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Sample records for addition multivariate analyses

  1. A review of multivariate analyses in imaging genetics.

    PubMed

    Liu, Jingyu; Calhoun, Vince D

    2014-01-01

    Recent advances in neuroimaging technology and molecular genetics provide the unique opportunity to investigate genetic influence on the variation of brain attributes. Since the year 2000, when the initial publication on brain imaging and genetics was released, imaging genetics has been a rapidly growing research approach with increasing publications every year. Several reviews have been offered to the research community focusing on various study designs. In addition to study design, analytic tools and their proper implementation are also critical to the success of a study. In this review, we survey recent publications using data from neuroimaging and genetics, focusing on methods capturing multivariate effects accommodating the large number of variables from both imaging data and genetic data. We group the analyses of genetic or genomic data into either a priori driven or data driven approach, including gene-set enrichment analysis, multifactor dimensionality reduction, principal component analysis, independent component analysis (ICA), and clustering. For the analyses of imaging data, ICA and extensions of ICA are the most widely used multivariate methods. Given detailed reviews of multivariate analyses of imaging data available elsewhere, we provide a brief summary here that includes a recently proposed method known as independent vector analysis. Finally, we review methods focused on bridging the imaging and genetic data by establishing multivariate and multiple genotype-phenotype-associations, including sparse partial least squares, sparse canonical correlation analysis, sparse reduced rank regression and parallel ICA. These methods are designed to extract latent variables from both genetic and imaging data, which become new genotypes and phenotypes, and the links between the new genotype-phenotype pairs are maximized using different cost functions. The relationship between these methods along with their assumptions, advantages, and limitations are discussed.

  2. Affymetrix GeneChip microarray preprocessing for multivariate analyses.

    PubMed

    McCall, Matthew N; Almudevar, Anthony

    2012-09-01

    Affymetrix GeneChip microarrays are the most widely used high-throughput technology to measure gene expression, and a wide variety of preprocessing methods have been developed to transform probe intensities reported by a microarray scanner into gene expression estimates. There have been numerous comparisons of these preprocessing methods, focusing on the most common analyses-detection of differential expression and gene or sample clustering. Recently, more complex multivariate analyses, such as gene co-expression, differential co-expression, gene set analysis and network modeling, are becoming more common; however, the same preprocessing methods are typically applied. In this article, we examine the effect of preprocessing methods on some of these multivariate analyses and provide guidance to the user as to which methods are most appropriate.

  3. Distinguishing Nonpareil marketing group almond cultivars through multivariate analyses.

    PubMed

    Ledbetter, Craig A; Sisterson, Mark S

    2013-09-01

    More than 80% of the world's almonds are grown in California with several dozen almond cultivars available commercially. To facilitate promotion and sale, almond cultivars are categorized into marketing groups based on kernel shape and appearance. Several marketing groups are recognized, with the Nonpareil Marketing Group (NMG) demanding the highest prices. Placement of cultivars into the NMG is historical and no objective standards exist for deciding whether newly developed cultivars belong in the NMG. Principal component analyses (PCA) were used to identify nut and kernel characteristics best separating the 4 NMG cultivars (Nonpareil, Jeffries, Kapareil, and Milow) from a representative of the California Marketing Group (cultivar Carmel) and the Mission Marketing Group (cultivar Padre). In addition, discriminant analyses were used to determine cultivar misclassification rates between and within the marketing groups. All 19 evaluated carpological characters differed significantly among the 6 cultivars and during 2 harvest seasons. A clear distinction of NMG cultivars from representatives of the California and Mission Marketing Groups was evident from a PCA involving the 6 cultivars. Further, NMG kernels were successfully discriminated from kernels representing the California and Mission Marketing Groups with overall kernel misclassification of only 2% using 16 of the 19 evaluated characters. Pellicle luminosity was the most discriminating character, regardless of the character set used in analyses. Results provide an objective classification of NMG almond kernels, clearly distinguishing them from kernels of cultivars representing the California and Mission Marketing Groups.

  4. Search for single top production using multivariate analyses at CDF

    SciTech Connect

    Hirschbuhl, Dominic; /Karlsruhe U., EKP

    2007-10-01

    This article reports on recent searches for single-top-quark production by the CDF collaboration at the Tevatron using a data set that corresponds to an integrated luminosity of 955 pb{sup -1}. Three different analyses techniques are employed, one using likelihood discriminants, one neural networks and one matrix elements. The sensitivity to single-top production at the rate predicted by the standard model ranges from 2.1 to 2.6 {sigma}. While the first two analyses observe a deficit of single-top like events compared to the expectation, the matrix element method observes an excess corresponding to a background fluctuation of 2.3 {sigma}. The null results of the likelihood and neural network analyses translate in upper limits on the cross section of 2.6 pb for the t-channel production mode and 3.7 pb for the s-channel mode at the 95% C.L. The matrix element result corresponds to a measurement of 2.7{sub -1.3}{sup +1.5} pb for the combined t- and s-channel single-top cross section. In addition, CDF has searched for non-standard model production of single-top-quarks via the s-channel exchange of a heavy W{prime} boson. No signal of this process is found resulting in lower mass limits of 760 GeV/c{sup 2} in case the mass of the right-handed neutrino is smaller than the mass of the right-handed W{prime} or 790 GeV/c{sup 2} in the opposite case.

  5. A Cyber-Attack Detection Model Based on Multivariate Analyses

    NASA Astrophysics Data System (ADS)

    Sakai, Yuto; Rinsaka, Koichiro; Dohi, Tadashi

    In the present paper, we propose a novel cyber-attack detection model based on two multivariate-analysis methods to the audit data observed on a host machine. The statistical techniques used here are the well-known Hayashi's quantification method IV and cluster analysis method. We quantify the observed qualitative audit event sequence via the quantification method IV, and collect similar audit event sequence in the same groups based on the cluster analysis. It is shown in simulation experiments that our model can improve the cyber-attack detection accuracy in some realistic cases where both normal and attack activities are intermingled.

  6. Multi-Window Classical Least Squares Multivariate Calibration Methods for Quantitative ICP-AES Analyses

    SciTech Connect

    CHAMBERS,WILLIAM B.; HAALAND,DAVID M.; KEENAN,MICHAEL R.; MELGAARD,DAVID K.

    1999-10-01

    The advent of inductively coupled plasma-atomic emission spectrometers (ICP-AES) equipped with charge-coupled-device (CCD) detector arrays allows the application of multivariate calibration methods to the quantitative analysis of spectral data. We have applied classical least squares (CLS) methods to the analysis of a variety of samples containing up to 12 elements plus an internal standard. The elements included in the calibration models were Ag, Al, As, Au, Cd, Cr, Cu, Fe, Ni, Pb, Pd, and Se. By performing the CLS analysis separately in each of 46 spectral windows and by pooling the CLS concentration results for each element in all windows in a statistically efficient manner, we have been able to significantly improve the accuracy and precision of the ICP-AES analyses relative to the univariate and single-window multivariate methods supplied with the spectrometer. This new multi-window CLS (MWCLS) approach simplifies the analyses by providing a single concentration determination for each element from all spectral windows. Thus, the analyst does not have to perform the tedious task of reviewing the results from each window in an attempt to decide the correct value among discrepant analyses in one or more windows for each element. Furthermore, it is not necessary to construct a spectral correction model for each window prior to calibration and analysis: When one or more interfering elements was present, the new MWCLS method was able to reduce prediction errors for a selected analyte by more than 2 orders of magnitude compared to the worst case single-window multivariate and univariate predictions. The MWCLS detection limits in the presence of multiple interferences are 15 rig/g (i.e., 15 ppb) or better for each element. In addition, errors with the new method are only slightly inflated when only a single target element is included in the calibration (i.e., knowledge of all other elements is excluded during calibration). The MWCLS method is found to be vastly

  7. Similarities and differences of metal distributions in the tissues of molluscs by using multivariate analyses.

    PubMed

    Yap, Chee Kong; Edward, Franklin Berandah; Tan, Soon Guan

    2010-06-01

    Multivariate analysis including correlation, multiple stepwise linear regression, and cluster analyses were applied to investigate the heavy metal concentrations (Cd, Cu, Fe, Ni, Pb, and Zn) in the different parts of bivalves and gastropods. It was also aimed to distinguish statistically the differences between the marine bivalves and the gastropods with regards to the accumulation of heavy metals in the different tissues. The different parts of four species of bivalves and four species of gastropods were obtained and analyzed for heavy metals. The multivariate analyses were then applied on the data. From the multivariate analyses conducted, there were correlations found between the soft tissues of bivalves and gastropods, but none was found between the shells and the soft tissues of most of the molluscs (except for Cerithidea obtusa and Puglina cochlidium). The significant correlations (P < 0.05) found between the soft tissues were further complemented by the multiple stepwise linear regressions where heavy metals in the total soft tissues were influenced by the accumulation in the different types of soft tissues. The present study found that the distributions of heavy metals in the different parts of molluscs were related to their feeding habits and living habitats. The statistical approaches proposed in this study are recommended for use in biomonitoring studies, since multivariate analyses can reduce the cost and time involved in identifying an effective tissue to monitor the heavy metal(s) bioavailability and contamination in tropical coastal waters.

  8. The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data

    PubMed Central

    Hebart, Martin N.; Görgen, Kai; Haynes, John-Dylan

    2015-01-01

    The multivariate analysis of brain signals has recently sparked a great amount of interest, yet accessible and versatile tools to carry out decoding analyses are scarce. Here we introduce The Decoding Toolbox (TDT) which represents a user-friendly, powerful and flexible package for multivariate analysis of functional brain imaging data. TDT is written in Matlab and equipped with an interface to the widely used brain data analysis package SPM. The toolbox allows running fast whole-brain analyses, region-of-interest analyses and searchlight analyses, using machine learning classifiers, pattern correlation analysis, or representational similarity analysis. It offers automatic creation and visualization of diverse cross-validation schemes, feature scaling, nested parameter selection, a variety of feature selection methods, multiclass capabilities, and pattern reconstruction from classifier weights. While basic users can implement a generic analysis in one line of code, advanced users can extend the toolbox to their needs or exploit the structure to combine it with external high-performance classification toolboxes. The toolbox comes with an example data set which can be used to try out the various analysis methods. Taken together, TDT offers a promising option for researchers who want to employ multivariate analyses of brain activity patterns. PMID:25610393

  9. The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data.

    PubMed

    Hebart, Martin N; Görgen, Kai; Haynes, John-Dylan

    2014-01-01

    The multivariate analysis of brain signals has recently sparked a great amount of interest, yet accessible and versatile tools to carry out decoding analyses are scarce. Here we introduce The Decoding Toolbox (TDT) which represents a user-friendly, powerful and flexible package for multivariate analysis of functional brain imaging data. TDT is written in Matlab and equipped with an interface to the widely used brain data analysis package SPM. The toolbox allows running fast whole-brain analyses, region-of-interest analyses and searchlight analyses, using machine learning classifiers, pattern correlation analysis, or representational similarity analysis. It offers automatic creation and visualization of diverse cross-validation schemes, feature scaling, nested parameter selection, a variety of feature selection methods, multiclass capabilities, and pattern reconstruction from classifier weights. While basic users can implement a generic analysis in one line of code, advanced users can extend the toolbox to their needs or exploit the structure to combine it with external high-performance classification toolboxes. The toolbox comes with an example data set which can be used to try out the various analysis methods. Taken together, TDT offers a promising option for researchers who want to employ multivariate analyses of brain activity patterns. PMID:25610393

  10. A guide to statistical analysis in microbial ecology: a community-focused, living review of multivariate data analyses.

    PubMed

    Buttigieg, Pier Luigi; Ramette, Alban

    2014-12-01

    The application of multivariate statistical analyses has become a consistent feature in microbial ecology. However, many microbial ecologists are still in the process of developing a deep understanding of these methods and appreciating their limitations. As a consequence, staying abreast of progress and debate in this arena poses an additional challenge to many microbial ecologists. To address these issues, we present the GUide to STatistical Analysis in Microbial Ecology (GUSTA ME): a dynamic, web-based resource providing accessible descriptions of numerous multivariate techniques relevant to microbial ecologists. A combination of interactive elements allows users to discover and navigate between methods relevant to their needs and examine how they have been used by others in the field. We have designed GUSTA ME to become a community-led and -curated service, which we hope will provide a common reference and forum to discuss and disseminate analytical techniques relevant to the microbial ecology community.

  11. A guide to statistical analysis in microbial ecology: a community-focused, living review of multivariate data analyses.

    PubMed

    Buttigieg, Pier Luigi; Ramette, Alban

    2014-12-01

    The application of multivariate statistical analyses has become a consistent feature in microbial ecology. However, many microbial ecologists are still in the process of developing a deep understanding of these methods and appreciating their limitations. As a consequence, staying abreast of progress and debate in this arena poses an additional challenge to many microbial ecologists. To address these issues, we present the GUide to STatistical Analysis in Microbial Ecology (GUSTA ME): a dynamic, web-based resource providing accessible descriptions of numerous multivariate techniques relevant to microbial ecologists. A combination of interactive elements allows users to discover and navigate between methods relevant to their needs and examine how they have been used by others in the field. We have designed GUSTA ME to become a community-led and -curated service, which we hope will provide a common reference and forum to discuss and disseminate analytical techniques relevant to the microbial ecology community. PMID:25314312

  12. Identifying Prognostic SNPs in Clinical Cohorts: Complementing Univariate Analyses by Resampling and Multivariable Modeling

    PubMed Central

    Hieke, Stefanie; Benner, Axel; Schlenk, Richard F.; Schumacher, Martin; Bullinger, Lars; Binder, Harald

    2016-01-01

    Clinical cohorts with time-to-event endpoints are increasingly characterized by measurements of a number of single nucleotide polymorphisms that is by a magnitude larger than the number of measurements typically considered at the gene level. At the same time, the size of clinical cohorts often is still limited, calling for novel analysis strategies for identifying potentially prognostic SNPs that can help to better characterize disease processes. We propose such a strategy, drawing on univariate testing ideas from epidemiological case-controls studies on the one hand, and multivariable regression techniques as developed for gene expression data on the other hand. In particular, we focus on stable selection of a small set of SNPs and corresponding genes for subsequent validation. For univariate analysis, a permutation-based approach is proposed to test at the gene level. We use regularized multivariable regression models for considering all SNPs simultaneously and selecting a small set of potentially important prognostic SNPs. Stability is judged according to resampling inclusion frequencies for both the univariate and the multivariable approach. The overall strategy is illustrated with data from a cohort of acute myeloid leukemia patients and explored in a simulation study. The multivariable approach is seen to automatically focus on a smaller set of SNPs compared to the univariate approach, roughly in line with blocks of correlated SNPs. This more targeted extraction of SNPs results in more stable selection at the SNP as well as at the gene level. Thus, the multivariable regression approach with resampling provides a perspective in the proposed analysis strategy for SNP data in clinical cohorts highlighting what can be added by regularized regression techniques compared to univariate analyses. PMID:27159447

  13. Multivariate analyses of locoregional recurrences and skin complications after postmastectomy radiotherapy using electrons or photons

    SciTech Connect

    Huang, E.-Y.; Chen, H.-C.; Sun, L.-M.; Fang, F.-M.; Hsu, H.-C.; Hsiung, C.-Y.; Huang, Y.-J.; Wang, C.-Y.; Wang, C.-J. . E-mail: cjw1010@adm.cgmh.org.tw

    2006-08-01

    Purpose: We retrospectively analyzed factors of locoregional (LR) recurrence and skin complications in patients after postmastectomy radiotherapy (PMRT). Methods and Materials: From January 1988 to December 1999, a total of 246 women with Stage II and III breast cancer received PMRT. Doses of 46 to 52.2 Gy/23 to 29 fractions were delivered to the chest wall (CW) and peripheral lymphatic drainage with 12 to 15 MeV single-portal electrons or 6MV photons. Of the patients, 84 patients received an additional 6 to 20 Gy boost to the surgical scar using 9 MeV electrons. We used the Cox regression model for multivariate analyses of CW, supraclavicular nodes (SCN), and LR recurrence. Results: N3 stage (positive nodes >9) (p = 0.003) and diabetes (p = 0.004) were independent factors of CW recurrence. Analysis of ipsilateral SCN recurrence showed that N3 stage (p < 0.001) and electrons (p = 0.006) were independent factors. For LR recurrence, N3 (p < 0.001), T3 to T4 (p = 0.033) and electrons (p = 0.003) were significant factors. Analysis of skin telangiectasia revealed that electrons (p < 0.001) and surgical scar boost (p = 0.003) were independent factors. Conclusions: Photons are superior to single-portal electrons in patients receiving postmastectomy radiotherapy because of better locoregional control and less skin telangiectasia. In patients in whom the number of positive axillary nodes is >9, more aggressive treatment may be considered for better locoregional control.

