature of the extraction solvent (pure water) was altered while keeping other factors constant using a homemade PHWE system. Samples extracted at different temperatures (50, 100, and 150°C) were assayed for antioxidant activities and the effect of the temperature on the extraction process was evaluated. The samples were further analysed by mass spectrometry to elucidate their metabolite compositions. Principal component analysis (PCA) evaluation of the UPLC-MS data showed distinctive differential metabolite patterns. Here, temperature changes during PHWE were shown to affect the levels of metabolites with known pharmacological activities, such as chlorogenic acids and flavonoids. Our overall findings suggest that, if not well optimised, the extraction temperature could compromise the “pharmacological potency” of the extracts. The use of MS in combination with PCA was furthermore shown to be an excellent approach to evaluate the quality and content of pharmacologically important extracts. PMID:25371697

Surface atrial frequency analysis in patients with atrial fibrillation: a tool for evaluating the effects of intervention.

PubMed

Raine, Dan; Langley, Philip; Murray, Alan; Dunuwille, Asunga; Bourke, John P

2004-09-01

The aims of this study were to evaluate (1) principal component analysis as a technique for extracting the atrial signal waveform from the standard 12-lead ECG and (2) its ability to distinguish changes in atrial fibrillation (AF) frequency parameters over time and in response to pharmacologic manipulation using drugs with different effects on atrial electrophysiology. Twenty patients with persistent AF were studied. Continuous 12-lead Holter ECGs were recorded for 60 minutes, first, in the drug-free state. Mean and variability of atrial waveform frequency were measured using an automated computer technique. This extracted the atrial signal by principal component analysis and identified the main frequency component using Fourier analysis. Patients were then allotted sequentially to receive 1 of 4 drugs intravenously (amiodarone, flecainide, sotalol, or metoprolol), and changes induced in mean and variability of atrial waveform frequency measured. Mean and variability of atrial waveform frequency did not differ within patients between the two 30-minute sections of the drug-free state. As hypothesized, significant changes in mean and variability of atrial waveform frequency were detected after manipulation with amiodarone (mean: 5.77 vs 4.86 Hz; variability: 0.55 vs 0.31 Hz), flecainide (mean: 5.33 vs 4.72 Hz; variability: 0.71 vs 0.31 Hz), and sotalol (mean: 5.94 vs 4.90 Hz; variability: 0.73 vs 0.40 Hz) but not with metoprolol (mean: 5.41 vs 5.17 Hz; variability: 0.81 vs 0.82 Hz). A technique for continuously analyzing atrial frequency characteristics of AF from the surface ECG has been developed and validated.

The influence of iliotibial band syndrome history on running biomechanics examined via principal components analysis.

PubMed

Foch, Eric; Milner, Clare E

2014-01-03

Iliotibial band syndrome (ITBS) is a common knee overuse injury among female runners. Atypical discrete trunk and lower extremity biomechanics during running may be associated with the etiology of ITBS. Examining discrete data points limits the interpretation of a waveform to a single value. Characterizing entire kinematic and kinetic waveforms may provide additional insight into biomechanical factors associated with ITBS. Therefore, the purpose of this cross-sectional investigation was to determine whether female runners with previous ITBS exhibited differences in kinematics and kinetics compared to controls using a principal components analysis (PCA) approach. Forty participants comprised two groups: previous ITBS and controls. Principal component scores were retained for the first three principal components and were analyzed using independent t-tests. The retained principal components accounted for 93-99% of the total variance within each waveform. Runners with previous ITBS exhibited low principal component one scores for frontal plane hip angle. Principal component one accounted for the overall magnitude in hip adduction which indicated that runners with previous ITBS assumed less hip adduction throughout stance. No differences in the remaining retained principal component scores for the waveforms were detected among groups. A smaller hip adduction angle throughout the stance phase of running may be a compensatory strategy to limit iliotibial band strain. This running strategy may have persisted after ITBS symptoms subsided. © 2013 Published by Elsevier Ltd.

PCA Tomography: how to extract information from data cubes

NASA Astrophysics Data System (ADS)

Steiner, J. E.; Menezes, R. B.; Ricci, T. V.; Oliveira, A. S.

2009-05-01

Astronomy has evolved almost exclusively by the use of spectroscopic and imaging techniques, operated separately. With the development of modern technologies, it is possible to obtain data cubes in which one combines both techniques simultaneously, producing images with spectral resolution. To extract information from them can be quite complex, and hence the development of new methods of data analysis is desirable. We present a method of analysis of data cube (data from single field observations, containing two spatial and one spectral dimension) that uses Principal Component Analysis (PCA) to express the data in the form of reduced dimensionality, facilitating efficient information extraction from very large data sets. PCA transforms the system of correlated coordinates into a system of uncorrelated coordinates ordered by principal components of decreasing variance. The new coordinates are referred to as eigenvectors, and the projections of the data on to these coordinates produce images we will call tomograms. The association of the tomograms (images) to eigenvectors (spectra) is important for the interpretation of both. The eigenvectors are mutually orthogonal, and this information is fundamental for their handling and interpretation. When the data cube shows objects that present uncorrelated physical phenomena, the eigenvector's orthogonality may be instrumental in separating and identifying them. By handling eigenvectors and tomograms, one can enhance features, extract noise, compress data, extract spectra, etc. We applied the method, for illustration purpose only, to the central region of the low ionization nuclear emission region (LINER) galaxy NGC 4736, and demonstrate that it has a type 1 active nucleus, not known before. Furthermore, we show that it is displaced from the centre of its stellar bulge. Based on observations obtained at the Gemini Observatory, which is operated by the Association of Universities for Research in Astronomy, Inc., under a cooperative agreement with the National Science Foundation on behalf of the Gemini partnership: the National Science Foundation (United States), the Science and Technology Facilities Council (United Kingdom), the National Research Council (Canada), CONICYT (Chile), the Australian Research Council (Australia), Ministério da Ciência e Tecnologia (Brazil) and SECYT (Argentina). E-mail: steiner@astro.iag.usp.br

Multilayer neural networks for reduced-rank approximation.

PubMed

Diamantaras, K I; Kung, S Y

1994-01-01

This paper is developed in two parts. First, the authors formulate the solution to the general reduced-rank linear approximation problem relaxing the invertibility assumption of the input autocorrelation matrix used by previous authors. The authors' treatment unifies linear regression, Wiener filtering, full rank approximation, auto-association networks, SVD and principal component analysis (PCA) as special cases. The authors' analysis also shows that two-layer linear neural networks with reduced number of hidden units, trained with the least-squares error criterion, produce weights that correspond to the generalized singular value decomposition of the input-teacher cross-correlation matrix and the input data matrix. As a corollary the linear two-layer backpropagation model with reduced hidden layer extracts an arbitrary linear combination of the generalized singular vector components. Second, the authors investigate artificial neural network models for the solution of the related generalized eigenvalue problem. By introducing and utilizing the extended concept of deflation (originally proposed for the standard eigenvalue problem) the authors are able to find that a sequential version of linear BP can extract the exact generalized eigenvector components. The advantage of this approach is that it's easier to update the model structure by adding one more unit or pruning one or more units when the application requires it. An alternative approach for extracting the exact components is to use a set of lateral connections among the hidden units trained in such a way as to enforce orthogonality among the upper- and lower-layer weights. The authors call this the lateral orthogonalization network (LON) and show via theoretical analysis-and verify via simulation-that the network extracts the desired components. The advantage of the LON-based model is that it can be applied in a parallel fashion so that the components are extracted concurrently. Finally, the authors show the application of their results to the solution of the identification problem of systems whose excitation has a non-invertible autocorrelation matrix. Previous identification methods usually rely on the invertibility assumption of the input autocorrelation, therefore they can not be applied to this case.

Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis

PubMed Central

2011-01-01

Background The computer-aided identification of specific gait patterns is an important issue in the assessment of Parkinson's disease (PD). In this study, a computer vision-based gait analysis approach is developed to assist the clinical assessments of PD with kernel-based principal component analysis (KPCA). Method Twelve PD patients and twelve healthy adults with no neurological history or motor disorders within the past six months were recruited and separated according to their "Non-PD", "Drug-On", and "Drug-Off" states. The participants were asked to wear light-colored clothing and perform three walking trials through a corridor decorated with a navy curtain at their natural pace. The participants' gait performance during the steady-state walking period was captured by a digital camera for gait analysis. The collected walking image frames were then transformed into binary silhouettes for noise reduction and compression. Using the developed KPCA-based method, the features within the binary silhouettes can be extracted to quantitatively determine the gait cycle time, stride length, walking velocity, and cadence. Results and Discussion The KPCA-based method uses a feature-extraction approach, which was verified to be more effective than traditional image area and principal component analysis (PCA) approaches in classifying "Non-PD" controls and "Drug-Off/On" PD patients. Encouragingly, this method has a high accuracy rate, 80.51%, for recognizing different gaits. Quantitative gait parameters are obtained, and the power spectrums of the patients' gaits are analyzed. We show that that the slow and irregular actions of PD patients during walking tend to transfer some of the power from the main lobe frequency to a lower frequency band. Our results indicate the feasibility of using gait performance to evaluate the motor function of patients with PD. Conclusion This KPCA-based method requires only a digital camera and a decorated corridor setup. The ease of use and installation of the current method provides clinicians and researchers a low cost solution to monitor the progression of and the treatment to PD. In summary, the proposed method provides an alternative to perform gait analysis for patients with PD. PMID:22074315

Metabolic analysis of elicited cell suspension cultures of Cannabis sativa L. by (1)H-NMR spectroscopy.

PubMed

Pec, Jaroslav; Flores-Sanchez, Isvett Josefina; Choi, Young Hae; Verpoorte, Robert

2010-07-01

Cannabis sativa L. plants produce a diverse array of secondary metabolites. Cannabis cell cultures were treated with jasmonic acid (JA) and pectin as elicitors to evaluate their effect on metabolism from two cell lines using NMR spectroscopy and multivariate data analysis. According to principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA), the chloroform extract of the pectin-treated cultures were more different than control and JA-treated cultures; but in the methanol/water extract the metabolome of the JA-treated cells showed clear differences with control and pectin-treated cultures. Tyrosol, an antioxidant metabolite, was detected in cannabis cell cultures. The tyrosol content increased after eliciting with JA.

Spatially resolved bimodal spectroscopy for classification/evaluation of mouse skin inflammatory and pre-cancerous stages

NASA Astrophysics Data System (ADS)

Díaz-Ayil, Gilberto; Amouroux, Marine; Clanché, Fabien; Granjon, Yves; Blondel, Walter C. P. M.

2009-07-01

Spatially-resolved bimodal spectroscopy (multiple AutoFluorescence AF excitation and Diffuse Reflectance DR), was used in vivo to discriminate various healthy and precancerous skin stages in a pre-clinical model (UV-irradiated mouse): Compensatory Hyperplasia CH, Atypical Hyperplasia AH and Dysplasia D. A specific data preprocessing scheme was applied to intensity spectra (filtering, spectral correction and intensity normalization), and several sets of spectral characteristics were automatically extracted and selected based on their discrimination power, statistically tested for every pair-wise comparison of histological classes. Data reduction with Principal Components Analysis (PCA) was performed and 3 classification methods were implemented (k-NN, LDA and SVM), in order to compare diagnostic performance of each method. Diagnostic performance was studied and assessed in terms of Sensibility (Se) and Specificity (Sp) as a function of the selected features, of the combinations of 3 different inter-fibres distances and of the numbers of principal components, such that: Se and Sp ~ 100% when discriminating CH vs. others; Sp ~ 100% and Se > 95% when discriminating Healthy vs. AH or D; Sp ~ 74% and Se ~ 63% for AH vs. D.

Evaluation of automated sample preparation, retention time locked gas chromatography-mass spectrometry and data analysis methods for the metabolomic study of Arabidopsis species.

PubMed

Gu, Qun; David, Frank; Lynen, Frédéric; Rumpel, Klaus; Dugardeyn, Jasper; Van Der Straeten, Dominique; Xu, Guowang; Sandra, Pat

2011-05-27

In this paper, automated sample preparation, retention time locked gas chromatography-mass spectrometry (GC-MS) and data analysis methods for the metabolomics study were evaluated. A miniaturized and automated derivatisation method using sequential oximation and silylation was applied to a polar extract of 4 types (2 types×2 ages) of Arabidopsis thaliana, a popular model organism often used in plant sciences and genetics. Automation of the derivatisation process offers excellent repeatability, and the time between sample preparation and analysis was short and constant, reducing artifact formation. Retention time locked (RTL) gas chromatography-mass spectrometry was used, resulting in reproducible retention times and GC-MS profiles. Two approaches were used for data analysis. XCMS followed by principal component analysis (approach 1) and AMDIS deconvolution combined with a commercially available program (Mass Profiler Professional) followed by principal component analysis (approach 2) were compared. Several features that were up- or down-regulated in the different types were detected. Copyright © 2011 Elsevier B.V. All rights reserved.

Discrimination of Rhizoma Gastrodiae (Tianma) using 3D synchronous fluorescence spectroscopy coupled with principal component analysis

NASA Astrophysics Data System (ADS)

Fan, Qimeng; Chen, Chaoyin; Huang, Zaiqiang; Zhang, Chunmei; Liang, Pengjuan; Zhao, Shenglan

2015-02-01

Rhizoma Gastrodiae (Tianma) of different variants and different geographical origins has vital difference in quality and physiological efficacy. This paper focused on the classification and identification of Tianma of six types (two variants from three different geographical origins) using three dimensional synchronous fluorescence spectroscopy (3D-SFS) coupled with principal component analysis (PCA). 3D-SF spectra of aqueous extracts, which were obtained from Tianma of the six types, were measured by a LS-50B luminescence spectrofluorometer. The experimental results showed that the characteristic fluorescent spectral regions of the 3D-SF spectra were similar, while the intensities of characteristic regions are different significantly. Coupled these differences in peak intensities with PCA, Tianma of six types could be discriminated successfully. In conclusion, 3D-SFS coupled with PCA, which has such advantages as effective, specific, rapid, non-polluting, has an edge for discrimination of the similar Chinese herbal medicine. And the proposed methodology is a useful tool to classify and identify Tianma of different variants and different geographical origins.

Build a Robust Learning Feature Descriptor by Using a New Image Visualization Method for Indoor Scenario Recognition

PubMed Central

Wang, Xin; Deng, Zhongliang

2017-01-01

In order to recognize indoor scenarios, we extract image features for detecting objects, however, computers can make some unexpected mistakes. After visualizing the histogram of oriented gradient (HOG) features, we find that the world through the eyes of a computer is indeed different from human eyes, which assists researchers to see the reasons that cause a computer to make errors. Additionally, according to the visualization, we notice that the HOG features can obtain rich texture information. However, a large amount of background interference is also introduced. In order to enhance the robustness of the HOG feature, we propose an improved method for suppressing the background interference. On the basis of the original HOG feature, we introduce a principal component analysis (PCA) to extract the principal components of the image colour information. Then, a new hybrid feature descriptor, which is named HOG–PCA (HOGP), is made by deeply fusing these two features. Finally, the HOGP is compared to the state-of-the-art HOG feature descriptor in four scenes under different illumination. In the simulation and experimental tests, the qualitative and quantitative assessments indicate that the visualizing images of the HOGP feature are close to the observation results obtained by human eyes, which is better than the original HOG feature for object detection. Furthermore, the runtime of our proposed algorithm is hardly increased in comparison to the classic HOG feature. PMID:28677635

Sufficient Forecasting Using Factor Models

PubMed Central

Fan, Jianqing; Xue, Lingzhou; Yao, Jiawei

2017-01-01

We consider forecasting a single time series when there is a large number of predictors and a possible nonlinear effect. The dimensionality was first reduced via a high-dimensional (approximate) factor model implemented by the principal component analysis. Using the extracted factors, we develop a novel forecasting method called the sufficient forecasting, which provides a set of sufficient predictive indices, inferred from high-dimensional predictors, to deliver additional predictive power. The projected principal component analysis will be employed to enhance the accuracy of inferred factors when a semi-parametric (approximate) factor model is assumed. Our method is also applicable to cross-sectional sufficient regression using extracted factors. The connection between the sufficient forecasting and the deep learning architecture is explicitly stated. The sufficient forecasting correctly estimates projection indices of the underlying factors even in the presence of a nonparametric forecasting function. The proposed method extends the sufficient dimension reduction to high-dimensional regimes by condensing the cross-sectional information through factor models. We derive asymptotic properties for the estimate of the central subspace spanned by these projection directions as well as the estimates of the sufficient predictive indices. We further show that the natural method of running multiple regression of target on estimated factors yields a linear estimate that actually falls into this central subspace. Our method and theory allow the number of predictors to be larger than the number of observations. We finally demonstrate that the sufficient forecasting improves upon the linear forecasting in both simulation studies and an empirical study of forecasting macroeconomic variables. PMID:29731537

Tensor decomposition-based and principal-component-analysis-based unsupervised feature extraction applied to the gene expression and methylation profiles in the brains of social insects with multiple castes.

PubMed

Taguchi, Y-H

2018-05-08

Even though coexistence of multiple phenotypes sharing the same genomic background is interesting, it remains incompletely understood. Epigenomic profiles may represent key factors, with unknown contributions to the development of multiple phenotypes, and social-insect castes are a good model for elucidation of the underlying mechanisms. Nonetheless, previous studies have failed to identify genes associated with aberrant gene expression and methylation profiles because of the lack of suitable methodology that can address this problem properly. A recently proposed principal component analysis (PCA)-based and tensor decomposition (TD)-based unsupervised feature extraction (FE) can solve this problem because these two approaches can deal with gene expression and methylation profiles even when a small number of samples is available. PCA-based and TD-based unsupervised FE methods were applied to the analysis of gene expression and methylation profiles in the brains of two social insects, Polistes canadensis and Dinoponera quadriceps. Genes associated with differential expression and methylation between castes were identified, and analysis of enrichment of Gene Ontology terms confirmed reliability of the obtained sets of genes from the biological standpoint. Biologically relevant genes, shown to be associated with significant differential gene expression and methylation between castes, were identified here for the first time. The identification of these genes may help understand the mechanisms underlying epigenetic control of development of multiple phenotypes under the same genomic conditions.

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.

HL-60 differentiating activity and flavonoid content of the readily extractable fraction prepared from citrus juices.

PubMed

Kawaii, S; Tomono, Y; Katase, E; Ogawa, K; Yano, M

1999-01-01

Citrus plants are rich sources of various bioactive flavonoids. To eliminate masking effects caused by hesperidin, naringin, and neoeriocitrin, the abundant flavonoid glycosides which make up 90% of the conventionally prepared sample, the readily extractable fraction from Citrus juice was prepared by adsorbing on HP-20 resin and eluting with EtOH and acetone from the resin and was subjected to HL-60 differentiation assay and quantitative analysis of major flavonoids. Screening of 34 Citrus juices indicated that King (C. nobilis) had a potent activity for inducing differentiation of HL-60, and the active principles were isolated and identified as four polymethoxylated flavonoids, namely, nobiletin, 3,3',4',5,6,7, 8-heptamethoxyflavone, natsudaidain, and tangeretin. HPLC analysis of the readily extractable fraction also indicated that King contained high amounts of these polymethoxylated flavonoids among the Citrus juices examined. Principal component and cluster analyses of the readily extractable flavonoids indicated peculiarities of King and Bergamot.

Nonlinear features for classification and pose estimation of machined parts from single views

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1998-10-01

A new nonlinear feature extraction method is presented for classification and pose estimation of objects from single views. The feature extraction method is called the maximum representation and discrimination feature (MRDF) method. The nonlinear MRDF transformations to use are obtained in closed form, and offer significant advantages compared to nonlinear neural network implementations. The features extracted are useful for both object discrimination (classification) and object representation (pose estimation). We consider MRDFs on image data, provide a new 2-stage nonlinear MRDF solution, and show it specializes to well-known linear and nonlinear image processing transforms under certain conditions. We show the use of MRDF in estimating the class and pose of images of rendered solid CAD models of machine parts from single views using a feature-space trajectory neural network classifier. We show new results with better classification and pose estimation accuracy than are achieved by standard principal component analysis and Fukunaga-Koontz feature extraction methods.

Identification and Characterization of Diploid and Tetraploid in Platycodon grandiflorum.

PubMed

Shin, Jeoung-Hwa; Ahn, Yun Gyong; Jung, Ju-Hee; Woo, Sun-Hee; Kim, Hag-Hyun; Gorinstein, Shela; Boo, Hee-Ock

2017-03-01

Platycodon grandiflorum (PG), a species of herbaceous flowering perennial plant of the family Campanulaceae, has been used as a traditional oriental medicine for bronchitis, asthma, pulmonary tuberculosis, diabetes, hepatic fibrosis, bone disorders and many others similar diseases and as a food supplement. For the primary profiling of PG gas chromatography coupled with high resolution - time of flight mass spectrometry (GC/HR-TOF MS) was used as an analytical tool. A comparison of optimal extraction of metabolites was carried out with a number of solvents [hexane, methylene chloride, methanol, ethanol, methanol: ethanol (70:30, v:v)]. In extracts with methanol: ethanol (70:30 v:v) were detected higher amounts of metabolites than with other solvents. Principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) plots showed significant differences between the diploid and tetraploid metabolite profiles. Extracts of tetraploid showed higher amounts of amino acids, while extracts of diploid contained more organic acids and sugars. Graphical Abstract ᅟ.

Investigating sub-2 μm particle stationary phase supercritical fluid chromatography coupled to mass spectrometry for chemical profiling of chamomile extracts.

PubMed

Jones, Michael D; Avula, Bharathi; Wang, Yan-Hong; Lu, Lu; Zhao, Jianping; Avonto, Cristina; Isaac, Giorgis; Meeker, Larry; Yu, Kate; Legido-Quigley, Cristina; Smith, Norman; Khan, Ikhlas A

2014-10-17

Roman and German chamomile are widely used throughout the world. Chamomiles contain a wide variety of active constituents including sesquiterpene lactones. Various extraction techniques were performed on these two types of chamomile. A packed-column supercritical fluid chromatography-mass spectrometry method was designed for the identification of sesquiterpenes and other constituents from chamomile extracts with no derivatization step prior to analysis. Mass spectrometry detection was achieved by using electrospray ionization. All of the compounds of interest were separated within 15 min. The chamomile extracts were analyzed and compared for similarities and distinct differences. Multivariate statistical analysis including principal component analysis and orthogonal partial least squares-discriminant analysis (OPLS-DA) were used to differentiate between the chamomile samples. German chamomile samples confirmed the presence of cis- and trans-tonghaosu, chrysosplenols, apigenin diglucoside whereas Roman chamomile samples confirmed the presence of apigenin, nobilin, 1,10-epioxynobilin, and hydroxyisonobilin. Copyright © 2014 Elsevier B.V. All rights reserved.

Authentication of beef versus horse meat using 60 MHz 1H NMR spectroscopy

PubMed Central

Jakes, W.; Gerdova, A.; Defernez, M.; Watson, A.D.; McCallum, C.; Limer, E.; Colquhoun, I.J.; Williamson, D.C.; Kemsley, E.K.

2015-01-01

This work reports a candidate screening protocol to distinguish beef from horse meat based upon comparison of triglyceride signatures obtained by 60 MHz 1H NMR spectroscopy. Using a simple chloroform-based extraction, we obtained classic low-field triglyceride spectra from typically a 10 min acquisition time. Peak integration was sufficient to differentiate samples of fresh beef (76 extractions) and horse (62 extractions) using Naïve Bayes classification. Principal component analysis gave a two-dimensional “authentic” beef region (p = 0.001) against which further spectra could be compared. This model was challenged using a subset of 23 freeze–thawed training samples. The outcomes indicated that storing samples by freezing does not adversely affect the analysis. Of a further collection of extractions from previously unseen samples, 90/91 beef spectra were classified as authentic, and 16/16 horse spectra as non-authentic. We conclude that 60 MHz 1H NMR represents a feasible high-throughput approach for screening raw meat. PMID:25577043

A method of vehicle license plate recognition based on PCANet and compressive sensing

NASA Astrophysics Data System (ADS)

Ye, Xianyi; Min, Feng

2018-03-01

The manual feature extraction of the traditional method for vehicle license plates has no good robustness to change in diversity. And the high feature dimension that is extracted with Principal Component Analysis Network (PCANet) leads to low classification efficiency. For solving these problems, a method of vehicle license plate recognition based on PCANet and compressive sensing is proposed. First, PCANet is used to extract the feature from the images of characters. And then, the sparse measurement matrix which is a very sparse matrix and consistent with Restricted Isometry Property (RIP) condition of the compressed sensing is used to reduce the dimensions of extracted features. Finally, the Support Vector Machine (SVM) is used to train and recognize the features whose dimension has been reduced. Experimental results demonstrate that the proposed method has better performance than Convolutional Neural Network (CNN) in the recognition and time. Compared with no compression sensing, the proposed method has lower feature dimension for the increase of efficiency.

Ship Detection Based on Multiple Features in Random Forest Model for Hyperspectral Images

NASA Astrophysics Data System (ADS)

Li, N.; Ding, L.; Zhao, H.; Shi, J.; Wang, D.; Gong, X.

2018-04-01

A novel method for detecting ships which aim to make full use of both the spatial and spectral information from hyperspectral images is proposed. Firstly, the band which is high signal-noise ratio in the range of near infrared or short-wave infrared spectrum, is used to segment land and sea on Otsu threshold segmentation method. Secondly, multiple features that include spectral and texture features are extracted from hyperspectral images. Principal components analysis (PCA) is used to extract spectral features, the Grey Level Co-occurrence Matrix (GLCM) is used to extract texture features. Finally, Random Forest (RF) model is introduced to detect ships based on the extracted features. To illustrate the effectiveness of the method, we carry out experiments over the EO-1 data by comparing single feature and different multiple features. Compared with the traditional single feature method and Support Vector Machine (SVM) model, the proposed method can stably achieve the target detection of ships under complex background and can effectively improve the detection accuracy of ships.

[A Feature Extraction Method for Brain Computer Interface Based on Multivariate Empirical Mode Decomposition].

PubMed

Wang, Jinjia; Liu, Yuan

2015-04-01

This paper presents a feature extraction method based on multivariate empirical mode decomposition (MEMD) combining with the power spectrum feature, and the method aims at the non-stationary electroencephalogram (EEG) or magnetoencephalogram (MEG) signal in brain-computer interface (BCI) system. Firstly, we utilized MEMD algorithm to decompose multichannel brain signals into a series of multiple intrinsic mode function (IMF), which was proximate stationary and with multi-scale. Then we extracted and reduced the power characteristic from each IMF to a lower dimensions using principal component analysis (PCA). Finally, we classified the motor imagery tasks by linear discriminant analysis classifier. The experimental verification showed that the correct recognition rates of the two-class and four-class tasks of the BCI competition III and competition IV reached 92.0% and 46.2%, respectively, which were superior to the winner of the BCI competition. The experimental proved that the proposed method was reasonably effective and stable and it would provide a new way for feature extraction.

Non-negative matrix factorization in texture feature for classification of dementia with MRI data

NASA Astrophysics Data System (ADS)

Sarwinda, D.; Bustamam, A.; Ardaneswari, G.

2017-07-01

This paper investigates applications of non-negative matrix factorization as feature selection method to select the features from gray level co-occurrence matrix. The proposed approach is used to classify dementia using MRI data. In this study, texture analysis using gray level co-occurrence matrix is done to feature extraction. In the feature extraction process of MRI data, we found seven features from gray level co-occurrence matrix. Non-negative matrix factorization selected three features that influence of all features produced by feature extractions. A Naïve Bayes classifier is adapted to classify dementia, i.e. Alzheimer's disease, Mild Cognitive Impairment (MCI) and normal control. The experimental results show that non-negative factorization as feature selection method able to achieve an accuracy of 96.4% for classification of Alzheimer's and normal control. The proposed method also compared with other features selection methods i.e. Principal Component Analysis (PCA).

Unsupervised Feature Learning for Heart Sounds Classification Using Autoencoder

NASA Astrophysics Data System (ADS)

Hu, Wei; Lv, Jiancheng; Liu, Dongbo; Chen, Yao

2018-04-01

Cardiovascular disease seriously threatens the health of many people. It is usually diagnosed during cardiac auscultation, which is a fast and efficient method of cardiovascular disease diagnosis. In recent years, deep learning approach using unsupervised learning has made significant breakthroughs in many fields. However, to our knowledge, deep learning has not yet been used for heart sound classification. In this paper, we first use the average Shannon energy to extract the envelope of the heart sounds, then find the highest point of S1 to extract the cardiac cycle. We convert the time-domain signals of the cardiac cycle into spectrograms and apply principal component analysis whitening to reduce the dimensionality of the spectrogram. Finally, we apply a two-layer autoencoder to extract the features of the spectrogram. The experimental results demonstrate that the features from the autoencoder are suitable for heart sound classification.

Smell identification of spices using nanomechanical membrane-type surface stress sensors

NASA Astrophysics Data System (ADS)

Imamura, Gaku; Shiba, Kota; Yoshikawa, Genki

2016-11-01

Artificial olfaction, that is, a chemical sensor system that identifies samples by smell, has not been fully achieved because of the complex perceptional mechanism of olfaction. To realize an artificial olfactory system, not only an array of chemical sensors but also a valid feature extraction method is required. In this study, we achieved the identification of spices by smell using nanomechanical membrane-type surface stress sensors (MSS). Features were extracted from the sensing signals obtained from four MSS coated with different types of polymers, focusing on the chemical interactions between polymers and odor molecules. The principal component analysis (PCA) of the dataset consisting of the extracted parameters demonstrated the separation of each spice on the scatter plot. We discuss the strategy for improving odor identification based on the relationship between the results of PCA and the chemical species in the odors.

Development of methodology for identification the nature of the polyphenolic extracts by FTIR associated with multivariate analysis.

PubMed

Grasel, Fábio dos Santos; Ferrão, Marco Flôres; Wolf, Carlos Rodolfo

2016-01-15

Tannins are polyphenolic compounds of complex structures formed by secondary metabolism in several plants. These polyphenolic compounds have different applications, such as drugs, anti-corrosion agents, flocculants, and tanning agents. This study analyses six different type of polyphenolic extracts by Fourier transform infrared spectroscopy (FTIR) combined with multivariate analysis. Through both principal component analysis (PCA) and hierarchical cluster analysis (HCA), we observed well-defined separation between condensed (quebracho and black wattle) and hydrolysable (valonea, chestnut, myrobalan, and tara) tannins. For hydrolysable tannins, it was also possible to observe the formation of two different subgroups between samples of chestnut and valonea and between samples of tara and myrobalan. Among all samples analysed, the chestnut and valonea showed the greatest similarity, indicating that these extracts contain equivalent chemical compositions and structure and, therefore, similar properties. Copyright © 2015 Elsevier B.V. All rights reserved.

Antioxidant Characterization of Oak Extracts Combining Spectrophotometric Assays and Chemometrics

PubMed Central

Popović, Boris M.; Štajner, Dubravka; Orlović, Saša; Galić, Zoran

2013-01-01

Antioxidant characteristics of leaves, twigs, and acorns from two Serbian oak species Quercus robur L. and Quercus petraea L. from Vojvodina province (northern Serbia) were investigated. 80% ethanol (in water) extracts were used for antiradical power (ARP) determinations against DPPH•, •NO, and O2 •− radicals, ferric reducing antioxidant power (FRAP), total phenol, tannin, flavonoid, and proanthocyanidin contents. Permanganate reducing antioxidant capacity (PRAC) was determined using water extracts. Beside, mentioned parameters, soluble proteins, lipid peroxidation (LP), pigments and proline contents were also determined. The data of different procedures were compared and analyzed by multivariate techniques (correlation matrix calculation and principal component analysis (PCA)). PCA found that investigated organs of two different oak tree species possess similar antioxidant characteristics. The superior antioxidant characteristics showed oak leaves over twigs and acorns and seem to be promising source of antioxidants with possible use in industry and pharmacy. PMID:24453789

Characteristic aroma components of rennet casein.

PubMed

Karagül-Yüceer, Yonca; Vlahovich, Katrina N; Drake, MaryAnne; Cadwallader, Keith R

2003-11-05

Rennet casein, produced by enzymatic (rennet) precipitation of casein from pasteurized skim milk, is used in both industrial (technical) and food applications. The flavor of rennet casein powder is an important quality parameter; however, the product often contains an odor described as like that of animal/wet dog. Two commercial rennet casein powders were evaluated to determine the compounds responsible for the typical odor. Aroma extracts were prepared by high-vacuum distillation of direct solvent (ether) extracts and analyzed by gas chromatography-olfactometry (GCO), aroma extract dilution analysis (AEDA), and GC-mass spectrometry (MS). Odorants detected by GCO were typical of those previously reported in skim milk powders and consisted mainly of short-chain volatile acids, phenolic compounds, lactones, and furanones. Results of AEDA indicated o-aminoacetophenone to be a potent odorant; however, sensory descriptive sensory analysis of model aroma systems revealed that the typical odor of rennet casein was principally caused by hexanoic acid, indole, guaiacol, and p-cresol.

Metal nanoparticles (other than gold or silver) prepared using plant extracts for medical applications

NASA Astrophysics Data System (ADS)

Pasca, Roxana-Diana; Santa, Szabolcs; Racz, Levente Zsolt; Racz, Csaba Pal

2016-12-01

There are many modalities to prepare metal nanoparticles, but the reducing of the metal ions with plant extracts is one of the most promising because it is considerate less toxic for the environment, suitable for the use of those nanoparticles in vivo and not very expensive. Various metal ions have been already studied such as: cobalt, copper, iron, platinum, palladium, zinc, indium, manganese and mercury and the number of plant extracts used is continuously increasing. The prepared systems were characterized afterwards with a great number of methods of investigation: both spectroscopic (especially UV-Vis spectroscopy) and microscopic (in principal, electron microscopy-TEM) methods. The applications of the metal nanoparticles obtained are diverse and not completely known, but the medical applications of such nanoparticles occupy a central place, due to their nontoxic components, but some diverse industrial applications do not have to be forgotten.

Spectral analysis of stellar light curves by means of neural networks

NASA Astrophysics Data System (ADS)

Tagliaferri, R.; Ciaramella, A.; Milano, L.; Barone, F.; Longo, G.

1999-06-01

Periodicity analysis of unevenly collected data is a relevant issue in several scientific fields. In astrophysics, for example, we have to find the fundamental period of light or radial velocity curves which are unevenly sampled observations of stars. Classical spectral analysis methods are unsatisfactory to solve the problem. In this paper we present a neural network based estimator system which performs well the frequency extraction in unevenly sampled signals. It uses an unsupervised Hebbian nonlinear neural algorithm to extract, from the interpolated signal, the principal components which, in turn, are used by the MUSIC frequency estimator algorithm to extract the frequencies. The neural network is tolerant to noise and works well also with few points in the sequence. We benchmark the system on synthetic and real signals with the Periodogram and with the Cramer-Rao lower bound. This work was been partially supported by IIASS, by MURST 40\\% and by the Italian Space Agency.

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

PubMed

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

2013-12-01

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

Therapeutic effects of Qian-Yu decoction and its three extracts on carrageenan-induced chronic prostatitis/chronic pelvic pain syndrome in rats.

PubMed

Zhang, Keda; Zeng, Xiaobin; Chen, Yonggang; Zhao, Rong; Wang, Hui; Wu, Jinhu

2017-01-25

Qian-Yu decoction (QYD) is a traditional Chinese medicinal recipe composed of Radix astragali (Astragalus membranaceus (Fisch.) Bunge var. mongholicus (Bunge) P.K. Hsiao, Fabaceae ), Herba epimedii (Epimedium brevicornum Maxim., Berberidaceae), Herba leonuri (Leonurus japonicus Houtt., Lamiaceae), Cortex phellodendri (Phellodendron chinense Schneid., Rutaceae) and Radix achyranthis bidentatae (Achyranthes bidentata Bl., Amaranthaceae). This study aimed to evaluate the therapeutic activity of QYD against carrageenan-induced chronic prostatic/chronic pelvic pain syndrome (CP/CPPS) in rats and further elucidate its effective components. Three types of components, total polysaccharides, total flavonoids and total saponins were separately extracted from QYD. Carrageenan-induced CP/CPPS rats were intragastrically administered with lyophilized product of QYD, individual extracts and all the combined forms of extracts for three weeks. Prostatic index (PI) was determined and histopathological analysis was performed. The levels of tumor necrosis factor alpha (TNF-α), interleukin-1 beta (IL-1β), cyclooxygenase-2 (COX-2) and prostaglandin E2 (PEG2) in rat prostate tissues were measured using ELISA. The production of inducible nitric oxide synthase (iNOS) was evaluated by an enzymatic activity assay, and the release of nitric oxide (NO) was determined by a nitrate/nitrite assay. Treatment with QYD significantly ameliorated the histological changes of CP/CPPS rats and reduced the PI by 44.3%, with a marked downregulation of TNF-α (42.8% reduction), IL-1β (45.3%), COX-2 (36.6%), PGE2 (44.2%), iNOS (54.1%) and NO (46.0%). Each of three extracts attenuated the symptom of CP/CPPS, but much more weakly than QYD. The combined administration of three extracts showed efficacy comparable to that of QYD while better than that of any combination of two extracts. A principal component analysis of the six inflammatory mediators as variables indicated that the effects of TS on CP/CPPS were rather different from those of TF and TP, which were similar. QYD can be beneficial in prevention and treatment of CP/CPPS. Polysaccharides, flavonoids and saponins, as the major effective components of QYD, exert a cooperative effect on CP/CPPS.

Relationship between skin cell wall composition and anthocyanin extractability of Vitis vinifera L. cv. Tempranillo at different grape ripeness degree.

PubMed

Hernández-Hierro, José Miguel; Quijada-Morín, Natalia; Martínez-Lapuente, Leticia; Guadalupe, Zenaida; Ayestarán, Belén; Rivas-Gonzalo, Julián C; Escribano-Bailón, M Teresa

2014-03-01

The relationship between cell wall composition and extractability of anthocyanins from red grape skins was assessed in Tempranillo grape samples harvested at three stages of ripening (pre-harvest, harvest and over-ripening) and three different contents of soluble solids (22, 24 and 26 °Brix) within each stage. Cell wall material was isolated and analysed in order to determine cellulose, lignin, non-cellulosic polysaccharides, protein, total polyphenols index and the degree of esterification of pectins. Results showed the influence of ripeness degree and contents of soluble solids on cell wall composition. Furthermore, principal components analysis was applied to the obtained data set in order to establish relationships between cell wall composition and extractability of anthocyanins. Total insoluble material exhibits the biggest opposition to anthocyanin extraction, while the highest amounts of cellulose, rhamnogalacturonans-II and polyphenols were positively correlated with anthocyanin extraction. Moreover, multiple linear regression was performed to assess the influence of the cell wall composition on the extraction of anthocyanin compounds. A model connecting cell wall composition and anthocyanin extractabilities was built, explaining 96.2% of the observed variability. Copyright © 2013 Elsevier Ltd. All rights reserved.

Effect of guava (Psidium guajava L.) leaf extract on glucose uptake in rat hepatocytes.

PubMed

Cheng, Fang-Chi; Shen, Szu-Chuan; Wu, James Swi-Bea

2009-06-01

People in oriental countries, including Japan and Taiwan, boil guava leaves (Psidium guajava L.) in water and drink the extract as a folk medicine for diabetes. The present study investigated the enhancement of aqueous guava leaf extract on glucose uptake in rat clone 9 hepatocytes and searched for the active compound. The extract was eluted with MeOH-H(2)O solutions through Diaion, Sephadex, and MCI-gel columns to separate into fractions with different polarities. The uptake test of 2-[1-(14)C] deoxy-D-glucose in rat clone 9 hepatocytes was performed to evaluate the hypoglycemic effect of these fractions. The active compound was identified by nuclear magnetic resonance analysis and high-performance liquid chromatography (HPLC). The results revealed that phenolics are the principal component of the extract, that high polarity fractions of the guava leaf extract are enhancers to glucose uptake in rat clone 9 hepatocytes, and that quercetin is the major active compound. We suggest that quercetin in the aqueous extract of guava leaves promotes glucose uptake in liver cells, and contributes to the alleviation of hypoglycemia in diabetes as a consequence.

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.

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

PubMed

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

2014-11-01

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

SU-E-I-58: Objective Models of Breast Shape Undergoing Mammography and Tomosynthesis Using Principal Component Analysis.

PubMed

Feng, Ssj; Sechopoulos, I

2012-06-01

To develop an objective model of the shape of the compressed breast undergoing mammographic or tomosynthesis acquisition. Automated thresholding and edge detection was performed on 984 anonymized digital mammograms (492 craniocaudal (CC) view mammograms and 492 medial lateral oblique (MLO) view mammograms), to extract the edge of each breast. Principal Component Analysis (PCA) was performed on these edge vectors to identify a limited set of parameters and eigenvectors that. These parameters and eigenvectors comprise a model that can be used to describe the breast shapes present in acquired mammograms and to generate realistic models of breasts undergoing acquisition. Sample breast shapes were then generated from this model and evaluated. The mammograms in the database were previously acquired for a separate study and authorized for use in further research. The PCA successfully identified two principal components and their corresponding eigenvectors, forming the basis for the breast shape model. The simulated breast shapes generated from the model are reasonable approximations of clinically acquired mammograms. Using PCA, we have obtained models of the compressed breast undergoing mammographic or tomosynthesis acquisition based on objective analysis of a large image database. Up to now, the breast in the CC view has been approximated as a semi-circular tube, while there has been no objectively-obtained model for the MLO view breast shape. Such models can be used for various breast imaging research applications, such as x-ray scatter estimation and correction, dosimetry estimates, and computer-aided detection and diagnosis. © 2012 American Association of Physicists in Medicine.

PCA based clustering for brain tumor segmentation of T1w MRI images.

PubMed

Kaya, Irem Ersöz; Pehlivanlı, Ayça Çakmak; Sekizkardeş, Emine Gezmez; Ibrikci, Turgay

2017-03-01

Medical images are huge collections of information that are difficult to store and process consuming extensive computing time. Therefore, the reduction techniques are commonly used as a data pre-processing step to make the image data less complex so that a high-dimensional data can be identified by an appropriate low-dimensional representation. PCA is one of the most popular multivariate methods for data reduction. This paper is focused on T1-weighted MRI images clustering for brain tumor segmentation with dimension reduction by different common Principle Component Analysis (PCA) algorithms. Our primary aim is to present a comparison between different variations of PCA algorithms on MRIs for two cluster methods. Five most common PCA algorithms; namely the conventional PCA, Probabilistic Principal Component Analysis (PPCA), Expectation Maximization Based Principal Component Analysis (EM-PCA), Generalize Hebbian Algorithm (GHA), and Adaptive Principal Component Extraction (APEX) were applied to reduce dimensionality in advance of two clustering algorithms, K-Means and Fuzzy C-Means. In the study, the T1-weighted MRI images of the human brain with brain tumor were used for clustering. In addition to the original size of 512 lines and 512 pixels per line, three more different sizes, 256 × 256, 128 × 128 and 64 × 64, were included in the study to examine their effect on the methods. The obtained results were compared in terms of both the reconstruction errors and the Euclidean distance errors among the clustered images containing the same number of principle components. According to the findings, the PPCA obtained the best results among all others. Furthermore, the EM-PCA and the PPCA assisted K-Means algorithm to accomplish the best clustering performance in the majority as well as achieving significant results with both clustering algorithms for all size of T1w MRI images. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Cognitive performance predicts treatment decisional abilities in mild to moderate dementia.

PubMed

Gurrera, R J; Moye, J; Karel, M J; Azar, A R; Armesto, J C

2006-05-09

To examine the contribution of neuropsychological test performance to treatment decision-making capacity in community volunteers with mild to moderate dementia. The authors recruited volunteers (44 men, 44 women) with mild to moderate dementia from the community. Subjects completed a battery of 11 neuropsychological tests that assessed auditory and visual attention, logical memory, language, and executive function. To measure decision making capacity, the authors administered the Capacity to Consent to Treatment Interview, the Hopemont Capacity Assessment Interview, and the MacCarthur Competence Assessment Tool--Treatment. Each of these instruments individually scores four decisional abilities serving capacity: understanding, appreciation, reasoning, and expression of choice. The authors used principal components analysis to generate component scores for each ability across instruments, and to extract principal components for neuropsychological performance. Multiple linear regression analyses demonstrated that neuropsychological performance significantly predicted all four abilities. Specifically, it predicted 77.8% of the common variance for understanding, 39.4% for reasoning, 24.6% for appreciation, and 10.2% for expression of choice. Except for reasoning and appreciation, neuropsychological predictor (beta) profiles were unique for each ability. Neuropsychological performance substantially and differentially predicted capacity for treatment decisions in individuals with mild to moderate dementia. Relationships between elemental cognitive function and decisional capacity may differ in individuals whose decisional capacity is impaired by other disorders, such as mental illness.

Rapid differentiation of Chinese hop varieties (Humulus lupulus) using volatile fingerprinting by HS-SPME-GC-MS combined with multivariate statistical analysis.

PubMed

Liu, Zechang; Wang, Liping; Liu, Yumei

2018-01-18

Hops impart flavor to beer, with the volatile components characterizing the various hop varieties and qualities. Fingerprinting, especially flavor fingerprinting, is often used to identify 'flavor products' because inconsistencies in the description of flavor may lead to an incorrect definition of beer quality. Compared to flavor fingerprinting, volatile fingerprinting is simpler and easier. We performed volatile fingerprinting using head space-solid phase micro-extraction gas chromatography-mass spectrometry combined with similarity analysis and principal component analysis (PCA) for evaluating and distinguishing between three major Chinese hops. Eighty-four volatiles were identified, which were classified into seven categories. Volatile fingerprinting based on similarity analysis did not yield any obvious result. By contrast, hop varieties and qualities were identified using volatile fingerprinting based on PCA. The potential variables explained the variance in the three hop varieties. In addition, the dendrogram and principal component score plot described the differences and classifications of hops. Volatile fingerprinting plus multivariate statistical analysis can rapidly differentiate between the different varieties and qualities of the three major Chinese hops. Furthermore, this method can be used as a reference in other fields. © 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.

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

PubMed Central

Hao, Guozhu

2016-01-01

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

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

PubMed

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

2016-01-01

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

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.

Defining genetic and chemical diversity in wheat grain by 1H-NMR spectroscopy of polar metabolites.

PubMed

Shewry, Peter R; Corol, Delia I; Jones, Huw D; Beale, Michael H; Ward, Jane L

2017-07-01

The application of high-throughput 1H nuclear magnetic resonance (1H-NMR) of unpurified extracts to determine genetic diversity and the contents of polar components in grain of wheat. Milled whole wheat grain was extracted with 80:20 D 2 O:CD 3 OD containing 0.05% d 4 -trimethylsilylpropionate. 1H-NMR spectra were acquired under automation at 300°K using an Avance Spectrometer operating at 600.0528 MHz. Regions for individual metabolites were identified by comparison to a library of known standards run under identical conditions. The individual 1H-NMR peaks or levels of known metabolites were then compared by Principal Component Analysis using SIMCA-P software. High-throughput 1H-NMR is an excellent tool to compare the extent of genetic diversity within and between wheat species, and to quantify specific components (including glycine betaine, choline, and asparagine) in individual genotypes. It can also be used to monitor changes in composition related to environmental factors and to support comparisons of the substantial equivalence of transgenic lines. © 2017 Rothamsted Research. Molecular Nutrition & Food Research Published by Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Nonlinear Principal Components Analysis: Introduction and Application

ERIC Educational Resources Information Center

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

2007-01-01

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

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

USDA-ARS?s Scientific Manuscript database

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

Similarities between principal components of protein dynamics and random diffusion

NASA Astrophysics Data System (ADS)

Hess, Berk

2000-12-01

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

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

Validation of green-solvent extraction combined with chromatographic chemical fingerprint to evaluate quality of Stevia rebaudiana Bertoni.

PubMed

Teo, Chin Chye; Tan, Swee Ngin; Yong, Jean Wan Hong; Hew, Choy Sin; Ong, Eng Shi

2009-02-01

An approach that combined green-solvent methods of extraction with chromatographic chemical fingerprint and pattern recognition tools such as principal component analysis (PCA) was used to evaluate the quality of medicinal plants. Pressurized hot water extraction (PHWE) and microwave-assisted extraction (MAE) were used and their extraction efficiencies to extract two bioactive compounds, namely stevioside (SV) and rebaudioside A (RA), from Stevia rebaudiana Bertoni (SB) under different cultivation conditions were compared. The proposed methods showed that SV and RA could be extracted from SB using pure water under optimized conditions. The extraction efficiency of the methods was observed to be higher or comparable to heating under reflux with water. The method precision (RSD, n = 6) was found to vary from 1.91 to 2.86% for the two different methods on different days. Compared to PHWE, MAE has higher extraction efficiency with shorter extraction time. MAE was also found to extract more chemical constituents and provide distinctive chemical fingerprints for quality control purposes. Thus, a combination of MAE with chromatographic chemical fingerprints and PCA provided a simple and rapid approach for the comparison and classification of medicinal plants from different growth conditions. Hence, the current work highlighted the importance of extraction method in chemical fingerprinting for the classification of medicinal plants from different cultivation conditions with the aid of pattern recognition tools used.

CHEMICAL CHARACTERIZATION OF A HYPOGLYCEMIC EXTRACT FROM CUCURBITA FICIFOLIA BOUCHE THAT INDUCES LIVER GLYCOGEN ACCUMULATION IN DIABETIC MICE

PubMed Central

Jessica, Garcia Gonzalez; Mario, Garcia Lorenzana; Alejandro, Zamilpa; Cesar, Almanza Perez Julio; Ivan, Jasso Villagomez E; Ruben, Roman Ramos; Javier, Alarcon-Aguilar Francisco

2017-01-01

Background: The aqueous extract of Cucurbita ficifolia (C. ficifolia) fruit has demonstrated hypoglycemic effect, which may be attributed to some components in the extract. However, the major secondary metabolites in this fruit have not yet been identified and little is known about its extra-pancreatic action, in particular, on liver carbohydrate metabolism. Therefore, in addition to the isolation and structural elucidation of the principal components in the aqueous extract of C. ficifolia, the aim of this study was to determine whether or not the hypoglycemic effect of the aqueous extract of Cucurbita ficifolia (C. ficifolia) fruit is due to accumulation of liver glycogen in diabetic mice. Materials and Methods: The aqueous extract from fruit of C. ficifolia was fractionated and its main secondary metabolites were purified and chemically characterized (NMR and GC-MS). Alloxan-induced diabetic mice received daily by gavage the aqueous extract (30 days). The liver glycogen content was quantified by spectroscopic method and by PAS stain; ALT and AST by spectrometric method; glycogen synthase, glycogen phosphorylase and GLUT2 by Western blot; the mRNA expression of GLUT2 and glucagon-receptor by RT-PCR; while serum insulin was quantified by ELISA method. A liver histological analysis was also performed by H&E stain. Results: Chemical fingerprint showed five majoritarian compounds in the aqueous extract of C. ficifolia: p-coumaric acid, p-hydroxybenzoic acid, salicin, stigmast-7,2,2-dien-3-ol and stigmast-7-en-3-ol. The histological analysis showed accumulation of liver glycogen. Also, increased glycogen synthase and decreased glycogen phosphorylase were observed. Interestingly, the histological architecture evidenced a liver-protective effect due the extract. Conclusion: Five compounds were identified in C. ficifolia aqueous extract. The hypoglycemic effect of this extract may be partially explained by liver glycogen accumulation. The bioactive compound responsible for the hypoglycemic effect of this extract will be elucidated in subsequent studies. PMID:28480434

Feature extraction in MFL signals of machined defects in steel tubes

NASA Astrophysics Data System (ADS)

Perazzo, R.; Pignotti, A.; Reich, S.; Stickar, P.

2001-04-01

Thirty defects of various shapes were machined on the external and internal wall surfaces of a 177 mm diameter ferromagnetic steel pipe. MFL signals were digitized and recorded at a frequency of 4 Khz. Various magnetizing currents and relative tube-probe velocities of the order of 2m/s were used. The identification of the location of the defect by a principal component/neural network analysis of the signal is shown to be more effective than the standard procedure of classification based on the average signal frequency.

Age-dependent changes from allylphenol to prenylated benzoic acid production in Piper gaudichaudianum Kunth.

PubMed

Gaia, Anderson M; Yamaguchi, Lydia F; Jeffrey, Christopher S; Kato, Massuo J

2014-10-01

HPLC-DAD and principal component analysis (PCA) of the (1)H NMR spectrum of crude plant extracts showed high chemical variability among seedlings and adult organs of Piper gaudichaudianum. While gaudichaudianic acid was the major compound in the adult leaves, apiole and dillapiole were the major compounds in their seedling leaves. By the 15th month of seedling growth, the levels of apiole and dillapiole decreased and gaudichaudianic acid appeared along with two compounds, biosynthetically related to gaudichaudianic acid. Copyright © 2014 Elsevier Ltd. All rights reserved.

Oenology: red wine procyanidins and vascular health.

PubMed

Corder, R; Mullen, W; Khan, N Q; Marks, S C; Wood, E G; Carrier, M J; Crozier, A

2006-11-30

Regular, moderate consumption of red wine is linked to a reduced risk of coronary heart disease and to lower overall mortality, but the relative contribution of wine's alcohol and polyphenol components to these effects is unclear. Here we identify procyanidins as the principal vasoactive polyphenols in red wine and show that they are present at higher concentrations in wines from areas of southwestern France and Sardinia, where traditional production methods ensure that these compounds are efficiently extracted during vinification. These regions also happen to be associated with increased longevity in the population.

Chemotypes of essential oil of unripe galls of Pistacia atlantica Desf. from Algeria.

PubMed

Sifi, Ibrahim; Gourine, Nadhir; Gaydou, Emile M; Yousfi, Mohamed

2015-01-01

The essential oils (EOs) of unripe galls (from male and female plants) of a total number of 52 samples of Pistacia atlantica collected from different regions in Algeria were analysed by GC/MS and GC. The yields of the extraction of the EO by hydrodistillation vary from low to high values (0.08-1.89% v/w). The results of both methods of principal component analysis and hierarchical ascendant classification revealed the presence of two different chemotypes: α-pinene chemotype and α-pinene/sabinene/terpinen-4-ol chemotype.

Infrared spectroscopic imaging for noninvasive detection of latent fingerprints.

PubMed

Crane, Nicole J; Bartick, Edward G; Perlman, Rebecca Schwartz; Huffman, Scott

2007-01-01

The capability of Fourier transform infrared (FTIR) spectroscopic imaging to provide detailed images of unprocessed latent fingerprints while also preserving important trace evidence is demonstrated. Unprocessed fingerprints were developed on various porous and nonporous substrates. Data-processing methods used to extract the latent fingerprint ridge pattern from the background material included basic infrared spectroscopic band intensities, addition and subtraction of band intensity measurements, principal components analysis (PCA) and calculation of second derivative band intensities, as well as combinations of these various techniques. Additionally, trace evidence within the fingerprints was recovered and identified.

The t-CWT: a new ERP detection and quantification method based on the continuous wavelet transform and Student's t-statistics.

PubMed

Bostanov, Vladimir; Kotchoubey, Boris

2006-12-01

This study was aimed at developing a method for extraction and assessment of event-related brain potentials (ERP) from single-trials. This method should be applicable in the assessment of single persons' ERPs and should be able to handle both single ERP components and whole waveforms. We adopted a recently developed ERP feature extraction method, the t-CWT, for the purposes of hypothesis testing in the statistical assessment of ERPs. The t-CWT is based on the continuous wavelet transform (CWT) and Student's t-statistics. The method was tested in two ERP paradigms, oddball and semantic priming, by assessing individual-participant data on a single-trial basis, and testing the significance of selected ERP components, P300 and N400, as well as of whole ERP waveforms. The t-CWT was also compared to other univariate and multivariate ERP assessment methods: peak picking, area computation, discrete wavelet transform (DWT) and principal component analysis (PCA). The t-CWT produced better results than all of the other assessment methods it was compared with. The t-CWT can be used as a reliable and powerful method for ERP-component detection and testing of statistical hypotheses concerning both single ERP components and whole waveforms extracted from either single persons' or group data. The t-CWT is the first such method based explicitly on the criteria of maximal statistical difference between two average ERPs in the time-frequency domain and is particularly suitable for ERP assessment of individual data (e.g. in clinical settings), but also for the investigation of small and/or novel ERP effects from group data.

Determination, speciation and distribution of mercury in soil in the surroundings of a former chlor-alkali plant: assessment of sequential extraction procedure and analytical technique

PubMed Central

2013-01-01

Background The paper presents the evaluation of soil contamination with total, water-available, mobile, semi-mobile and non-mobile Hg fractions in the surroundings of a former chlor-alkali plant in connection with several chemical soil characteristics. Principal Component Analysis and Cluster Analysis were used to evaluate the chemical composition variability of soil and factors influencing the fate of Hg in such areas. The sequential extraction EPA 3200-Method and the determination technique based on capacitively coupled microplasma optical emission spectrometry were checked. Results A case study was conducted in the Turda town, Romania. The results revealed a high contamination with Hg in the area of the former chlor-alkali plant and waste landfills, where soils were categorized as hazardous waste. The weight of the Hg fractions decreased in the order semi-mobile > non-mobile > mobile > water leachable. Principal Component Analysis revealed 7 factors describing chemical composition variability of soil, of which 3 attributed to Hg species. Total Hg, semi-mobile, non-mobile and mobile fractions were observed to have a strong influence, while the water leachable fraction a weak influence. The two-dimensional plot of PCs highlighted 3 groups of sites according to the Hg contamination factor. The statistical approach has shown that the Hg fate in soil is dependent on pH, content of organic matter, Ca, Fe, Mn, Cu and SO42- rather than natural components, such as aluminosilicates. Cluster analysis of soil characteristics revealed 3 clusters, one of which including Hg species. Soil contamination with Cu as sulfate and Zn as nitrate was also observed. Conclusions The approach based on speciation and statistical interpretation of data developed in this study could be useful in the investigation of other chlor-alkali contaminated areas. According to the Bland and Altman test the 3-step sequential extraction scheme is suitable for Hg speciation in soil, while the used determination method of Hg is appropriate. PMID:24252185

Coordinative structuring of gait kinematics during adaptation to variable and asymmetric split-belt treadmill walking - A principal component analysis approach.