  14. Classification characteristics of multivariate analyses for groundwater chemistry in the nitrate contaminated area

    NASA Astrophysics Data System (ADS)

    Nakagawa, K.; Amano, H.

    2015-12-01

    Groundwater nitrate pollution in agricultural field is a common problem in many parts of the world. To design effective countermeasures for the pollution, understanding of contaminant transport and source identification will be in need. Classification of groundwater chemistry is useful tool to compare between water from different sources. In this study, results of 4 multivariate analyses for groundwater chemistry were compared. In the multivariate analyses, 277 sampling points data of major ion concentrations (Cl-, NO3-, SO42-, HCO3-, Na+, K+, Mg2+, Ca2+) were used (2011-2013). 4 multivariate analysis methods were as follows; 1) HCA (Hierarchical Cluster Analysis); Based on 8 major ion concentrations (8 dimensional 277 vectors), HCA was performed. 2) HCA with PCA (Principal Component Analysis); PCA was performed with major ion concentrations. Based on 2 principal components scores obtained from PCA (2 dimensional 277 vectors), HCA was performed. 3) HCA with SOM (Self Organized Map); SOM was performed with major ion concentrations. Obtained 84 reference vectors used for HCA (8 dimensional 84 vectors). 4) HCA, SOM with PCA; PCA was performed with major ion concentrations. Based on 2 principal component scores obtained from PCA, SOM was performed. Finally, HCA was performed with 80 reference vectors (2 dimensional 80 vectors). The number of clusters were fixed to 5, which was determined based on DBI (Davies-Bouldin Index) at the method 3). Broadly characteristics of each cluster is same among all multivariate analysis methods. According to this characteristics, 2 of 5 clusters show nitrate pollution, which are exceeding Japanese drinking water standards (10 mg/L). This 2 clusters can be distinguished by level of nitrate concentration. Other 3 clusters can also distinguished by level of major ion concentrations. Each cluster may be related to land use, because spatial distribution of sampling points shown by clusters congregate in specific locations. In the methods 1

  15. Prevalence and Predictive Factors of Sexual Dysfunction in Iranian Women: Univariate and Multivariate Logistic Regression Analyses

    PubMed Central

    Direkvand-Moghadam, Ashraf; Suhrabi, Zainab; Akbari, Malihe

    2016-01-01

    Background Female sexual dysfunction, which can occur during any stage of a normal sexual activity, is a serious condition for individuals and couples. The present study aimed to determine the prevalence and predictive factors of female sexual dysfunction in women referred to health centers in Ilam, the Western Iran, in 2014. Methods In the present cross-sectional study, 444 women who attended health centers in Ilam were enrolled from May to September 2014. Participants were selected according to the simple random sampling method. Univariate and multivariate logistic regression analyses were used to predict the risk factors of female sexual dysfunction. Diffe rences with an alpha error of 0.05 were regarded as statistically significant. Results Overall, 75.9% of the study population exhibited sexual dysfunction. Univariate logistic regression analysis demonstrated that there was a significant association between female sexual dysfunction and age, menarche age, gravidity, parity, and education (P<0.05). Multivariate logistic regression analysis indicated that, menarche age (odds ratio, 1.26), education level (odds ratio, 1.71), and gravida (odds ratio, 1.59) were independent predictive variables for female sexual dysfunction. Conclusion The majority of Iranian women suffer from sexual dysfunction. A lack of awareness of Iranian women's sexual pleasure and formal training on sexual function and its influencing factors, such as menarche age, gravida, and level of education, may lead to a high prevalence of female sexual dysfunction. PMID:27688863

  16. Prevalence and Predictive Factors of Sexual Dysfunction in Iranian Women: Univariate and Multivariate Logistic Regression Analyses

    PubMed Central

    Direkvand-Moghadam, Ashraf; Suhrabi, Zainab; Akbari, Malihe

    2016-01-01

    Background Female sexual dysfunction, which can occur during any stage of a normal sexual activity, is a serious condition for individuals and couples. The present study aimed to determine the prevalence and predictive factors of female sexual dysfunction in women referred to health centers in Ilam, the Western Iran, in 2014. Methods In the present cross-sectional study, 444 women who attended health centers in Ilam were enrolled from May to September 2014. Participants were selected according to the simple random sampling method. Univariate and multivariate logistic regression analyses were used to predict the risk factors of female sexual dysfunction. Diffe rences with an alpha error of 0.05 were regarded as statistically significant. Results Overall, 75.9% of the study population exhibited sexual dysfunction. Univariate logistic regression analysis demonstrated that there was a significant association between female sexual dysfunction and age, menarche age, gravidity, parity, and education (P<0.05). Multivariate logistic regression analysis indicated that, menarche age (odds ratio, 1.26), education level (odds ratio, 1.71), and gravida (odds ratio, 1.59) were independent predictive variables for female sexual dysfunction. Conclusion The majority of Iranian women suffer from sexual dysfunction. A lack of awareness of Iranian women's sexual pleasure and formal training on sexual function and its influencing factors, such as menarche age, gravida, and level of education, may lead to a high prevalence of female sexual dysfunction.

  17. Spectroscopy and multivariate analyses applications related to solid rocket nozzle bondline

    NASA Technical Reports Server (NTRS)

    Arendale, W. F.; Hatcher, Richard; Benson, Brian; Workman, Gary L.

    1991-01-01

    Chemical composition and molecular orientation define the properties of materials. Information related to chemical composition and molecular configuration is obtained by various forms of spectroscopy. Software algorithms developed for multivariate analyses, expert systems, and Artificial Intelligence (AI) are used to conduct repetitive operations. The techniques are believed to be of particular significance toward achieving TQM objectives. The objective was to obtain information related to the quality of the bondline in the solid rocket motor, SRM, nozzle. Hysol 934 NA, a room temperature curing epoxide resin, is used as the bonding agent. A good bond requires that the adhesive be placed on a properly prepared metal surface, the adhesives Part A and B be mixed in appropriate ratio from material within shelf life specifications. Spectroscopic data was obtained for surfaces prepared according to specifications, contaminated metal surfaces, samples of the epoxide adhesive at times that represent shelf aging from 3 months to 2 years, several mix ratio of A to B, and curing material. Temperature was found to be a significant factor. The study concentrated on pot life and mix ratio.

  18. Multivariate qualitative analysis of banned additives in food safety using surface enhanced Raman scattering spectroscopy

    NASA Astrophysics Data System (ADS)

    He, Shixuan; Xie, Wanyi; Zhang, Wei; Zhang, Liqun; Wang, Yunxia; Liu, Xiaoling; Liu, Yulong; Du, Chunlei

    2015-02-01

    A novel strategy which combines iteratively cubic spline fitting baseline correction method with discriminant partial least squares qualitative analysis is employed to analyze the surface enhanced Raman scattering (SERS) spectroscopy of banned food additives, such as Sudan I dye and Rhodamine B in food, Malachite green residues in aquaculture fish. Multivariate qualitative analysis methods, using the combination of spectra preprocessing iteratively cubic spline fitting (ICSF) baseline correction with principal component analysis (PCA) and discriminant partial least squares (DPLS) classification respectively, are applied to investigate the effectiveness of SERS spectroscopy for predicting the class assignments of unknown banned food additives. PCA cannot be used to predict the class assignments of unknown samples. However, the DPLS classification can discriminate the class assignment of unknown banned additives using the information of differences in relative intensities. The results demonstrate that SERS spectroscopy combined with ICSF baseline correction method and exploratory analysis methodology DPLS classification can be potentially used for distinguishing the banned food additives in field of food safety.

  19. Multivariate qualitative analysis of banned additives in food safety using surface enhanced Raman scattering spectroscopy.

    PubMed

    He, Shixuan; Xie, Wanyi; Zhang, Wei; Zhang, Liqun; Wang, Yunxia; Liu, Xiaoling; Liu, Yulong; Du, Chunlei

    2015-02-25

    A novel strategy which combines iteratively cubic spline fitting baseline correction method with discriminant partial least squares qualitative analysis is employed to analyze the surface enhanced Raman scattering (SERS) spectroscopy of banned food additives, such as Sudan I dye and Rhodamine B in food, Malachite green residues in aquaculture fish. Multivariate qualitative analysis methods, using the combination of spectra preprocessing iteratively cubic spline fitting (ICSF) baseline correction with principal component analysis (PCA) and discriminant partial least squares (DPLS) classification respectively, are applied to investigate the effectiveness of SERS spectroscopy for predicting the class assignments of unknown banned food additives. PCA cannot be used to predict the class assignments of unknown samples. However, the DPLS classification can discriminate the class assignment of unknown banned additives using the information of differences in relative intensities. The results demonstrate that SERS spectroscopy combined with ICSF baseline correction method and exploratory analysis methodology DPLS classification can be potentially used for distinguishing the banned food additives in field of food safety.

  20. Multivariate qualitative analysis of banned additives in food safety using surface enhanced Raman scattering spectroscopy.

    PubMed

    He, Shixuan; Xie, Wanyi; Zhang, Wei; Zhang, Liqun; Wang, Yunxia; Liu, Xiaoling; Liu, Yulong; Du, Chunlei

    2015-02-25

    A novel strategy which combines iteratively cubic spline fitting baseline correction method with discriminant partial least squares qualitative analysis is employed to analyze the surface enhanced Raman scattering (SERS) spectroscopy of banned food additives, such as Sudan I dye and Rhodamine B in food, Malachite green residues in aquaculture fish. Multivariate qualitative analysis methods, using the combination of spectra preprocessing iteratively cubic spline fitting (ICSF) baseline correction with principal component analysis (PCA) and discriminant partial least squares (DPLS) classification respectively, are applied to investigate the effectiveness of SERS spectroscopy for predicting the class assignments of unknown banned food additives. PCA cannot be used to predict the class assignments of unknown samples. However, the DPLS classification can discriminate the class assignment of unknown banned additives using the information of differences in relative intensities. The results demonstrate that SERS spectroscopy combined with ICSF baseline correction method and exploratory analysis methodology DPLS classification can be potentially used for distinguishing the banned food additives in field of food safety. PMID:25300041

  1. Analysing DHPC/DMPC bicelles by diffusion NMR and multivariate decomposition.

    PubMed

    Björnerås, Johannes; Nilsson, Mathias; Mäler, Lena

    2015-11-01

    Mixtures of lipids and detergents are known to form bicelles at certain parameter ranges, but many uncertainties remain concerning the details of the phase behaviour of these mixtures and the morphology of the formed lipid assemblies. Here we used nuclear magnetic resonance (NMR) diffusion data in combination with the multivariate processing method speedy component resolution (SCORE) to analyse mixtures of 1,2-dihexanoyl-sn-glycero-3-phosphocholine (DHPC) and 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) with the relative concentration q=[DMPC]/[DHPC]=0.5 at total lipid concentrations ranging from 15 to 300 mM. With this approach we were able to resolve the heavily overlapping mixture spectra into component spectra and obtained reliable diffusion coefficients for lipid concentrations in the range 15 to 300 mM, although at high concentrations (250-300 mM), non-negativity constraints or overfactoring was required to successfully decompose the data. At 50-300 mM total lipid concentration, the radii estimated from the diffusion coefficient of DMPC indicate assemblies of the appropriate bicelle size, although small size variations exist, while at lower concentrations the morphology appears to change to larger assemblies. Taken together, the results suggest that for q=0.5 DMPC/DHPC mixtures there is a relatively broad concentration range above 50 mM where bicelles may reliably be assumed to adopt the 'classical' bicelle morphology. The study clearly demonstrates the usefulness of our approach for accurately determining physical properties of complex mixtures such as bicelles. Both reliable diffusion coefficients and chemical shifts can be derived from overlapping data. This should prove useful for analysing the behaviour of other, more complex, lipid mixtures.

  2. Power of univariate and multivariate analyses of repeated measurements in controlled clinical trials.

    PubMed

    Overall, J E; Atlas, R S

    1999-04-01

    The power of univariate and multivariate tests of significance is compared in relation to linear and nonlinear patterns of treatment effects in a repeated measurement design. Bonferroni correction was used to control the experiment-wise error rate in combining results from univariate tests of significance accomplished separately on average level, linear, quadratic, and cubic trend components. Multivariate tests on these same components of the overall treatment effect, as well as a multivariate test for between-groups difference on the original repeated measurements, were also evaluated for power against the same representative patterns of treatment effects. Results emphasize the advantage of parsimony that is achieved by transforming multiple repeated measurements into a reduced set of mean ngful composite variables representing average levels and rates of change. The Bonferroni correction applied to the separate univariate tests provided experiment-wise protection against Type I error, produced slightly greater experiment-wise power than a multivariate test applied to the same components of the data patterns, and provided substantially greater power than a multivariate test on the complete set of original repeated measurements. The separate univariate tests provide interpretive advantage regarding locus of the treatment effects. PMID:10348408

  3. Multivariate analyses with end-member mixing to characterize groundwater flow: Wind Cave and associated aquifers

    USGS Publications Warehouse

    Long, Andrew J.; Valder, Joshua F.

    2011-01-01

    Principal component analysis (PCA) applied to hydrochemical data has been used with end-member mixing to characterize groundwater flow to a limited extent, but aspects of this approach are unresolved. Previous similar approaches typically have assumed that the extreme-value samples identified by PCA represent end members. The method presented herein is different from previous work in that (1) end members were not assumed to have been sampled but rather were estimated and constrained by prior knowledge; (2) end-member mixing was quantified in relation to hydrogeologic domains, which focuses model results on major hydrologic processes; (3) a method to select an appropriate number of end members using a series of cluster analyses is presented; and (4) conservative tracers were weighted preferentially in model calibration, which distributed model errors of optimized values, or residuals, more appropriately than would otherwise be the case. The latter item also provides an estimate of the relative influence of geochemical evolution along flow paths in comparison to mixing. This method was applied to groundwater in Wind Cave and the associated karst aquifer in the Black Hills of South Dakota, USA. The end-member mixing model was used to test a hypothesis that five different end-member waters are mixed in the groundwater system comprising five hydrogeologic domains. The model estimated that Wind Cave received most of its groundwater inflow from local surface recharge with an additional 33% from an upgradient aquifer. Artesian springs in the vicinity of Wind Cave primarily received water from regional groundwater flow.