PubMed

Hinkel-Lipsker, Jacob W; Hahn, Michael E

2018-06-01

Gait adaptation is a task that requires fine-tuned coordination of all degrees of freedom in the lower limbs by the central nervous system. However, when individuals change their gait it is unknown how this coordination is organized, and how it can be influenced by contextual interference during practice. Such knowledge could provide information about measurement of gait adaptation during rehabilitation. Able-bodied individuals completed an acute bout of asymmetric split-belt treadmill walking, where one limb was driven at a constant velocity and the other according to one of three designed practice paradigms: serial practice, where the variable limb belt velocity increased over time; random blocked practice, where every 20 strides the variable limb belt velocity changed randomly; random practice, where every stride the variable limb belt velocity changed randomly. On the second day, subjects completed one of two different transfer tests; one with a belt asymmetry close to that experienced on the acquisition day (transfer 1; 1.5:1), and one with a greater asymmetry (transfer 2; 2:1) . To reduce this inherently high-dimensional dataset, principal component analyses were used for kinematic data collected throughout the acquisition and transfer phases; resulting in extraction of the first two principal components (PCs). For acquisition, PC1 and PC2 were related to sagittal and frontal plane control. For transfer 1, PC1 and PC2 were related to frontal plane control of the base of support and whole-body center of mass. For transfer 2, PC1 did not have any variables with high enough coefficients deemed to be relevant, and PC2 was related to sagittal plane control. Observations of principal component scores indicate that variance structuring differs among practice groups during acquisition and transfer 1, but not transfer 2. These results demonstrate the main kinematic coordinative structures that exist during gait adaptation, and that control of sagittal plane and frontal plane motion are perhaps a trade-off during acquisition of a novel asymmetric gait pattern. Copyright © 2018 Elsevier B.V. All rights reserved.

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 various extraction techniques for the determination of polycyclic aromatic hydrocarbons in worms.

PubMed

Mooibroek, D; Hoogerbrugge, R; Stoffelsen, B H G; Dijkman, E; Berkhoff, C J; Hogendoorn, E A

2002-10-25

Two less laborious extraction methods, viz. (i) a simplified liquid extraction using light petroleum or (ii) microwave-assisted solvent extraction (MASE), for the analysis of polycyclic aromatic hydrocarbons (PAHs) in samples of the compost worm Eisenia andrei, were compared with a reference method. After extraction and concentration, analytical methodology consisted of a cleanup of (part) of the extract with high-performance gel permeation chromatography (HPGPC) and instrumental analysis of 15 PAHs with reversed-phase liquid chromatography with fluorescence detection (RPLC-FLD). Comparison of the methods was done by analysing samples with incurred residues (n=15, each method) originating from an experiment in which worms were exposed to a soil contaminated with PAHs. Simultaneously, the performance of the total lipid determination of each method was established. Evaluation of the data by means of principal component analysis (PCA) and analysis of variance (ANOVA) revealed that the performance of the light petroleum method for both the extraction of PAHs (concentration range 1-30 ng/g) and lipid content corresponds very well with the reference method. Compared to the reference method, the MASE method yielded somewhat lower concentrations for the less volatile PAHs, e.g., dibenzo[ah]anthracene and benzo[ghi]perylene and provided a significant higher amount of co-extracted material.

Insight into the time-resolved extraction of aroma compounds during espresso coffee preparation: online monitoring by PTR-ToF-MS.

PubMed

Sánchez-López, José A; Zimmermann, Ralf; Yeretzian, Chahan

2014-12-02

Using proton-transfer-reaction time-of-flight mass-spectrometry (PTR-ToF-MS), we investigated the extraction dynamic of 95 ion traces in real time (time resolution = 1 s) during espresso coffee preparation. Fifty-two of these ions were tentatively identified. This was achieved by online sampling of the volatile organic compounds (VOCs) in close vicinity to the coffee flow, at the exit of the extraction hose of the espresso machine (single serve capsules). Ten replicates of six different single serve coffee types were extracted to a final weight between 20-120 g, according to the recommended cup size of the respective coffee capsule (Ristretto, Espresso, and Lungo), and analyzed. The results revealed considerable differences in the extraction kinetics between compounds, which led to a fast evolution of the volatile profiles in the extract flow and consequently to an evolution of the final aroma balance in the cup. Besides exploring the time-resolved extraction dynamics of VOCs, the dynamic data also allowed the coffees types (capsules) to be distinguished from one another. Both hierarchical cluster analysis (HCA) and principal component analysis (PCA) showed full separation between the coffees types. The methodology developed provides a fast and simple means of studying the extraction dynamics of VOCs and differentiating between different coffee types.

Inhibition of secretary PLA₂--VRV-PL-VIIIa of Russell's viper venom by standard aqueous stem bark extract of Mangifera indica L.

PubMed

Dhananjaya, B L; Sudarshan, S

2015-03-01

The aqueous extract of Mangifera indica is known to possess anti-snake venom activities. However, its inhibitory potency and mechanism of action on multi-toxic phospholipases A2s, which are the most toxic and lethal component of snake venom is still unknown. Therefore, this study was carried out to evaluate the modulatory effect of standard aqueous bark extract of M. indica on VRV-PL-VIIIa of Indian Russells viper venom. Mangifera indica extract dose dependently inhibited the GIIB sPLA2 (VRV-PL-VIIIa) activity with an IC50 value of 6.8±0.3 μg/ml. M. indica extract effectively inhibited the indirect hemolytic activity up to 96% at ~40 μg/ml concentration. Further, M. indica extract at different concentrations (0-50 μg/ml) inhibited the edema formed in a dose dependent manner. It was found that there was no relieve of inhibitory effect of the extract when examined as a function of increased substrate and calcium concentration. The inhibition was irreversible as evident from binding studies. The in vitro inhibition is well correlated with in situ and in vivo edema inducing activities. As the inhibition is independent of substrate, calcium concentration and was irreversible, it can be concluded that M. indica extracts mode of inhibition could be due to direct interaction of components present in the extract with PLA2 enzyme. In conclusion, the aqueous extract of M. indica effectively inhibits svPLA2 (Snake venom phospholipase A2) enzymatic and its associated toxic activities, which substantiate its anti-snake venom properties. Further in-depth studies are interesting to known on the role and mechanism of the principal inhibitory constituents present in the extract, so as to develop them into potent anti-snake venom and as an anti-inflammatory agent.

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.

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...

Perturbational formulation of principal component analysis in molecular dynamics simulation.

PubMed

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

2008-10-01

Conformational fluctuations of a molecule are important to its function since such intrinsic fluctuations enable the molecule to respond to the external environmental perturbations. For extracting large conformational fluctuations, which predict the primary conformational change by the perturbation, principal component analysis (PCA) has been used in molecular dynamics simulations. However, several versions of PCA, such as Cartesian coordinate PCA and dihedral angle PCA (dPCA), are limited to use with molecules with a single dominant state or proteins where the dihedral angle represents an important internal coordinate. Other PCAs with general applicability, such as the PCA using pairwise atomic distances, do not represent the physical meaning clearly. Therefore, a formulation that provides general applicability and clearly represents the physical meaning is yet to be developed. For developing such a formulation, we consider the conformational distribution change by the perturbation with arbitrary linearly independent perturbation functions. Within the second order approximation of the Kullback-Leibler divergence by the perturbation, the PCA can be naturally interpreted as a method for (1) decomposing a given perturbation into perturbations that independently contribute to the conformational distribution change or (2) successively finding the perturbation that induces the largest conformational distribution change. In this perturbational formulation of PCA, (i) the eigenvalue measures the Kullback-Leibler divergence from the unperturbed to perturbed distributions, (ii) the eigenvector identifies the combination of the perturbation functions, and (iii) the principal component determines the probability change induced by the perturbation. Based on this formulation, we propose a PCA using potential energy terms, and we designate it as potential energy PCA (PEPCA). The PEPCA provides both general applicability and clear physical meaning. For demonstrating its power, we apply the PEPCA to an alanine dipeptide molecule in vacuum as a minimal model of a nonsingle dominant conformational biomolecule. The first and second principal components clearly characterize two stable states and the transition state between them. Positive and negative components with larger absolute values of the first and second eigenvectors identify the electrostatic interactions, which stabilize or destabilize each stable state and the transition state. Our result therefore indicates that PCA can be applied, by carefully selecting the perturbation functions, not only to identify the molecular conformational fluctuation but also to predict the conformational distribution change by the perturbation beyond the limitation of the previous methods.

Perturbational formulation of principal component analysis in molecular dynamics simulation

NASA Astrophysics Data System (ADS)

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

2008-10-01

Conformational fluctuations of a molecule are important to its function since such intrinsic fluctuations enable the molecule to respond to the external environmental perturbations. For extracting large conformational fluctuations, which predict the primary conformational change by the perturbation, principal component analysis (PCA) has been used in molecular dynamics simulations. However, several versions of PCA, such as Cartesian coordinate PCA and dihedral angle PCA (dPCA), are limited to use with molecules with a single dominant state or proteins where the dihedral angle represents an important internal coordinate. Other PCAs with general applicability, such as the PCA using pairwise atomic distances, do not represent the physical meaning clearly. Therefore, a formulation that provides general applicability and clearly represents the physical meaning is yet to be developed. For developing such a formulation, we consider the conformational distribution change by the perturbation with arbitrary linearly independent perturbation functions. Within the second order approximation of the Kullback-Leibler divergence by the perturbation, the PCA can be naturally interpreted as a method for (1) decomposing a given perturbation into perturbations that independently contribute to the conformational distribution change or (2) successively finding the perturbation that induces the largest conformational distribution change. In this perturbational formulation of PCA, (i) the eigenvalue measures the Kullback-Leibler divergence from the unperturbed to perturbed distributions, (ii) the eigenvector identifies the combination of the perturbation functions, and (iii) the principal component determines the probability change induced by the perturbation. Based on this formulation, we propose a PCA using potential energy terms, and we designate it as potential energy PCA (PEPCA). The PEPCA provides both general applicability and clear physical meaning. For demonstrating its power, we apply the PEPCA to an alanine dipeptide molecule in vacuum as a minimal model of a nonsingle dominant conformational biomolecule. The first and second principal components clearly characterize two stable states and the transition state between them. Positive and negative components with larger absolute values of the first and second eigenvectors identify the electrostatic interactions, which stabilize or destabilize each stable state and the transition state. Our result therefore indicates that PCA can be applied, by carefully selecting the perturbation functions, not only to identify the molecular conformational fluctuation but also to predict the conformational distribution change by the perturbation beyond the limitation of the previous methods.

Psychometric properties of the children's sleep habits questionnaire in children with autism spectrum disorder.

PubMed

Johnson, Cynthia R; DeMand, Alexandra; Lecavalier, Luc; Smith, Tristram; Aman, Michael; Foldes, Emily; Scahill, Lawrence

2016-04-01

Sleep disturbances in autism spectrum disorder (ASD) are very common. Psychometrically sound instruments are essential to assess these disturbances. Children's Sleep Habit Questionnaire (CSHQ) is a widely used measure in ASD. The purpose of this study was to explore the psychometric properties of the CSHQ in a sample of children with ASD. Parents/caregivers of 310 children (mean age: 4.7) with ASD completed the CSHQ at study enrollment. Correlations between intelligence quotient (IQ) scores and the original CSHQ scales were calculated. Item endorsement frequencies and percentages were also calculated. A principal component analysis (PCA) was performed, and internal consistency was assessed for the newly extracted components. Correlations between IQ scores and CSHQ subscales and total scores ranged from .015 to .001 suggesting a weak, if any, association. Item endorsement frequencies were high for bedtime resistance items, but lower for parasomnia and sleep-disordered breathing items. A PCA suggested that a five-component solution best fits the data. Internal consistency of the newly extracted five components ranged α = .87-.50. Item endorsement frequencies were highest for bedtime resistance items. A PCA suggested a five-component solution. Three of the five components (Sleep Routine Problems, Insufficient Sleep, and Sleep-onset Association Problems) were types of sleep disturbances commonly reported in ASD, but the other two components (Parasomnia/Sleep-disordered Breathing and Sleep Anxiety) were less clear. Internal consistencies ranged from mediocre to good. Further development of this measure for use in children with ASD is encouraged. Copyright © 2016 Elsevier B.V. All rights reserved.

Chemical composition and sensory profile of pomelo (Citrus grandis (L.) Osbeck) juice.

PubMed

Cheong, Mun Wai; Liu, Shao Quan; Zhou, Weibiao; Curran, Philip; Yu, Bin

2012-12-15

Two cultivars (Citrus grandis (L.) Osbeck PO 51 and PO 52) of Malaysian pomelo juices were studied by examining their physicochemical properties (i.e. pH, °Brix and titratable acidity), volatile and non-volatile components (sugars and organic acids). Using solvent extraction and headspace solid-phase microextraction, 49 and 65 volatile compounds were identified by gas chromatography-mass spectrometer/flame ionisation detector, respectively. Compared to pink pomelo juice (cultivar PO 52), white pomelo juice (cultivar PO 51) contained lower amount of total volatiles but higher terpenoids. Descriptive sensory evaluation indicated that white pomelo juice was milder in taste especially acidity. Furthermore, principal component analysis and partial least square regression revealed a strong correlation in pomelo juices between their chemical components and some flavour attributes (i.e. acidic, fresh, peely and sweet). Hence, this research enabled a deeper insight into the flavour of this unique citrus fruit. Copyright © 2012 Elsevier Ltd. All rights reserved.

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.

Multivariate analysis of sexual size dimorphism in local turkeys (Meleagris gallopavo) in Nigeria.

PubMed

Ajayi, Oyeyemi O; Yakubu, Abdulmojeed; Jayeola, Oluwaseun O; Imumorin, Ikhide G; Takeet, Michael I; Ozoje, Michael O; Ikeobi, Christian O N; Peters, Sunday O

2012-06-01

Sexual size dimorphism is a key evolutionary feature that can lead to important biological insights. To improve methods of sexing live birds in the field, we assessed sexual size dimorphism in Nigerian local turkeys (Meleagris gallopavo) using multivariate techniques. Measurements were taken on 125 twenty-week-old birds reared under the intensive management system. The body parameters measured were body weight, body length, breast girth, thigh length, shank length, keel length, wing length and wing span. Univariate analysis revealed that toms (males) had significantly (P < 0.05) higher mean values than hens (females) in all the measured traits. Positive phenotypic correlations between body weight and body measurements ranged from 0.445 to 0.821 in toms and 0.053-0.660 in hens, respectively. Three principal components (PC1, PC2 and PC3) were extracted in toms, each accounting for 63.70%, 19.42% and 5.72% of the total variance, respectively. However, four principal components (PC1, PC2, PC3 and PC4) were extracted in hens, which explained 54.03%, 15.29%, 11.68% and 6.95%, respectively of the generalised variance. A stepwise discriminant function analysis of the eight morphological traits indicated that body weight, body length, tail length and wing span were the most discriminating variables in separating the sexes. The single discriminant function obtained was able to correctly classify 100% of the birds into their source population. The results obtained from the present study could aid future management decisions, ecological studies and conservation of local turkeys in a developing economy.

The dimensions of responsiveness of a health system: a Taiwanese perspective.

PubMed

Hsu, Chih-Cheng; Chen, Likwang; Hu, Yu-Whuei; Yip, Winnie; Shu, Chen-Chun

2006-03-17

Responsiveness is an indicator used to measure how well a health system performs relative to non-health aspects. This study assessed whether seven dimensions proposed by the World Health Organization (WHO) to measure responsiveness (dignity, autonomy, confidentiality, prompt attention, social support, basic amenities, and choices of providers) are applicable in evaluating the health system of Taiwan. A key informant survey and focus group research were used in this study. The translated WHO proposed questionnaire was sent to 205 nominated key informants by mail, and 132 (64.4%) were returned. We used principal component analysis to extract factors. Linear regression analysis was used to assess the relationship between the total score and the extracted factors. A qualitative content analysis was also carried out in focus group research. Principal component analysis produced five factors (respect, access, confidentiality, basic amenities, and social support) that explained 63.5% of the total variances. These five factors demonstrated acceptable internal consistency and four of them (except social support) were significantly correlated with the total responsiveness score. The focus group interviews revealed health providers' communication ability and medical ethics were also highly appraised by Taiwanese. When the performance of a health system is to be evaluated, elements of responsiveness proposed by WHO may have to be tailored to fit different cultural backgrounds. Four key features illustrate the uniqueness of Taiwanese perspectives: the idea of autonomy may not be conceptualized, prompt attention and choice of providers are on the same track, social support during care is trivially correlated to the total responsiveness score, and accountability of health providers is deemed essential to a health system.

Assessment of Gait Characteristics in Total Knee Arthroplasty Patients Using a Hierarchical Partial Least Squares Method.

PubMed

Wang, Wei; Ackland, David C; McClelland, Jodie A; Webster, Kate E; Halgamuge, Saman

2018-01-01

Quantitative gait analysis is an important tool in objective assessment and management of total knee arthroplasty (TKA) patients. Studies evaluating gait patterns in TKA patients have tended to focus on discrete data such as spatiotemporal information, joint range of motion and peak values of kinematics and kinetics, or consider selected principal components of gait waveforms for analysis. These strategies may not have the capacity to capture small variations in gait patterns associated with each joint across an entire gait cycle, and may ultimately limit the accuracy of gait classification. The aim of this study was to develop an automatic feature extraction method to analyse patterns from high-dimensional autocorrelated gait waveforms. A general linear feature extraction framework was proposed and a hierarchical partial least squares method derived for discriminant analysis of multiple gait waveforms. The effectiveness of this strategy was verified using a dataset of joint angle and ground reaction force waveforms from 43 patients after TKA surgery and 31 healthy control subjects. Compared with principal component analysis and partial least squares methods, the hierarchical partial least squares method achieved generally better classification performance on all possible combinations of waveforms, with the highest classification accuracy . The novel hierarchical partial least squares method proposed is capable of capturing virtually all significant differences between TKA patients and the controls, and provides new insights into data visualization. The proposed framework presents a foundation for more rigorous classification of gait, and may ultimately be used to evaluate the effects of interventions such as surgery and rehabilitation.

Prediction of protein structural classes by Chou's pseudo amino acid composition: approached using continuous wavelet transform and principal component analysis.

PubMed

Li, Zhan-Chao; Zhou, Xi-Bin; Dai, Zong; Zou, Xiao-Yong

2009-07-01

A prior knowledge of protein structural classes can provide useful information about its overall structure, so it is very important for quick and accurate determination of protein structural class with computation method in protein science. One of the key for computation method is accurate protein sample representation. Here, based on the concept of Chou's pseudo-amino acid composition (AAC, Chou, Proteins: structure, function, and genetics, 43:246-255, 2001), a novel method of feature extraction that combined continuous wavelet transform (CWT) with principal component analysis (PCA) was introduced for the prediction of protein structural classes. Firstly, the digital signal was obtained by mapping each amino acid according to various physicochemical properties. Secondly, CWT was utilized to extract new feature vector based on wavelet power spectrum (WPS), which contains more abundant information of sequence order in frequency domain and time domain, and PCA was then used to reorganize the feature vector to decrease information redundancy and computational complexity. Finally, a pseudo-amino acid composition feature vector was further formed to represent primary sequence by coupling AAC vector with a set of new feature vector of WPS in an orthogonal space by PCA. As a showcase, the rigorous jackknife cross-validation test was performed on the working datasets. The results indicated that prediction quality has been improved, and the current approach of protein representation may serve as a useful complementary vehicle in classifying other attributes of proteins, such as enzyme family class, subcellular localization, membrane protein types and protein secondary structure, etc.

Principal component analysis-based unsupervised feature extraction applied to in silico drug discovery for posttraumatic stress disorder-mediated heart disease.

PubMed

Taguchi, Y-h; Iwadate, Mitsuo; Umeyama, Hideaki

2015-04-30

Feature extraction (FE) is difficult, particularly if there are more features than samples, as small sample numbers often result in biased outcomes or overfitting. Furthermore, multiple sample classes often complicate FE because evaluating performance, which is usual in supervised FE, is generally harder than the two-class problem. Developing sample classification independent unsupervised methods would solve many of these problems. Two principal component analysis (PCA)-based FE, specifically, variational Bayes PCA (VBPCA) was extended to perform unsupervised FE, and together with conventional PCA (CPCA)-based unsupervised FE, were tested as sample classification independent unsupervised FE methods. VBPCA- and CPCA-based unsupervised FE both performed well when applied to simulated data, and a posttraumatic stress disorder (PTSD)-mediated heart disease data set that had multiple categorical class observations in mRNA/microRNA expression of stressed mouse heart. A critical set of PTSD miRNAs/mRNAs were identified that show aberrant expression between treatment and control samples, and significant, negative correlation with one another. Moreover, greater stability and biological feasibility than conventional supervised FE was also demonstrated. Based on the results obtained, in silico drug discovery was performed as translational validation of the methods. Our two proposed unsupervised FE methods (CPCA- and VBPCA-based) worked well on simulated data, and outperformed two conventional supervised FE methods on a real data set. Thus, these two methods have suggested equivalence for FE on categorical multiclass data sets, with potential translational utility for in silico drug discovery.

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

PubMed

Mueller, Gerhard; Mylonas, Demetrius; Schumacher, Petra

2018-07-01

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

A comparative evaluation of six principal IgY antibody extraction methods.

PubMed

Ren, Hao; Yang, Wenjing; Thirumalai, Diraviyam; Zhang, Xiaoying; Schade, Rüdiger

2016-03-01

Egg yolk has been considered a promising source of antibodies. Our study was designed to compare six principal IgY extraction methods (water dilution, polyethylene glycol [PEG] precipitation, caprylic acid extraction, chloroform extraction, phenol extraction, and carrageenan extraction), and to assess their relative extraction efficiencies and the purity of the resulting antibodies. The results showed that the organic solvents (chloroform or phenol) minimised the lipid ratio in the egg yolk. The water dilution, PEG precipitation and caprylic acid extraction methods resulted in high yields, and antibodies purified with PEG and carrageenan exhibited high purity. Our results indicate that phenol extraction would be more suitable for preparing high concentrations of IgY for non-therapeutic usage, while the water dilution and carrageenan extraction methods would be more appropriate for use in the preparation of IgY for oral administration. 2016 FRAME.

[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.

Health care resource utilization in patients with active epilepsy.

PubMed

Kurth, Tobias; Lewis, Barbara E; Walker, Alexander M

2010-05-01

To evaluate health care resource utilization (HRU) in active epilepsy. Thomson-Reuters insurance databases included 14 million persons in 2005-2007. We extracted information for individuals with insurance claims suggestive of epilepsy. Using iterative expert classification, we sorted patients by type of epilepsy. For each type we calculated prevalence and HRU. A distance analysis identified closely similar types, and a principal components analysis revealed dimensions of variation in HRU. The prevalence of active epilepsy was 3.4 per 1,000. Most common diagnoses among 46,847 patients were generalized convulsive epilepsy (33.3%) and complex partial seizures (24.8%). Patients averaged 10 physician visits per year, 24 diagnostic tests/procedures per year, >30 drug dispensings per year, and <1 emergency room (ER) visit per year, the minority of each of these being related to epilepsy. Female patients generally had more HRU, and HRU increased with age. Patients were hospitalized most frequently for disorders other than epilepsy. HRU was similar for most epilepsy types, excepting grand mal status, epilepsia partialis continua, and infantile spasms. The first principal components of HRU variation was nonepilepsy HRU, followed by components of epilepsy-related medications, other epilepsy/emergency care, and epilepsy visits/diagnostic procedures. The prevalence of active epilepsy in the United States is substantially less than the prevalence of any history of recurrent seizure. Nonepilepsy-related HRU dominated HRU in epilepsy patients and was the principal source of variation. There is a core set of epilepsy diagnoses, the HRU patterns of which are indistinguishable, whereas patients with grand mal status, epilepsia partialis continua, and infantile spasms all have distinct patterns. To provide more specific insights into the economic impact of the condition, studies of HRU in epilepsy should make a distinction about epilepsy-related and unrelated care.

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

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.

The use of experimental structures to model protein dynamics.

PubMed

Katebi, Ataur R; Sankar, Kannan; Jia, Kejue; Jernigan, Robert L

2015-01-01

The number of solved protein structures submitted in the Protein Data Bank (PDB) has increased dramatically in recent years. For some specific proteins, this number is very high-for example, there are over 550 solved structures for HIV-1 protease, one protein that is essential for the life cycle of human immunodeficiency virus (HIV) which causes acquired immunodeficiency syndrome (AIDS) in humans. The large number of structures for the same protein and its variants include a sample of different conformational states of the protein. A rich set of structures solved experimentally for the same protein has information buried within the dataset that can explain the functional dynamics and structural mechanism of the protein. To extract the dynamics information and functional mechanism from the experimental structures, this chapter focuses on two methods-Principal Component Analysis (PCA) and Elastic Network Models (ENM). PCA is a widely used statistical dimensionality reduction technique to classify and visualize high-dimensional data. On the other hand, ENMs are well-established simple biophysical method for modeling the functionally important global motions of proteins. This chapter covers the basics of these two. Moreover, an improved ENM version that utilizes the variations found within a given set of structures for a protein is described. As a practical example, we have extracted the functional dynamics and mechanism of HIV-1 protease dimeric structure by using a set of 329 PDB structures of this protein. We have described, step by step, how to select a set of protein structures, how to extract the needed information from the PDB files for PCA, how to extract the dynamics information using PCA, how to calculate ENM modes, how to measure the congruency between the dynamics computed from the principal components (PCs) and the ENM modes, and how to compute entropies using the PCs. We provide the computer programs or references to software tools to accomplish each step and show how to use these programs and tools. We also include computer programs to generate movies based on PCs and ENM modes and describe how to visualize them.

The Use of Experimental Structures to Model Protein Dynamics

PubMed Central

Katebi, Ataur R.; Sankar, Kannan; Jia, Kejue; Jernigan, Robert L.

2014-01-01

Summary The number of solved protein structures submitted in the Protein Data Bank (PDB) has increased dramatically in recent years. For some specific proteins, this number is very high – for example, there are over 550 solved structures for HIV-1 protease, one protein that is essential for the life cycle of human immunodeficiency virus (HIV) which causes acquired immunodeficiency syndrome (AIDS) in humans. The large number of structures for the same protein and its variants include a sample of different conformational states of the protein. A rich set of structures solved experimentally for the same protein has information buried within the dataset that can explain the functional dynamics and structural mechanism of the protein. To extract the dynamics information and functional mechanism from the experimental structures, this chapter focuses on two methods – Principal Component Analysis (PCA) and Elastic Network Models (ENM). PCA is a widely used statistical dimensionality reduction technique to classify and visualize high-dimensional data. On the other hand, ENMs are well-established simple biophysical method for modeling the functionally important global motions of proteins. This chapter covers the basics of these two. Moreover, an improved ENM version that utilizes the variations found within a given set of structures for a protein is described. As a practical example, we have extracted the functional dynamics and mechanism of HIV-1 protease dimeric structure by using a set of 329 PDB structures of this protein. We have described, step by step, how to select a set of protein structures, how to extract the needed information from the PDB files for PCA, how to extract the dynamics information using PCA, how to calculate ENM modes, how to measure the congruency between the dynamics computed from the principal components (PCs) and the ENM modes, and how to compute entropies using the PCs. We provide the computer programs or references to software tools to accomplish each step and show how to use these programs and tools. We also include computer programs to generate movies based on PCs and ENM modes and describe how to visualize them. PMID:25330965

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:…

Characterization of Volatile Compounds of Eleven Achillea Species from Turkey and Biological Activities of Essential Oil and Methanol Extract of A. hamzaoglui Arabacı & Budak.

PubMed

Turkmenoglu, Fatma Pinar; Agar, Osman Tuncay; Akaydin, Galip; Hayran, Mutlu; Demirci, Betul

2015-06-22

According to distribution of genus Achillea, two main centers of diversity occur in S.E. Europe and S.W. Asia. Diversified essential oil compositions from Balkan Peninsula have been numerously reported. However, report on essential oils of Achillea species growing in Turkey, which is one of the main centers of diversity, is very limited. This paper represents the chemical compositions of the essential oils obtained by hydrodistillation from the aerial parts of eleven Achillea species, identified simultaneously by gas chromatography and gas chromatography-mass spectrometry. The main components were found to be 1,8-cineole, p-cymene, viridiflorol, nonacosane, α-bisabolol, caryophyllene oxide, α-bisabolon oxide A, β-eudesmol, 15-hexadecanolide and camphor. The chemical principal component analysis based on thirty compounds identified three species groups and a subgroup, where each group constituted a chemotype. This is the first report on the chemical composition of A. hamzaoglui essential oil; as well as the antioxidant and antimicrobial evaluation of its essential oil and methanolic extract.

Kernel Principal Component Analysis for dimensionality reduction in fMRI-based diagnosis of ADHD.