  4. Differentiating Puerariae Lobatae Radix and Puerariae Thomsonii Radix using HPTLC coupled with multivariate classification analyses.

    PubMed

    Wong, Ka H; Razmovski-Naumovski, Valentina; Li, Kong M; Li, George Q; Chan, Kelvin

    2014-07-01

    Puerariae Lobatae Radix (PLR), the root of Pueraria lobata, is a traditional Chinese medicine for treating diabetes and cardiovascular diseases. Puerariae Thomsonii Radix (PTR), the root of Pueraria thomsonii, is a closely related species to PLR and has been used as a PLR substitute in clinical practice. The aim of this study was to compare the classification accuracy of high performance thin-layer chromatography (HPTLC) with that of ultra-performance liquid chromatography (UPLC) in differentiating PLR from PTR. The Matlab functions were used to facilitate the digitalisation and pre-processing of the HPTLC plates. Seven multivariate classification methods were evaluated for the two chromatographic methods. The results demonstrated that the HPTLC classification models were comparable to the UPLC classification models. In particular, k-nearest neighbours, partial least square-discriminant analysis, principal component analysis-discriminant analysis and support vector machine-discriminant analysis showed the highest rate of correct species classification, whilst the lowest classification rate was obtained from soft independent modelling of class analogy. In conclusion, HPTLC combined with multivariate analysis is a promising technique for the quality control and differentiation of PLR and PTR. PMID:24631955

  5. Cucumis monosomic alien addition lines: morphological, cytological, and genotypic analyses.

    PubMed

    Chen, Jin-Feng; Luo, Xiang-Dong; Qian, Chun-Tao; Jahn, Molly M; Staub, Jack E; Zhuang, Fei-Yun; Lou, Qun-Feng; Ren, Gang

    2004-05-01

    Cucumis hystrix Chakr. (HH, 2n=24), a wild relative of the cultivated cucumber, possesses several potentially valuable disease-resistance and abiotic stress-tolerance traits for cucumber ( C. sativus L., CC, 2n=14) improvement. Numerous attempts have been made to transfer desirable traits since the successful interspecific hybridization between C. hystrix and C. sativus, one of which resulted in the production of an allotriploid (HCC, 2n=26: one genome of C. hystrix and two of C. sativus). When this genotype was treated with colchicine to induce polyploidy, two monosomic alien addition lines (MAALs) (plant nos. 87 and 517: 14 CC+1 H, 2n=15) were recovered among 252 viable plants. Each of these plants was morphologically distinct from allotriploids and cultivated cucumbers. Cytogenetic and molecular marker analyses were performed to confirm the genetic constitution and further characterize these two MAALs. Chromosome counts made from at least 30 meristematic cells from each plant confirmed 15 nuclear chromosomes. In pollen mother cells of plant nos. 87 and 517, seven bivalents and one univalent were observed at diakinesis and metaphase I; the frequency of trivalent formation was low (about 4-5%). At anaphase I and II, stochastic and asymmetric division led to the formation of two gamete classes: n=7 and n=8; however, pollen fertility was relatively high. Pollen stainability in plant no. 87 was 86.7% and in plant no. 517 was 93.2%. Random amplified polymorphic DNA analysis was performed using 100 random 10-base primers. Genotypes obtained with eight primers (A-9, A-11, AH-13, AI-19, AJ-18, AJ-20, E-19, and N-20) showed a band common to the two MAAL plants and C. hystrix that was absent in C. sativus, confirming that the alien chromosomes present in the MAALs were derived from C. hystrix. Morphological differences and differences in banding patterns were also observed between plant nos. 87 and 517 after amplification with primers AI-5, AJ-13, N-12, and N-20

  6. Synchrotron Infrared Spectroscopy with Multivariate Spectral Analyses Potentially Facilitates the Classification of Inherent Structures of Feed-Type of Sorghum

    SciTech Connect

    Yu Peiqiang; Damiran, Daalkhaijav; Liu Dasen

    2010-02-03

    The objective of this study was to investigate the inherent structural-chemical features of Chinese feed-type sorghum seed using synchrotron-radiation Fourier transform infrared microspectroscopy (SRFTIRM) with two multivariate molecular spectral analysis techniques: Agglomerative Hierarchical cluster (AHCA) and principal component analyses (PCA). The results show that by application of these two multivariate techniques with the infrared spectroscopy of the SRFTIRM, it makes possible to discriminate and classify the inherent molecular structural features among the different layers of sorghum with a great efficiency. With the SRFTIRM, images of the molecular chemistry of sorghum could be generated at an ultra-spatial resolution. The features of nutrient matrix and nutrient make-up and interactions could be revealed.

  7. Multivariate analyses with end-member mixing to characterize groundwater flow: Wind Cave and associated aquifers

    USGS Publications Warehouse

    Long, A.J.; Valder, J.F.

    2011-01-01

    Principal component analysis (PCA) applied to hydrochemical data has been used with end-member mixing to characterize groundwater flow to a limited extent, but aspects of this approach are unresolved. Previous similar approaches typically have assumed that the extreme-value samples identified by PCA represent end members. The method presented herein is different from previous work in that (1) end members were not assumed to have been sampled but rather were estimated and constrained by prior knowledge; (2) end-member mixing was quantified in relation to hydrogeologic domains, which focuses model results on major hydrologic processes; (3) a method to select an appropriate number of end members using a series of cluster analyses is presented; and (4) conservative tracers were weighted preferentially in model calibration, which distributed model errors of optimized values, or residuals, more appropriately than would otherwise be the case. The latter item also provides an estimate of the relative influence of geochemical evolution along flow paths in comparison to mixing. This method was applied to groundwater in Wind Cave and the associated karst aquifer in the Black Hills of South Dakota, USA. The end-member mixing model was used to test a hypothesis that five different end-member waters are mixed in the groundwater system comprising five hydrogeologic domains. The model estimated that Wind Cave received most of its groundwater inflow from local surface recharge with an additional 33% from an upgradient aquifer. Artesian springs in the vicinity of Wind Cave primarily received water from regional groundwater flow. ?? 2011.

  8. Multivariate Analyses of Small Theropod Dinosaur Teeth and Implications for Paleoecological Turnover through Time

    PubMed Central

    Larson, Derek W.; Currie, Philip J.

    2013-01-01

    Isolated small theropod teeth are abundant in vertebrate microfossil assemblages, and are frequently used in studies of species diversity in ancient ecosystems. However, determining the taxonomic affinities of these teeth is problematic due to an absence of associated diagnostic skeletal material. Species such as Dromaeosaurus albertensis, Richardoestesia gilmorei, and Saurornitholestes langstoni are known from skeletal remains that have been recovered exclusively from the Dinosaur Park Formation (Campanian). It is therefore likely that teeth from different formations widely disparate in age or geographic position are not referable to these species. Tooth taxa without any associated skeletal material, such as Paronychodon lacustris and Richardoestesia isosceles, have also been identified from multiple localities of disparate ages throughout the Late Cretaceous. To address this problem, a dataset of measurements of 1183 small theropod teeth (the most specimen-rich theropod tooth dataset ever constructed) from North America ranging in age from Santonian through Maastrichtian were analyzed using multivariate statistical methods: canonical variate analysis, pairwise discriminant function analysis, and multivariate analysis of variance. The results indicate that teeth referred to the same taxon from different formations are often quantitatively distinct. In contrast, isolated teeth found in time equivalent formations are not quantitatively distinguishable from each other. These results support the hypothesis that small theropod taxa, like other dinosaurs in the Late Cretaceous, tend to be exclusive to discrete host formations. The methods outlined have great potential for future studies of isolated teeth worldwide, and may be the most useful non-destructive technique known of extracting the most data possible from isolated and fragmentary specimens. The ability to accurately assess species diversity and turnover through time based on isolated teeth will help illuminate

  9. Memory Reactivation Predicts Resistance to Retroactive Interference: Evidence from Multivariate Classification and Pattern Similarity Analyses

    PubMed Central

    Rugg, Michael D.

    2016-01-01

    Memory reactivation—the reinstatement of processes and representations engaged when an event is initially experienced—is believed to play an important role in strengthening and updating episodic memory. The present study examines how memory reactivation during a potentially interfering event influences memory for a previously experienced event. Participants underwent fMRI during the encoding phase of an AB/AC interference task in which some words were presented twice in association with two different encoding tasks (AB and AC trials) and other words were presented once (DE trials). The later memory test required retrieval of the encoding tasks associated with each of the study words. Retroactive interference was evident for the AB encoding task and was particularly strong when the AC encoding task was remembered rather than forgotten. We used multivariate classification and pattern similarity analysis (PSA) to measure reactivation of the AB encoding task during AC trials. The results demonstrated that reactivation of generic task information measured with multivariate classification predicted subsequent memory for the AB encoding task regardless of whether interference was strong and weak (trials for which the AC encoding task was remembered or forgotten, respectively). In contrast, reactivation of neural patterns idiosyncratic to a given AB trial measured with PSA only predicted memory when the strength of interference was low. These results suggest that reactivation of features of an initial experience shared across numerous events in the same category, but not features idiosyncratic to a particular event, are important in resisting retroactive interference caused by new learning. SIGNIFICANCE STATEMENT Reactivating a previously encoded memory is believed to provide an opportunity to strengthen the memory, but also to return the memory to a labile state, making it susceptible to interference. However, there is debate as to how memory reactivation elicited by

  10. Multivariate analyses applied to fetal, neonatal and pediatric MRI of neurodevelopmental disorders

    PubMed Central

    Levman, Jacob; Takahashi, Emi

    2015-01-01

    Multivariate analysis (MVA) is a class of statistical and pattern recognition methods that involve the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of medical neuroimaging-related challenges including identifying variables associated with a measure of clinical importance (i.e. patient outcome), creating diagnostic tests, assisting in characterizing developmental disorders, understanding disease etiology, development and progression, assisting in treatment monitoring and much more. Compared to adults, imaging of developing immature brains has attracted less attention from MVA researchers. However, remarkable MVA research growth has occurred in recent years. This paper presents the results of a systematic review of the literature focusing on MVA technologies applied to neurodevelopmental disorders in fetal, neonatal and pediatric magnetic resonance imaging (MRI) of the brain. The goal of this manuscript is to provide a concise review of the state of the scientific literature on studies employing brain MRI and MVA in a pre-adult population. Neurological developmental disorders addressed in the MVA research contained in this review include autism spectrum disorder, attention deficit hyperactivity disorder, epilepsy, schizophrenia and more. While the results of this review demonstrate considerable interest from the scientific community in applications of MVA technologies in pediatric/neonatal/fetal brain MRI, the field is still young and considerable research growth remains ahead of us. PMID:26640765

  11. Multivariate Analyses Applied to Healthy Neurodevelopment in Fetal, Neonatal, and Pediatric MRI

    PubMed Central

    Levman, Jacob; Takahashi, Emi

    2016-01-01

    Multivariate analysis (MVA) is a class of statistical and pattern recognition techniques that involve the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of neurological medical imaging related challenges including the evaluation of healthy brain development, the automated analysis of brain tissues and structures through image segmentation, evaluating the effects of genetic and environmental factors on brain development, evaluating sensory stimulation's relationship with functional brain activity and much more. Compared to adult imaging, pediatric, neonatal and fetal imaging have attracted less attention from MVA researchers, however, recent years have seen remarkable MVA research growth in pre-adult populations. This paper presents the results of a systematic review of the literature focusing on MVA applied to healthy subjects in fetal, neonatal and pediatric magnetic resonance imaging (MRI) of the brain. While the results of this review demonstrate considerable interest from the scientific community in applications of MVA technologies in brain MRI, the field is still young and significant research growth will continue into the future. PMID:26834576

  12. Multivariate analyses applied to fetal, neonatal and pediatric MRI of neurodevelopmental disorders.

    PubMed

    Levman, Jacob; Takahashi, Emi

    2015-01-01

    Multivariate analysis (MVA) is a class of statistical and pattern recognition methods that involve the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of medical neuroimaging-related challenges including identifying variables associated with a measure of clinical importance (i.e. patient outcome), creating diagnostic tests, assisting in characterizing developmental disorders, understanding disease etiology, development and progression, assisting in treatment monitoring and much more. Compared to adults, imaging of developing immature brains has attracted less attention from MVA researchers. However, remarkable MVA research growth has occurred in recent years. This paper presents the results of a systematic review of the literature focusing on MVA technologies applied to neurodevelopmental disorders in fetal, neonatal and pediatric magnetic resonance imaging (MRI) of the brain. The goal of this manuscript is to provide a concise review of the state of the scientific literature on studies employing brain MRI and MVA in a pre-adult population. Neurological developmental disorders addressed in the MVA research contained in this review include autism spectrum disorder, attention deficit hyperactivity disorder, epilepsy, schizophrenia and more. While the results of this review demonstrate considerable interest from the scientific community in applications of MVA technologies in pediatric/neonatal/fetal brain MRI, the field is still young and considerable research growth remains ahead of us. PMID:26640765

  13. Pre-Adult MRI of Brain Cancer and Neurological Injury: Multivariate Analyses

    PubMed Central

    Levman, Jacob; Takahashi, Emi

    2016-01-01

    Brain cancer and neurological injuries, such as stroke, are life-threatening conditions for which further research is needed to overcome the many challenges associated with providing optimal patient care. Multivariate analysis (MVA) is a class of pattern recognition technique involving the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of neuroimaging challenges, including identifying variables associated with patient outcomes; understanding an injury’s etiology, development, and progression; creating diagnostic tests; assisting in treatment monitoring; and more. Compared to adults, imaging of the developing brain has attracted less attention from MVA researchers, however, remarkable MVA growth has occurred in recent years. This paper presents the results of a systematic review of the literature focusing on MVA technologies applied to brain injury and cancer in neurological fetal, neonatal, and pediatric magnetic resonance imaging (MRI). With a wide variety of MRI modalities providing physiologically meaningful biomarkers and new biomarker measurements constantly under development, MVA techniques hold enormous potential toward combining available measurements toward improving basic research and the creation of technologies that contribute to improving patient care. PMID:27446888

  14. Pre-Adult MRI of Brain Cancer and Neurological Injury: Multivariate Analyses.

    PubMed

    Levman, Jacob; Takahashi, Emi

    2016-01-01

    Brain cancer and neurological injuries, such as stroke, are life-threatening conditions for which further research is needed to overcome the many challenges associated with providing optimal patient care. Multivariate analysis (MVA) is a class of pattern recognition technique involving the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of neuroimaging challenges, including identifying variables associated with patient outcomes; understanding an injury's etiology, development, and progression; creating diagnostic tests; assisting in treatment monitoring; and more. Compared to adults, imaging of the developing brain has attracted less attention from MVA researchers, however, remarkable MVA growth has occurred in recent years. This paper presents the results of a systematic review of the literature focusing on MVA technologies applied to brain injury and cancer in neurological fetal, neonatal, and pediatric magnetic resonance imaging (MRI). With a wide variety of MRI modalities providing physiologically meaningful biomarkers and new biomarker measurements constantly under development, MVA techniques hold enormous potential toward combining available measurements toward improving basic research and the creation of technologies that contribute to improving patient care. PMID:27446888