PubMed

Sidhu, Gagan S; Asgarian, Nasimeh; Greiner, Russell; Brown, Matthew R G

2012-01-01

This study explored various feature extraction methods for use in automated diagnosis of Attention-Deficit Hyperactivity Disorder (ADHD) from functional Magnetic Resonance Image (fMRI) data. Each participant's data consisted of a resting state fMRI scan as well as phenotypic data (age, gender, handedness, IQ, and site of scanning) from the ADHD-200 dataset. We used machine learning techniques to produce support vector machine (SVM) classifiers that attempted to differentiate between (1) all ADHD patients vs. healthy controls and (2) ADHD combined (ADHD-c) type vs. ADHD inattentive (ADHD-i) type vs. controls. In different tests, we used only the phenotypic data, only the imaging data, or else both the phenotypic and imaging data. For feature extraction on fMRI data, we tested the Fast Fourier Transform (FFT), different variants of Principal Component Analysis (PCA), and combinations of FFT and PCA. PCA variants included PCA over time (PCA-t), PCA over space and time (PCA-st), and kernelized PCA (kPCA-st). Baseline chance accuracy was 64.2% produced by guessing healthy control (the majority class) for all participants. Using only phenotypic data produced 72.9% accuracy on two class diagnosis and 66.8% on three class diagnosis. Diagnosis using only imaging data did not perform as well as phenotypic-only approaches. Using both phenotypic and imaging data with combined FFT and kPCA-st feature extraction yielded accuracies of 76.0% on two class diagnosis and 68.6% on three class diagnosis-better than phenotypic-only approaches. Our results demonstrate the potential of using FFT and kPCA-st with resting-state fMRI data as well as phenotypic data for automated diagnosis of ADHD. These results are encouraging given known challenges of learning ADHD diagnostic classifiers using the ADHD-200 dataset (see Brown et al., 2012).

Automated diagnosis of Alzheimer's disease with multi-atlas based whole brain segmentations

NASA Astrophysics Data System (ADS)

Luo, Yuan; Tang, Xiaoying

2017-03-01

Voxel-based analysis is widely used in quantitative analysis of structural brain magnetic resonance imaging (MRI) and automated disease detection, such as Alzheimer's disease (AD). However, noise at the voxel level may cause low sensitivity to AD-induced structural abnormalities. This can be addressed with the use of a whole brain structural segmentation approach which greatly reduces the dimension of features (the number of voxels). In this paper, we propose an automatic AD diagnosis system that combines such whole brain segmen- tations with advanced machine learning methods. We used a multi-atlas segmentation technique to parcellate T1-weighted images into 54 distinct brain regions and extract their structural volumes to serve as the features for principal-component-analysis-based dimension reduction and support-vector-machine-based classification. The relationship between the number of retained principal components (PCs) and the diagnosis accuracy was systematically evaluated, in a leave-one-out fashion, based on 28 AD subjects and 23 age-matched healthy subjects. Our approach yielded pretty good classification results with 96.08% overall accuracy being achieved using the three foremost PCs. In addition, our approach yielded 96.43% specificity, 100% sensitivity, and 0.9891 area under the receiver operating characteristic curve.

Tongue Motion Patterns in Post-Glossectomy and Typical Speakers: A Principal Components Analysis

PubMed Central

Stone, Maureen; Langguth, Julie M.; Woo, Jonghye; Chen, Hegang; Prince, Jerry L.

2015-01-01

Purpose In this study, the authors examined changes in tongue motion caused by glossectomy surgery. A speech task that involved subtle changes in tongue-tip positioning (the motion from /i/ to /s/) was measured. The hypothesis was that patients would have limited motion on the tumor (resected) side and would compensate with greater motion on the nontumor side in order to elevate the tongue tip and blade for /s/. Method Velocity fields were extracted from tagged magnetic resonance images in the left, middle, and right tongue of 3 patients and 10 controls. Principal components (PCs) analysis quantified motion differences and distinguished between the subject groups. Results PCs 1 and 2 represented variance in (a) size and independence of the tongue tip, and (b) direction of motion of the tip, body, or both. Patients and controls were correctly separated by a small number of PCs. Conclusions Motion of the tumor slice was different between patients and controls, but the nontumor side of the patients’ tongues did not show excessive or adaptive motion. Both groups contained apical and laminal /s/ users, and 1 patient created apical /s/ in a highly unusual manner. PMID:24023377

Reconstructing the free-energy landscape of Met-enkephalin using dihedral principal component analysis and well-tempered metadynamics

NASA Astrophysics Data System (ADS)

Sicard, François; Senet, Patrick

2013-06-01

Well-Tempered Metadynamics (WTmetaD) is an efficient method to enhance the reconstruction of the free-energy surface of proteins. WTmetaD guarantees a faster convergence in the long time limit in comparison with the standard metadynamics. It still suffers, however, from the same limitation, i.e., the non-trivial choice of pertinent collective variables (CVs). To circumvent this problem, we couple WTmetaD with a set of CVs generated from a dihedral Principal Component Analysis (dPCA) on the Ramachandran dihedral angles describing the backbone structure of the protein. The dPCA provides a generic method to extract relevant CVs built from internal coordinates, and does not depend on the alignment to an arbitrarily chosen reference structure as usual in Cartesian PCA. We illustrate the robustness of this method in the case of a reference model protein, the small and very diffusive Met-enkephalin pentapeptide. We propose a justification a posteriori of the considered number of CVs necessary to bias the metadynamics simulation in terms of the one-dimensional free-energy profiles associated with Ramachandran dihedral angles along the amino-acid sequence.

Reconstructing the free-energy landscape of Met-enkephalin using dihedral principal component analysis and well-tempered metadynamics.

PubMed

Sicard, François; Senet, Patrick

2013-06-21

Well-Tempered Metadynamics (WTmetaD) is an efficient method to enhance the reconstruction of the free-energy surface of proteins. WTmetaD guarantees a faster convergence in the long time limit in comparison with the standard metadynamics. It still suffers, however, from the same limitation, i.e., the non-trivial choice of pertinent collective variables (CVs). To circumvent this problem, we couple WTmetaD with a set of CVs generated from a dihedral Principal Component Analysis (dPCA) on the Ramachandran dihedral angles describing the backbone structure of the protein. The dPCA provides a generic method to extract relevant CVs built from internal coordinates, and does not depend on the alignment to an arbitrarily chosen reference structure as usual in Cartesian PCA. We illustrate the robustness of this method in the case of a reference model protein, the small and very diffusive Met-enkephalin pentapeptide. We propose a justification a posteriori of the considered number of CVs necessary to bias the metadynamics simulation in terms of the one-dimensional free-energy profiles associated with Ramachandran dihedral angles along the amino-acid sequence.

Effects of plant polyphenols and α-tocopherol on lipid oxidation, microbiological characteristics, and biogenic amines formation in dry-cured bacons.

PubMed

Wang, Yongli; Li, Feng; Zhuang, Hong; Li, Lianghao; Chen, Xiao; Zhang, Jianhao

2015-03-01

Effects of plant polyphenols (tea polyphenol [TP], grape seed extract [GSE], and gingerol) and α-tocopherol on physicochemical parameters, microbiological counts, and biogenic amines were determined in dry-cured bacons at the end of ripening. Results showed that plant polyphenols and α-tocopherol significantly decreased pH, thiobarbituric acid reactive substances content, and total volatile basic nitrogen (TVBN) compared with the control (P < 0.05). Microbial counts and biogenic amine contents in dry-cured bacons were affected by plant polyphenols or α-tocopherol, with TP being the most effective (P < 0.05) in reducing aerobic plate counts, Enterobacteriaceae, Micrococcaceae, yeast, and molds, as well as in inhibiting formation of putrescine, cadaverine, tyramine, and spermine. Principal component analysis indicated that the first 2 principal components (PC) explained about 85.5% of the total variation. PC1 was related with physicochemical factors, parts of biogenic amines, and spoilage microorganisms, whereas PC2 grouped the TVBN, tyramine, 2-phenylethylamine, yeast, and molds. These findings suggest that plant polyphenols, especially TP, could be used to process dry-cured bacons to improve the quality and safety of finished products. © 2015 Institute of Food Technologists®

Quantification of intensity variations in functional MR images using rotated principal components

NASA Astrophysics Data System (ADS)

Backfrieder, W.; Baumgartner, R.; Sámal, M.; Moser, E.; Bergmann, H.

1996-08-01

In functional MRI (fMRI), the changes in cerebral haemodynamics related to stimulated neural brain activity are measured using standard clinical MR equipment. Small intensity variations in fMRI data have to be detected and distinguished from non-neural effects by careful image analysis. Based on multivariate statistics we describe an algorithm involving oblique rotation of the most significant principal components for an estimation of the temporal and spatial distribution of the stimulated neural activity over the whole image matrix. This algorithm takes advantage of strong local signal variations. A mathematical phantom was designed to generate simulated data for the evaluation of the method. In simulation experiments, the potential of the method to quantify small intensity changes, especially when processing data sets containing multiple sources of signal variations, was demonstrated. In vivo fMRI data collected in both visual and motor stimulation experiments were analysed, showing a proper location of the activated cortical regions within well known neural centres and an accurate extraction of the activation time profile. The suggested method yields accurate absolute quantification of in vivo brain activity without the need of extensive prior knowledge and user interaction.

Detection of Fungus Infection on Petals of Rapeseed (Brassica napus L.) Using NIR Hyperspectral Imaging

NASA Astrophysics Data System (ADS)

Zhao, Yan-Ru; Yu, Ke-Qiang; Li, Xiaoli; He, Yong

2016-12-01

Infected petals are often regarded as the source for the spread of fungi Sclerotinia sclerotiorum in all growing process of rapeseed (Brassica napus L.) plants. This research aimed to detect fungal infection of rapeseed petals by applying hyperspectral imaging in the spectral region of 874-1734 nm coupled with chemometrics. Reflectance was extracted from regions of interest (ROIs) in the hyperspectral image of each sample. Firstly, principal component analysis (PCA) was applied to conduct a cluster analysis with the first several principal components (PCs). Then, two methods including X-loadings of PCA and random frog (RF) algorithm were used and compared for optimizing wavebands selection. Least squares-support vector machine (LS-SVM) methodology was employed to establish discriminative models based on the optimal and full wavebands. Finally, area under the receiver operating characteristics curve (AUC) was utilized to evaluate classification performance of these LS-SVM models. It was found that LS-SVM based on the combination of all optimal wavebands had the best performance with AUC of 0.929. These results were promising and demonstrated the potential of applying hyperspectral imaging in fungus infection detection on rapeseed petals.

Differentiation of tea varieties using UV-Vis spectra and pattern recognition techniques

NASA Astrophysics Data System (ADS)

Palacios-Morillo, Ana; Alcázar, Ángela.; de Pablos, Fernando; Jurado, José Marcos

2013-02-01

Tea, one of the most consumed beverages all over the world, is of great importance in the economies of a number of countries. Several methods have been developed to classify tea varieties or origins based in pattern recognition techniques applied to chemical data, such as metal profile, amino acids, catechins and volatile compounds. Some of these analytical methods become tedious and expensive to be applied in routine works. The use of UV-Vis spectral data as discriminant variables, highly influenced by the chemical composition, can be an alternative to these methods. UV-Vis spectra of methanol-water extracts of tea have been obtained in the interval 250-800 nm. Absorbances have been used as input variables. Principal component analysis was used to reduce the number of variables and several pattern recognition methods, such as linear discriminant analysis, support vector machines and artificial neural networks, have been applied in order to differentiate the most common tea varieties. A successful classification model was built by combining principal component analysis and multilayer perceptron artificial neural networks, allowing the differentiation between tea varieties. This rapid and simple methodology can be applied to solve classification problems in food industry saving economic resources.

Medical diagnosis of atherosclerosis from Carotid Artery Doppler Signals using principal component analysis (PCA), k-NN based weighting pre-processing and Artificial Immune Recognition System (AIRS).

PubMed

Latifoğlu, Fatma; Polat, Kemal; Kara, Sadik; Güneş, Salih

2008-02-01

In this study, we proposed a new medical diagnosis system based on principal component analysis (PCA), k-NN based weighting pre-processing, and Artificial Immune Recognition System (AIRS) for diagnosis of atherosclerosis from Carotid Artery Doppler Signals. The suggested system consists of four stages. First, in the feature extraction stage, we have obtained the features related with atherosclerosis disease using Fast Fourier Transformation (FFT) modeling and by calculating of maximum frequency envelope of sonograms. Second, in the dimensionality reduction stage, the 61 features of atherosclerosis disease have been reduced to 4 features using PCA. Third, in the pre-processing stage, we have weighted these 4 features using different values of k in a new weighting scheme based on k-NN based weighting pre-processing. Finally, in the classification stage, AIRS classifier has been used to classify subjects as healthy or having atherosclerosis. Hundred percent of classification accuracy has been obtained by the proposed system using 10-fold cross validation. This success shows that the proposed system is a robust and effective system in diagnosis of atherosclerosis disease.

Sand/cement ratio evaluation on mortar using neural networks and ultrasonic transmission inspection.

PubMed

Molero, M; Segura, I; Izquierdo, M A G; Fuente, J V; Anaya, J J

2009-02-01

The quality and degradation state of building materials can be determined by nondestructive testing (NDT). These materials are composed of a cementitious matrix and particles or fragments of aggregates. Sand/cement ratio (s/c) provides the final material quality; however, the sand content can mask the matrix properties in a nondestructive measurement. Therefore, s/c ratio estimation is needed in nondestructive characterization of cementitious materials. In this study, a methodology to classify the sand content in mortar is presented. The methodology is based on ultrasonic transmission inspection, data reduction, and features extraction by principal components analysis (PCA), and neural network classification. This evaluation is carried out with several mortar samples, which were made while taking into account different cement types and s/c ratios. The estimated s/c ratio is determined by ultrasonic spectral attenuation with three different broadband transducers (0.5, 1, and 2 MHz). Statistical PCA to reduce the dimension of the captured traces has been applied. Feed-forward neural networks (NNs) are trained using principal components (PCs) and their outputs are used to display the estimated s/c ratios in false color images, showing the s/c ratio distribution of the mortar samples.

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.

Authentication of beef versus horse meat using 60 MHz 1H NMR spectroscopy.

PubMed

Jakes, W; Gerdova, A; Defernez, M; Watson, A D; McCallum, C; Limer, E; Colquhoun, I J; Williamson, D C; Kemsley, E K

2015-05-15

This work reports a candidate screening protocol to distinguish beef from horse meat based upon comparison of triglyceride signatures obtained by 60 MHz (1)H NMR spectroscopy. Using a simple chloroform-based extraction, we obtained classic low-field triglyceride spectra from typically a 10 min acquisition time. Peak integration was sufficient to differentiate samples of fresh beef (76 extractions) and horse (62 extractions) using Naïve Bayes classification. Principal component analysis gave a two-dimensional "authentic" beef region (p=0.001) against which further spectra could be compared. This model was challenged using a subset of 23 freeze-thawed training samples. The outcomes indicated that storing samples by freezing does not adversely affect the analysis. Of a further collection of extractions from previously unseen samples, 90/91 beef spectra were classified as authentic, and 16/16 horse spectra as non-authentic. We conclude that 60 MHz (1)H NMR represents a feasible high-throughput approach for screening raw meat. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading

PubMed Central

Cho, Nam-Hoon; Choi, Heung-Kook

2014-01-01

One of the most significant processes in cancer cell and tissue image analysis is the efficient extraction of features for grading purposes. This research applied two types of three-dimensional texture analysis methods to the extraction of feature values from renal cell carcinoma tissue images, and then evaluated the validity of the methods statistically through grade classification. First, we used a confocal laser scanning microscope to obtain image slices of four grades of renal cell carcinoma, which were then reconstructed into 3D volumes. Next, we extracted quantitative values using a 3D gray level cooccurrence matrix (GLCM) and a 3D wavelet based on two types of basis functions. To evaluate their validity, we predefined 6 different statistical classifiers and applied these to the extracted feature sets. In the grade classification results, 3D Haar wavelet texture features combined with principal component analysis showed the best discrimination results. Classification using 3D wavelet texture features was significantly better than 3D GLCM, suggesting that the former has potential for use in a computer-based grading system. PMID:25371701

Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing

PubMed Central

Wen, Tailai; Huang, Daoyu; Lu, Kun; Deng, Changjian; Zeng, Tanyue; Yu, Song; He, Zhiyi

2018-01-01

The aim of this research was to enhance the classification accuracy of an electronic nose (E-nose) in different detecting applications. During the learning process of the E-nose to predict the types of different odors, the prediction accuracy was not quite satisfying because the raw features extracted from sensors’ responses were regarded as the input of a classifier without any feature extraction processing. Therefore, in order to obtain more useful information and improve the E-nose’s classification accuracy, in this paper, a Weighted Kernels Fisher Discriminant Analysis (WKFDA) combined with Quantum-behaved Particle Swarm Optimization (QPSO), i.e., QWKFDA, was presented to reprocess the original feature matrix. In addition, we have also compared the proposed method with quite a few previously existing ones including Principal Component Analysis (PCA), Locality Preserving Projections (LPP), Fisher Discriminant Analysis (FDA) and Kernels Fisher Discriminant Analysis (KFDA). Experimental results proved that QWKFDA is an effective feature extraction method for E-nose in predicting the types of wound infection and inflammable gases, which shared much higher classification accuracy than those of the contrast methods. PMID:29382146

Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing.

PubMed

Wen, Tailai; Yan, Jia; Huang, Daoyu; Lu, Kun; Deng, Changjian; Zeng, Tanyue; Yu, Song; He, Zhiyi

2018-01-29

The aim of this research was to enhance the classification accuracy of an electronic nose (E-nose) in different detecting applications. During the learning process of the E-nose to predict the types of different odors, the prediction accuracy was not quite satisfying because the raw features extracted from sensors' responses were regarded as the input of a classifier without any feature extraction processing. Therefore, in order to obtain more useful information and improve the E-nose's classification accuracy, in this paper, a Weighted Kernels Fisher Discriminant Analysis (WKFDA) combined with Quantum-behaved Particle Swarm Optimization (QPSO), i.e., QWKFDA, was presented to reprocess the original feature matrix. In addition, we have also compared the proposed method with quite a few previously existing ones including Principal Component Analysis (PCA), Locality Preserving Projections (LPP), Fisher Discriminant Analysis (FDA) and Kernels Fisher Discriminant Analysis (KFDA). Experimental results proved that QWKFDA is an effective feature extraction method for E-nose in predicting the types of wound infection and inflammable gases, which shared much higher classification accuracy than those of the contrast methods.

Functional data analysis of sleeping energy expenditure.

PubMed

Lee, Jong Soo; Zakeri, Issa F; Butte, Nancy F

2017-01-01

Adequate sleep is crucial during childhood for metabolic health, and physical and cognitive development. Inadequate sleep can disrupt metabolic homeostasis and alter sleeping energy expenditure (SEE). Functional data analysis methods were applied to SEE data to elucidate the population structure of SEE and to discriminate SEE between obese and non-obese children. Minute-by-minute SEE in 109 children, ages 5-18, was measured in room respiration calorimeters. A smoothing spline method was applied to the calorimetric data to extract the true smoothing function for each subject. Functional principal component analysis was used to capture the important modes of variation of the functional data and to identify differences in SEE patterns. Combinations of functional principal component analysis and classifier algorithm were used to classify SEE. Smoothing effectively removed instrumentation noise inherent in the room calorimeter data, providing more accurate data for analysis of the dynamics of SEE. SEE exhibited declining but subtly undulating patterns throughout the night. Mean SEE was markedly higher in obese than non-obese children, as expected due to their greater body mass. SEE was higher among the obese than non-obese children (p<0.01); however, the weight-adjusted mean SEE was not statistically different (p>0.1, after post hoc testing). Functional principal component scores for the first two components explained 77.8% of the variance in SEE and also differed between groups (p = 0.037). Logistic regression, support vector machine or random forest classification methods were able to distinguish weight-adjusted SEE between obese and non-obese participants with good classification rates (62-64%). Our results implicate other factors, yet to be uncovered, that affect the weight-adjusted SEE of obese and non-obese children. Functional data analysis revealed differences in the structure of SEE between obese and non-obese children that may contribute to disruption of metabolic homeostasis.

Value assignment and uncertainty evaluation for single-element reference solutions

NASA Astrophysics Data System (ADS)

Possolo, Antonio; Bodnar, Olha; Butler, Therese A.; Molloy, John L.; Winchester, Michael R.

2018-06-01

A Bayesian statistical procedure is proposed for value assignment and uncertainty evaluation for the mass fraction of the elemental analytes in single-element solutions distributed as NIST standard reference materials. The principal novelty that we describe is the use of information about relative differences observed historically between the measured values obtained via gravimetry and via high-performance inductively coupled plasma optical emission spectrometry, to quantify the uncertainty component attributable to between-method differences. This information is encapsulated in a prior probability distribution for the between-method uncertainty component, and it is then used, together with the information provided by current measurement data, to produce a probability distribution for the value of the measurand from which an estimate and evaluation of uncertainty are extracted using established statistical procedures.

Short-term PV/T module temperature prediction based on PCA-RBF neural network

NASA Astrophysics Data System (ADS)

Li, Jiyong; Zhao, Zhendong; Li, Yisheng; Xiao, Jing; Tang, Yunfeng

2018-02-01

Aiming at the non-linearity and large inertia of temperature control in PV/T system, short-term temperature prediction of PV/T module is proposed, to make the PV/T system controller run forward according to the short-term forecasting situation to optimize control effect. Based on the analysis of the correlation between PV/T module temperature and meteorological factors, and the temperature of adjacent time series, the principal component analysis (PCA) method is used to pre-process the original input sample data. Combined with the RBF neural network theory, the simulation results show that the PCA method makes the prediction accuracy of the network model higher and the generalization performance stronger than that of the RBF neural network without the main component extraction.

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

NASA Astrophysics Data System (ADS)

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

2015-05-01

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

Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns.

PubMed

Akama, Hiroyuki; Miyake, Maki; Jung, Jaeyoung; Murphy, Brian

2015-01-01

In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF). This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk) and co-occurrence adjustment (degree balance and distribution). We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of) the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.

Tracking Equilibrium and Nonequilibrium Shifts in Data with TREND.

PubMed

Xu, Jia; Van Doren, Steven R

2017-01-24

Principal component analysis (PCA) discovers patterns in multivariate data that include spectra, microscopy, and other biophysical measurements. Direct application of PCA to crowded spectra, images, and movies (without selecting peaks or features) was shown recently to identify their equilibrium or temporal changes. To enable the community to utilize these capabilities with a wide range of measurements, we have developed multiplatform software named TREND to Track Equilibrium and Nonequilibrium population shifts among two-dimensional Data frames. TREND can also carry this out by independent component analysis. We highlight a few examples of finding concurrent processes. TREND extracts dual phases of binding to two sites directly from the NMR spectra of the titrations. In a cardiac movie from magnetic resonance imaging, TREND resolves principal components (PCs) representing breathing and the cardiac cycle. TREND can also reconstruct the series of measurements from selected PCs, as illustrated for a biphasic, NMR-detected titration and the cardiac MRI movie. Fidelity of reconstruction of series of NMR spectra or images requires more PCs than needed to plot the largest population shifts. TREND reads spectra from many spectroscopies in the most common formats (JCAMP-DX and NMR) and multiple movie formats. The TREND package thus provides convenient tools to resolve the processes recorded by diverse biophysical methods. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Cognitive performance predicts treatment decisional abilities in mild to moderate dementia

PubMed Central

Gurrera, R.J.; Moye, J.; Karel, M.J.; Azar, A.R.; Armesto, J.C.

2016-01-01

Objective To examine the contribution of neuropsychological test performance to treatment decision-making capacity in community volunteers with mild to moderate dementia. Methods The authors recruited volunteers (44 men, 44 women) with mild to moderate dementia from the community. Subjects completed a battery of 11 neuropsychological tests that assessed auditory and visual attention, logical memory, language, and executive function. To measure decision making capacity, the authors administered the Capacity to Consent to Treatment Interview, the Hopemont Capacity Assessment Interview, and the MacCarthur Competence Assessment Tool—Treatment. Each of these instruments individually scores four decisional abilities serving capacity: understanding, appreciation, reasoning, and expression of choice. The authors used principal components analysis to generate component scores for each ability across instruments, and to extract principal components for neuropsychological performance. Results Multiple linear regression analyses demonstrated that neuropsychological performance significantly predicted all four abilities. Specifically, it predicted 77.8% of the common variance for understanding, 39.4% for reasoning, 24.6% for appreciation, and 10.2% for expression of choice. Except for reasoning and appreciation, neuropsychological predictor (β) profiles were unique for each ability. Conclusions Neuropsychological performance substantially and differentially predicted capacity for treatment decisions in individuals with mild to moderate dementia. Relationships between elemental cognitive function and decisional capacity may differ in individuals whose decisional capacity is impaired by other disorders, such as mental illness. PMID:16682669

Sampling methods for the study of volatile profile of PDO wine vinegars. A comparison using multivariate data analysis.

PubMed

Ríos-Reina, Rocío; Morales, M Lourdes; García-González, Diego L; Amigo, José M; Callejón, Raquel M

2018-03-01

High-quality wine vinegars have been registered in Spain under protected designation of origin (PDO): "Vinagre de Jerez", "Vinagre de Condado de Huelva" and "Vinagre de Montilla-Moriles". The raw material, production and aging processes determine their quality and their aromatic composition. Vinegar volatile profile is usually analyzed by gas chromatography-mass spectrometry (GC-MS), being necessary a previous extraction step. Thus, three different sampling methods (Headspace solid phase microextraction "HS-SPME", Headspace stir bar sorptive extraction "HSSE" and Dynamic headspace extraction "DHS") were studied for the analysis of the volatile composition of Spanish PDO wine vinegars. Multivariate curve resolution (MCR) was used to solve chromatographic problems, improving the results obtained. Principal component analysis (PCA) showed that not all the sampling methods were equally suitable for the characterization and differentiation between PDOs and categories, being HSSE the technique that made able the best vinegar characterization. Copyright © 2017 Elsevier Ltd. All rights reserved.

Rapid fingerprinting and classification of extra virgin olive oil by microjet sampling and extractive electrospray ionization mass spectrometry.

PubMed

Law, Wai Siang; Chen, Huan Wen; Balabin, Roman; Berchtold, Christian; Meier, Lukas; Zenobi, Renato

2010-04-01

Microjet sampling in combination with extractive electrospray ionization (EESI) mass spectrometry (MS) was applied to the rapid characterization and classification of extra virgin olive oil (EVOO) without any sample pretreatment. When modifying the composition of the primary ESI spray solvent, mass spectra of an identical EVOO sample showed differences. This demonstrates the capability of this technique to extract molecules with varying polarities, hence generating rich molecular information of the EVOO. Moreover, with the aid of microjet sampling, compounds of different volatilities (e.g.E-2-hexenal, trans-trans-2,4-heptadienal, tyrosol and caffeic acid) could be sampled simultaneously. EVOO data was also compared with that of other edible oils. Principal Component Analysis (PCA) was performed to discriminate EVOO and EVOO adulterated with edible oils. Microjet sampling EESI-MS was found to be a simple, rapid (less than 2 min analysis time per sample) and powerful method to obtain MS fingerprints of EVOO without requiring any complicated sample pretreatment steps.

An Efficient VLSI Architecture for Multi-Channel Spike Sorting Using a Generalized Hebbian Algorithm

PubMed Central

Chen, Ying-Lun; Hwang, Wen-Jyi; Ke, Chi-En

2015-01-01

A novel VLSI architecture for multi-channel online spike sorting is presented in this paper. In the architecture, the spike detection is based on nonlinear energy operator (NEO), and the feature extraction is carried out by the generalized Hebbian algorithm (GHA). To lower the power consumption and area costs of the circuits, all of the channels share the same core for spike detection and feature extraction operations. Each channel has dedicated buffers for storing the detected spikes and the principal components of that channel. The proposed circuit also contains a clock gating system supplying the clock to only the buffers of channels currently using the computation core to further reduce the power consumption. The architecture has been implemented by an application-specific integrated circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture has lower power consumption and hardware area costs for real-time multi-channel spike detection and feature extraction. PMID:26287193

Automatic optical detection and classification of marine animals around MHK converters using machine vision

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

Brunton, Steven

Optical systems provide valuable information for evaluating interactions and associations between organisms and MHK energy converters and for capturing potentially rare encounters between marine organisms and MHK device. The deluge of optical data from cabled monitoring packages makes expert review time-consuming and expensive. We propose algorithms and a processing framework to automatically extract events of interest from underwater video. The open-source software framework consists of background subtraction, filtering, feature extraction and hierarchical classification algorithms. This principle classification pipeline was validated on real-world data collected with an experimental underwater monitoring package. An event detection rate of 100% was achieved using robustmore » principal components analysis (RPCA), Fourier feature extraction and a support vector machine (SVM) binary classifier. The detected events were then further classified into more complex classes – algae | invertebrate | vertebrate, one species | multiple species of fish, and interest rank. Greater than 80% accuracy was achieved using a combination of machine learning techniques.« less

Application of higher order SVD to vibration-based system identification and damage detection

NASA Astrophysics Data System (ADS)

Chao, Shu-Hsien; Loh, Chin-Hsiung; Weng, Jian-Huang

2012-04-01

Singular value decomposition (SVD) is a powerful linear algebra tool. It is widely used in many different signal processing methods, such principal component analysis (PCA), singular spectrum analysis (SSA), frequency domain decomposition (FDD), subspace identification and stochastic subspace identification method ( SI and SSI ). In each case, the data is arranged appropriately in matrix form and SVD is used to extract the feature of the data set. In this study three different algorithms on signal processing and system identification are proposed: SSA, SSI-COV and SSI-DATA. Based on the extracted subspace and null-space from SVD of data matrix, damage detection algorithms can be developed. The proposed algorithm is used to process the shaking table test data of the 6-story steel frame. Features contained in the vibration data are extracted by the proposed method. Damage detection can then be investigated from the test data of the frame structure through subspace-based and nullspace-based damage indices.