  15. Once upon Multivariate Analyses: When They Tell Several Stories about Biological Evolution

    PubMed Central

    Renaud, Sabrina; Dufour, Anne-Béatrice; Hardouin, Emilie A.; Ledevin, Ronan; Auffray, Jean-Christophe

    2015-01-01

    Geometric morphometrics aims to characterize of the geometry of complex traits. It is therefore by essence multivariate. The most popular methods to investigate patterns of differentiation in this context are (1) the Principal Component Analysis (PCA), which is an eigenvalue decomposition of the total variance-covariance matrix among all specimens; (2) the Canonical Variate Analysis (CVA, a.k.a. linear discriminant analysis (LDA) for more than two groups), which aims at separating the groups by maximizing the between-group to within-group variance ratio; (3) the between-group PCA (bgPCA) which investigates patterns of between-group variation, without standardizing by the within-group variance. Standardizing within-group variance, as performed in the CVA, distorts the relationships among groups, an effect that is particularly strong if the variance is similarly oriented in a comparable way in all groups. Such shared direction of main morphological variance may occur and have a biological meaning, for instance corresponding to the most frequent standing genetic variation in a population. Here we undertake a case study of the evolution of house mouse molar shape across various islands, based on the real dataset and simulations. We investigated how patterns of main variance influence the depiction of among-group differentiation according to the interpretation of the PCA, bgPCA and CVA. Without arguing about a method performing ‘better’ than another, it rather emerges that working on the total or between-group variance (PCA and bgPCA) will tend to put the focus on the role of direction of main variance as line of least resistance to evolution. Standardizing by the within-group variance (CVA), by dampening the expression of this line of least resistance, has the potential to reveal other relevant patterns of differentiation that may otherwise be blurred. PMID:26192946

  16. Once upon Multivariate Analyses: When They Tell Several Stories about Biological Evolution.

    PubMed

    Renaud, Sabrina; Dufour, Anne-Béatrice; Hardouin, Emilie A; Ledevin, Ronan; Auffray, Jean-Christophe

    2015-01-01

    Geometric morphometrics aims to characterize of the geometry of complex traits. It is therefore by essence multivariate. The most popular methods to investigate patterns of differentiation in this context are (1) the Principal Component Analysis (PCA), which is an eigenvalue decomposition of the total variance-covariance matrix among all specimens; (2) the Canonical Variate Analysis (CVA, a.k.a. linear discriminant analysis (LDA) for more than two groups), which aims at separating the groups by maximizing the between-group to within-group variance ratio; (3) the between-group PCA (bgPCA) which investigates patterns of between-group variation, without standardizing by the within-group variance. Standardizing within-group variance, as performed in the CVA, distorts the relationships among groups, an effect that is particularly strong if the variance is similarly oriented in a comparable way in all groups. Such shared direction of main morphological variance may occur and have a biological meaning, for instance corresponding to the most frequent standing genetic variation in a population. Here we undertake a case study of the evolution of house mouse molar shape across various islands, based on the real dataset and simulations. We investigated how patterns of main variance influence the depiction of among-group differentiation according to the interpretation of the PCA, bgPCA and CVA. Without arguing about a method performing 'better' than another, it rather emerges that working on the total or between-group variance (PCA and bgPCA) will tend to put the focus on the role of direction of main variance as line of least resistance to evolution. Standardizing by the within-group variance (CVA), by dampening the expression of this line of least resistance, has the potential to reveal other relevant patterns of differentiation that may otherwise be blurred.

  17. Multivariate analyses of Erzgebirge granite and rhyolite composition: Implications for classification of granites and their genetic relations

    USGS Publications Warehouse

    Forster, H.-J.; Davis, J.C.; Tischendorf, G.; Seltmann, R.

    1999-01-01

    High-precision major, minor and trace element analyses for 44 elements have been made of 329 Late Variscan granitic and rhyolitic rocks from the Erzgebirge metallogenic province of Germany. The intrusive histories of some of these granites are not completely understood and exposures of rock are not adequate to resolve relationships between what apparently are different plutons. Therefore, it is necessary to turn to chemical analyses to decipher the evolution of the plutons and their relationships. A new classification of Erzgebirge plutons into five major groups of granites, based on petrologic interpretations of geochemical and mineralogical relationships (low-F biotite granites; low-F two-mica granites; high-F, high-P2O5 Li-mica granites; high-F, low-P2O5 Li-mica granites; high-F, low-P2O5 biotite granites) was tested by multivariate techniques. Canonical analyses of major elements, minor elements, trace elements and ratio variables all distinguish the groups with differing amounts of success. Univariate ANOVA's, in combination with forward-stepwise and backward-elimination canonical analyses, were used to select ten variables which were most effective in distinguishing groups. In a biplot, groups form distinct clusters roughly arranged along a quadratic path. Within groups, individual plutons tend to be arranged in patterns possibly reflecting granitic evolution. Canonical functions were used to classify samples of rhyolites of unknown association into the five groups. Another canonical analysis was based on ten elements traditionally used in petrology and which were important in the new classification of granites. Their biplot pattern is similar to that from statistically chosen variables but less effective at distinguishing the five groups of granites. This study shows that multivariate statistical techniques can provide significant insight into problems of granitic petrogenesis and may be superior to conventional procedures for petrological interpretation.

  18. Instrumental and multivariate statistical analyses for the characterisation of the geographical origin of Apulian virgin olive oils.

    PubMed

    Longobardi, F; Ventrella, A; Casiello, G; Sacco, D; Catucci, L; Agostiano, A; Kontominas, M G

    2012-07-15

    In this paper, virgin olive oils (VOOs) coming from three different geographic origins of Apulia, were analysed for free acidity, peroxide value, spectrophotometric indexes, chlorophyll content, sterol, fatty acid, and triacylglycerol compositions. In order to predict the geographical origin of VOOs, different multivariate approaches were applied. By performing principal component analysis (PCA) a modest natural grouping of the VOOs was observed on the basis of their origin, and consequently three supervised techniques, i.e., general discriminant analysis (GDA), partial least squares-discriminant analysis (PLS-DA) and soft independent modelling of class analogy (SIMCA) were used and the results were compared. In particular, the best prediction ability was produced by applying GDA (average prediction ability of 82.5%), even if interesting results were obtained also by applying the other two classification techniques, i.e., 77.2% and 75.5% for PLS-DA and SIMCA, respectively.

  19. Impact of dose intensity of ponatinib on selected adverse events: Multivariate analyses from a pooled population of clinical trial patients.

    PubMed

    Dorer, David J; Knickerbocker, Ronald K; Baccarani, Michele; Cortes, Jorge E; Hochhaus, Andreas; Talpaz, Moshe; Haluska, Frank G

    2016-09-01

    Ponatinib is approved for adults with refractory chronic myeloid leukemia or Philadelphia chromosome-positive acute lymphoblastic leukemia, including those with the T315I BCR-ABL1 mutation. We pooled data from 3 clinical trials (N=671) to determine the impact of ponatinib dose intensity on the following adverse events: arterial occlusive events (cardiovascular, cerebrovascular, and peripheral vascular events), venous thromboembolic events, cardiac failure, thrombocytopenia, neutropenia, hypertension, pancreatitis, increased lipase, increased alanine aminotransferase, increased aspartate aminotransferase, rash, arthralgia, and hypertriglyceridemia. Multivariate analyses allowed adjustment for covariates potentially related to changes in dosing or an event. Logistic regression analysis identified significant associations between dose intensity and most events after adjusting for covariates. Pancreatitis, rash, and cardiac failure had the strongest associations with dose intensity (odds ratios >2). Time-to-event analyses showed significant associations between dose intensity and risk of arterial occlusive events and each subcategory. Further, these analyses suggested that a lag exists between a change in dose and the resulting change in event risk. No significant association between dose intensity and risk of venous thromboembolic events was evident. Collectively, these findings suggest a potential causal relationship between ponatinib dose and certain adverse events and support prospective investigations of approaches to lower average ponatinib dose intensity. PMID:27505637

  20. Impact of dose intensity of ponatinib on selected adverse events: Multivariate analyses from a pooled population of clinical trial patients.

    PubMed

    Dorer, David J; Knickerbocker, Ronald K; Baccarani, Michele; Cortes, Jorge E; Hochhaus, Andreas; Talpaz, Moshe; Haluska, Frank G

    2016-09-01

    Ponatinib is approved for adults with refractory chronic myeloid leukemia or Philadelphia chromosome-positive acute lymphoblastic leukemia, including those with the T315I BCR-ABL1 mutation. We pooled data from 3 clinical trials (N=671) to determine the impact of ponatinib dose intensity on the following adverse events: arterial occlusive events (cardiovascular, cerebrovascular, and peripheral vascular events), venous thromboembolic events, cardiac failure, thrombocytopenia, neutropenia, hypertension, pancreatitis, increased lipase, increased alanine aminotransferase, increased aspartate aminotransferase, rash, arthralgia, and hypertriglyceridemia. Multivariate analyses allowed adjustment for covariates potentially related to changes in dosing or an event. Logistic regression analysis identified significant associations between dose intensity and most events after adjusting for covariates. Pancreatitis, rash, and cardiac failure had the strongest associations with dose intensity (odds ratios >2). Time-to-event analyses showed significant associations between dose intensity and risk of arterial occlusive events and each subcategory. Further, these analyses suggested that a lag exists between a change in dose and the resulting change in event risk. No significant association between dose intensity and risk of venous thromboembolic events was evident. Collectively, these findings suggest a potential causal relationship between ponatinib dose and certain adverse events and support prospective investigations of approaches to lower average ponatinib dose intensity.

  1. Signature of Nonstationarity in Precipitation Extremes over Urbanizing Regions in India Identified through a Multivariate Frequency Analyses

    NASA Astrophysics Data System (ADS)

    Singh, Jitendra; Hari, Vittal; Sharma, Tarul; Karmakar, Subhankar; Ghosh, Subimal

    2016-04-01

    The statistical assumption of stationarity in hydrologic extreme time/event series has been relied heavily in frequency analysis. However, due to the analytically perceivable impacts of climate change, urbanization and concomitant land use pattern, assumption of stationarity in hydrologic time series will draw erroneous results, which in turn may affect the policy and decision-making. Past studies provided sufficient evidences on changes in the characteristics of Indian monsoon precipitation extremes and further it has been attributed to climate change and urbanization, which shows need of nonstationary analysis on the Indian monsoon extremes. Therefore, a comprehensive multivariate nonstationary frequency analysis has been conducted for the entire India to identify the precipitation characteristics (intensity, duration and depth) responsible for significant nonstationarity in the Indian monsoon. We use 1o resolution of precipitation data for a period of 1901-2004, in a Generalized Additive Model for Location, Scale and Shape (GAMLSS) framework. A cluster of GAMLSS models has been developed by considering nonstationarity in different combinations of distribution parameters through different regression techniques, and the best-fit model is further applied for bivariate analysis. A population density data has been utilized to identify the urban, urbanizing and rural regions. The results showed significant differences in the stationary and nonstationary bivariate return periods for the urbanizing grids, when compared to urbanized and rural grids. A comprehensive multivariate analysis has also been conducted to identify the precipitation characteristics particularly responsible for imprinting signature of nonstationarity.

  2. B-jet and c-jet identification with Neural Networks as well as combination of multivariate analyses for the search for of multivariate analyses for the search for single top-quark production

    SciTech Connect

    Renz, Manuel; /Karlsruhe U., EKP

    2008-06-01

    In the first part of this diploma thesis, the current version of the KIT Flavor Separator, a neural network which is able to distinguish between tagged b-quark jets and tagged c/light-quark jets, is presented. In comparison with previous versions four new input variables are utilized and new Monte Carlo samples with a larger number of simulated events are used for the training of the neural network. It is illustrated that the output of the neural network is continuously distributed between 1 and -1, whereas b-quark jets accumulate at 1, however, c-quark jets and light-quark jets have outputs next to -1. To ensure that the network output describes observed events correctly, the shapes of all input variables are compared in simulation and data. Thus the mismodelling of any input variable is excluded. Moreover, the b jet and light jet output distributions are compared with the output of samples of observed events, which are enhanced in the particular flavor. In contrast to previous versions, no b-jet output correction function has to be calculated, because the agreement between simulation and collision data is excellent for b-quark jets. For the light-jet output, correction functions are developed. Different applications of the KIT Flavor Separator are mentioned. For example it provides a precious input to all three CDF single top quark analyses. Furthermore, it is shown that the KIT Flavor Separator is a universal tool, which can be used in every high-p{sub T} analysis that requires the identification of b-quark jets with high efficiency. As it is pointed out, a further application is the estimation of the flavor composition of a given sample of observed events. In addition a neural network, which is able to separate c-quark jets from light-quark jets, is trained. It is shown, that all three flavors can be separated in the c-net-Flavor Separator plane. As a result, the uncertainties on the estimation of the flavor composition in events with one tagged jet are cut

  3. Application of multivariate analysis to the effects of additives on chemical and sensory quality of stored coffee brew.

    PubMed

    Pérez-Martínez, Mónica; Sopelana, Patricia; de Peña, M Paz; Cid, Concepción

    2008-12-24

    The aim of this work was to obtain a black coffee brew to be consumed hot by extension of its shelf life, by addition of additives. Four pH-regulator agents (sodium and potassium carbonates and bicarbonates), one pH regulator and antioxidant (sodium citrate), three antioxidants [sodium ascorbate, ethylenediaminetetracetic acid (EDTA), and sodium sulfite], and lactoserum were tested by sensory analysis. Sodium carbonate and bicarbonate were selected for a study of the physicochemical (soluble and volatile compounds related to the sensory properties) and sensorial quality of coffee brew stored for 90 days at 4 degrees C. Although both additives extended the shelf life of the coffee brew up to 60 days, sodium carbonate was the chosen additive because it was the most useful in limiting the pH decrease and perception of sourness, which are some of the main factors involved in the rejection of stored coffee brews, and it better maintained the aroma and taste/flavor. Moreover, the application of multivariate analysis facilitated first the description of the global changes of the coffee brews with or without additives throughout the storage using principal component analysis and second the obtainment of a simple equation only with pH and caffeic acid parameters to discriminate the three types of coffee brews and simplify the analytical process, by means of the stepwise discriminant analysis.

  4. Using multivariate analyses and GIS to identify pollutants and their spatial patterns in urban soils in Galway, Ireland.

    PubMed

    Zhang, Chaosheng

    2006-08-01

    Galway is a small but rapidly growing tourism city in western Ireland. To evaluate its environmental quality, a total of 166 surface soil samples (0-10 cm depth) were collected from parks and grasslands at the density of 1 sample per 0.25 km2 at the end of 2004. All samples were analysed using ICP-AES for the near-total concentrations of 26 chemical elements. Multivariate statistics and GIS techniques were applied to classify the elements and to identify elements influenced by human activities. Cluster analysis (CA) and principal component analysis (PCA) classified the elements into two groups: the first group predominantly derived from natural sources, the second being influenced by human activities. GIS mapping is a powerful tool in identifying the possible sources of pollutants. Relatively high concentrations of Cu, Pb and Zn were found in the city centre, old residential areas, and along major traffic routes, showing significant effects of traffic pollution. The element As is enriched in soils of the old built-up areas, which can be attributed to coal and peat combustion for home heating. Such significant spatial patterns of pollutants displayed by urban soils may imply potential health threat to residents of the contaminated areas of the city.

  5. A screening method based on UV-Visible spectroscopy and multivariate analysis to assess addition of filler juices and water to pomegranate juices.

    PubMed

    Boggia, Raffaella; Casolino, Maria Chiara; Hysenaj, Vilma; Oliveri, Paolo; Zunin, Paola

    2013-10-15

    Consumer demand for pomegranate juice has considerably grown, during the last years, for its potential health benefits. Since it is an expensive functional food, cheaper fruit juices addition (i.e., grape and apple juices) or its simple dilution, or polyphenols subtraction are deceptively used. At present, time-consuming analyses are used to control the quality of this product. Furthermore these analyses are expensive and require well-trained analysts. Thus, the purpose of this study was to propose a high-speed and easy-to-use shortcut. Based on UV-VIS spectroscopy and chemometrics, a screening method is proposed to quickly screening some common fillers of pomegranate juice that could decrease the antiradical scavenging capacity of pure products. The analytical method was applied to laboratory prepared juices, to commercial juices and to representative experimental mixtures at different levels of water and filler juices. The outcomes were evaluated by means of multivariate exploratory analysis. The results indicate that the proposed strategy can be a useful screening tool to assess addition of filler juices and water to pomegranate juices. PMID:23692760

  6. Evolvability of individual traits in a multivariate context: partitioning the additive genetic variance into common and specific components.

    PubMed

    McGuigan, Katrina; Blows, Mark W

    2010-07-01

    Genetic covariation among multiple traits will bias the direction of evolution. Although a trait's phenotypic context is crucial for understanding evolutionary constraints, the evolutionary potential of one (focal) trait, rather than the whole phenotype, is often of interest. The extent to which a focal trait can evolve independently depends on how much of the genetic variance in that trait is unique. Here, we present a hypothesis-testing framework for estimating the genetic variance in a focal trait that is independent of variance in other traits. We illustrate our analytical approach using two Drosophila bunnanda trait sets: a contact pheromone system comprised of cuticular hydrocarbons (CHCs), and wing shape, characterized by relative warps of vein position coordinates. Only 9% of the additive genetic variation in CHCs was trait specific, suggesting individual traits are unlikely to evolve independently. In contrast, most (72%) of the additive genetic variance in wing shape was trait specific, suggesting relative warp representations of wing shape could evolve independently. The identification of genetic variance in focal traits that is independent of other traits provides a way of studying the evolvability of individual traits within the broader context of the multivariate phenotype.

  7. Metabolic changes during a field experiment in a world-class windsurfing athlete: a trial with multivariate analyses.

    PubMed

    Resende, Nathália Maria; de Magalhães Neto, Anibal Monteiro; Bachini, Flávio; de Castro, Luis Eduardo Viveiros; Bassini, Adriana; Cameron, L C

    2011-10-01

    Physical exercise affects hematological equilibrium and metabolism. This study evaluated the biochemical and hematological responses of a male world-class athlete in sailing who is ranked among the top athletes on the official ISAF ranking list of windsurfing, class RS:X. The results describe the metabolic adaptations of this athlete in response to exercise in two training situations: the first when the athlete was using the usual training and dietary protocol, and the second following training and nutritional interventions based on a careful analysis of his diet and metabolic changes measured in a simulated competition. The intervention protocol for this study consisted of a 3-month facility-based program using neuromuscular training (NT), aerobic training (AT), and nutritional changes to promote anabolism and correct micronutrient malnutrition. Nutritional and training intervention produced an increase in the plasma availability of branched-chain amino acids (BCAAs), aromatic amino acids (AAAs), alanine, glutamate, and glutamine during exercise. Both training and nutritional interventions reduced ammonemia, uricemia, and uremia. In addition, we are able to correct a significant drop in potassium levels during races by correct supplementation. Due to the uniqueness of this experiment, these results may not apply to other windsurfers, but we nonetheless had the opportunity to characterize the metabolic adaptations of this athlete. We also proposed the importance of in-field metabolic analyses to the understanding, support, and training of world-class elite athletes. PMID:21978397

  8. Antioxidant and metabolite profiling of North American and neotropical blueberries using LC-TOF-MS and multivariate analyses.

    PubMed

    Ma, Chunhui; Dastmalchi, Keyvan; Flores, Gema; Wu, Shi-Biao; Pedraza-Peñalosa, Paola; Long, Chunlin; Kennelly, Edward J

    2013-04-10

    There are many neotropical blueberries, and recent studies have shown that some have even stronger antioxidant activity than the well-known edible North American blueberries. Antioxidant marker compounds were predicted by applying multivariate statistics to data from LC-TOF-MS analysis and antioxidant assays of 3 North American blueberry species (Vaccinium corymbosum, Vaccinium angustifolium, and a defined mixture of Vaccinium virgatum with V. corymbosum) and 12 neotropical blueberry species (Anthopterus wardii, Cavendishia grandifolia, Cavendishia isernii, Ceratostema silvicola, Disterigma rimbachii, Macleania coccoloboides, Macleania cordifolia, Macleania rupestris, Satyria boliviana, Sphyrospermum buxifolium, Sphyrospermum cordifolium, and Sphyrospermum ellipticum). Fourteen antioxidant markers were detected, and 12 of these, including 7 anthocyanins, 3 flavonols, 1 hydroxycinnamic acid, and 1 iridoid glycoside, were identified. This application of multivariate analysis to bioactivity and mass data can be used for identification of pharmacologically active natural products and may help to determine which neotropical blueberry species will be prioritized for agricultural development. Also, the compositional differences between North American and neotropical blueberries were determined by chemometric analysis, and 44 marker compounds including 16 anthocyanins, 15 flavonoids, 7 hydroxycinnamic acid derivatives, 5 triterpene glycosides, and 1 iridoid glycoside were identified.

  9. Application of nonparametric multivariate analyses to the authentication of wild and farmed European sea bass (Dicentrarchus labrax). Results of a survey on fish sampled in the retail trade.

    PubMed

    Fasolato, Luca; Novelli, Enrico; Salmaso, Luigi; Corain, Livio; Camin, Federica; Perini, Matteo; Antonetti, Paolo; Balzan, Stefania

    2010-10-27

    The aim of this study was to apply biometric measurements and analyses of proximate composition, fatty acid composition, and ratios of stable isotopes of carbon (δ(13)C) and nitrogen (δ(15)N) in muscle tissue to reliably differentiate between wild and farmed European sea bass (Dicentrarchus labrax). Farmed (n = 20) and wild (n = 19) European sea bass were purchased between March and May 2008 and used as standard samples. In the same months, a survey was conducted to evaluate the truthfulness of the statements on the labels of European sea bass sold in retail markets (declared farmed n = 34 and declared wild n = 33). In addition, data from the literature (reference) were employed to build the profile type of wild and farmed European sea bass. Primarily, an exploration and comparison of the analytical data of the standard data set based on principal component analysis and permutation test were performed. Afterward, an inferential statistical approach based on nonparametric combination test methodology (NPC) was applied on standard samples to check its suitability in discriminating the production method. This multivariate statistical analysis selected 30 variables on a total of 36 available. The validation of standard fish data set was accomplished by a novel nonparametric rank-based method according to profile type (just 1 misclassification over 39 samples). Both the NPC test and nonparametric rank-based method were then applied to survey fishes using the selected variables with the aim to classify the individual European sea bass as "true farmed" or "true wild". The former test segregated 10 fishes over 33 declared wild, whereas the results obtained by the nonparametric rank-based method showed that 11 of 33 declared wild European sea bass samples could be unquestionably attributed to the wild cluster. Moreover, considering the comparative contribution of profile type, a few surveyed farmed samples were ascribed to the wild cluster. PMID:20857938

  10. Application of nonparametric multivariate analyses to the authentication of wild and farmed European sea bass (Dicentrarchus labrax). Results of a survey on fish sampled in the retail trade.

    PubMed

    Fasolato, Luca; Novelli, Enrico; Salmaso, Luigi; Corain, Livio; Camin, Federica; Perini, Matteo; Antonetti, Paolo; Balzan, Stefania

    2010-10-27

    The aim of this study was to apply biometric measurements and analyses of proximate composition, fatty acid composition, and ratios of stable isotopes of carbon (δ(13)C) and nitrogen (δ(15)N) in muscle tissue to reliably differentiate between wild and farmed European sea bass (Dicentrarchus labrax). Farmed (n = 20) and wild (n = 19) European sea bass were purchased between March and May 2008 and used as standard samples. In the same months, a survey was conducted to evaluate the truthfulness of the statements on the labels of European sea bass sold in retail markets (declared farmed n = 34 and declared wild n = 33). In addition, data from the literature (reference) were employed to build the profile type of wild and farmed European sea bass. Primarily, an exploration and comparison of the analytical data of the standard data set based on principal component analysis and permutation test were performed. Afterward, an inferential statistical approach based on nonparametric combination test methodology (NPC) was applied on standard samples to check its suitability in discriminating the production method. This multivariate statistical analysis selected 30 variables on a total of 36 available. The validation of standard fish data set was accomplished by a novel nonparametric rank-based method according to profile type (just 1 misclassification over 39 samples). Both the NPC test and nonparametric rank-based method were then applied to survey fishes using the selected variables with the aim to classify the individual European sea bass as "true farmed" or "true wild". The former test segregated 10 fishes over 33 declared wild, whereas the results obtained by the nonparametric rank-based method showed that 11 of 33 declared wild European sea bass samples could be unquestionably attributed to the wild cluster. Moreover, considering the comparative contribution of profile type, a few surveyed farmed samples were ascribed to the wild cluster.

  11. Source apportionment of groundwater pollutants in Apulian agricultural sites using multivariate statistical analyses: case study of Foggia province

    PubMed Central

    2012-01-01

    Background Ground waters are an important resource of water supply for human health and activities. Groundwater uses and applications are often related to its composition, which is increasingly influenced by human activities. In fact the water quality of groundwater is affected by many factors including precipitation, surface runoff, groundwater flow, and the characteristics of the catchment area. During the years 2004-2007 the Agricultural and Food Authority of Apulia Region has implemented the project “Expansion of regional agro-meteorological network” in order to assess, monitor and manage of regional groundwater quality. The total wells monitored during this activity amounted to 473, and the water samples analyzed were 1021. This resulted in a huge and complex data matrix comprised of a large number of physical-chemical parameters, which are often difficult to interpret and draw meaningful conclusions. The application of different multivariate statistical techniques such as Cluster Analysis (CA), Principal Component Analysis (PCA), Absolute Principal Component Scores (APCS) for interpretation of the complex databases offers a better understanding of water quality in the study region. Results Form results obtained by Principal Component and Cluster Analysis applied to data set of Foggia province it’s evident that some sampling sites investigated show dissimilarities, mostly due to the location of the site, the land use and management techniques and groundwater overuse. By APCS method it’s been possible to identify three pollutant sources: Agricultural pollution 1 due to fertilizer applications, Agricultural pollution 2 due to microelements for agriculture and groundwater overuse and a third source that can be identified as soil run off and rock tracer mining. Conclusions Multivariate statistical methods represent a valid tool to understand complex nature of groundwater quality issues, determine priorities in the use of ground waters as irrigation water

  12. Hydrogeochemical Processes of Groundwater Using Multivariate Statistical Analyses and Inverse Geochemical Modeling in Samrak Park of Nakdong River Basin, Korea

    NASA Astrophysics Data System (ADS)

    Chung, Sang Yong

    2015-04-01

    Multivariate statistical methods and inverse geochemical modelling were used to assess the hydrogeochemical processes of groundwater in Nakdong River basin. The study area is located in a part of Nakdong River basin, the Busan Metropolitan City, Kora. Quaternary deposits forms Samrak Park region and are underlain by intrusive rocks of Bulkuksa group and sedimentary rocks of Yucheon group in the Cretaceous Period. The Samrak park region is acting as two aquifer systems of unconfined aquifer and confined aquifer. The unconfined aquifer consists of upper sand, and confined aquifer is comprised of clay, lower sand, gravel, weathered rock. Porosity and hydraulic conductivity of the area is 37 to 59% and 1.7 to 200m/day, respectively. Depth of the wells ranges from 9 to 77m. Piper's trilinear diagram, CaCl2 type was useful for unconfined aquifer and NaCl type was dominant for confined aquifer. By hierarchical cluster analysis (HCA), Group 1 and Group 2 are fully composed of unconfined aquifer and confined aquifer, respectively. In factor analysis (FA), Factor 1 is described by the strong loadings of EC, Na, K, Ca, Mg, Cl, HCO3, SO4 and Si, and Factor 2 represents the strong loadings of pH and Al. Base on the Gibbs diagram, the unconfined and confined aquifer samples are scattered discretely in the rock and evaporation areas. The principal hydrogeochemical processes occurring in the confined and unconfined aquifers are the ion exchange due to the phenomena of freshening under natural recharge and water-rock interactions followed by evaporation and dissolution. The saturation index of minerals such as Ca-montmorillonite, dolomite and calcite represents oversaturated, and the albite, gypsum and halite show undersaturated. Inverse geochemical modeling using PHREEQC code demonstrated that relatively few phases were required to derive the differences in groundwater chemistry along the flow path in the area. It also suggested that dissolution of carbonate and ion exchange

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

    PubMed Central

    2016-01-01

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

  14. Bivariate and multivariate analyses of the correlations between stability of the erythrocyte membrane, serum lipids and hematological variables.

    PubMed

    Bernardino Neto, M; de Avelar, E B; Arantes, T S; Jordão, I A; da Costa Huss, J C; de Souza, T M T; de Souza Penha, V A; da Silva, S C; de Souza, P C A; Tavares, M; Penha-Silva, N

    2013-01-01

    The observation that the fluidity must remain within a critical interval, outside which the stability and functionality of the cell tends to decrease, shows that stability, fluidity and function are related and that the measure of erythrocyte stability allows inferences about the fluidity or functionality of these cells. This study determined the biochemical and hematological variables that are directly or indirectly related to erythrocyte stability in a population of 71 volunteers. Data were evaluated by bivariate and multivariate analysis. The erythrocyte stability showed a greater association with hematological variables than the biochemical variables. The RDW stands out for its strong correlation with the stability of erythrocyte membrane, without being heavily influenced by other factors. Regarding the biochemical variables, the erythrocyte stability was more sensitive to LDL-C. Erythrocyte stability was significantly associated with RDW and LDL-C. Thus, the level of LDL-C is a consistent link between stability and functionality, suggesting that a measure of stability could be more one indirect parameter for assessing the risk of degenerative processes associated with high levels of LDL-C.

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

    PubMed

    Qi, Danyi; Roe, Brian E

    2016-01-01

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

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

    PubMed

    Qi, Danyi; Roe, Brian E

    2016-01-01

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

  17. Near-infrared noninvasive blood glucose prediction without using multivariate analyses: introduction of imaginary spectra due to scattering change in the skin.