An Efficient VLSI Architecture for Multi-Channel Spike Sorting Using a Generalized Hebbian Algorithm.

PubMed

Chen, Ying-Lun; Hwang, Wen-Jyi; Ke, Chi-En

2015-08-13

A novel VLSI architecture for multi-channel online spike sorting is presented in this paper. In the architecture, the spike detection is based on nonlinear energy operator (NEO), and the feature extraction is carried out by the generalized Hebbian algorithm (GHA). To lower the power consumption and area costs of the circuits, all of the channels share the same core for spike detection and feature extraction operations. Each channel has dedicated buffers for storing the detected spikes and the principal components of that channel. The proposed circuit also contains a clock gating system supplying the clock to only the buffers of channels currently using the computation core to further reduce the power consumption. The architecture has been implemented by an application-specific integrated circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture has lower power consumption and hardware area costs for real-time multi-channel spike detection and feature extraction.

Face-iris multimodal biometric scheme based on feature level fusion

NASA Astrophysics Data System (ADS)

Huo, Guang; Liu, Yuanning; Zhu, Xiaodong; Dong, Hongxing; He, Fei

2015-11-01

Unlike score level fusion, feature level fusion demands all the features extracted from unimodal traits with high distinguishability, as well as homogeneity and compatibility, which is difficult to achieve. Therefore, most multimodal biometric research focuses on score level fusion, whereas few investigate feature level fusion. We propose a face-iris recognition method based on feature level fusion. We build a special two-dimensional-Gabor filter bank to extract local texture features from face and iris images, and then transform them by histogram statistics into an energy-orientation variance histogram feature with lower dimensions and higher distinguishability. Finally, through a fusion-recognition strategy based on principal components analysis and support vector machine (FRSPS), feature level fusion and one-to-n identification are accomplished. The experimental results demonstrate that this method can not only effectively extract face and iris features but also provide higher recognition accuracy. Compared with some state-of-the-art fusion methods, the proposed method has a significant performance advantage.

Characterization of pectins extracted from pomegranate peel and their gelling properties.

PubMed

Abid, Mouna; Cheikhrouhou, S; Renard, Catherine M G C; Bureau, Sylvie; Cuvelier, Gérard; Attia, Hamadi; Ayadi, M A

2017-01-15

The composition of pomegranate peel, the main by-product during pomegranate processing, and some of the characteristics of the water-soluble pectins were investigated. Four tunisian pomegranate peels were subjected to hot aqueous extractions (86°C, 80min, 20mM nitric acid). Pomegranate peels yielded between 6.8% and 10.1% pectins. The extracted pectins were low methylated and were characterized by the predominance of homogalacturonan regions. Principal component analysis applied on FT-IR spectral data in the region between 4000 and 650cm(-1) differentiated the samples according to their degree of methylation. At pH 3, in the presence of 0.7% pectin, all solutions showed a rapid gel formation with G'>G″. With decreasing temperature from 90°C to 10°C, G' increased to reach a plateau at 10°C. The variation in the pectin gel formation between varieties was attributed to difference in pectin characteristics particularly the hydrodynamic volume and the neutral sugar content. Copyright © 2016 Elsevier Ltd. 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

Multi-angle backscatter classification and sub-bottom profiling for improved seafloor characterization

NASA Astrophysics Data System (ADS)

Alevizos, Evangelos; Snellen, Mirjam; Simons, Dick; Siemes, Kerstin; Greinert, Jens

2018-06-01

This study applies three classification methods exploiting the angular dependence of acoustic seafloor backscatter along with high resolution sub-bottom profiling for seafloor sediment characterization in the Eckernförde Bay, Baltic Sea Germany. This area is well suited for acoustic backscatter studies due to its shallowness, its smooth bathymetry and the presence of a wide range of sediment types. Backscatter data were acquired using a Seabeam1180 (180 kHz) multibeam echosounder and sub-bottom profiler data were recorded using a SES-2000 parametric sonar transmitting 6 and 12 kHz. The high density of seafloor soundings allowed extracting backscatter layers for five beam angles over a large part of the surveyed area. A Bayesian probability method was employed for sediment classification based on the backscatter variability at a single incidence angle, whereas Maximum Likelihood Classification (MLC) and Principal Components Analysis (PCA) were applied to the multi-angle layers. The Bayesian approach was used for identifying the optimum number of acoustic classes because cluster validation is carried out prior to class assignment and class outputs are ordinal categorical values. The method is based on the principle that backscatter values from a single incidence angle express a normal distribution for a particular sediment type. The resulting Bayesian classes were well correlated to median grain sizes and the percentage of coarse material. The MLC method uses angular response information from five layers of training areas extracted from the Bayesian classification map. The subsequent PCA analysis is based on the transformation of these five layers into two principal components that comprise most of the data variability. These principal components were clustered in five classes after running an external cluster validation test. In general both methods MLC and PCA, separated the various sediment types effectively, showing good agreement (kappa >0.7) with the Bayesian approach which also correlates well with ground truth data (r2 > 0.7). In addition, sub-bottom data were used in conjunction with the Bayesian classification results to characterize acoustic classes with respect to their geological and stratigraphic interpretation. The joined interpretation of seafloor and sub-seafloor data sets proved to be an efficient approach for a better understanding of seafloor backscatter patchiness and to discriminate acoustically similar classes in different geological/bathymetric settings.

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.

Identification and Quantification of the Main Active Anticancer Alkaloids from the Root of Glaucium flavum

PubMed Central

Bournine, Lamine; Bensalem, Sihem; Wauters, Jean-Noël; Iguer-Ouada, Mokrane; Maiza-Benabdesselam, Fadila; Bedjou, Fatiha; Castronovo, Vincent; Bellahcène, Akeila; Tits, Monique; Frédérich, Michel

2013-01-01

Glaucium flavum is used in Algerian folk medicine to remove warts (benign tumors). Its local appellations are Cheqiq el-asfar and Qarn el-djedyane. We have recently reported the anti-tumoral activity of Glaucium flavum root alkaloid extract against human cancer cells, in vitro and in vivo. The principal identified alkaloid in the extract was protopine. This study aims to determine which component(s) of Glaucium flavum root extract might possess potent antitumor activity on human cancer cells. Quantitative estimation of Glaucium flavum alkaloids was realized by HPLC-DAD. Glaucium flavum effect on human normal and cancer cell viability was determined using WST-1 assay. Quantification of alkaloids in Glaucium flavum revealed that the dried root part contained 0.84% of protopine and 0.07% of bocconoline (w/w), while the dried aerial part contained only 0.08% of protopine, glaucine as the main alkaloid, and no bocconoline. In vitro evaluation of the growth inhibitory activity on breast cancer and normal cells demonstrated that purified protopine did not reproduce the full cytotoxic activity of the alkaloid root extract on cancer cell lines. On the other hand, bocconoline inhibited strongly the viability of cancer cells with an IC50 of 7.8 μM and only a low cytotoxic effect was observed against normal human cells. Our results showed for the first time that protopine is the major root alkaloid of Glaucium flavum. Finally, we are the first to demonstrate a specific anticancer effect of Glaucium flavum root extract against breast cancer cells, which can be attributed, at least in part, to bocconoline. PMID:24317429

Identification and quantification of the main active anticancer alkaloids from the root of Glaucium flavum.

PubMed

Bournine, Lamine; Bensalem, Sihem; Wauters, Jean-Noël; Iguer-Ouada, Mokrane; Maiza-Benabdesselam, Fadila; Bedjou, Fatiha; Castronovo, Vincent; Bellahcène, Akeila; Tits, Monique; Frédérich, Michel

2013-12-02

Glaucium flavum is used in Algerian folk medicine to remove warts (benign tumors). Its local appellations are Cheqiq el-asfar and Qarn el-djedyane. We have recently reported the anti-tumoral activity of Glaucium flavum root alkaloid extract against human cancer cells, in vitro and in vivo. The principal identified alkaloid in the extract was protopine. This study aims to determine which component(s) of Glaucium flavum root extract might possess potent antitumor activity on human cancer cells. Quantitative estimation of Glaucium flavum alkaloids was realized by HPLC-DAD. Glaucium flavum effect on human normal and cancer cell viability was determined using WST-1 assay. Quantification of alkaloids in Glaucium flavum revealed that the dried root part contained 0.84% of protopine and 0.07% of bocconoline (w/w), while the dried aerial part contained only 0.08% of protopine, glaucine as the main alkaloid, and no bocconoline. In vitro evaluation of the growth inhibitory activity on breast cancer and normal cells demonstrated that purified protopine did not reproduce the full cytotoxic activity of the alkaloid root extract on cancer cell lines. On the other hand, bocconoline inhibited strongly the viability of cancer cells with an IC50 of 7.8 µM and only a low cytotoxic effect was observed against normal human cells. Our results showed for the first time that protopine is the major root alkaloid of Glaucium flavum. Finally, we are the first to demonstrate a specific anticancer effect of Glaucium flavum root extract against breast cancer cells, which can be attributed, at least in part, to bocconoline.

Classification of white wine aromas with an electronic nose.

PubMed

Lozano, J; Santos, J P; Horrillo, M C

2005-09-15

This paper reports the use of a tin dioxide multisensor array based electronic nose for recognition of 29 typical aromas in white wine. Headspace technique has been used to extract aroma of the wine. Multivariate analysis, including principal component analysis (PCA) as well as probabilistic neural networks (PNNs), has been used to identify the main aroma added to the wine. The results showed that in spite of the strong influence of ethanol and other majority compounds of wine, the system could discriminate correctly the aromatic compounds added to the wine with a minimum accuracy of 97.2%.

Diagnostic analysis of liver B ultrasonic texture features based on LM neural network

NASA Astrophysics Data System (ADS)

Chi, Qingyun; Hua, Hu; Liu, Menglin; Jiang, Xiuying

2017-03-01

In this study, B ultrasound images of 124 benign and malignant patients were randomly selected as the study objects. The B ultrasound images of the liver were treated by enhanced de-noising. By constructing the gray level co-occurrence matrix which reflects the information of each angle, Principal Component Analysis of 22 texture features were extracted and combined with LM neural network for diagnosis and classification. Experimental results show that this method is a rapid and effective diagnostic method for liver imaging, which provides a quantitative basis for clinical diagnosis of liver diseases.

Robust 2DPCA with non-greedy l1 -norm maximization for image analysis.

PubMed

Wang, Rong; Nie, Feiping; Yang, Xiaojun; Gao, Feifei; Yao, Minli

2015-05-01

2-D principal component analysis based on l1 -norm (2DPCA-L1) is a recently developed approach for robust dimensionality reduction and feature extraction in image domain. Normally, a greedy strategy is applied due to the difficulty of directly solving the l1 -norm maximization problem, which is, however, easy to get stuck in local solution. In this paper, we propose a robust 2DPCA with non-greedy l1 -norm maximization in which all projection directions are optimized simultaneously. Experimental results on face and other datasets confirm the effectiveness of the proposed approach.

Terminal steps of bacteriochlorophyll a phytol formation in purple photosynthetic bacteria

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

Shioi, Y.; Sasa, T.

1984-04-01

Four chemically different bacteriochlorophylls (Bchls) a esterified with geranylgeraniol, dihydrogeranyl-geraniol, tetrahydrogeraniol, and phytol have been detected by high-pressure liquid chromatography in cell extracts from Rhodopseudomonas sphaeroides and Chromatium vinosum. Bchl a containing phytol is the principal component, and the other three Bchls a comprise about 4% of the total Bchls a in stationary-phase cells of R. sphaeroides and C. vinosum. The high levels of the minor pigments occur in the beginning of Bchl a phytol formation, indicating that they are not degradation products, but intermediates of Bchl a phytol formation.

Methods for spectral image analysis by exploiting spatial simplicity

DOEpatents

Keenan, Michael R.

2010-05-25

Several full-spectrum imaging techniques have been introduced in recent years that promise to provide rapid and comprehensive chemical characterization of complex samples. One of the remaining obstacles to adopting these techniques for routine use is the difficulty of reducing the vast quantities of raw spectral data to meaningful chemical information. Multivariate factor analysis techniques, such as Principal Component Analysis and Alternating Least Squares-based Multivariate Curve Resolution, have proven effective for extracting the essential chemical information from high dimensional spectral image data sets into a limited number of components that describe the spectral characteristics and spatial distributions of the chemical species comprising the sample. There are many cases, however, in which those constraints are not effective and where alternative approaches may provide new analytical insights. For many cases of practical importance, imaged samples are "simple" in the sense that they consist of relatively discrete chemical phases. That is, at any given location, only one or a few of the chemical species comprising the entire sample have non-zero concentrations. The methods of spectral image analysis of the present invention exploit this simplicity in the spatial domain to make the resulting factor models more realistic. Therefore, more physically accurate and interpretable spectral and abundance components can be extracted from spectral images that have spatially simple structure.

Methods for spectral image analysis by exploiting spatial simplicity

DOEpatents

Keenan, Michael R.

2010-11-23

Several full-spectrum imaging techniques have been introduced in recent years that promise to provide rapid and comprehensive chemical characterization of complex samples. One of the remaining obstacles to adopting these techniques for routine use is the difficulty of reducing the vast quantities of raw spectral data to meaningful chemical information. Multivariate factor analysis techniques, such as Principal Component Analysis and Alternating Least Squares-based Multivariate Curve Resolution, have proven effective for extracting the essential chemical information from high dimensional spectral image data sets into a limited number of components that describe the spectral characteristics and spatial distributions of the chemical species comprising the sample. There are many cases, however, in which those constraints are not effective and where alternative approaches may provide new analytical insights. For many cases of practical importance, imaged samples are "simple" in the sense that they consist of relatively discrete chemical phases. That is, at any given location, only one or a few of the chemical species comprising the entire sample have non-zero concentrations. The methods of spectral image analysis of the present invention exploit this simplicity in the spatial domain to make the resulting factor models more realistic. Therefore, more physically accurate and interpretable spectral and abundance components can be extracted from spectral images that have spatially simple structure.

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.

Inhibition of Cytochrome P450 (CYP3A4) Activity by Extracts from 57 Plants Used in Traditional Chinese Medicine (TCM)

PubMed Central

Ashour, Mohamed L; Youssef, Fadia S; Gad, Haidy A; Wink, Michael

2017-01-01

Background: Herbal medicine is widely used all over the world for treating various health disorders. It is employed either alone or in combination with synthetic drugs or plants to be more effective. Objective: The assessment of the effect of both water and methanol extracts of 57 widely used plants from Traditional Chinese Medicine (TCM) against the main phase I metabolizing enzyme CYP3A4 in vitro for the first time. Materials and Methods: The inhibition of cytochrome P450 activity was evaluated using a luminescence assay. The principal component analysis (PCA) was used to correlate the inhibitory activity with the main secondary metabolites present in the plant extracts. Molecular modeling studies on CYP3A4 (PDB ID 4NY4) were carried out with 38 major compounds present in the most active plant extracts to validate the observed inhibitory effect. Results: Aqueous extracts of Acacia catechu, Andrographis paniculata, Arctium lappa, Areca catechu, Bupleurum marginatum, Chrysanthemum indicum, Dysosma versipellis, and Spatholobus suberectus inhibited CYP3A4 is more than 85% (at a dose of 100 μg/mL). The corresponding methanol extracts of A. catechu, A. paniculata, A. catechu, Mahonia bealei, and Sanguisorba officinalis inhibited the enzyme by more than 50%. Molecular modeling studies revealed that two polyphenols, namely hesperidin and rutin, revealed the highest fitting scores in the active sites of the CYP3A4 with binding energies equal to -74.09 and -71.34 kcal/mol, respectively. Conclusion: These results provide evidence that many TCM plants can inhibit CYP3A4, which might cause a potential interference with the metabolism of other concomitantly administered herbs or drugs. SUMMARY In this study, the inhibitory activity of the aqueous and methanol extracts of 57 widely used plants from Traditional Chinese Medicine (TCM) against the main phase I metabolizing enzyme CYP3A4 was tested in vitro for the first time.Aqueous extracts of Acacia catechu, Andrographis paniculata, Arctium lappa, Areca catechu, Bupleurum marginatum, Dysosma versipellis, and Spatholobus suberectus inhibited CYP3A4 by more than 85% (at a dose of 100 μg/mL).The activity could be attributed to the presence of polyphenolics as revealed from the multivariate chemometric analysis and molecular modeling study.These results provide evidence that many TCM plants can inhibit CYP3A4, which might cause a potential interference with the metabolism of other concomitantly administered herbs or drugs. Abbreviation used: CHARMm: Chemistry at HARvard Macromolecular Mechanics, CYP: Cytochrome P450, DMSO: Dimethyl Sulfoxide, PCA: Principal Component Analysis, PDB: Protein Data Bank, TCM: Traditional Chinese Medicine PMID:28539725

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

USGS Publications Warehouse

Brown, C. Erwin

1993-01-01

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

The quality of social relationships in ravens

PubMed Central

Fraser, Orlaith N.; Bugnyar, Thomas

2015-01-01

The quality of a social relationship represents the history of past social interactions between two individuals, from which the nature and outcome of future interactions can be predicted. Current theory predicts that relationship quality comprises three separate components, its value, compatibility and security. This study is the first to investigate the components of relationship quality in a large-brained bird. Following methods recently used to obtain quantitative measures of each relationship quality component in chimpanzees, Pan troglodytes, we entered data on seven behavioural variables from a group of 11 ravens, Corvus corax, into a principal components analysis. The characteristics of the extracted components matched those predicted for value, compatibility and security, and were labelled as such. When the effects of kinship and sex combination on each relationship quality component were analysed, we found that kin had more valuable relationships, whereas females had less secure and compatible relationships, although the effect of sex combination on compatibility only applied to nonkin. These patterns are consistent with what little knowledge we have of raven relationships from aviary studies and show that the components of relationship quality in ravens may indeed be analogous to those in chimpanzees. PMID:25821236

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...

An Efficient Method for Automatic Road Extraction Based on Multiple Features from LiDAR Data

NASA Astrophysics Data System (ADS)

Li, Y.; Hu, X.; Guan, H.; Liu, P.

2016-06-01

The road extraction in urban areas is difficult task due to the complicated patterns and many contextual objects. LiDAR data directly provides three dimensional (3D) points with less occlusions and smaller shadows. The elevation information and surface roughness are distinguishing features to separate roads. However, LiDAR data has some disadvantages are not beneficial to object extraction, such as the irregular distribution of point clouds and lack of clear edges of roads. For these problems, this paper proposes an automatic road centerlines extraction method which has three major steps: (1) road center point detection based on multiple feature spatial clustering for separating road points from ground points, (2) local principal component analysis with least squares fitting for extracting the primitives of road centerlines, and (3) hierarchical grouping for connecting primitives into complete roads network. Compared with MTH (consist of Mean shift algorithm, Tensor voting, and Hough transform) proposed in our previous article, this method greatly reduced the computational cost. To evaluate the proposed method, the Vaihingen data set, a benchmark testing data provided by ISPRS for "Urban Classification and 3D Building Reconstruction" project, was selected. The experimental results show that our method achieve the same performance by less time in road extraction using LiDAR data.

Combination of HPLC chromatogram and hypoglycemic effect identifies isoflavones as the principal active fraction of Belamcanda chinensis leaf extract in diabetes treatment.

PubMed

Chen, Yan; Wu, Chong-Ming; Dai, Rong-Ji; Li, Liang; Yu, Yu-Hong; Li, Yan; Meng, Wei-Wei; Zhang, Liang; Zhang, Yongqian; Deng, Yu-Lin

2011-02-15

In previous study, we demonstrated the hypoglycemic effect of aqueous extract of Belamcanda chinensis leaves in rats. Here, we separated the aqueous extract of B. chinensis leaves and investigated the spectrum-effect relationships between HPLC chromatograms and hypoglycemic activities of different isolates from B. chinensis leaf extract. Sequential solvent extraction with petroleum ether, chloroform, acetic ester and n-butanol provided several isolates showing similar hypoglycemic activities, making it difficult to discriminate the active fractions. Stepwise elution through HP20 macroporous resin by water, 40% and 95% ethanol provided isolates with distinct hypoglycemic activities, representing a simple, rapid and efficient preparative separation method. Combination of HPLC chromatogram and pharmacological effect targeted a hypoglycemic activity-related region in HPLC chromatogram. Each peak in this region was analyzed by UV spectrum scan. Most of them were flavonoids in which tectoridin and swertisin were known flavonoids with anti-diabetic activities. In together, this work provides a general model of combination of HPLC chromatography and pharmacological effect to study the spectrum-effect relationships of aqueous extract from B. chinensis leaves, which can be used to find principle components of B. chinensis on pharmacological activity. Copyright © 2011 Elsevier B.V. All rights reserved.

Undergraduate nursing assistant employment in aged care has benefits for new graduates.

PubMed

Algoso, Maricris; Ramjan, Lucie; East, Leah; Peters, Kath

2018-04-20

To determine how undergraduate assistant in nursing employment in aged care helps to prepare new graduates for clinical work as a registered nurse. The amount and quality of clinical experience afforded by university programs has been the subject of constant debate in the nursing profession. New graduate nurses are often deemed inadequately prepared for clinical practice and so many nursing students seek employment as assistants in nursing whilst studying to increase their clinical experience. This paper presents the first phase of a larger mixed-methods study to explore whether undergraduate assistant in nursing employment in aged care prepares new graduate nurses for the clinical work environment. The first phase involved the collection of quantitative data from a modified Preparation for Clinical Practice survey, which contained 50-scaled items relating to nursing practice. Ethics approval was obtained prior to commencing data collection. New graduate nurses who were previously employed as assistants in nursing in aged care and had at least 3 months' experience as a registered nurse, were invited to complete the survey. Social media and professional networks were used to distribute the survey between March 2015 and May 2016 and again in January 2017 - February 2017. Purposeful and snowballing sampling methods using social media and nursing networks were used to collect survey responses. Data were analysed using principal components analysis. 110 completed surveys were returned. Principal components analysis revealed four underlying constructs (components) of undergraduate assistant in nursing employment in aged care. These were emotional literacy (component 1), clinical skills (component 2), managing complex patient care (component 3) and health promotion (component 4). The 4 extracted components reflect the development of core nursing skills that transcend that of technical skills and includes the ability to situate oneself as a nurse in the care of an individual and in a healthcare team. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Effect of Different Solvents on the Measurement of Phenolics and the Antioxidant Activity of Mulberry (Morus atropurpurea Roxb.) with Accelerated Solvent Extraction.

PubMed

Yang, Jiufang; Ou, XiaoQun; Zhang, Xiaoxu; Zhou, ZiYing; Ma, LiYan

2017-03-01

The effects of 9 different solvents on the measurement of the total phenolics and antioxidant activities of mulberry fruits were studied using accelerated solvent extraction (ASE). Sixteen to 22 types of phenolics (flavonols, flavan-3-ols, flavanol, hydroxycinnamic acids, hydroxybenzoic acids, and stilbenes) from different mulberry extracts were characterized and quantified using HPLC-MS/MS. The principal component analysis (PCA) was used to determine the suitable solvents to distinguish between different classes of phenolics. Additionally, the phenolic extraction abilities of ASE and ultrasound-assisted extraction (UAE) were compared. The highest extraction efficiency could be achieved by using 50% acidified methanol (50MA) as ASE solvents with 15.14 mg/gallic acid equivalents g dry weight of mulberry fruit. The PCA results revealed that the 50MA followed by 50% acidified acetone (50AA) was the most efficient solvent for the extraction of phenolics, particularly flavonols (627.12 and 510.31 μg/g dry weight, respectively), while water (W) was not beneficial to the extraction of all categories of phenolics. Besides, the results of 3 antioxidant capability assays (DPPH, ABTS free radical-scavenging assay, and ferric-reducing antioxidant power assay) showed that water-based organic solvents increased the antioxidant capabilities of the extracts compared with water or pure organic solvents. ASE was more suitable for the extraction of phenolics than UAE. © 2017 Institute of Food Technologists®.

On reliable time-frequency characterization and delay estimation of stimulus frequency otoacoustic emissions

NASA Astrophysics Data System (ADS)

Biswal, Milan; Mishra, Srikanta

2018-05-01

The limited information on origin and nature of stimulus frequency otoacoustic emissions (SFOAEs) necessitates a thorough reexamination into SFOAE analysis procedures. This will lead to a better understanding of the generation of SFOAEs. The SFOAE response waveform in the time domain can be interpreted as a summation of amplitude modulated and frequency modulated component waveforms. The efficiency of a technique to segregate these components is critical to describe the nature of SFOAEs. Recent advancements in robust time-frequency analysis algorithms have staked claims on the more accurate extraction of these components, from composite signals buried in noise. However, their potential has not been fully explored for SFOAEs analysis. Indifference to distinct information, due to nature of these analysis techniques, may impact the scientific conclusions. This paper attempts to bridge this gap in literature by evaluating the performance of three linear time-frequency analysis algorithms: short-time Fourier transform (STFT), continuous Wavelet transform (CWT), S-transform (ST) and two nonlinear algorithms: Hilbert-Huang Transform (HHT), synchrosqueezed Wavelet transform (SWT). We revisit the extraction of constituent components and estimation of their magnitude and delay, by carefully evaluating the impact of variation in analysis parameters. The performance of HHT and SWT from the perspective of time-frequency filtering and delay estimation were found to be relatively less efficient for analyzing SFOAEs. The intrinsic mode functions of HHT does not completely characterize the reflection components and hence IMF based filtering alone, is not recommended for segregating principal emission from multiple reflection components. We found STFT, WT, and ST to be suitable for canceling multiple internal reflection components with marginal altering in SFOAE.

Fine-grained information extraction from German transthoracic echocardiography reports.

PubMed

Toepfer, Martin; Corovic, Hamo; Fette, Georg; Klügl, Peter; Störk, Stefan; Puppe, Frank

2015-11-12

Information extraction techniques that get structured representations out of unstructured data make a large amount of clinically relevant information about patients accessible for semantic applications. These methods typically rely on standardized terminologies that guide this process. Many languages and clinical domains, however, lack appropriate resources and tools, as well as evaluations of their applications, especially if detailed conceptualizations of the domain are required. For instance, German transthoracic echocardiography reports have not been targeted sufficiently before, despite of their importance for clinical trials. This work therefore aimed at development and evaluation of an information extraction component with a fine-grained terminology that enables to recognize almost all relevant information stated in German transthoracic echocardiography reports at the University Hospital of Würzburg. A domain expert validated and iteratively refined an automatically inferred base terminology. The terminology was used by an ontology-driven information extraction system that outputs attribute value pairs. The final component has been mapped to the central elements of a standardized terminology, and it has been evaluated according to documents with different layouts. The final system achieved state-of-the-art precision (micro average.996) and recall (micro average.961) on 100 test documents that represent more than 90 % of all reports. In particular, principal aspects as defined in a standardized external terminology were recognized with f 1=.989 (micro average) and f 1=.963 (macro average). As a result of keyword matching and restraint concept extraction, the system obtained high precision also on unstructured or exceptionally short documents, and documents with uncommon layout. The developed terminology and the proposed information extraction system allow to extract fine-grained information from German semi-structured transthoracic echocardiography reports with very high precision and high recall on the majority of documents at the University Hospital of Würzburg. Extracted results populate a clinical data warehouse which supports clinical research.

Hierarchical Regularity in Multi-Basin Dynamics on Protein Landscapes

NASA Astrophysics Data System (ADS)

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

2004-04-01

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

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…

Assessing similarity analysis of chromatographic fingerprints of Cyclopia subternata extracts as potential screening tool for in vitro glucose utilisation.

PubMed

Schulze, Alexandra E; De Beer, Dalene; Mazibuko, Sithandiwe E; Muller, Christo J F; Roux, Candice; Willenburg, Elize L; Nyunaï, Nyemb; Louw, Johan; Manley, Marena; Joubert, Elizabeth

2016-01-01

Similarity analysis of the phenolic fingerprints of a large number of aqueous extracts of Cyclopia subternata, obtained by high-performance liquid chromatography (HPLC), was evaluated as a potential tool to screen extracts for relative bioactivity. The assessment was based on the (dis)similarity of their fingerprints to that of a reference active extract of C. subternata, proven to enhance glucose uptake in vitro and in vivo. In vitro testing of extracts, selected as being most similar (n = 5; r ≥ 0.962) and most dissimilar (n = 5; r ≤ 0.688) to the reference active extract, showed that no clear pattern in terms of relative glucose uptake efficacy in C2C12 myocytes emerged, irrespective of the dose. Some of the most dissimilar extracts had higher glucose-lowering activity than the reference active extract. Principal component analysis revealed the major compounds responsible for the most variation within the chromatographic fingerprints, as mangiferin, isomangiferin, iriflophenone-3-C-β-D-glucoside-4-O-β-D-glucoside, iriflophenone-3-C-β-D-glucoside, scolymoside, and phloretin-3',5'-di-C-β-D-glucoside. Quantitative analysis of the selected extracts showed that the most dissimilar extracts contained the highest mangiferin and isomangiferin levels, whilst the most similar extracts had the highest scolymoside content. These compounds demonstrated similar glucose uptake efficacy in C2C12 myocytes. It can be concluded that (dis)similarity of chromatographic fingerprints of extracts of unknown activity to that of a proven bioactive extract does not necessarily translate to lower or higher bioactivity.

Unique and shared areas of cognitive function in children with intractable frontal or temporal lobe epilepsy.

PubMed

Law, Nicole; Widjaja, Elysa; Smith, Mary Lou

2018-03-01

Previous findings have been mixed in terms of identifying a distinct pattern of neuropsychological deficits in children with frontal lobe epilepsy (FLE) and in those with temporal lobe epilepsy (TLE). The current study investigated the neuropsychological similarities and differences across these two pediatric medically intractable localization-related epilepsies. Thirty-eight children with FLE, 20 children with TLE, and 40 healthy children (HC) participated in this study. A comprehensive battery of standardized tests assessed five neuropsychological domains including intelligence, language, memory, executive function, and motor function. A principal component analysis (PCA) was used to distill our neuropsychological measures into latent components to compare between groups. Principal component analysis extracted 5 latent components: executive function (F1), verbal semantics (F2), motor (F3), nonverbal cognition/impulsivity (F4), and verbal cognition/attention (F5). The group with FLE differed from the HC group on F1, F2, F4, and F5, and had worse performance than the group with TLE on F1; the group with TLE had lower performance relative to the HC group on F2. Our findings suggest that, in comparison with neurotypically developing children, children with medically intractable FLE have more widespread neuropsychological impairments than do children with TLE. The differences between the two patient groups were greatest for the factor score most clearly related to executive function. The results provide mixed support for the concept of specificity in neuropsychological dysfunction among different subtypes of localization-related medically intractable childhood epilepsies. Copyright © 2018 Elsevier Inc. All rights reserved.

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

PubMed

Nguyen, Phuong H

2007-05-15

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

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…

A simplified soil extraction sequence to monitor the main and trace element speciation in soil after compost and mineral fertilizer additions upon the composition of wheat grains

NASA Astrophysics Data System (ADS)

Sager, Manfred; Erhart, Eva

2016-04-01

High quality biological waste treatment aims at producing compost in order to maintain a clean environment and to sustain soil organic carbon levels. Fertilization with compost as a source of organic carbon, nutrients, and accessory elements, as well as fertilization with mineral N- and PK fertilizer have been tested in a field experiment on a calcaric Fluvisol in the Danube wetlands, at 4 levels each. Yields of wheat were recorded, and grains and soils were sampled from each treatment, and analyzed for main and trace element composition. The corresponding soils were characterized by mobile phases, obtained by leaching with 0,16M acetic acid to cover exchangeables plus carbonates, and subsequently by 0,1M oxalate buffer pH 3 to dissolve the pedogenic oxides. Total amounts were obtained from digests with perchloric- nitric-hydrofluoric acid. For quasi-total amounts, aqua regia was replaced by pressure decomposition with KClO3 in dilute nitric acid. The proposed extraction sequence permits to analyze and interpret soil for main elements, trace elements, nutrients and anions simultaneously. Factor analyses of soil extracts obtained from dilute acetic acid revealed Ba-Be-Cd-Cu-Li-S (traces), Ca-Mg-Mn (main carbonates), Al-Fe-B, Y, and P-K (nutrients) as chemically feasible principal components. Subsequent soil extracts from oxalate contained Al-B-Co-K-Na-Pb-Si-V-S (maybe acid silicate weathering), Cr-Li-Ni-Sr-Ti (maybe basic silicate weathering), Be-Cu-Fe-P, Co-Mg-Mn-Zn (Mn-oxides) and Ba-Sc as principal components. Factor analyses of total element data distinguished the principal components Ce-La-Li-Sc-Y-P (rare earths), Al-Ca-Fe-K-Mg-Na-P (main elements), Cd-Co-Cr-Cu-Ni-Zn (trace elements), As-Pb (contaminants), Ba-Mn-Sr, and Ti, which looks chemically feasible also. Factor analyses of those soil fractions which presumably form the main fractions of exchangeables, carbonates, pedogenic oxides and silicates, showed no cross connections, except for P. Oxalate-soluble Fe together with P and S was independent from oxalate-soluble Al-Mn-Si. In the crops, all element levels were within a non-contaminated and non-deficient range, therefore correlations with concentrations as well as loads in the wheat grains where largely not pronounced. Maximum correlations between plant and soil data were obtained with Li and Be. The load data (concentration times yield, given in g/ha) were much more intercorrelated than the concentrations. Regarding the same element, correlation coefficients between loads and respective concentrations were larger than 0,800 for Al, Ba, Cd, Co, Cr, Li, Mo, Na, Ni, Se, and Sr, which means the transfer remained independent from the load. In case of Ca, Mg, P, S, Zn, however, correlation coefficients between loads and concentrations were < 0,500, thus the transfer was not constant because of obvious metabolic influences. The proposed method of soil characterization was applied at a field trial here for the first time, and offers new possibilities of intercorrelations between plant uptake and geochemical soil fractions.

The standard aqueous stem bark extract of Mangifera indica L. inhibits toxic PLA2 - NN-XIb-PLA2 of Indian cobra venom.

PubMed

Dhananjaya, Bhadrapura Lakkappa; Sudarshan, Shivalingaiah; Dongol, Yashad; More, Sunil S

2016-05-01

The aqueous extract of Mangifera indica is known to possess diverse medicinal properties, which also includes anti-snake venom activities. However, its inhibitory potency and mechanism of action on multi-toxic snake venom phospholipases A2s are still unknown. Therefore, the objective of this study was to evaluate the modulatory effect of standard aqueous bark extract of M. indica on NN-XIb-PLA2 of Indian cobra venom. The in vitro sPLA2, in situ hemolytic and in vivo edema inhibition effect were carried out as described. Also the effect of substrate and calcium concentration was carried out. M. indica extract dose dependently inhibited the GIA sPLA2 (NN-XIb-PLA2) activity with an IC50 value of 7.6 μg/ml. M. indica extract effectively inhibited the indirect hemolytic activity up to 98% at ∼40 μg/ml concentration. Further, M. indica extract (0-50 μg/ml) inhibited the edema formed in a dose dependent manner. When examined as a function of increased substrate and calcium concentration, there was no relieve of inhibitory effect of M. indica extract on the NN-XIb-PLA2. Further, the inhibition was irreversible as evident from binding studies. The in vitro inhibition is well correlated with in situ and in vivo edema inhibiting activities of M. indica. As the inhibition is independent of substrate and calcium and was irreversible, it can be concluded that M. indica extract mode of inhibition could be due to direct interaction of components present in the extract with the PLA2 enzyme. The aqueous extract of M. indica effectively inhibits svPLA2 enzymatic and its associated toxic activities, which substantiate their anti-snake venom properties. Further in-depth studies on the role and mechanism of the principal constituents present in the extract, responsible for the anti-PLA2 activity will be interesting to develop them into potent antisnake component and also as an anti-inflammatory agent.

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

The anti-inflammatory activity of standard aqueous stem bark extract of Mangifera indica L. as evident in inhibition of Group IA sPLA2.

PubMed

Dhananjaya, Bhadrapura Lakkappa; Shivalingaiah, Sudharshan

2016-03-01

The standard aqueous stem bark extract is consumed as herbal drink and used in the pharmaceutical formulations to treat patients suffering from various disease conditions in Cuba. This study was carried out to evaluate the modulatory effect of standard aqueous bark extract of M. indica on Group IA sPLA2. M. indica extract, dose dependently inhibited the GIA sPLA2 (NN-XIa-PLA2) activity with an IC50 value 8.1 µg/ml. M. indica extract effectively inhibited the indirect hemolytic activity up to 98% at ~40 µg/ml concentration and at various concentrations (0-50 µg/ml), it dose dependently inhibited the edema formation. When examined as a function of increased substrate and calcium concentration, there was no relieve of inhibitory effect on the GIA sPLA2. Furthermore, the inhibition was irreversible as evidenced from binding studies. It is observed that the aqueous extract ofM. indica effectively inhibits sPLA2 and it is associated inflammatory activities, which substantiate their anti-inflammatory properties. The mode of inhibition could be due to direct interaction of components present in the extract, with sPLA2 enzyme. Further studies on understanding the principal constituents, responsible for the anti-inflammatory activity would be interesting to develop this into potent anti-inflammatory agent.

Extraction methods of Amaranthus sp. grain oil isolation.

PubMed

Krulj, Jelena; Brlek, Tea; Pezo, Lato; Brkljača, Jovana; Popović, Sanja; Zeković, Zoran; Bodroža Solarov, Marija

2016-08-01

Amaranthus sp. is a fast-growing crop with well-known beneficial nutritional values (rich in protein, fat, dietary fiber, ash, and minerals, especially calcium and sodium, and containing a higher amount of lysine than conventional cereals). Amaranthus sp. is an underexploited plant source of squalene, a compound of high importance in the food, cosmetic and pharmaceutical industries. This paper has examined the effects of the different extraction methods (Soxhlet, supercritical fluid and accelerated solvent extraction) on the oil and squalene yield of three genotypes of Amaranthus sp. grain. The highest yield of the extracted oil (78.1 g kg(-1) ) and squalene (4.7 g kg(-1) ) in grain was obtained by accelerated solvent extraction (ASE) in genotype 16. Post hoc Tukey's HSD test at 95% confidence limit showed significant differences between observed samples. Principal component analysis (PCA) and cluster analysis (CA) were used for assessing the effect of different genotypes and extraction methods on oil and squalene yield, and also the fatty acid composition profile. Using coupled PCA and CA of observed samples, possible directions for improving the quality of product can be realized. The results of this study indicate that it is very important to choose both the right genotype and the right method of extraction for optimal oil and squalene yield. © 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.

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).

1 H NMR study and multivariate data analysis of reindeer skin tanning methods.

PubMed

Zhu, Lizheng; Ilott, Andrew J; Del Federico, Eleonora; Kehlet, Cindie; Klokkernes, Torunn; Jerschow, Alexej

2017-04-01

Reindeer skin clothing has been an essential component in the lives of indigenous people of the arctic and sub-arctic regions, keeping them warm during harsh winters. However, the skin processing technology, which often conveys the history and tradition of the indigenous group, has not been well documented. In this study, NMR spectra and relaxation behaviors of reindeer skin samples treated with a variety of vegetable tannin extracts, oils and fatty substances are studied and compared. With the assistance of principal component analysis (PCA), one can recognize patterns and identify groupings of differently treated samples. These methods could be important aids in efforts to conserve museum leather artifacts with unknown treatment methods and in the analysis of reindeer skin tanning processes. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Metabolomics-Driven Nutraceutical Evaluation of Diverse Green Tea Cultivars

PubMed Central

Ida, Megumi; Kosaka, Reia; Miura, Daisuke; Wariishi, Hiroyuki; Maeda-Yamamoto, Mari; Nesumi, Atsushi; Saito, Takeshi; Kanda, Tomomasa; Yamada, Koji; Tachibana, Hirofumi

2011-01-01

Background Green tea has various health promotion effects. Although there are numerous tea cultivars, little is known about the differences in their nutraceutical properties. Metabolic profiling techniques can provide information on the relationship between the metabolome and factors such as phenotype or quality. Here, we performed metabolomic analyses to explore the relationship between the metabolome and health-promoting attributes (bioactivity) of diverse Japanese green tea cultivars. Methodology/Principal Findings We investigated the ability of leaf extracts from 43 Japanese green tea cultivars to inhibit thrombin-induced phosphorylation of myosin regulatory light chain (MRLC) in human umbilical vein endothelial cells (HUVECs). This thrombin-induced phosphorylation is a potential hallmark of vascular endothelial dysfunction. Among the tested cultivars, Cha Chuukanbohon Nou-6 (Nou-6) and Sunrouge (SR) strongly inhibited MRLC phosphorylation. To evaluate the bioactivity of green tea cultivars using a metabolomics approach, the metabolite profiles of all tea extracts were determined by high-performance liquid chromatography-mass spectrometry (LC-MS). Multivariate statistical analyses, principal component analysis (PCA) and orthogonal partial least-squares-discriminant analysis (OPLS-DA), revealed differences among green tea cultivars with respect to their ability to inhibit MRLC phosphorylation. In the SR cultivar, polyphenols were associated with its unique metabolic profile and its bioactivity. In addition, using partial least-squares (PLS) regression analysis, we succeeded in constructing a reliable bioactivity-prediction model to predict the inhibitory effect of tea cultivars based on their metabolome. This model was based on certain identified metabolites that were associated with bioactivity. When added to an extract from the non-bioactive cultivar Yabukita, several metabolites enriched in SR were able to transform the extract into a bioactive extract. Conclusions/Significance Our findings suggest that metabolic profiling is a useful approach for nutraceutical evaluation of the health promotion effects of diverse tea cultivars. This may propose a novel strategy for functional food design. PMID:21853132

[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.

Modelling Nonlinear Dynamic Textures using Hybrid DWT-DCT and Kernel PCA with GPU

NASA Astrophysics Data System (ADS)

Ghadekar, Premanand Pralhad; Chopade, Nilkanth Bhikaji

2016-12-01

Most of the real-world dynamic textures are nonlinear, non-stationary, and irregular. Nonlinear motion also has some repetition of motion, but it exhibits high variation, stochasticity, and randomness. Hybrid DWT-DCT and Kernel Principal Component Analysis (KPCA) with YCbCr/YIQ colour coding using the Dynamic Texture Unit (DTU) approach is proposed to model a nonlinear dynamic texture, which provides better results than state-of-art methods in terms of PSNR, compression ratio, model coefficients, and model size. Dynamic texture is decomposed into DTUs as they help to extract temporal self-similarity. Hybrid DWT-DCT is used to extract spatial redundancy. YCbCr/YIQ colour encoding is performed to capture chromatic correlation. KPCA is applied to capture nonlinear motion. Further, the proposed algorithm is implemented on Graphics Processing Unit (GPU), which comprise of hundreds of small processors to decrease time complexity and to achieve parallelism.

Computationally Efficient Clustering of Audio-Visual Meeting Data

NASA Astrophysics Data System (ADS)

Hung, Hayley; Friedland, Gerald; Yeo, Chuohao

This chapter presents novel computationally efficient algorithms to extract semantically meaningful acoustic and visual events related to each of the participants in a group discussion using the example of business meeting recordings. The recording setup involves relatively few audio-visual sensors, comprising a limited number of cameras and microphones. We first demonstrate computationally efficient algorithms that can identify who spoke and when, a problem in speech processing known as speaker diarization. We also extract visual activity features efficiently from MPEG4 video by taking advantage of the processing that was already done for video compression. Then, we present a method of associating the audio-visual data together so that the content of each participant can be managed individually. The methods presented in this article can be used as a principal component that enables many higher-level semantic analysis tasks needed in search, retrieval, and navigation.

Static terrestrial laser scanning of juvenile understory trees for field phenotyping

NASA Astrophysics Data System (ADS)

Wang, Huanhuan; Lin, Yi

2014-11-01

This study was to attempt the cutting-edge 3D remote sensing technique of static terrestrial laser scanning (TLS) for parametric 3D reconstruction of juvenile understory trees. The data for test was collected with a Leica HDS6100 TLS system in a single-scan way. The geometrical structures of juvenile understory trees are extracted by model fitting. Cones are used to model trunks and branches. Principal component analysis (PCA) is adopted to calculate their major axes. Coordinate transformation and orthogonal projection are used to estimate the parameters of the cones. Then, AutoCAD is utilized to simulate the morphological characteristics of the understory trees, and to add secondary branches and leaves in a random way. Comparison of the reference values and the estimated values gives the regression equation and shows that the proposed algorithm of extracting parameters is credible. The results have basically verified the applicability of TLS for field phenotyping of juvenile understory trees.

Sunflower seeds as eliciting agents of Compositae dermatitis.

PubMed

Paulsen, Evy; El-Houri, Rime B; Andersen, Klaus E; Christensen, Lars P

2015-03-01

Sunflowers may cause dermatitis because of allergenic sesquiterpene lactones (SLs). Contact sensitization to sunflower seeds has also been reported, but the allergens are unknown. To analyse sunflower seeds for the presence of SLs and to assess the prevalence of sunflower sensitization in Compositae-allergic individuals. Sunflower-sensitive patients were identified by aimed patch testing. A dichloromethane extract of whole sunflower seeds was analysed by liquid chromatography-mass spectrometry and high-performance liquid chromatography. The prevalence of sensitivity to sunflower in Compositae-allergic individuals was 56%. A solvent wash of whole sunflower seeds yielded an extract containing SLs, the principal component tentatively being identified as argophyllin A or B, other SLs being present in minute amounts. The concentration of SLs on the sunflower seeds is considered high enough to elicit dermatitis in sensitive persons, and it seems appropriate to warn Compositae-allergic subjects against handling sunflower seeds. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Extracting insights from the shape of complex data using topology

PubMed Central

Lum, P. Y.; Singh, G.; Lehman, A.; Ishkanov, T.; Vejdemo-Johansson, M.; Alagappan, M.; Carlsson, J.; Carlsson, G.

2013-01-01

This paper applies topological methods to study complex high dimensional data sets by extracting shapes (patterns) and obtaining insights about them. Our method combines the best features of existing standard methodologies such as principal component and cluster analyses to provide a geometric representation of complex data sets. Through this hybrid method, we often find subgroups in data sets that traditional methodologies fail to find. Our method also permits the analysis of individual data sets as well as the analysis of relationships between related data sets. We illustrate the use of our method by applying it to three very different kinds of data, namely gene expression from breast tumors, voting data from the United States House of Representatives and player performance data from the NBA, in each case finding stratifications of the data which are more refined than those produced by standard methods. PMID:23393618

PEM-PCA: a parallel expectation-maximization PCA face recognition architecture.

PubMed

Rujirakul, Kanokmon; So-In, Chakchai; Arnonkijpanich, Banchar

2014-01-01

Principal component analysis or PCA has been traditionally used as one of the feature extraction techniques in face recognition systems yielding high accuracy when requiring a small number of features. However, the covariance matrix and eigenvalue decomposition stages cause high computational complexity, especially for a large database. Thus, this research presents an alternative approach utilizing an Expectation-Maximization algorithm to reduce the determinant matrix manipulation resulting in the reduction of the stages' complexity. To improve the computational time, a novel parallel architecture was employed to utilize the benefits of parallelization of matrix computation during feature extraction and classification stages including parallel preprocessing, and their combinations, so-called a Parallel Expectation-Maximization PCA architecture. Comparing to a traditional PCA and its derivatives, the results indicate lower complexity with an insignificant difference in recognition precision leading to high speed face recognition systems, that is, the speed-up over nine and three times over PCA and Parallel PCA.

Geographical identification of saffron (Crocus sativus L.) by linear discriminant analysis applied to the UV-visible spectra of aqueous extracts.

PubMed

D'Archivio, Angelo Antonio; Maggi, Maria Anna

2017-03-15

We attempted geographical classification of saffron using UV-visible spectroscopy, conventionally adopted for quality grading according to the ISO Normative 3632. We investigated 81 saffron samples produced in L'Aquila, Città della Pieve, Cascia, and Sardinia (Italy) and commercial products purchased in various supermarkets. Exploratory principal component analysis applied to the UV-vis spectra of saffron aqueous extracts revealed a clear differentiation of the samples belonging to different quality categories, but a poor separation according to the geographical origin of the spices. On the other hand, linear discriminant analysis based on 8 selected absorbance values, concentrated near 279, 305 and 328nm, allowed a good distinction of the spices coming from different sites. Under severe validation conditions (30% and 50% of saffron samples in the evaluation set), correct predictions were 85 and 83%, respectively. Copyright © 2016 Elsevier Ltd. All rights reserved.

Automated feature extraction in color retinal images by a model based approach.

PubMed

Li, Huiqi; Chutatape, Opas

2004-02-01

Color retinal photography is an important tool to detect the evidence of various eye diseases. Novel methods to extract the main features in color retinal images have been developed in this paper. Principal component analysis is employed to locate optic disk; A modified active shape model is proposed in the shape detection of optic disk; A fundus coordinate system is established to provide a better description of the features in the retinal images; An approach to detect exudates by the combined region growing and edge detection is proposed. The success rates of disk localization, disk boundary detection, and fovea localization are 99%, 94%, and 100%, respectively. The sensitivity and specificity of exudate detection are 100% and 71%, correspondingly. The success of the proposed algorithms can be attributed to the utilization of the model-based methods. The detection and analysis could be applied to automatic mass screening and diagnosis of the retinal diseases.

Extracting insights from the shape of complex data using topology.

PubMed

Lum, P Y; Singh, G; Lehman, A; Ishkanov, T; Vejdemo-Johansson, M; Alagappan, M; Carlsson, J; Carlsson, G

2013-01-01

This paper applies topological methods to study complex high dimensional data sets by extracting shapes (patterns) and obtaining insights about them. Our method combines the best features of existing standard methodologies such as principal component and cluster analyses to provide a geometric representation of complex data sets. Through this hybrid method, we often find subgroups in data sets that traditional methodologies fail to find. Our method also permits the analysis of individual data sets as well as the analysis of relationships between related data sets. We illustrate the use of our method by applying it to three very different kinds of data, namely gene expression from breast tumors, voting data from the United States House of Representatives and player performance data from the NBA, in each case finding stratifications of the data which are more refined than those produced by standard methods.

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.

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.

The software-cycle model for re-engineering and reuse

NASA Technical Reports Server (NTRS)

Bailey, John W.; Basili, Victor R.

1992-01-01

This paper reports on the progress of a study which will contribute to our ability to perform high-level, component-based programming by describing means to obtain useful components, methods for the configuration and integration of those components, and an underlying economic model of the costs and benefits associated with this approach to reuse. One goal of the study is to develop and demonstrate methods to recover reusable components from domain-specific software through a combination of tools, to perform the identification, extraction, and re-engineering of components, and domain experts, to direct the applications of those tools. A second goal of the study is to enable the reuse of those components by identifying techniques for configuring and recombining the re-engineered software. This component-recovery or software-cycle model addresses not only the selection and re-engineering of components, but also their recombination into new programs. Once a model of reuse activities has been developed, the quantification of the costs and benefits of various reuse options will enable the development of an adaptable economic model of reuse, which is the principal goal of the overall study. This paper reports on the conception of the software-cycle model and on several supporting techniques of software recovery, measurement, and reuse which will lead to the development of the desired economic model.

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…

Metabolite fingerprinting of Punica granatum L. (pomegranate) polyphenols by means of high-performance liquid chromatography with diode array and electrospray ionization-mass spectrometry detection.

PubMed

Brighenti, Virginia; Groothuis, Sebastiaan Frearick; Prencipe, Francesco Pio; Amir, Rachel; Benvenuti, Stefania; Pellati, Federica

2017-01-13

The present study was aimed at the development of a new analytical method for the comprehensive multi-component analysis of polyphenols in Punica granatum L. (pomegranate) juice and peel. While pomegranate juice was directly analysed after simple centrifugation, different extraction techniques, including maceration, heat reflux extraction, ultrasound-assisted extraction and microwave-assisted extraction, were compared in order to obtain a high yield of the target analytes from pomegranate peel. Dynamic maceration with a mixture of water and ethanol 80:20 (v/v) with 0.1% of hydrochloric acid as the extraction solvent provided the best result in terms of recovery of pomegranate secondary metabolites. The quali- and quantitative analysis of pomegranate polyphenols was performed by high-performance liquid chromatography with diode array and electrospray ionization-mass spectrometry detection. The application of fused-core column technology allowed us to obtain an improvement of the chromatographic performance in comparison with that of conventional particulate stationary phases, thus enabling a good separation of all constituents in a shorter time and with low solvent usage. The analytical method was completely validated to show compliance with the International Conference on Harmonization of Technical Requirements for the Registration of Pharmaceuticals for Human Use guidelines and successfully applied to the characterisation of commercial and experimental pomegranate samples, thus demonstrating its efficiency as a tool for the fingerprinting of this plant material. The quantitative data collected were submitted to principal component analysis, in order to highlight the possible presence of pomegranate samples with high content of secondary metabolites. From the statistical analysis, four experimental samples showed a notable content of bioactive compounds in the peels, while commercial ones still represent the best source of healthy juice. Copyright © 2016 Elsevier B.V. All rights reserved.

Discrimination and similarity evaluation of tissue-cultured and wild Dendrobium species using Fourier transform infrared spectroscopy

NASA Astrophysics Data System (ADS)

Chen, Nai-dong; Chen, Han; Li, Jun; Sang, Mang-mang; Ding, Shen; Yu, Hao

2015-04-01

The FTIR method was applied to evaluate the similarity of tissue-cultured and wild Dendrobium huoshanense C.Z. Tang et S.J. Cheng, Dendrobium officinale Kimura et Migo and Dendrobium moniliforme (Linn.) Sw and discriminate different Dendrobium species, especially D. huoshanense and its main goldbrick Dendrobium henanense J.L. Lu et L.X. Gao. Despite the general pattern of the IR spectra, different intensities, shapes and peak positions were found in the IR spectra of these samples, especially in the range of 1800-600 cm-1, which could be used to discriminate them. The methanol, aqueous extracting procedure and the second derivative transformation obviously enlarged the tiny spectral differences among these samples. The similarity evaluation based on the IR spectra and the second derivative IR spectrum revealed that the similarity of the methanol extracts between tissue-cultured and wild Dendrobiums might be lower than that between different Dendrobium species. The similarities of the powders and aqueous extracts between tissue-cultured and wild Dendrobiums were higher than those between different Dendrobium species. The further principal component analysis showed that the first three components explained 99.7%, 87.7% and 85.1% of data variance for powder, methanol extract and aqueous extract, respectively, demonstrating a good discrimination between samples. Our research suggested that the variations of secondary metabolites between different origins of the investigated Dendrobiums might be higher than what we had supposed. Tissue culture techniques were widely used in the conversation of rare and endangered medicinal amedica, however, our study suggested that the chemical constituents of tissue-cultured plants might be quite different from their wild correspondences.

Economic and cultural correlates of subjective wellbeing in countries using data from the Organisation for Economic Co-Operation and Development (OECD).

PubMed

Gaygisiz, Esma

2010-06-01

The correlations among indicators of objective well-being, cultural dimensions, and subjective well-being were investigated using Organisation for Economic Co-operation and Development (OECD) data from 35 countries. The subjective well-being measures included life satisfaction as well as six positive and six negative indexes of experience. Positive and negative experience scores were subjected to principal component analysis, and two positive experience components (labeled as "positive experiences" and "time management") and two negative experience components (labeled as "pain, worry, and sadness" and "anger and boredom") were extracted. Objective well-being included economic indicators, education, and health. The cultural variables included Hofstede's and Schwartz's cultural dimensions, national Big Five personality scores, and national IQs. High life satisfaction was positively related to Gross Domestic Product, life expectancy, education, individualism, affective and intellectual autonomy, egalitarianism, and conscientiousness, whereas low life satisfaction was related to unemployment, unequal income distribution, power distance, masculinity uncertainty avoidance, embeddedness, hierarchy, and neuroticism.

Application of the principal component analysis (PCA) to HVSR data aimed at the seismic characterization of earthquake prone areas

NASA Astrophysics Data System (ADS)

Paolucci, Enrico; Lunedei, Enrico; Albarello, Dario

2017-10-01

In this work, we propose a procedure based on principal component analysis on data sets consisting of many horizontal to vertical spectral ratio (HVSR or H/V) curves obtained by single-station ambient vibration acquisitions. This kind of analysis aimed at the seismic characterization of the investigated area by identifying sites characterized by similar HVSR curves. It also allows to extract the typical HVSR patterns of the explored area and to establish their relative importance, providing an estimate of the level of heterogeneity under the seismic point of view. In this way, an automatic explorative seismic characterization of the area becomes possible by only considering ambient vibration data. This also implies that the relevant outcomes can be safely compared with other available information (geological data, borehole measurements, etc.) without any conceptual trade-off. The whole algorithm is remarkably fast: on a common personal computer, the processing time takes few seconds for a data set including 100-200 HVSR measurements. The procedure has been tested in three study areas in the Central-Northern Italy characterized by different geological settings. Outcomes demonstrate that this technique is effective and well correlates with most significant seismostratigraphical heterogeneities present in each of the study areas.

Systematic comparison of the behaviors produced by computational models of epileptic neocortex.

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

Warlaumont, A. S.; Lee, H. C.; Benayoun, M.