    PubMed

    Maruo, Katsuhiko; Yamada, Yukio

    2015-04-01

    A noninvasive measurement method is proposed and examined to continuously predict blood glucose contents using near-infrared diffuse reflection difference spectra measured at the skin tissue without using multivariate analyses. Using the modified Beer’s law, the difference spectra are assumed to be synthesized from four major components in the human skin (water, protein, glucose, and fat) and a scattering equivalent component called baseline. As a result, one of the origins of the errors in blood glucose prediction using near-infrared is found to be the similarity of the shapes of the absorption spectrum between glucose and baseline. After separating the glucose contributions from the difference spectra at the characteristic wavelengths of baseline and fat, an imaginary component combining baseline and fat is introduced by considering that both the change in the fat contribution and the generation of baseline originate from the change in scattering in the skin. The imaginary component enables us to reduce the errors in blood glucose prediction. In contrast to the methods using multivariate analyses, the calculation process of the blood glucose contents from the measured reflection spectra is clear in this method, thus, it is easy to estimate the origins of the changes and contributions of the components in the measured difference spectra. The proposed method may become a useful tool for realization of noninvasive blood glucose prediction using near-infrared spectroscopy. PMID:25859836

  18. Multivariate spatial analyses of the distribution and origin of trace and major elements in soils surrounding a secondary lead smelter.

    PubMed

    Schneider, Arnaud R; Morvan, Xavier; Saby, Nicolas P A; Cancès, Benjamin; Ponthieu, Marie; Gommeaux, Maxime; Marin, Béatrice

    2016-08-01

    Major and trace elements in soils originate from natural processes and different anthropogenic activities which are difficult to discriminate. On a 17-ha impacted site in northern France, two industrial sources of soil contamination were xidentified: a former iron foundry and a current secondary lead smelter. To discriminate and map natural and anthropogenic sources of major and trace elements on this site, the rarely applied MULTISPATI-principal component analysis (PCA) method was used. Using a 20-m × 20-m grid, 247 topsoil horizons were sampled and analysed with a field-portable X-ray fluorescence analyser for screening soil contamination. The study site was heavily contaminated with Pb and, to a lesser degree, with Sn. Summary statistics and enrichment factors allowed the differentiation of the main lithogenic or anthropogenic origin of the elements. The MULTISPATI-PCA method, which explained 73.9 % of the variability with the three first factors, evidenced strong spatial structures. Those spatial structures were attributed to different natural and artificial processes in the study area. The first axis can be interpreted as a lithogenic effect. Axes 2 and 3 reflect the two different contamination sources. Pb, Sn and S originated from the secondary lead smelter while Fe and Ca were mainly derived from the old iron foundry activity and the old railway built with foundry sand. This study demonstrated that the MULTISPATI-PCA method can be successfully used to investigate multicontaminated sites to discriminate the various sources of contamination.

  19. Multivariate spatial analyses of the distribution and origin of trace and major elements in soils surrounding a secondary lead smelter.

    PubMed

    Schneider, Arnaud R; Morvan, Xavier; Saby, Nicolas P A; Cancès, Benjamin; Ponthieu, Marie; Gommeaux, Maxime; Marin, Béatrice

    2016-08-01

    Major and trace elements in soils originate from natural processes and different anthropogenic activities which are difficult to discriminate. On a 17-ha impacted site in northern France, two industrial sources of soil contamination were xidentified: a former iron foundry and a current secondary lead smelter. To discriminate and map natural and anthropogenic sources of major and trace elements on this site, the rarely applied MULTISPATI-principal component analysis (PCA) method was used. Using a 20-m × 20-m grid, 247 topsoil horizons were sampled and analysed with a field-portable X-ray fluorescence analyser for screening soil contamination. The study site was heavily contaminated with Pb and, to a lesser degree, with Sn. Summary statistics and enrichment factors allowed the differentiation of the main lithogenic or anthropogenic origin of the elements. The MULTISPATI-PCA method, which explained 73.9 % of the variability with the three first factors, evidenced strong spatial structures. Those spatial structures were attributed to different natural and artificial processes in the study area. The first axis can be interpreted as a lithogenic effect. Axes 2 and 3 reflect the two different contamination sources. Pb, Sn and S originated from the secondary lead smelter while Fe and Ca were mainly derived from the old iron foundry activity and the old railway built with foundry sand. This study demonstrated that the MULTISPATI-PCA method can be successfully used to investigate multicontaminated sites to discriminate the various sources of contamination. PMID:27094274

  20. Discrimination, correlation, and provenance of Bed I tephrostratigraphic markers, Olduvai Gorge, Tanzania, based on multivariate analyses of phenocryst compositions

    NASA Astrophysics Data System (ADS)

    Habermann, Jörg M.; McHenry, Lindsay J.; Stollhofen, Harald; Tolosana-Delgado, Raimon; Stanistreet, Ian G.; Deino, Alan L.

    2016-06-01

    The chronology of Pleistocene flora and fauna, including hominin remains and associated Oldowan industries in Bed I, Olduvai Gorge, Tanzania, is primarily based on 40Ar/39Ar dating of intercalated tuffs and lavas, combined with detailed tephrostratigraphic correlations within the basin. Although a high-resolution chronostratigraphic framework has been established for the eastern part of the Olduvai Basin, the western subbasin is less well known due in part to major lateral facies changes within Bed I combined with discontinuous exposure. We address these correlation difficulties using the discriminative power of the chemical composition of the major juvenile mineral phases (augite, anorthoclase, plagioclase) from tuffs, volcaniclastic sandstones, siliciclastic units, and lavas. We statistically evaluate these compositions, obtained from electron probe micro-analysis, applying principal component analysis and discriminant analysis to develop discriminant models that successfully classify most Bed I volcanic units. The correlations, resulting from integrated analyses of all target minerals, provide a basin-wide Bed I chemostratigraphic framework at high lateral and vertical resolution, consistent with the known geological context, that expands and refines the geochemical databases currently available. Correlation of proximal ignimbrites at the First Fault with medial and distal Lower Bed I successions of the western basin enables assessment of lateral facies and thickness trends that confirm Ngorongoro Volcano as the primary source for Lower Bed I, whereas Upper Bed I sediment supply is mainly from Olmoti Volcano. Compositional similarity between Tuff IA, Bed I lava, and Mafic Tuffs II and III single-grain fingerprints, together with north- and northwestward thinning of Bed I lava, suggests a common Ngorongoro source for these units. The techniques applied herein improve upon previous work by evaluating compositional affinities with statistical rigor rather than

  1. ADDITIONAL STRESS AND FRACTURE MECHANICS ANALYSES OF PRESSURIZED WATER REACTOR PRESSURE VESSEL NOZZLES

    SciTech Connect

    Walter, Matthew; Yin, Shengjun; Stevens, Gary; Sommerville, Daniel; Palm, Nathan; Heinecke, Carol

    2012-01-01

    In past years, the authors have undertaken various studies of nozzles in both boiling water reactors (BWRs) and pressurized water reactors (PWRs) located in the reactor pressure vessel (RPV) adjacent to the core beltline region. Those studies described stress and fracture mechanics analyses performed to assess various RPV nozzle geometries, which were selected based on their proximity to the core beltline region, i.e., those nozzle configurations that are located close enough to the core region such that they may receive sufficient fluence prior to end-of-life (EOL) to require evaluation of embrittlement as part of the RPV analyses associated with pressure-temperature (P-T) limits. In this paper, additional stress and fracture analyses are summarized that were performed for additional PWR nozzles with the following objectives: To expand the population of PWR nozzle configurations evaluated, which was limited in the previous work to just two nozzles (one inlet and one outlet nozzle). To model and understand differences in stress results obtained for an internal pressure load case using a two-dimensional (2-D) axi-symmetric finite element model (FEM) vs. a three-dimensional (3-D) FEM for these PWR nozzles. In particular, the ovalization (stress concentration) effect of two intersecting cylinders, which is typical of RPV nozzle configurations, was investigated. To investigate the applicability of previously recommended linear elastic fracture mechanics (LEFM) hand solutions for calculating the Mode I stress intensity factor for a postulated nozzle corner crack for pressure loading for these PWR nozzles. These analyses were performed to further expand earlier work completed to support potential revision and refinement of Title 10 to the U.S. Code of Federal Regulations (CFR), Part 50, Appendix G, Fracture Toughness Requirements, and are intended to supplement similar evaluation of nozzles presented at the 2008, 2009, and 2011 Pressure Vessels and Piping (PVP

  2. Additional Measurements and Analyses of H217O and H218O

    NASA Astrophysics Data System (ADS)

    Pearson, John; Yu, Shanshan; Walters, Adam; Daly, Adam M.

    2015-06-01

    Historically the analysis of the spectrum of water has been a balance between the quality of the data set and the applicability of the Hamiltonian to a highly non-rigid molecule. Recently, a number of different non-rigid analysis approaches have successfully been applied to 16O water resulting in a self-consistent set of transitions and energy levels to high J which allowed the spectrum to be modeled to experimental precision. The data set for 17O and 18O water was previously reviewed and many of the problematic measurements identified, but Hamiltonian modeling of the remaining data resulted in significantly poorer quality fits than that for the 16O parent. As a result, we have made additional microwave measurements and modeled the existing 17O and 18O data sets with an Euler series model. This effort has illuminated a number of additional problematic measurements in the previous data sets and has resulted in analyses of 17O and 18O water that are of similar quality to the 16O analysis. We report the new lines, the analyses and make recommendations on the quality of the experimental data sets. SS. Yu, J.C. Pearson, B.J. Drouin et al. J. Mol. Spectrosc. 279,~16-25 (2012) J. Tennyson, P.F. Bernath, L.R. Brown et al. J. Quant. Spectrosc. Rad. Trans. 117, 29-58 (2013) J. Tennyson, P.F. Bernath, L.R. Brown et al. J. Quant. Spectrosc. Rad. Trans. 110, 573-596 (2009) H.M. Pickett, J.C. Pearson, C.E. Miller J. Mol. Spectrosc. 233, 174-179 (2005)

  3. Metabolite profiling in 18 Saudi date palm fruit cultivars and their antioxidant potential via UPLC-qTOF-MS and multivariate data analyses.

    PubMed

    Farag, Mohamed A; Handoussa, Heba; Fekry, Mostafa I; Wessjohann, Ludger A

    2016-02-01

    Date palm fruit (Phoenix dactylifera) is not only one of the most economically significant plants in the Middle East, but also valued for its nutritional impact, and for which development of analytical methods is ongoing to help distinguish its many cultivars. This study attempts to characterize the primary and secondary metabolite profiles of 18 date cultivars from Saudi Arabia. A total of 44 metabolites extracted from the fruit peel were evaluated in a UPLC-qTOF-MS based metabolomics analysis including flavonoids, phenolic acids and fatty acids. The predominant flavones were glycosides of luteolin and chrysoeriol, as well as quercetin conjugates, whereas caffeoyl shikimic acid was the main hydroxycinnamic acid conjugate. GC-MS was further utilized to identify the primary metabolites in fruits (i.e. sugars) with glucose and fructose accounting for up to 95% of TIC among most cultivars. PCA and OPLS analyses revealed that flavone versus flavonol distribution in fruit were the main contributors for cultivar segregation. The antioxidant activity of date fruit samples was correlated with their total phenolics as determined by DPPH and CUPRAC assays. Dkheni Saudi and Shalabi Madina cultivars, appearing as the most distant in clustering analyses exhibited the strongest antioxidant effect suggesting that multivariate data analysis could help determine which date cultivars ought to be prioritized for future agricultural development. PMID:26781334

  4. Advanced discriminating criteria for natural organic substances of cultural heritage interest: spectral decomposition and multivariate analyses of FT-Raman and FT-IR signatures.

    PubMed

    Daher, Céline; Bellot-Gurlet, Ludovic; Le Hô, Anne-Solenn; Paris, Céline; Regert, Martine

    2013-10-15

    Natural organic substances are involved in many aspects of the cultural heritage field. Their presence in different forms (raw, heated, mixed), with various conservation states, constitutes a real challenge regarding their recognition and discrimination. Their characterization usually involves the use of separative techniques which imply destructive sampling and specific analytical preparations. Here we propose a non destructive approach using FT-Raman and infrared spectroscopies for the identification and differentiation of natural organic substances. Because of their related functional groups, they usually present similar vibrational signatures. Nevertheless the use of appropriate signal treatment and statistical analysis was successfully carried out to overcome this limitation, then proposing new objective discriminating methodology to identify these substances. Spectral decomposition calculations were performed on the CH stretching region of a large set of reference materials such as resins, oils, animal glues, and gums. Multivariate analyses (Principal Component Analyses) were then performed on the fitting parameters, and new discriminating criteria were established. A set of previously characterized archeological resins, with different surface aspects or alteration states, was analyzed using the same methodology. These testing samples validate the efficiency of our discriminating criteria established on the reference corpus. Moreover, we proved that some alteration or ageing of organic materials is not an issue to their recognition.

  5. Selected Gray Matter Volumes and Gender but Not Basal Ganglia nor Cerebellum Gyri Discriminate Left Versus Right Cerebral Hemispheres: Multivariate Analyses in human Brains at 3T.

    PubMed

    Roldan-Valadez, Ernesto; Suarez-May, Marcela A; Favila, Rafael; Aguilar-Castañeda, Erika; Rios, Camilo

    2015-07-01

    Interest in the lateralization of the human brain is evident through a multidisciplinary number of scientific studies. Understanding volumetric brain asymmetries allows the distinction between normal development stages and behavior, as well as brain diseases. We aimed to evaluate volumetric asymmetries in order to select the best gyri able to classify right- versus left cerebral hemispheres. A cross-sectional study performed in 47 right-handed young-adults healthy volunteers. SPM-based software performed brain segmentation, automatic labeling and volumetric analyses for 54 regions involving the cerebral lobes, basal ganglia and cerebellum from each cerebral hemisphere. Multivariate discriminant analysis (DA) allowed the assembling of a predictive model. DA revealed one discriminant function that significantly differentiated left vs. right cerebral hemispheres: Wilks' λ = 0.008, χ(2) (9) = 238.837, P < 0.001. The model explained 99.20% of the variation in the grouping variable and depicted an overall predictive accuracy of 98.8%. With the influence of gender; the selected gyri able to discriminate between hemispheres were middle orbital frontal gyrus (g.), angular g., supramarginal g., middle cingulum g., inferior orbital frontal g., calcarine g., inferior parietal lobule and the pars triangularis inferior frontal g. Specific brain gyri are able to accurately classify left vs. right cerebral hemispheres by using a multivariate approach; the selected regions correspond to key brain areas involved in attention, internal thought, vision and language; our findings favored the concept that lateralization has been evolutionary favored by mental processes increasing cognitive efficiency and brain capacity.

  6. Multivariate and phylogenetic analyses assessing the response of bacterial mat communities from an ancient oligotrophic aquatic ecosystem to different scenarios of long-term environmental disturbance.

    PubMed

    Pajares, Silvia; Souza, Valeria; Eguiarte, Luis E

    2015-01-01

    Understanding the response of bacterial communities to environmental change is extremely important in predicting the effect of biogeochemical modifications in ecosystem functioning. The Cuatro Cienegas Basin is an ancient oasis in the Mexican Chihuahuan desert that hosts a wide diversity of microbial mats and stromatolites that have survived in extremely oligotrophic pools with nearly constant conditions. However, thus far, the response of these unique microbial communities to long-term environmental disturbances remains unexplored. We therefore studied the compositional stability of these bacterial mat communities by using a replicated (3x) mesocosm experiment: a) Control; b) Fluct: fluctuating temperature; c) 40C: increase to 40 ºC; d) UVplus: artificial increase in UV radiation; and f) UVmin: UV radiation protection. In order to observe the changes in biodiversity, we obtained 16S rRNA gene clone libraries from microbial mats at the end of the experiment (eight months) and analyzed them using multivariate and phylogenetic tools. Sequences were assigned to 13 major lineages, among which Cyanobacteria (38.8%) and Alphaproteobacteria (25.5%) were the most abundant. The less extreme treatments (Control and UVmin) had a more similar composition and distribution of the phylogenetic groups with the natural pools than the most extreme treatments (Fluct, 40C, and UVplus), which showed drastic changes in the community composition and structure, indicating a different community response to each environmental disturbance. An increase in bacterial diversity was found in the UVmin treatment, suggesting that protected environments promote the establishment of complex bacterial communities, while stressful environments reduce diversity and increase the dominance of a few Cyanobacterial OTUs (mainly Leptolyngbya sp) through environmental filtering. Mesocosm experiments using complex bacterial communities, along with multivariate and phylogenetic analyses of molecular data, can

  7. Multivariate and Phylogenetic Analyses Assessing the Response of Bacterial Mat Communities from an Ancient Oligotrophic Aquatic Ecosystem to Different Scenarios of Long-Term Environmental Disturbance

    PubMed Central

    Pajares, Silvia; Souza, Valeria; Eguiarte, Luis E.

    2015-01-01

    Understanding the response of bacterial communities to environmental change is extremely important in predicting the effect of biogeochemical modifications in ecosystem functioning. The Cuatro Cienegas Basin is an ancient oasis in the Mexican Chihuahuan desert that hosts a wide diversity of microbial mats and stromatolites that have survived in extremely oligotrophic pools with nearly constant conditions. However, thus far, the response of these unique microbial communities to long-term environmental disturbances remains unexplored. We therefore studied the compositional stability of these bacterial mat communities by using a replicated (3x) mesocosm experiment: a) Control; b) Fluct: fluctuating temperature; c) 40C: increase to 40 ºC; d) UVplus: artificial increase in UV radiation; and f) UVmin: UV radiation protection. In order to observe the changes in biodiversity, we obtained 16S rRNA gene clone libraries from microbial mats at the end of the experiment (eight months) and analyzed them using multivariate and phylogenetic tools. Sequences were assigned to 13 major lineages, among which Cyanobacteria (38.8%) and Alphaproteobacteria (25.5%) were the most abundant. The less extreme treatments (Control and UVmin) had a more similar composition and distribution of the phylogenetic groups with the natural pools than the most extreme treatments (Fluct, 40C, and UVplus), which showed drastic changes in the community composition and structure, indicating a different community response to each environmental disturbance. An increase in bacterial diversity was found in the UVmin treatment, suggesting that protected environments promote the establishment of complex bacterial communities, while stressful environments reduce diversity and increase the dominance of a few Cyanobacterial OTUs (mainly Leptolyngbya sp) through environmental filtering. Mesocosm experiments using complex bacterial communities, along with multivariate and phylogenetic analyses of molecular data, can

  8. Multivariate analyses of individual variation in soccer skill as a tool for talent identification and development: utilising evolutionary theory in sports science.

    PubMed

    Wilson, Robbie S; James, Rob S; David, Gwendolyn; Hermann, Ecki; Morgan, Oliver J; Niehaus, Amanda C; Hunter, Andrew; Thake, Doug; Smith, Michelle D

    2016-11-01

    The development of a comprehensive protocol for quantifying soccer-specific skill could markedly improve both talent identification and development. Surprisingly, most protocols for talent identification in soccer still focus on the more generic athletic attributes of team sports, such as speed, strength, agility and endurance, rather than on a player's technical skills. We used a multivariate methodology borrowed from evolutionary analyses of adaptation to develop our quantitative assessment of individual soccer-specific skill. We tested the performance of 40 individual academy-level players in eight different soccer-specific tasks across an age range of 13-18 years old. We first quantified the repeatability of each skill performance then explored the effects of age on soccer-specific skill, correlations between each of the pairs of skill tasks independent of age, and finally developed an individual metric of overall skill performance that could be easily used by coaches. All of our measured traits were highly repeatable when assessed over a short period and we found that an individual's overall skill - as well as their performance in their best task - was strongly positively correlated with age. Most importantly, our study established a simple but comprehensive methodology for assessing skill performance in soccer players, thus allowing coaches to rapidly assess the relative abilities of their players, identify promising youths and work on eliminating skill deficits in players.

  9. Univariate and multivariate molecular spectral analyses of lipid related molecular structural components in relation to nutrient profile in feed and food mixtures

    NASA Astrophysics Data System (ADS)

    Abeysekara, Saman; Damiran, Daalkhaijav; Yu, Peiqiang

    2013-02-01

    The objectives of this study were (i) to determine lipid related molecular structures components (functional groups) in feed combination of cereal grain (barley, Hordeum vulgare) and wheat (Triticum aestivum) based dried distillers grain solubles (wheat DDGSs) from bioethanol processing at five different combination ratios using univariate and multivariate molecular spectral analyses with infrared Fourier transform molecular spectroscopy, and (ii) to correlate lipid-related molecular-functional structure spectral profile to nutrient profiles. The spectral intensity of (i) CH3 asymmetric, CH2 asymmetric, CH3 symmetric and CH2 symmetric groups, (ii) unsaturation (Cdbnd C) group, and (iii) carbonyl ester (Cdbnd O) group were determined. Spectral differences of functional groups were detected by hierarchical cluster analysis (HCA) and principal components analysis (PCA). The results showed that the combination treatments significantly inflicted modifications (P < 0.05) in nutrient profile and lipid related molecular spectral intensity (CH2 asymmetric stretching peak height, CH2 symmetric stretching peak height, ratio of CH2 to CH3 symmetric stretching peak intensity, and carbonyl peak area). Ratio of CH2 to CH3 symmetric stretching peak intensity, and carbonyl peak significantly correlated with nutrient profiles. Both PCA and HCA differentiated lipid-related spectrum. In conclusion, the changes of lipid molecular structure spectral profiles through feed combination could be detected using molecular spectroscopy. These changes were associated with nutrient profiles and functionality.

  10. Multivariate analyses of individual variation in soccer skill as a tool for talent identification and development: utilising evolutionary theory in sports science.

    PubMed

    Wilson, Robbie S; James, Rob S; David, Gwendolyn; Hermann, Ecki; Morgan, Oliver J; Niehaus, Amanda C; Hunter, Andrew; Thake, Doug; Smith, Michelle D

    2016-11-01

    The development of a comprehensive protocol for quantifying soccer-specific skill could markedly improve both talent identification and development. Surprisingly, most protocols for talent identification in soccer still focus on the more generic athletic attributes of team sports, such as speed, strength, agility and endurance, rather than on a player's technical skills. We used a multivariate methodology borrowed from evolutionary analyses of adaptation to develop our quantitative assessment of individual soccer-specific skill. We tested the performance of 40 individual academy-level players in eight different soccer-specific tasks across an age range of 13-18 years old. We first quantified the repeatability of each skill performance then explored the effects of age on soccer-specific skill, correlations between each of the pairs of skill tasks independent of age, and finally developed an individual metric of overall skill performance that could be easily used by coaches. All of our measured traits were highly repeatable when assessed over a short period and we found that an individual's overall skill - as well as their performance in their best task - was strongly positively correlated with age. Most importantly, our study established a simple but comprehensive methodology for assessing skill performance in soccer players, thus allowing coaches to rapidly assess the relative abilities of their players, identify promising youths and work on eliminating skill deficits in players. PMID:26920468

  11. Univariate and multivariate molecular spectral analyses of lipid related molecular structural components in relation to nutrient profile in feed and food mixtures.

    PubMed

    Abeysekara, Saman; Damiran, Daalkhaijav; Yu, Peiqiang

    2013-02-01

    The objectives of this study were (i) to determine lipid related molecular structures components (functional groups) in feed combination of cereal grain (barley, Hordeum vulgare) and wheat (Triticum aestivum) based dried distillers grain solubles (wheat DDGSs) from bioethanol processing at five different combination ratios using univariate and multivariate molecular spectral analyses with infrared Fourier transform molecular spectroscopy, and (ii) to correlate lipid-related molecular-functional structure spectral profile to nutrient profiles. The spectral intensity of (i) CH(3) asymmetric, CH(2) asymmetric, CH(3) symmetric and CH(2) symmetric groups, (ii) unsaturation (CC) group, and (iii) carbonyl ester (CO) group were determined. Spectral differences of functional groups were detected by hierarchical cluster analysis (HCA) and principal components analysis (PCA). The results showed that the combination treatments significantly inflicted modifications (P<0.05) in nutrient profile and lipid related molecular spectral intensity (CH(2) asymmetric stretching peak height, CH(2) symmetric stretching peak height, ratio of CH(2) to CH(3) symmetric stretching peak intensity, and carbonyl peak area). Ratio of CH(2) to CH(3) symmetric stretching peak intensity, and carbonyl peak significantly correlated with nutrient profiles. Both PCA and HCA differentiated lipid-related spectrum. In conclusion, the changes of lipid molecular structure spectral profiles through feed combination could be detected using molecular spectroscopy. These changes were associated with nutrient profiles and functionality.

  12. Using Additional Analyses to Clarify the Functions of Problem Behavior: An Analysis of Two Cases

    ERIC Educational Resources Information Center

    Payne, Steven W.; Dozier, Claudia L.; Neidert, Pamela L.; Jowett, Erica S.; Newquist, Matthew H.

    2014-01-01

    Functional analyses (FA) have proven useful for identifying contingencies that influence problem behavior. Research has shown that some problem behavior may only occur in specific contexts or be influenced by multiple or idiosyncratic variables. When these contexts or sources of influence are not assessed in an FA, further assessment may be…

  13. Additives

    NASA Technical Reports Server (NTRS)

    Smalheer, C. V.

    1973-01-01

    The chemistry of lubricant additives is discussed to show what the additives are chemically and what functions they perform in the lubrication of various kinds of equipment. Current theories regarding the mode of action of lubricant additives are presented. The additive groups discussed include the following: (1) detergents and dispersants, (2) corrosion inhibitors, (3) antioxidants, (4) viscosity index improvers, (5) pour point depressants, and (6) antifouling agents.

  14. Investigation of intervertebral disc degeneration using multivariate FTIR spectroscopic imaging† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c5fd00160a Click here for additional data file.

    PubMed Central

    Peeters, Mirte; Detiger, Suzanne E. L.; Helder, Marco N.; Smit, Theo H.; Le Maitre, Christine L.; Sammon, Chris

    2016-01-01

    Traditionally tissue samples are analysed using protein or enzyme specific stains on serial sections to build up a picture of the distribution of components contained within them. In this study we investigated the potential of multivariate curve resolution-alternating least squares (MCR-ALS) to deconvolute 2nd derivative spectra of Fourier transform infrared (FTIR) microscopic images measured in transflectance mode of goat and human paraffin embedded intervertebral disc (IVD) tissue sections, to see if this methodology can provide analogous information to that provided by immunohistochemical stains and bioassays but from a single section. MCR-ALS analysis of non-degenerate and enzymatically in vivo degenerated goat IVDs reveals five matrix components displaying distribution maps matching histological stains for collagen, elastin and proteoglycan (PG), as well as immunohistochemical stains for collagen type I and II. Interestingly, two components exhibiting characteristic spectral and distribution profiles of proteoglycans were found, and relative component/tissue maps of these components (labelled PG1 and PG2) showed distinct distributions in non-degenerate versus mildly degenerate goat samples. MCR-ALS analysis of human IVD sections resulted in comparable spectral profiles to those observed in the goat samples, highlighting the inter species transferability of the presented methodology. Multivariate FTIR image analysis of a set of 43 goat IVD sections allowed the extraction of semi-quantitative information from component/tissue gradients taken across the IVD width of collagen type I, collagen type II, PG1 and PG2. Regional component/tissue parameters were calculated and significant correlations were found between histological grades of degeneration and PG parameters (PG1: p = 0.0003, PG2: p < 0.0001); glycosaminoglycan (GAG) content and PGs (PG1: p = 0.0055, PG2: p = 0.0001); and MRI T2* measurements and PGs (PG1: p = 0.0021, PG2: p < 0.0001). Additionally

  15. Metagenomic analyses of the late Pleistocene permafrost - additional tools for reconstruction of environmental conditions

    NASA Astrophysics Data System (ADS)

    Rivkina, Elizaveta; Petrovskaya, Lada; Vishnivetskaya, Tatiana; Krivushin, Kirill; Shmakova, Lyubov; Tutukina, Maria; Meyers, Arthur; Kondrashov, Fyodor

    2016-04-01

    A comparative analysis of the metagenomes from two 30 000-year-old permafrost samples, one of lake-alluvial origin and the other from late Pleistocene Ice Complex sediments, revealed significant differences within microbial communities. The late Pleistocene Ice Complex sediments (which have been characterized by the absence of methane with lower values of redox potential and Fe2+ content) showed a low abundance of methanogenic archaea and enzymes from both the carbon and nitrogen cycles, but a higher abundance of enzymes associated with the sulfur cycle. The metagenomic and geochemical analyses described in the paper provide evidence that the formation of the sampled late Pleistocene Ice Complex sediments likely took place under much more aerobic conditions than lake-alluvial sediments.

  16. Monitoring the quality consistency of Weibizhi tablets by micellar electrokinetic chromatography fingerprints combined with multivariate statistical analyses, the simple quantified ratio fingerprint method, and the fingerprint-efficacy relationship.

    PubMed

    Liu, Yingchun; Sun, Guoxiang; Wang, Yan; Yang, Lanping; Yang, Fangliang

    2015-06-01

    Micellar electrokinetic chromatography fingerprinting combined with quantification was successfully developed and applied to monitor the quality consistency of Weibizhi tablets, which is a classical compound preparation used to treat gastric ulcers. A background electrolyte composed of 57 mmol/L sodium borate, 21 mmol/L sodium dodecylsulfate and 100 mmol/L sodium hydroxide was used to separate compounds. To optimize capillary electrophoresis conditions, multivariate statistical analyses were applied. First, the most important factors influencing sample electrophoretic behavior were identified as background electrolyte concentrations. Then, a Box-Benhnken design response surface strategy using resolution index RF as an integrated response was set up to correlate factors with response. RF reflects the effective signal amount, resolution, and signal homogenization in an electropherogram, thus, it was regarded as an excellent indicator. In fingerprint assessments, simple quantified ratio fingerprint method was established for comprehensive quality discrimination of traditional Chinese medicines/herbal medicines from qualitative and quantitative perspectives, by which the quality of 27 samples from the same manufacturer were well differentiated. In addition, the fingerprint-efficacy relationship between fingerprints and antioxidant activities was established using partial least squares regression, which provided important medicinal efficacy information for quality control. The present study offered an efficient means for monitoring Weibizhi tablet quality consistency.

  17. Additional Development and Systems Analyses of Pneumatic Technology for High Speed Civil Transport Aircraft

    NASA Technical Reports Server (NTRS)

    Englar, Robert J.; Willie, F. Scott; Lee, Warren J.