2010-12-01

Two existing models of brain dynamics in epilepsy, one detailed (i.e., realistic) and one abstract (i.e., simplified) are compared in terms of behavioral range and match to in vitro mouse recordings. A new method is introduced for comparing across computational models that may have very different forms. First, high-level metrics were extracted from model and in vitro output time series. A principal components analysis was then performed over these metrics to obtain a reduced set of derived features. These features define a low-dimensional behavior space in which quantitative measures of behavioral range and degree of match to real data canmore » be obtained. The detailed and abstract models and the mouse recordings overlapped considerably in behavior space. Both the range of behaviors and similarity to mouse data were similar between the detailed and abstract models. When no high-level metrics were used and principal components analysis was computed over raw time series, the models overlapped minimally with the mouse recordings. The method introduced here is suitable for comparing across different kinds of model data and across real brain recordings. It appears that, despite differences in form and computational expense, detailed and abstract models do not necessarily differ in their behaviors.« less

Local Geographic Variation of Public Services Inequality: Does the Neighborhood Scale Matter?

PubMed Central

Wei, Chunzhu; Cabrera-Barona, Pablo; Blaschke, Thomas

2016-01-01

This study aims to explore the effect of the neighborhood scale when estimating public services inequality based on the aggregation of social, environmental, and health-related indicators. Inequality analyses were carried out at three neighborhood scales: the original census blocks and two aggregated neighborhood units generated by the spatial “k”luster analysis by the tree edge removal (SKATER) algorithm and the self-organizing map (SOM) algorithm. Then, we combined a set of health-related public services indicators with the geographically weighted principal components analyses (GWPCA) and the principal components analyses (PCA) to measure the public services inequality across all multi-scale neighborhood units. Finally, a statistical test was applied to evaluate the scale effects in inequality measurements by combining all available field survey data. We chose Quito as the case study area. All of the aggregated neighborhood units performed better than the original census blocks in terms of the social indicators extracted from a field survey. The SKATER and SOM algorithms can help to define the neighborhoods in inequality analyses. Moreover, GWPCA performs better than PCA in multivariate spatial inequality estimation. Understanding the scale effects is essential to sustain a social neighborhood organization, which, in turn, positively affects social determinants of public health and public quality of life. PMID:27706072

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

PubMed Central

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

2018-01-01

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

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

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

Application of kernel principal component analysis and computational machine learning to exploration of metabolites strongly associated with diet.

PubMed

Shiokawa, Yuka; Date, Yasuhiro; Kikuchi, Jun

2018-02-21

Computer-based technological innovation provides advancements in sophisticated and diverse analytical instruments, enabling massive amounts of data collection with relative ease. This is accompanied by a fast-growing demand for technological progress in data mining methods for analysis of big data derived from chemical and biological systems. From this perspective, use of a general "linear" multivariate analysis alone limits interpretations due to "non-linear" variations in metabolic data from living organisms. Here we describe a kernel principal component analysis (KPCA)-incorporated analytical approach for extracting useful information from metabolic profiling data. To overcome the limitation of important variable (metabolite) determinations, we incorporated a random forest conditional variable importance measure into our KPCA-based analytical approach to demonstrate the relative importance of metabolites. Using a market basket analysis, hippurate, the most important variable detected in the importance measure, was associated with high levels of some vitamins and minerals present in foods eaten the previous day, suggesting a relationship between increased hippurate and intake of a wide variety of vegetables and fruits. Therefore, the KPCA-incorporated analytical approach described herein enabled us to capture input-output responses, and should be useful not only for metabolic profiling but also for profiling in other areas of biological and environmental systems.

The Development and Validation of a Rapid Assessment Tool of Primary Care in China

PubMed Central

Mei, Jie; Liang, Yuan; Shi, LeiYu; Zhao, JingGe; Wang, YuTan; Kuang, Li

2016-01-01

Introduction. With Chinese health care reform increasingly emphasizing the importance of primary care, the need for a tool to evaluate primary care performance and service delivery is clear. This study presents a methodology for a rapid assessment of primary care organizations and service delivery in China. Methods. The study translated and adapted the Primary Care Assessment Tool-Adult Edition (PCAT-AE) into a Chinese version to measure core dimensions of primary care, namely, first contact, continuity, comprehensiveness, and coordination. A cross-sectional survey was conducted to assess the validity and reliability of the Chinese Rapid Primary Care Assessment Tool (CR-PCAT). Eight community health centers in Guangdong province have been selected to participate in the survey. Results. A total of 1465 effective samples were included for data analysis. Eight items were eliminated following principal component analysis and reliability testing. The principal component analysis extracted five multiple-item scales (first contact utilization, first contact accessibility, ongoing care, comprehensiveness, and coordination). The tests of scaling assumptions were basically met. Conclusion. The standard psychometric evaluation indicates that the scales have achieved relatively good reliability and validity. The CR-PCAT provides a rapid and reliable measure of four core dimensions of primary care, which could be applied in various scenarios. PMID:26885509

Evaluation of mass spectrometric data using principal component analysis for determination of the effects of organic lakes on protein binder identification.

PubMed

Hrdlickova Kuckova, Stepanka; Rambouskova, Gabriela; Hynek, Radovan; Cejnar, Pavel; Oltrogge, Doris; Fuchs, Robert

2015-11-01

Matrix-assisted laser desorption/ionisation-time of flight (MALDI-TOF) mass spectrometry is commonly used for the identification of proteinaceous binders and their mixtures in artworks. The determination of protein binders is based on a comparison between the m/z values of tryptic peptides in the unknown sample and a reference one (egg, casein, animal glues etc.), but this method has greater potential to study changes due to ageing and the influence of organic/inorganic components on protein identification. However, it is necessary to then carry out statistical evaluation on the obtained data. Before now, it has been complicated to routinely convert the mass spectrometric data into a statistical programme, to extract and match the appropriate peaks. Only several 'homemade' computer programmes without user-friendly interfaces are available for these purposes. In this paper, we would like to present our completely new, publically available, non-commercial software, ms-alone and multiMS-toolbox, for principal component analyses of MALDI-TOF MS data for R software, and their application to the study of the influence of heterogeneous matrices (organic lakes) for protein identification. Using this new software, we determined the main factors that influence the protein analyses of artificially aged model mixtures of organic lakes and fish glue, prepared according to historical recipes that were used for book illumination, using MALDI-TOF peptide mass mapping. Copyright © 2015 John Wiley & Sons, Ltd.

A Functional Monomer Is Not Enough: Principal Component Analysis of the Influence of Template Complexation in Pre-Polymerization Mixtures on Imprinted Polymer Recognition and Morphology

PubMed Central

Golker, Kerstin; Karlsson, Björn C. G.; Rosengren, Annika M.; Nicholls, Ian A.

2014-01-01

In this report, principal component analysis (PCA) has been used to explore the influence of template complexation in the pre-polymerization phase on template molecularly imprinted polymer (MIP) recognition and polymer morphology. A series of 16 bupivacaine MIPs were studied. The ethylene glycol dimethacrylate (EGDMA)-crosslinked polymers had either methacrylic acid (MAA) or methyl methacrylate (MMA) as the functional monomer, and the stoichiometry between template, functional monomer and crosslinker was varied. The polymers were characterized using radioligand equilibrium binding experiments, gas sorption measurements, swelling studies and data extracted from molecular dynamics (MD) simulations of all-component pre-polymerization mixtures. The molar fraction of the functional monomer in the MAA-polymers contributed to describing both the binding, surface area and pore volume. Interestingly, weak positive correlations between the swelling behavior and the rebinding characteristics of the MAA-MIPs were exposed. Polymers prepared with MMA as a functional monomer and a polymer prepared with only EGDMA were found to share the same characteristics, such as poor rebinding capacities, as well as similar surface area and pore volume, independent of the molar fraction MMA used in synthesis. The use of PCA for interpreting relationships between MD-derived descriptions of events in the pre-polymerization mixture, recognition properties and morphologies of the corresponding polymers illustrates the potential of PCA as a tool for better understanding these complex materials and for their rational design. PMID:25391043

Regional prioritisation of flood risk in mountainous areas

NASA Astrophysics Data System (ADS)

Rogelis, M. C.; Werner, M.; Obregón, N.; Wright, G.

2015-07-01

A regional analysis of flood risk was carried out in the mountainous area surrounding the city of Bogotá (Colombia). Vulnerability at regional level was assessed on the basis of a principal component analysis carried out with variables recognised in literature to contribute to vulnerability; using watersheds as the unit of analysis. The area exposed was obtained from a simplified flood analysis at regional level to provide a mask where vulnerability variables were extracted. The vulnerability indicator obtained from the principal component analysis was combined with an existing susceptibility indicator, thus providing an index that allows the watersheds to be prioritised in support of flood risk management at regional level. Results show that the components of vulnerability can be expressed in terms of four constituent indicators; socio-economic fragility, which is composed of demography and lack of well-being; lack of resilience, which is composed of education, preparedness and response capacity, rescue capacity, social cohesion and participation; and physical exposure is composed of exposed infrastructure and exposed population. A sensitivity analysis shows that the classification of vulnerability is robust for watersheds with low and high values of the vulnerability indicator, while some watersheds with intermediate values of the indicator are sensitive to shifting between medium and high vulnerability. The complex interaction between vulnerability and hazard is evidenced in the case study. Environmental degradation in vulnerable watersheds shows the influence that vulnerability exerts on hazard and vice versa, thus establishing a cycle that builds up risk conditions.

A functional monomer is not enough: principal component analysis of the influence of template complexation in pre-polymerization mixtures on imprinted polymer recognition and morphology.

PubMed

Golker, Kerstin; Karlsson, Björn C G; Rosengren, Annika M; Nicholls, Ian A

2014-11-10

In this report, principal component analysis (PCA) has been used to explore the influence of template complexation in the pre-polymerization phase on template molecularly imprinted polymer (MIP) recognition and polymer morphology. A series of 16 bupivacaine MIPs were studied. The ethylene glycol dimethacrylate (EGDMA)-crosslinked polymers had either methacrylic acid (MAA) or methyl methacrylate (MMA) as the functional monomer, and the stoichiometry between template, functional monomer and crosslinker was varied. The polymers were characterized using radioligand equilibrium binding experiments, gas sorption measurements, swelling studies and data extracted from molecular dynamics (MD) simulations of all-component pre-polymerization mixtures. The molar fraction of the functional monomer in the MAA-polymers contributed to describing both the binding, surface area and pore volume. Interestingly, weak positive correlations between the swelling behavior and the rebinding characteristics of the MAA-MIPs were exposed. Polymers prepared with MMA as a functional monomer and a polymer prepared with only EGDMA were found to share the same characteristics, such as poor rebinding capacities, as well as similar surface area and pore volume, independent of the molar fraction MMA used in synthesis. The use of PCA for interpreting relationships between MD-derived descriptions of events in the pre-polymerization mixture, recognition properties and morphologies of the corresponding polymers illustrates the potential of PCA as a tool for better understanding these complex materials and for their rational design.

Tailored multivariate analysis for modulated enhanced diffraction

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

Caliandro, Rocco; Guccione, Pietro; Nico, Giovanni

2015-10-21

Modulated enhanced diffraction (MED) is a technique allowing the dynamic structural characterization of crystalline materials subjected to an external stimulus, which is particularly suited forin situandoperandostructural investigations at synchrotron sources. Contributions from the (active) part of the crystal system that varies synchronously with the stimulus can be extracted by an offline analysis, which can only be applied in the case of periodic stimuli and linear system responses. In this paper a new decomposition approach based on multivariate analysis is proposed. The standard principal component analysis (PCA) is adapted to treat MED data: specific figures of merit based on their scoresmore » and loadings are found, and the directions of the principal components obtained by PCA are modified to maximize such figures of merit. As a result, a general method to decompose MED data, called optimum constrained components rotation (OCCR), is developed, which produces very precise results on simulated data, even in the case of nonperiodic stimuli and/or nonlinear responses. The multivariate analysis approach is able to supply in one shot both the diffraction pattern related to the active atoms (through the OCCR loadings) and the time dependence of the system response (through the OCCR scores). When applied to real data, OCCR was able to supply only the latter information, as the former was hindered by changes in abundances of different crystal phases, which occurred besides structural variations in the specific case considered. To develop a decomposition procedure able to cope with this combined effect represents the next challenge in MED analysis.« less

Developing a complex independent component analysis technique to extract non-stationary patterns from geophysical time-series

NASA Astrophysics Data System (ADS)

Forootan, Ehsan; Kusche, Jürgen

2016-04-01

Geodetic/geophysical observations, such as the time series of global terrestrial water storage change or sea level and temperature change, represent samples of physical processes and therefore contain information about complex physical interactionswith many inherent time scales. Extracting relevant information from these samples, for example quantifying the seasonality of a physical process or its variability due to large-scale ocean-atmosphere interactions, is not possible by rendering simple time series approaches. In the last decades, decomposition techniques have found increasing interest for extracting patterns from geophysical observations. Traditionally, principal component analysis (PCA) and more recently independent component analysis (ICA) are common techniques to extract statistical orthogonal (uncorrelated) and independent modes that represent the maximum variance of observations, respectively. PCA and ICA can be classified as stationary signal decomposition techniques since they are based on decomposing the auto-covariance matrix or diagonalizing higher (than two)-order statistical tensors from centered time series. However, the stationary assumption is obviously not justifiable for many geophysical and climate variables even after removing cyclic components e.g., the seasonal cycles. In this paper, we present a new decomposition method, the complex independent component analysis (CICA, Forootan, PhD-2014), which can be applied to extract to non-stationary (changing in space and time) patterns from geophysical time series. Here, CICA is derived as an extension of real-valued ICA (Forootan and Kusche, JoG-2012), where we (i) define a new complex data set using a Hilbert transformation. The complex time series contain the observed values in their real part, and the temporal rate of variability in their imaginary part. (ii) An ICA algorithm based on diagonalization of fourth-order cumulants is then applied to decompose the new complex data set in (i). (iii) Dominant non-stationary patterns are recognized as independent complex patterns that can be used to represent the space and time amplitude and phase propagations. We present the results of CICA on simulated and real cases e.g., for quantifying the impact of large-scale ocean-atmosphere interaction on global mass changes. Forootan (PhD-2014) Statistical signal decomposition techniques for analyzing time-variable satellite gravimetry data, PhD Thesis, University of Bonn, http://hss.ulb.uni-bonn.de/2014/3766/3766.htm Forootan and Kusche (JoG-2012) Separation of global time-variable gravity signals into maximally independent components, Journal of Geodesy 86 (7), 477-497, doi: 10.1007/s00190-011-0532-5

Real-time Adaptive EEG Source Separation using Online Recursive Independent Component Analysis

PubMed Central

Hsu, Sheng-Hsiou; Mullen, Tim; Jung, Tzyy-Ping; Cauwenberghs, Gert

2016-01-01

Independent Component Analysis (ICA) has been widely applied to electroencephalographic (EEG) biosignal processing and brain-computer interfaces. The practical use of ICA, however, is limited by its computational complexity, data requirements for convergence, and assumption of data stationarity, especially for high-density data. Here we study and validate an optimized online recursive ICA algorithm (ORICA) with online recursive least squares (RLS) whitening for blind source separation of high-density EEG data, which offers instantaneous incremental convergence upon presentation of new data. Empirical results of this study demonstrate the algorithm's: (a) suitability for accurate and efficient source identification in high-density (64-channel) realistically-simulated EEG data; (b) capability to detect and adapt to non-stationarity in 64-ch simulated EEG data; and (c) utility for rapidly extracting principal brain and artifact sources in real 61-channel EEG data recorded by a dry and wearable EEG system in a cognitive experiment. ORICA was implemented as functions in BCILAB and EEGLAB and was integrated in an open-source Real-time EEG Source-mapping Toolbox (REST), supporting applications in ICA-based online artifact rejection, feature extraction for real-time biosignal monitoring in clinical environments, and adaptable classifications in brain-computer interfaces. PMID:26685257

NMR-Based Metabolomic Study on Isatis tinctoria: Comparison of Different Accessions, Harvesting Dates, and the Effect of Repeated Harvesting.

PubMed

Guldbrandsen, Niels; Kostidis, Sarantos; Schäfer, Hartmut; De Mieri, Maria; Spraul, Manfred; Skaltsounis, Alexios-Leandros; Mikros, Emmanuel; Hamburger, Matthias

2015-05-22

Isatis tinctoria is an ancient dye and medicinal plant with potent anti-inflammatory and antiallergic properties. Metabolic differences were investigated by NMR spectroscopy of accessions from different origins that were grown under identical conditions on experimental plots. For these accessions, metabolite profiles at different harvesting dates were analyzed, and single and repeatedly harvested plants were compared. Leaf samples were shock-frozen in liquid N2 immediately after being harvested, freeze-dried, and cryomilled prior to extraction. Extracts were prepared by pressurized liquid extraction with ethyl acetate and 70% aqueous methanol. NMR spectra were analyzed using a combination of different methods of multivariate data analysis such as principal component analysis (PCA), canonical analysis (CA), and k-nearest neighbor concept (k-NN). Accessions and harvesting dates were well separated in the PCA/CA/k-NN analysis in both extracts. Pairwise statistical total correlation spectroscopy (STOCSY) revealed unsaturated fatty acids, porphyrins, carbohydrates, indole derivatives, isoprenoids, phenylpropanoids, and minor aromatic compounds as the cause of these differences. In addition, the metabolite profile was affected by the repeated harvest regime, causing a decrease of 1,5-anhydroglucitol, sucrose, unsaturated fatty acids, porphyrins, isoprenoids, and a flavonoid.

[Discomfort associated with dental extraction surgery and development of a questionnaire (QCirDental). Part I: Impacts and internal consistency].

PubMed

Bortoluzzi, Marcelo Carlos; Martins, Luciana Dorochenko; Takahashi, André; Ribeiro, Bianca; Martins, Ligiane; Pinto, Marcia Helena Baldani

2018-01-01

The scope of this study was to develop and validate a questionnaire (QCirDental) to measure the impacts associated with dental extraction surgery. The QCirDental questionnaire was developed in two steps; (1) question and item generation and selection, and (2) pretest of the questionnaire with evaluation of the its measurement properties (internal consistency and responsiveness). The sample was composed of 123 patients. None of the patients had any difficulty in understanding the QCirDental. The instrument was found to have excellent internal consistency with Cronbach's alpha reliability coefficient of 0.83. The principal component analysis (Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0,72 and Bartlett's Test of Sphericity with p < 0.001) showed six (6) dimensions explaining 67.5% of the variance. The QCirDental presented excellent internal consistency, being a questionnaire that is easy to read and understand with adequate semantic and content validity. More than 80% of the patients who underwent dental extraction reported some degree of discomfort within the perioperative period which highlights the necessity to assess the quality of care and impacts of dental extraction surgery.

Towards practical time-of-flight secondary ion mass spectrometry lignocellulolytic enzyme assays

PubMed Central

2013-01-01

Background Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) is a surface sensitive mass spectrometry technique with potential strengths as a method for detecting enzymatic activity on solid materials. In particular, ToF-SIMS has been applied to detect the enzymatic degradation of woody lignocellulose. Proof-of-principle experiments previously demonstrated the detection of both lignin-degrading and cellulose-degrading enzymes on solvent-extracted hardwood and softwood. However, these preliminary experiments suffered from low sample throughput and were restricted to samples which had been solvent-extracted in order to minimize the potential for mass interferences between low molecular weight extractive compounds and polymeric lignocellulose components. Results The present work introduces a new, higher-throughput method for processing powdered wood samples for ToF-SIMS, meanwhile exploring likely sources of sample contamination. Multivariate analysis (MVA) including Principal Component Analysis (PCA) and Multivariate Curve Resolution (MCR) was regularly used to check for sample contamination as well as to detect extractives and enzyme activity. New data also demonstrates successful ToF-SIMS analysis of unextracted samples, placing an emphasis on identifying the low-mass secondary ion peaks related to extractives, revealing how extractives change previously established peak ratios used to describe enzyme activity, and elucidating peak intensity patterns for better detection of cellulase activity in the presence of extractives. The sensitivity of ToF-SIMS to a range of cellulase doses is also shown, along with preliminary experiments augmenting the cellulase cocktail with other proteins. Conclusions These new procedures increase the throughput of sample preparation for ToF-SIMS analysis of lignocellulose and expand the applications of the method to include unextracted lignocellulose. These are important steps towards the practical use of ToF-SIMS as a tool to screen for changes in plant composition, whether the transformation of the lignocellulose is achieved through enzyme application, plant mutagenesis, or other treatments. PMID:24034438

Chemical information obtained from Auger depth profiles by means of advanced factor analysis (MLCFA)

NASA Astrophysics Data System (ADS)

De Volder, P.; Hoogewijs, R.; De Gryse, R.; Fiermans, L.; Vennik, J.

1993-01-01

The advanced multivariate statistical technique "maximum likelihood common factor analysis (MLCFA)" is shown to be superior to "principal component analysis (PCA)" for decomposing overlapping peaks into their individual component spectra of which neither the number of components nor the peak shape of the component spectra is known. An examination of the maximum resolving power of both techniques, MLCFA and PCA, by means of artificially created series of multicomponent spectra confirms this finding unambiguously. Substantial progress in the use of AES as a chemical-analysis technique is accomplished through the implementation of MLCFA. Chemical information from Auger depth profiles is extracted by investigating the variation of the line shape of the Auger signal as a function of the changing chemical state of the element. In particular, MLCFA combined with Auger depth profiling has been applied to problems related to steelcord-rubber tyre adhesion. MLCFA allows one to elucidate the precise nature of the interfacial layer of reaction products between natural rubber vulcanized on a thin brass layer. This study reveals many interesting chemical aspects of the oxi-sulfidation of brass undetectable with classical AES.

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.

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.

An ECG signals compression method and its validation using NNs.

PubMed

Fira, Catalina Monica; Goras, Liviu

2008-04-01

This paper presents a new algorithm for electrocardiogram (ECG) signal compression based on local extreme extraction, adaptive hysteretic filtering and Lempel-Ziv-Welch (LZW) coding. The algorithm has been verified using eight of the most frequent normal and pathological types of cardiac beats and an multi-layer perceptron (MLP) neural network trained with original cardiac patterns and tested with reconstructed ones. Aspects regarding the possibility of using the principal component analysis (PCA) to cardiac pattern classification have been investigated as well. A new compression measure called "quality score," which takes into account both the reconstruction errors and the compression ratio, is proposed.

Extracting Topological Relations Between Indoor Spaces from Point Clouds

NASA Astrophysics Data System (ADS)

Tran, H.; Khoshelham, K.; Kealy, A.; Díaz-Vilariño, L.

2017-09-01

3D models of indoor environments are essential for many application domains such as navigation guidance, emergency management and a range of indoor location-based services. The principal components defined in different BIM standards contain not only building elements, such as floors, walls and doors, but also navigable spaces and their topological relations, which are essential for path planning and navigation. We present an approach to automatically reconstruct topological relations between navigable spaces from point clouds. Three types of topological relations, namely containment, adjacency and connectivity of the spaces are modelled. The results of initial experiments demonstrate the potential of the method in supporting indoor navigation.

PIXE analysis on Maya blue in Prehispanic and colonial mural paintings

NASA Astrophysics Data System (ADS)

Sánchez del Río, M.; Martinetto, P.; Solís, C.; Reyes-Valerio, C.

2006-08-01

Particle induced X-ray emission (PIXE) experiments have been carried out at the AGLAE facility (Paris) on several mural samples containing Maya blue from different Prehispanic archaeological sites (Cacaxtla, El Tajín, Tamuin, Santa Cecilia Acatitlán) and from several colonial convents in the Mexican plateau (Jiutepec, Totimehuacán, Tezontepec and Cuauhtinchán). The analysis of the concentration of several elements permitted to extract some information on the technique used for painting the mural, usually fresco. Principal component analysis permitted to classify the samples into groups. This grouping is discussed in relation to geographic and historic data.

Detection of Poisonous Herbs by Terahertz Time-Domain Spectroscopy

NASA Astrophysics Data System (ADS)

Zhang, H.; Li, Z.; Chen, T.; Liu, J.-J.

2018-03-01

The aim of this paper is the application of terahertz (THz) spectroscopy combined with chemometrics techniques to distinguish poisonous and non-poisonous herbs which both have a similar appearance. Spectra of one poisonous and two non-poisonous herbs (Gelsemium elegans, Lonicera japonica Thunb, and Ficus Hirta Vahl) were obtained in the range 0.2-1.4 THz by using a THz time-domain spectroscopy system. Principal component analysis (PCA) was used for feature extraction. The prediction accuracy of classification is between 97.78 to 100%. The results demonstrate an efficient and applicative method to distinguish poisonous herbs, and it may be implemented by using THz spectroscopy combined with chemometric algorithms.

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.

Allelopathic and bioherbicidal potential of Cladonia verticillaris on the germination and growth of Lactuca sativa.

PubMed

Tigre, R C; Silva, N H; Santos, M G; Honda, N K; Falcão, E P S; Pereira, E C

2012-10-01

Responses to germination and initial growth of Lactuca sativa (lettuce) submitted to organic extracts and purified compounds of Cladonia verticillaris ("salambaia") were analyzed in this work. The experiments were conducted in laboratory conditions using extracts and pure compounds at different concentrations. None of the assays showed any influence on the germination of L. sativa seeds using C. verticillaris extracts; however, modifications in leaf area and seedling hypocotyl and root development occurred. In the growth experiments, seedlings exposed to ether or acetone extract showed diminished hypocotyl growth in detriment to the root stimulus, compared to controls. Increases in extract concentrations led to the formation of abnormal seedlings. To determine the allelochemicals of C. verticillaris, its principal components, fumarprotocetraric and protocetraric acids, were isolated and then analyzed by high performance liquid chromatography (HPLC). When the seedlings were exposed to the two acids separately, presented increased leaf area at all concentrations. In contrast, hypocotyl and root stimulus was observed only in the presence of protocetraric acid at different concentrations. Fumarprotocetraric as well as protocetraric acids, isolated and purified from C. verticillaris and Parmotrema dilatatum respectively, influenced the development of L. sativa seedlings at high concentrations, indicating a possible bioherbicide potential of these acids. Crown Copyright © 2012. Published by Elsevier Inc. All rights reserved.

Development of the upper-body dressing scale for a buttoned shirt: a preliminary correlational study.

PubMed

Suzuki, Makoto; Yamada, Sumio; Omori, Mikayo; Hatakeyama, Mayumi; Sugimura, Yuko; Matsushita, Kazuhiko; Tagawa, Yoshikatsu

2008-09-01

A patient with poststroke hemiparesis learns to use the nonparetic arm to compensate for the weakness of the paretic arm to achieve independence in dressing. This is the learning process of new component actions on dressing. The purpose of this study was to develop the Upper-Body Dressing Scale (UBDS) for buttoned shirt dressing, which evaluates the component actions of upper-body dressing, and to provide preliminary data on internal consistency of the UBDS, as well as its reproducibility, validity, and sensitivity to clinical change. Correlational study of concurrent validity and reliability in which 63 consecutive stroke patients were enrolled in the study and were assessed repeatedly by the UBDS and the dressing item of Functional Independent Measure (FIM). Fifty-one patients completed the 3-wk study. The Cronbach's coefficient alpha of UBDS was 0.88. The principal component analysis extracted two components, which explained 62.3% of total variance. All items of the scale had high loading on the first component (0.65-0.83). Actions on the paralytic side were the positive loadings and actions on the healthy side were the negative loadings on the second component. Intraclass correlation coefficient was 0.87. The level of correlation between UBDS score and FIM dressing item scores was -0.72. Logistic regression analysis showed that only the score of UBDS on the first day of evaluation was a significant independent predictor of dressing ability (odds ratio, 0.82; 95% confidence interval, 0.71-0.95). The UBDS scores for paralytic hand passed into the sleeve, sleeve pulled up beyond the elbow joint, and sleeve pulled up beyond the shoulder joint were worse than the score for the other components of the task. These component actions had positive loading on the second component, which was identified by the principal component analysis. The UBDS has good internal consistency, reproducibility, validity, and sensitivity to clinical changes of patients with poststroke hemiparesis. This detailed UBDS assessment enables us to document the most difficult stages in dressing and to assess motor and process skills for independence of dressing.

Extracting the regional common-mode component of GPS station position time series from dense continuous network

NASA Astrophysics Data System (ADS)

Tian, Yunfeng; Shen, Zheng-Kang

2016-02-01

We develop a spatial filtering method to remove random noise and extract the spatially correlated transients (i.e., common-mode component (CMC)) that deviate from zero mean over the span of detrended position time series of a continuous Global Positioning System (CGPS) network. The technique utilizes a weighting scheme that incorporates two factors—distances between neighboring sites and their correlations of long-term residual position time series. We use a grid search algorithm to find the optimal thresholds for deriving the CMC that minimizes the root-mean-square (RMS) of the filtered residual position time series. Comparing to the principal component analysis technique, our method achieves better (>13% on average) reduction of residual position scatters for the CGPS stations in western North America, eliminating regional transients of all spatial scales. It also has advantages in data manipulation: less intervention and applicable to a dense network of any spatial extent. Our method can also be used to detect CMC irrespective of its origins (i.e., tectonic or nontectonic), if such signals are of particular interests for further study. By varying the filtering distance range, the long-range CMC related to atmospheric disturbance can be filtered out, uncovering CMC associated with transient tectonic deformation. A correlation-based clustering algorithm is adopted to identify stations cluster that share the common regional transient characteristics.

Untargeted MS-based small metabolite identification from the plant leaves and stems of Impatiens balsamina.

PubMed

Chua, Lee Suan

2016-09-01

The identification of plant metabolites is very important for the understanding of plant physiology including plant growth, development and defense mechanism, particularly for herbal medicinal plants. The metabolite profile could possibly be used for future drug discovery since the pharmacological activities of the indigenous herbs have been proven for centuries. An untargeted mass spectrometric approach was used to identify metabolites from the leaves and stems of Impatiens balsamina using LC-DAD-MS/MS. The putative compounds are mostly from the groups of phenolic, organic and amino acids which are essential for plant growth and as intermediates for other compounds. Alanine appeared to be the main amino acid in the plant because many alanine derived metabolites were detected. There are also several secondary metabolites from the groups of benzopyrones, benzofuranones, naphthoquinones, alkaloids and flavonoids. The widely reported bioactive components such as kaempferol, quercetin and their glycosylated, lawsone and its derivatives were detected in this study. The results also revealed that aqueous methanol could extract flavonoids better than water, and mostly, flavonoids were detected from the leaf samples. The score plots of component analysis show that there is a minor variance in the metabolite profiles of water and aqueous methanolic extracts with 21.5 and 30.5% of the total variance for the first principal component at the positive and negative ion modes, respectively. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Essential oil from Ocimum basilicum (Omani Basil): a desert crop.

PubMed

Al-Maskri, Ahmed Yahya; Hanif, Muhammad Asif; Al-Maskari, Masoud Yahya; Abraham, Alfie Susan; Al-sabahi, Jamal Nasser; Al-Mantheri, Omar

2011-10-01

The focus of the present study was on the influence of season on yield, chemical composition, antioxidant and antifungal activities of Omani basil (Ocimum basilicum) oil. The present study involved only one of the eight Omani basil varieties. The hydro-distilled essential oil yields were computed to be 0.1%, 0.3% and 0.1% in the winter, spring and summer seasons, respectively. The major components identified were L- linalool (26.5-56.3%), geraniol (12.1-16.5%), 1,8-cineole (2.5-15.1%), p-allylanisole (0.2-13.8%) and DL-limonene (0.2-10.4%). A noteworthy extra component was beta- farnesene, which was exclusively detected in the oil extracted during winter and spring at 6.3% and 5.8%, respectively. The essential oil composition over the different seasons was quite idiosyncratic, in which the principal components of one season were either trivial or totally absent in another. The essential oil extracted in spring exhibited the highest antioxidant activity (except DPPH scavenging ability) in comparison with the oils from other seasons. The basil oil was tested against pathogenic fungi viz. Aspergillus niger, A. fumigatus, Penicillium italicum and Rhizopus stolonifer using a disc diffusion method, and by determination of minimum inhibitory concentration. Surprisingly high antifungal values were found highlighting the potential of Omani basil as a preservative in the food and medical industries.

Antibacterial activity of Limonium brasiliense (Baicuru) against multidrug-resistant bacteria using a statistical mixture design.

PubMed

Blainski, Andressa; Gionco, Barbara; Oliveira, Admilton G; Andrade, Galdino; Scarminio, Ieda S; Silva, Denise B; Lopes, Norberto P; Mello, João C P

2017-02-23

Limonium brasiliense (Boiss.) Kuntze (Plumbaginaceae) is commonly known as "baicuru" or "guaicuru" and preparations of its dried rhizomes have been popularly used in the treatment of premenstrual syndrome and menstrual disorder, and as an antiseptic in genito-urinary infections. This study evaluated the potential antibacterial activity of rhizome extracts against multidrug-resistant bacterial strains using statistical mixture design. The statistical design of four components (water, methanol, acetone, and ethanol) produced 15 different extracts and also a confirmatory experiment, which was performed using water:acetone (3:7, v/v). The crude extracts and their ethyl-acetate fractions were tested against vancomycin-resistant Enterococcus faecium (VREfm), methicillin-resistant Staphylococcus aureus (MRSA) and Klebsiella pneumoniae carbapenemase (KPC)-producing K. pneumoniae, all of which have been implicated in hospital and community-acquired infections. The dry residue, total polyphenol, gallocatechin and epigallocatechin contents of the extracts were also tested and statistical analysis was applied in order to define the fit models to predict the result of each parameter for any mixture of components. The principal component and hierarchical clustering analyses (PCA and HCA) of chromatographic data, as well as mass spectrometry (MS) analysis were performanced to determine the main compounds present in the extracts. The Gram-positive bacteria were susceptible to inhibition of bacterial growth, in special the ethyl-acetate fraction of ternary extracts from water:acetone:ethanol and methanol:acetone:ethanol against, respectively, VREfm (MIC=19µg/mL) and MRSA (MIC=39µg/mL). On the other hand, moderate activity of the ethyl-acetate fractions from primary (except water), secondary and ternary extracts (MIC=625µg/mL) was noted against KPC. The quadratic and special cubic models were significant for polyphenols and gallocatechin contents, respectively. Fit models to dry residue and epigallocatechin contents were not possible. PCA and HCA of the chromatographic fingerprints were disturbed by displacement retention time of some peaks, but the ultraviolet spectra indicated the homogeneous presence of flavan-3-ols characteristic of tannins. The MS confirmed the presence of gallic acid, gallocatechin, and epigallocatechin in extracts, and suggested the presence of monomers and dimers of B- and A-type prodelphinidins gallate, as well as a methyl gallate. Our results showed the antibacterial potential of L. brasiliense extracts against multidrug-resistant Gram-positive bacteria, such as VREfm and MRSA. The statistical design was a important tool to evaluate the biological activity by optimized form. The presence of some phenolic compounds was also demonstrated in extracts. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Developing a Complex Independent Component Analysis (CICA) Technique to Extract Non-stationary Patterns from Geophysical Time Series

NASA Astrophysics Data System (ADS)

Forootan, Ehsan; Kusche, Jürgen; Talpe, Matthieu; Shum, C. K.; Schmidt, Michael

2017-12-01

In recent decades, decomposition techniques have enabled increasingly more applications for dimension reduction, as well as extraction of additional information from geophysical time series. Traditionally, the principal component analysis (PCA)/empirical orthogonal function (EOF) method and more recently the independent component analysis (ICA) have been applied to extract, statistical orthogonal (uncorrelated), and independent modes that represent the maximum variance of time series, respectively. PCA and ICA can be classified as stationary signal decomposition techniques since they are based on decomposing the autocovariance matrix and diagonalizing higher (than two) order statistical tensors from centered time series, respectively. However, the stationarity assumption in these techniques is not justified for many geophysical and climate variables even after removing cyclic components, e.g., the commonly removed dominant seasonal cycles. In this paper, we present a novel decomposition method, the complex independent component analysis (CICA), which can be applied to extract non-stationary (changing in space and time) patterns from geophysical time series. Here, CICA is derived as an extension of real-valued ICA, where (a) we first define a new complex dataset that contains the observed time series in its real part, and their Hilbert transformed series as its imaginary part, (b) an ICA algorithm based on diagonalization of fourth-order cumulants is then applied to decompose the new complex dataset in (a), and finally, (c) the dominant independent complex modes are extracted and used to represent the dominant space and time amplitudes and associated phase propagation patterns. The performance of CICA is examined by analyzing synthetic data constructed from multiple physically meaningful modes in a simulation framework, with known truth. Next, global terrestrial water storage (TWS) data from the Gravity Recovery And Climate Experiment (GRACE) gravimetry mission (2003-2016), and satellite radiometric sea surface temperature (SST) data (1982-2016) over the Atlantic and Pacific Oceans are used with the aim of demonstrating signal separations of the North Atlantic Oscillation (NAO) from the Atlantic Multi-decadal Oscillation (AMO), and the El Niño Southern Oscillation (ENSO) from the Pacific Decadal Oscillation (PDO). CICA results indicate that ENSO-related patterns can be extracted from the Gravity Recovery And Climate Experiment Terrestrial Water Storage (GRACE TWS) with an accuracy of 0.5-1 cm in terms of equivalent water height (EWH). The magnitude of errors in extracting NAO or AMO from SST data using the complex EOF (CEOF) approach reaches up to 50% of the signal itself, while it is reduced to 16% when applying CICA. Larger errors with magnitudes of 100% and 30% of the signal itself are found while separating ENSO from PDO using CEOF and CICA, respectively. We thus conclude that the CICA is more effective than CEOF in separating non-stationary patterns.

Authentication of Piper betle L. folium and quantification of their antifungal-activity.

PubMed

Wirasuta, I Made Agus Gelgel; Srinadi, I Gusti Ayu Made; Dwidasmara, Ida Bagus Gede; Ardiyanti, Ni Luh Putu Putri; Trisnadewi, I Gusti Ayu Arya; Paramita, Ni Luh Putu Vidya

2017-07-01

The TLC profiles of intra- and inter-day precision for Piper betle L . (PBL) folium methanol extract was studied for their peak marker recognition and identification. The Numerical chromatographic parameters (NCPs) of the peak markers, the hierarchical clustering analysis (HCA) and the principal component analysis (PCA) were applied to authenticate the PBL. folium extract from other Piper species folium extract and to ensure the antifungal activity quality of the PBL essential oil. The spotted extract was developed with the mobile phase of toluene: ethyl acetate; 93:7, (v/v). The eluted plate was viewed with the TLC-Visualizer, scanned under absorption and fluorescent mode detection, and on each sample the in-situ UV spectra were recorded between 190 to 400 nm. The NCPs profiles of intra- and inter-day precision results offered multi-dimensional chromatogram fingerprints for better marker peak pattern recognition and identification. Using the r -value fingerprints data series generated with this method allowed more precise discrimination the PBL. from other Piper species compared to the marker peak area fingerprint method. The cosine pair comparison was a simple method for authentication of two different fingerprints. The ward linkage clustering and the pair cross-correlation comparison were better chemometric methods to determine the consistency peak area ratio between fingerprints. The first component PCA-loading values of peak marker area fingerprints were correlated linearly to both the bio-marker concentration as well as the antifungal activity. This relationship could be used to control the quality and pharmacological potency. This simple method was developed for the authentication and quantification of herbal medicine.

A Comparison of Protein Extraction Methods Suitable for Gel-Based Proteomic Studies of Aphid Proteins

PubMed Central

Cilia, M.; Fish, T.; Yang, X.; Mclaughlin, M.; Thannhauser, T. W.

2009-01-01

Protein extraction methods can vary widely in reproducibility and in representation of the total proteome, yet there are limited data comparing protein isolation methods. The methodical comparison of protein isolation methods is the first critical step for proteomic studies. To address this, we compared three methods for isolation, purification, and solubilization of insect proteins. The aphid Schizaphis graminum, an agricultural pest, was the source of insect tissue. Proteins were extracted using TCA in acetone (TCA-acetone), phenol, or multi-detergents in a chaotrope solution. Extracted proteins were solubilized in a multiple chaotrope solution and examined using 1-D and 2-D electrophoresis and compared directly using 2-D Difference Gel Electrophoresis (2-D DIGE). Mass spectrometry was used to identify proteins from each extraction type. We were unable to ascribe the differences in the proteins extracted to particular physical characteristics, cell location, or biological function. The TCA-acetone extraction yielded the greatest amount of protein from aphid tissues. Each extraction method isolated a unique subset of the aphid proteome. The TCA-acetone method was explored further for its quantitative reliability using 2-D DIGE. Principal component analysis showed that little of the variation in the data was a result of technical issues, thus demonstrating that the TCA-acetone extraction is a reliable method for preparing aphid proteins for a quantitative proteomics experiment. These data suggest that although the TCA-acetone method is a suitable method for quantitative aphid proteomics, a combination of extraction approaches is recommended for increasing proteome coverage when using gel-based separation techniques. PMID:19721822

Extraction of motor activity from the cervical spinal cord of behaving rats

NASA Astrophysics Data System (ADS)

Prasad, Abhishek; Sahin, Mesut

2006-12-01

Injury at the cervical region of the spinal cord results in the loss of the skeletal muscle control from below the shoulders and hence causes quadriplegia. The brain-computer interface technique is one way of generating a substitute for the lost command signals in these severely paralyzed individuals using the neural signals from the brain. In this study, we are investigating the feasibility of an alternative method where the volitional signals are extracted from the cervical spinal cord above the point of injury. A microelectrode array assembly was implanted chronically at the C5-C6 level of the spinal cord in rats. Neural recordings were made during the face cleaning behavior with forelimbs as this task involves cyclic forelimb movements and does not require any training. The correlation between the volitional motor signals and the elbow movements was studied. Linear regression technique was used to reconstruct the arm movement from the rectified-integrated version of the principal neural components. The results of this study demonstrate the feasibility of extracting the motor signals from the cervical spinal cord and using them for reconstruction of the elbow movements.

Wireless AE Event and Environmental Monitoring for Wind Turbine Blades at Low Sampling Rates

NASA Astrophysics Data System (ADS)

Bouzid, Omar M.; Tian, Gui Y.; Cumanan, K.; Neasham, J.

Integration of acoustic wireless technology in structural health monitoring (SHM) applications introduces new challenges due to requirements of high sampling rates, additional communication bandwidth, memory space, and power resources. In order to circumvent these challenges, this chapter proposes a novel solution through building a wireless SHM technique in conjunction with acoustic emission (AE) with field deployment on the structure of a wind turbine. This solution requires a low sampling rate which is lower than the Nyquist rate. In addition, features extracted from aliased AE signals instead of reconstructing the original signals on-board the wireless nodes are exploited to monitor AE events, such as wind, rain, strong hail, and bird strike in different environmental conditions in conjunction with artificial AE sources. Time feature extraction algorithm, in addition to the principal component analysis (PCA) method, is used to extract and classify the relevant information, which in turn is used to classify or recognise a testing condition that is represented by the response signals. This proposed novel technique yields a significant data reduction during the monitoring process of wind turbine blades.

Thermal effusivity measurement of conventional and organic coffee oils via photopyroelectric technique.

PubMed

Bedoya, A; Gordillo-Delgado, F; Cruz-Santillana, Y E; Plazas, J; Marin, E

2017-12-01

In this work, oil samples extracted from organic and conventional coffee beans were studied. A fatty acids profile analysis was done using gas chromatography and physicochemical analysis of density and acidity index to verify the oil purity. Additionally, Mid-Infrared Fourier Transform Photoacoustic Spectroscopy (FTIR-PAS) aided by Principal Component Analysis (PCA) was used to identify differences between the intensities of the absorption bands related to functional groups. Thermal effusivity values between 592±3 and 610±4Ws 1/2 m -2 K -1 were measured using the photopyroelectric technique in a front detection configuration. The acidity index was between 1.11 and 1.27% and the density changed between 0.921 and 0.94g/mL. These variables, as well as the extraction yield between 12,6 and 14,4%, showed a similar behavior than that observed for the thermal effusivity, demonstrating that this parameter can be used as a criterion for discrimination between oil samples extracted from organic and conventional coffee beans. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

Wire bonding quality monitoring via refining process of electrical signal from ultrasonic generator

NASA Astrophysics Data System (ADS)

Feng, Wuwei; Meng, Qingfeng; Xie, Youbo; Fan, Hong

2011-04-01

In this paper, a technique for on-line quality detection of ultrasonic wire bonding is developed. The electrical signals from the ultrasonic generator supply, namely, voltage and current, are picked up by a measuring circuit and transformed into digital signals by a data acquisition system. A new feature extraction method is presented to characterize the transient property of the electrical signals and further evaluate the bond quality. The method includes three steps. First, the captured voltage and current are filtered by digital bandpass filter banks to obtain the corresponding subband signals such as fundamental signal, second harmonic, and third harmonic. Second, each subband envelope is obtained using the Hilbert transform for further feature extraction. Third, the subband envelopes are, respectively, separated into three phases, namely, envelope rising, stable, and damping phases, to extract the tiny waveform changes. The different waveform features are extracted from each phase of these subband envelopes. The principal components analysis (PCA) method is used for the feature selection in order to remove the relevant information and reduce the dimension of original feature variables. Using the selected features as inputs, an artificial neural network (ANN) is constructed to identify the complex bond fault pattern. By analyzing experimental data with the proposed feature extraction method and neural network, the results demonstrate the advantages of the proposed feature extraction method and the constructed artificial neural network in detecting and identifying bond quality.

Novel algorithm for simultaneous component detection and pseudo-molecular ion characterization in liquid chromatography-mass spectrometry.

PubMed

Zhang, Yufeng; Wang, Xiaoan; Wo, Siukwan; Ho, Hingman; Han, Quanbin; Fan, Xiaohui; Zuo, Zhong

2015-01-01

Resolving components and determining their pseudo-molecular ions (PMIs) are crucial steps in identifying complex herbal mixtures by liquid chromatography-mass spectrometry. To tackle such labor-intensive steps, we present here a novel algorithm for simultaneous detection of components and their PMIs. Our method consists of three steps: (1) obtaining a simplified dataset containing only mono-isotopic masses by removal of background noise and isotopic cluster ions based on the isotopic distribution model derived from all the reported natural compounds in dictionary of natural products; (2) stepwise resolving and removing all features of the highest abundant component from current simplified dataset and calculating PMI of each component according to an adduct-ion model, in which all non-fragment ions in a mass spectrum are considered as PMI plus one or several neutral species; (3) visual classification of detected components by principal component analysis (PCA) to exclude possible non-natural compounds (such as pharmaceutical excipients). This algorithm has been successfully applied to a standard mixture and three herbal extract/preparations. It indicated that our algorithm could detect components' features as a whole and report their PMI with an accuracy of more than 98%. Furthermore, components originated from excipients/contaminants could be easily separated from those natural components in the bi-plots of PCA. Copyright © 2014 Elsevier B.V. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

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

PubMed

Dascălu, Cristina Gena; Antohe, Magda Ecaterina

2009-01-01

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

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.

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.…

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.

Principal Components Analysis Studies of Martian Clouds

NASA Astrophysics Data System (ADS)

Klassen, D. R.; Bell, J. F., III

2001-11-01

We present the principal components analysis (PCA) of absolutely calibrated multi-spectral images of Mars as a function of Martian season. The PCA technique is a mathematical rotation and translation of the data from a brightness/wavelength space to a vector space of principal ``traits'' that lie along the directions of maximal variance. The first of these traits, accounting for over 90% of the data variance, is overall brightness and represented by an average Mars spectrum. Interpretation of the remaining traits, which account for the remaining ~10% of the variance, is not always the same and depends upon what other components are in the scene and thus, varies with Martian season. For example, during seasons with large amounts of water ice in the scene, the second trait correlates with the ice and anti-corrlates with temperature. We will investigate the interpretation of the second, and successive important PCA traits. Although these PCA traits are orthogonal in their own vector space, it is unlikely that any one trait represents a singular, mineralogic, spectral end-member. It is more likely that there are many spectral endmembers that vary identically to within the noise level, that the PCA technique will not be able to distinguish them. Another possibility is that similar absorption features among spectral endmembers may be tied to one PCA trait, for example ''amount of 2 \\micron\\ absorption''. We thus attempt to extract spectral endmembers by matching linear combinations of the PCA traits to USGS, JHU, and JPL spectral libraries as aquired through the JPL Aster project. The recovered spectral endmembers are then linearly combined to model the multi-spectral image set. We present here the spectral abundance maps of the water ice/frost endmember which allow us to track Martian clouds and ground frosts. This work supported in part through NASA Planetary Astronomy Grant NAG5-6776. All data gathered at the NASA Infrared Telescope Facility in collaboration with the telescope operators and with thanks to the support staff and day crew.

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.

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 directed partial least squares analysis for combining nuclear magnetic resonance and mass spectrometry data in metabolomics: application to the detection of breast cancer.

PubMed

Gu, Haiwei; Pan, Zhengzheng; Xi, Bowei; Asiago, Vincent; Musselman, Brian; Raftery, Daniel

2011-02-07

Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are the two most commonly used analytical tools in metabolomics, and their complementary nature makes the combination particularly attractive. A combined analytical approach can improve the potential for providing reliable methods to detect metabolic profile alterations in biofluids or tissues caused by disease, toxicity, etc. In this paper, (1)H NMR spectroscopy and direct analysis in real time (DART)-MS were used for the metabolomics analysis of serum samples from breast cancer patients and healthy controls. Principal component analysis (PCA) of the NMR data showed that the first principal component (PC1) scores could be used to separate cancer from normal samples. However, no such obvious clustering could be observed in the PCA score plot of DART-MS data, even though DART-MS can provide a rich and informative metabolic profile. Using a modified multivariate statistical approach, the DART-MS data were then reevaluated by orthogonal signal correction (OSC) pretreated partial least squares (PLS), in which the Y matrix in the regression was set to the PC1 score values from the NMR data analysis. This approach, and a similar one using the first latent variable from PLS-DA of the NMR data resulted in a significant improvement of the separation between the disease samples and normals, and a metabolic profile related to breast cancer could be extracted from DART-MS. The new approach allows the disease classification to be expressed on a continuum as opposed to a binary scale and thus better represents the disease and healthy classifications. An improved metabolic profile obtained by combining MS and NMR by this approach may be useful to achieve more accurate disease detection and gain more insight regarding disease mechanisms and biology. Copyright © 2010 Elsevier B.V. All rights reserved.

Detection and discrimination of cotton foreign matter using push-broom based hyperspectral imaging: system design and capability.

PubMed

Jiang, Yu; Li, Changying

2015-01-01

Cotton quality, a major factor determining both cotton profitability and marketability, is affected by not only the overall quantity of but also the type of the foreign matter. Although current commercial instruments can measure the overall amount of the foreign matter, no instrument can differentiate various types of foreign matter. The goal of this study was to develop a hyperspectral imaging system to discriminate major types of foreign matter in cotton lint. A push-broom based hyperspectral imaging system with a custom-built multi-thread software was developed to acquire hyperspectral images of cotton fiber with 15 types of foreign matter commonly found in the U.S. cotton lint. A total of 450 (30 replicates for each foreign matter) foreign matter samples were cut into 1 by 1 cm2 pieces and imaged on the lint surface using reflectance mode in the spectral range from 400-1000 nm. The mean spectra of the foreign matter and lint were extracted from the user-defined region-of-interests in the hyperspectral images. The principal component analysis was performed on the mean spectra to reduce the feature dimension from the original 256 bands to the top 3 principal components. The score plots of the 3 principal components were used to examine clusterization patterns for classifying the foreign matter. These patterns were further validated by statistical tests. The experimental results showed that the mean spectra of all 15 types of cotton foreign matter were different from that of the lint. Nine types of cotton foreign matter formed distinct clusters in the score plots. Additionally, all of them were significantly different from each other at the significance level of 0.05 except brown leaf and bract. The developed hyperspectral imaging system is effective to detect and classify cotton foreign matter on the lint surface and has the potential to be implemented in commercial cotton classing offices.

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

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

Shape information from glucose curves: Functional data analysis compared with traditional summary measures

PubMed Central

2013-01-01

Background Plasma glucose levels are important measures in medical care and research, and are often obtained from oral glucose tolerance tests (OGTT) with repeated measurements over 2–3 hours. It is common practice to use simple summary measures of OGTT curves. However, different OGTT curves can yield similar summary measures, and information of physiological or clinical interest may be lost. Our mean aim was to extract information inherent in the shape of OGTT glucose curves, compare it with the information from simple summary measures, and explore the clinical usefulness of such information. Methods OGTTs with five glucose measurements over two hours were recorded for 974 healthy pregnant women in their first trimester. For each woman, the five measurements were transformed into smooth OGTT glucose curves by functional data analysis (FDA), a collection of statistical methods developed specifically to analyse curve data. The essential modes of temporal variation between OGTT glucose curves were extracted by functional principal component analysis. The resultant functional principal component (FPC) scores were compared with commonly used simple summary measures: fasting and two-hour (2-h) values, area under the curve (AUC) and simple shape index (2-h minus 90-min values, or 90-min minus 60-min values). Clinical usefulness of FDA was explored by regression analyses of glucose tolerance later in pregnancy. Results Over 99% of the variation between individually fitted curves was expressed in the first three FPCs, interpreted physiologically as “general level” (FPC1), “time to peak” (FPC2) and “oscillations” (FPC3). FPC1 scores correlated strongly with AUC (r=0.999), but less with the other simple summary measures (−0.42≤r≤0.79). FPC2 scores gave shape information not captured by simple summary measures (−0.12≤r≤0.40). FPC2 scores, but not FPC1 nor the simple summary measures, discriminated between women who did and did not develop gestational diabetes later in pregnancy. Conclusions FDA of OGTT glucose curves in early pregnancy extracted shape information that was not identified by commonly used simple summary measures. This information discriminated between women with and without gestational diabetes later in pregnancy. PMID:23327294

Chemometrics-based Approach in Analysis of Arnicae flos

PubMed Central

Zheleva-Dimitrova, Dimitrina Zh.; Balabanova, Vessela; Gevrenova, Reneta; Doichinova, Irini; Vitkova, Antonina

2015-01-01

Introduction: Arnica montana flowers have a long history as herbal medicines for external use on injuries and rheumatic complaints. Objective: To investigate Arnicae flos of cultivated accessions from Bulgaria, Poland, Germany, Finland, and Pharmacy store for phenolic derivatives and sesquiterpene lactones (STLs). Materials and Methods: Samples of Arnica from nine origins were prepared by ultrasound-assisted extraction with 80% methanol for phenolic compounds analysis. Subsequent reverse-phase high-performance liquid chromatography (HPLC) separation of the analytes was performed using gradient elution and ultraviolet detection at 280 and 310 nm (phenolic acids), and 360 nm (flavonoids). Total STLs were determined in chloroform extracts by solid-phase extraction-HPLC at 225 nm. The HPLC generated chromatographic data were analyzed using principal component analysis (PCA) and hierarchical clustering (HC). Results: The highest total amount of phenolic acids was found in the sample from Botanical Garden at Joensuu University, Finland (2.36 mg/g dw). Astragalin, isoquercitrin, and isorhamnetin 3-glucoside were the main flavonol glycosides being present up to 3.37 mg/g (astragalin). Three well-defined clusters were distinguished by PCA and HC. Cluster C1 comprised of the German and Finnish accessions characterized by the highest content of flavonols. Cluster C2 included the Bulgarian and Polish samples presenting a low content of flavonoids. Cluster C3 consisted only of one sample from a pharmacy store. Conclusion: A validated HPLC method for simultaneous determination of phenolic acids, flavonoid glycosides, and aglycones in A. montana flowers was developed. The PCA loading plot showed that quercetin, kaempferol, and isorhamnetin can be used to distinguish different Arnica accessions. SUMMARY A principal component analysis (PCA) on 13 phenolic compounds and total amount of sesquiterpene lactones in Arnicae flos collection tended to cluster the studied 9 accessions into three main groups. The profiles obtained demonstrated that the samples from Germany and Finland are characterized by greater amounts of phenolic derivatives than the Bulgarian and Polish ones. The PCA loading plot showed that quercetin, kaemferol and isorhamnetin can be used to distinguish different arnica accessions. PMID:27013791

Shape information from glucose curves: functional data analysis compared with traditional summary measures.

PubMed

Frøslie, Kathrine Frey; Røislien, Jo; Qvigstad, Elisabeth; Godang, Kristin; Bollerslev, Jens; Voldner, Nanna; Henriksen, Tore; Veierød, Marit B

2013-01-17

Plasma glucose levels are important measures in medical care and research, and are often obtained from oral glucose tolerance tests (OGTT) with repeated measurements over 2-3  hours. It is common practice to use simple summary measures of OGTT curves. However, different OGTT curves can yield similar summary measures, and information of physiological or clinical interest may be lost. Our mean aim was to extract information inherent in the shape of OGTT glucose curves, compare it with the information from simple summary measures, and explore the clinical usefulness of such information. OGTTs with five glucose measurements over two hours were recorded for 974 healthy pregnant women in their first trimester. For each woman, the five measurements were transformed into smooth OGTT glucose curves by functional data analysis (FDA), a collection of statistical methods developed specifically to analyse curve data. The essential modes of temporal variation between OGTT glucose curves were extracted by functional principal component analysis. The resultant functional principal component (FPC) scores were compared with commonly used simple summary measures: fasting and two-hour (2-h) values, area under the curve (AUC) and simple shape index (2-h minus 90-min values, or 90-min minus 60-min values). Clinical usefulness of FDA was explored by regression analyses of glucose tolerance later in pregnancy. Over 99% of the variation between individually fitted curves was expressed in the first three FPCs, interpreted physiologically as "general level" (FPC1), "time to peak" (FPC2) and "oscillations" (FPC3). FPC1 scores correlated strongly with AUC (r=0.999), but less with the other simple summary measures (-0.42≤r≤0.79). FPC2 scores gave shape information not captured by simple summary measures (-0.12≤r≤0.40). FPC2 scores, but not FPC1 nor the simple summary measures, discriminated between women who did and did not develop gestational diabetes later in pregnancy. FDA of OGTT glucose curves in early pregnancy extracted shape information that was not identified by commonly used simple summary measures. This information discriminated between women with and without gestational diabetes later in pregnancy.