    1999-01-01

    In the Task I portion of this NASA research grant, configuration development and experimental investigations have been conducted on a series of pneumatic high-lift and control surface devices applied to a generic High Speed Civil Transport (HSCT) model configuration to determine their potential for improved aerodynamic performance, plus stability and control of higher performance aircraft. These investigations were intended to optimize pneumatic lift and drag performance; provide adequate control and longitudinal stability; reduce separation flowfields at high angle of attack; increase takeoff/climbout lift-to-drag ratios; and reduce system complexity and weight. Experimental aerodynamic evaluations were performed on a semi-span HSCT generic model with improved fuselage fineness ratio and with interchangeable plain flaps, blown flaps, pneumatic Circulation Control Wing (CCW) high-lift configurations, plain and blown canards, a novel Circulation Control (CC) cylinder blown canard, and a clean cruise wing for reference. Conventional tail power was also investigated for longitudinal trim capability. Also evaluated was unsteady pulsed blowing of the wing high-lift system to determine if reduced pulsed mass flow rates and blowing requirements could be made to yield the same lift as that resulting from steady-state blowing. Depending on the pulsing frequency applied, reduced mass flow rates were indeed found able to provide lift augmentation at lesser blowing values than for the steady conditions. Significant improvements in the aerodynamic characteristics leading to improved performance and stability/control were identified, and the various components were compared to evaluate the pneumatic potential of each. Aerodynamic results were provided to the Georgia Tech Aerospace System Design Lab. to conduct the companion system analyses and feasibility study (Task 2) of theses concepts applied to an operational advanced HSCT aircraft. Results and conclusions from these

  18. Personal, Social, and Game-Related Correlates of Active and Non-Active Gaming Among Dutch Gaming Adolescents: Survey-Based Multivariable, Multilevel Logistic Regression Analyses

    PubMed Central

    de Vet, Emely; Chinapaw, Mai JM; de Boer, Michiel; Seidell, Jacob C; Brug, Johannes

    2014-01-01

    Background Playing video games contributes substantially to sedentary behavior in youth. A new generation of video games—active games—seems to be a promising alternative to sedentary games to promote physical activity and reduce sedentary behavior. At this time, little is known about correlates of active and non-active gaming among adolescents. Objective The objective of this study was to examine potential personal, social, and game-related correlates of both active and non-active gaming in adolescents. Methods A survey assessing game behavior and potential personal, social, and game-related correlates was conducted among adolescents (12-16 years, N=353) recruited via schools. Multivariable, multilevel logistic regression analyses, adjusted for demographics (age, sex and educational level of adolescents), were conducted to examine personal, social, and game-related correlates of active gaming ≥1 hour per week (h/wk) and non-active gaming >7 h/wk. Results Active gaming ≥1 h/wk was significantly associated with a more positive attitude toward active gaming (OR 5.3, CI 2.4-11.8; P<.001), a less positive attitude toward non-active games (OR 0.30, CI 0.1-0.6; P=.002), a higher score on habit strength regarding gaming (OR 1.9, CI 1.2-3.2; P=.008) and having brothers/sisters (OR 6.7, CI 2.6-17.1; P<.001) and friends (OR 3.4, CI 1.4-8.4; P=.009) who spend more time on active gaming and a little bit lower score on game engagement (OR 0.95, CI 0.91-0.997; P=.04). Non-active gaming >7 h/wk was significantly associated with a more positive attitude toward non-active gaming (OR 2.6, CI 1.1-6.3; P=.035), a stronger habit regarding gaming (OR 3.0, CI 1.7-5.3; P<.001), having friends who spend more time on non-active gaming (OR 3.3, CI 1.46-7.53; P=.004), and a more positive image of a non-active gamer (OR 2, CI 1.07–3.75; P=.03). Conclusions Various factors were significantly associated with active gaming ≥1 h/wk and non-active gaming >7 h/wk. Active gaming is most

  19. Reprocessing the Southern Hemisphere ADditional OZonesondes (SHADOZ) Database for Long-Term Trend Analyses

    NASA Astrophysics Data System (ADS)

    Witte, J. C.; Thompson, A. M.; Coetzee, G.; Fujiwara, M.; Johnson, B. J.; Sterling, C. W.; Cullis, P.; Ashburn, C. E.; Jordan, A. F.

    2015-12-01

    SHADOZ is a large archive of tropical balloon-bone ozonesonde data at NASA/Goddard Space Flight Center with data from 14 tropical and subtropical stations provided by collaborators in Europe, Asia, Latin America and Africa . The SHADOZ time series began in 1998, using electrochemical concentration cell (ECC) ozonesondes. Like many long-term sounding stations, SHADOZ is characterized by variations in operating procedures, launch protocols, and data processing such that biases within a data record and among sites appear. In addition, over time, the radiosonde and ozonesonde instruments and data processing protocols have changed, adding to the measurement uncertainties at individual stations and limiting the reliability of ozone profile trends and continuous satellite validation. Currently, the ozonesonde community is engaged in reprocessing ECC data, with an emphasis on homogenization of the records to compensate for the variations in instrumentation and technique. The goals are to improve the information and integrity of each measurement record and to support calculation of more reliable trends. We illustrate the reprocessing activity of SHADOZ with selected stations. We will (1) show reprocessing steps based on the recent WMO report that provides post-processing guidelines for ozonesondes; (2) characterize uncertainties in various parts of the ECC conditioning process; and (3) compare original and reprocessed data to co-located ground and satellite measurements of column ozone.

  20. A review of recent methods for the determination of ranges of feasible solutions resulting from soft modelling analyses of multivariate data.

    PubMed

    Golshan, Azadeh; Abdollahi, Hamid; Beyramysoltan, Samira; Maeder, Marcel; Neymeyr, Klaus; Rajkó, Robert; Sawall, Mathias; Tauler, Romá

    2016-03-10

    Soft modelling or multivariate curve resolution (MCR) are well-known methodologies for the analysis of multivariate data in many different application fields. Results obtained by soft modelling methods are very likely impaired by rotational and scaling ambiguities, i.e. a full range of feasible solutions can describe the data equally well while fulfilling the constraints of the system. These issues are severely limiting the applicability of these methods and therefore, they can be considered as the most challenging ones. The purpose of the current review is to describe and critically compare the available methods that attempt at determining the range of ambiguity for the case of 3-component systems. Theoretical and practical aspects are discussed, based on a collection of simulated examples containing noise-free and noisy data sets as well as an experimental example. PMID:26893081

  1. Delineation and evaluation of hydrologic-landscape regions in the United States using geographic information system tools and multivariate statistical analyses.

    USGS Publications Warehouse

    Wolock, D.M.; Winter, T.C.; McMahon, G.

    2004-01-01

    Hydrologic-landscape regions in the United States were delineated by using geographic information system (GIS) tools combined with principal components and cluster analyses. The GIS and statistical analyses were applied to land-surface form, geologic texture (permeability of the soil and bedrock), and climate variables that describe the physical and climatic setting of 43,931 small (approximately 200 km2) watersheds in the United States. (The term "watersheds" is defined in this paper as the drainage areas of tributary streams, headwater streams, and stream segments lying between two confluences.) The analyses grouped the watersheds into 20 noncontiguous regions based on similarities in land-surface form, geologic texture, and climate characteristics. The percentage of explained variance (R-squared value) in an analysis of variance was used to compare the hydrologic-landscape regions to 19 square geometric regions and the 21 U.S. Environmental Protection Agency level-II ecoregions. Hydrologic-landscape regions generally were better than ecoregions at delineating regions of distinct land-surface form and geologic texture. Hydrologic-landscape regions and ecoregions were equally effective at defining regions in terms of climate, land cover, and water-quality characteristics. For about half of the landscape, climate, and water-quality characteristics, the R-squared values of square geometric regions were as high as hydrologic-landscape regions or ecoregions.

  2. Multivariate normality

    NASA Technical Reports Server (NTRS)

    Crutcher, H. L.; Falls, L. W.

    1976-01-01

    Sets of experimentally determined or routinely observed data provide information about the past, present and, hopefully, future sets of similarly produced data. An infinite set of statistical models exists which may be used to describe the data sets. The normal distribution is one model. If it serves at all, it serves well. If a data set, or a transformation of the set, representative of a larger population can be described by the normal distribution, then valid statistical inferences can be drawn. There are several tests which may be applied to a data set to determine whether the univariate normal model adequately describes the set. The chi-square test based on Pearson's work in the late nineteenth and early twentieth centuries is often used. Like all tests, it has some weaknesses which are discussed in elementary texts. Extension of the chi-square test to the multivariate normal model is provided. Tables and graphs permit easier application of the test in the higher dimensions. Several examples, using recorded data, illustrate the procedures. Tests of maximum absolute differences, mean sum of squares of residuals, runs and changes of sign are included in these tests. Dimensions one through five with selected sample sizes 11 to 101 are used to illustrate the statistical tests developed.

  3. PARAMETRIC AND NON PARAMETRIC (MARS: MULTIVARIATE ADDITIVE REGRESSION SPLINES) LOGISTIC REGRESSIONS FOR PREDICTION OF A DICHOTOMOUS RESPONSE VARIABLE WITH AN EXAMPLE FOR PRESENCE/ABSENCE OF AMPHIBIANS

    EPA Science Inventory

    The purpose of this report is to provide a reference manual that could be used by investigators for making informed use of logistic regression using two methods (standard logistic regression and MARS). The details for analyses of relationships between a dependent binary response ...

  4. Estimating the Mixing Proportions of Groundwater Sources in the San Joaquin Valley, California Based on Multivariate Analyses of Major Minerals and Isotopic Data

    NASA Astrophysics Data System (ADS)

    Sartono, O.; Marrero-Cuebas, R.; Suen, C. J.

    2008-12-01

    Groundwater samples from the San Joaquin Valley represent mixtures of waters from different sources. The pristine ground water in the east side of the valley floor originates mostly from precipitation recharge in the Sierra Mountains. Because the valley is predominantly an agricultural area, groundwater compositions are subsequently modified when mixed with deep percolating irrigation waters from farmlands and other contaminated sources, such as waste waters from confined animal facilities and municipal waste treatment facilities. We analyzed 125 groundwater samples from agricultural and domestic water-supply wells in a mixed suburban and agricultural area for major minerals and nitrate isotope ratios (delta N-15 and delta O-18). The data were subjected to statistical correlation and principal components analyses. The results indicate that the groundwater compositions are less dependent on their locations but apparently, they are more dependent on their original sources and the mixing proportions among the different possible sources. Based on these analyses, the ratios of mixing and the amounts of chemical fluxes due to groundwater recharge derived from agricultural and other anthropogenic sources can be estimated.

  5. Multivariate Analysis in Metabolomics

    PubMed Central

    Worley, Bradley; Powers, Robert

    2015-01-01

    Metabolomics aims to provide a global snapshot of all small-molecule metabolites in cells and biological fluids, free of observational biases inherent to more focused studies of metabolism. However, the staggeringly high information content of such global analyses introduces a challenge of its own; efficiently forming biologically relevant conclusions from any given metabolomics dataset indeed requires specialized forms of data analysis. One approach to finding meaning in metabolomics datasets involves multivariate analysis (MVA) methods such as principal component analysis (PCA) and partial least squares projection to latent structures (PLS), where spectral features contributing most to variation or separation are identified for further analysis. However, as with any mathematical treatment, these methods are not a panacea; this review discusses the use of multivariate analysis for metabolomics, as well as common pitfalls and misconceptions. PMID:26078916

  6. Multivariate genetic analyses of cognition and academic achievement from two population samples of 174,000 and 166,000 school children.

    PubMed

    Calvin, Catherine M; Deary, Ian J; Webbink, Dinand; Smith, Pauline; Fernandes, Cres; Lee, Sang Hong; Luciano, Michelle; Visscher, Peter M

    2012-09-01

    The genetic influence on the association between contemporaneously measured intelligence and academic achievement in childhood was examined in nationally representative cohorts from England and The Netherlands using a whole population indirect twin design, including singleton data. We identified 1,056 same-sex (SS) and 495 opposite-sex (OS) twin pairs among 174,098 British 11 year-olds with test scores from 2004, and, 785 SS and 327 OS twin pairs among 120,995 Dutch schoolchildren, aged 8, 10 or 12 years, with assessments from 1994 to 2002. The estimate of intelligence heritability was large in both cohorts, consistent with previous studies (h (2) = 0.70 ± 0.14, England; h (2) = 0.43 ± 0.28-0.67 ± 0.31, The Netherlands), as was the heritability of academic achievement variables (h (2) = 0.51 ± 0.16-0.81 ± 0.16, England; h (2) = 0.36 ± 0.27-0.74 ± 0.27, The Netherlands). Additive genetic covariance explained the large majority of the phenotypic correlations between intelligence and academic achievement scores in England, when standardised to a bivariate heritability (Biv h (2) = 0.76 ± 0.15-0.88 ± 0.16), and less consistent but often large proportions of the phenotypic correlations in The Netherlands (Biv h (2) = 0.33 ± 0.52-1.00 ± 0.43). In the British cohort both nonverbal and verbal reasoning showed very high additive genetic covariance with achievement scores (Biv h (2) = 0.94-0.98; Biv h (2) = 0.77-1.00 respectively). In The Netherlands, covariance estimates were consistent across age groups. The heritability of intelligence-academic achievement associations in two population cohorts of elementary schoolchildren, using a twin pair extraction method, is at the high end of estimates reported by studies of largely preselected twin samples. PMID:22700061

  7. Development of sourdough fermented date seed for improving the quality and shelf life of flat bread: study with univariate and multivariate analyses.

    PubMed

    Habibi Najafi, Mohammad B; Pourfarzad, Amir; Zahedi, Hoda; Ahmadian-Kouchaksaraie, Zahra; Haddad Khodaparast, Mohammad H

    2016-01-01

    The aim of this work was to study the effects of a novel sourdough system prepared by wheat flour supplemented by combination of pulverized date seed, Lactobacillus plantarum, and/or Lactobacillus brevis as well as Saccharomyces cerevisiae on the sourdough characteristics, quality, sensory, texture, shelf life and image properties of Barbari flat bread. The highest sourdough acidity and bread specific volume was obtained with co-culture of Lb. plantarum + Lb. brevis + S. cerevisiae. The results suggest that fermentation is a potential bioprocessing technology for improving sensory aspects of bread supplemented with pulverized date seed, as a dietary fiber resource. Texture analysis of bread samples during 7 days of storage indicated that the presence of pulverized date seed in sourdough was able to diminish bread staling. The interaction of baker's yeast and lactic acid bacteria (LAB) has led to increase the particle average size of bread crumb and decrease the area fraction than the LAB samples. It was observed that all treatments of sourdough Barbari breads had higher cell wall thickness than the control Barbari bread. Avrami non-linear regression equation was chosen as useful mathematical model to properly study bread hardening kinetics. In addition, principal component analysis (PCA) allowed discriminating among sourdough and bread specialties. Partial least squares regression (PLSR) models were applied to determine the relationships between sensory and instrumental data.

  8. Using multivariate analyses to compare subsets of electrodes and potentials within an electrode array for predicting sugar concentrations in mixed solutions.

    SciTech Connect

    Stork, Christopher Lyle; Steen, William Arthur

    2008-04-01

    A non-selective electrode array is presented for the quantification of fructose, galactose, and glucose in mixed solutions. A unique feature of this electrode array relative to other published work is the wide diversity of electrode materials incorporated within the array, being constructed of 41 different metals and metal alloys. Cyclic voltammograms were acquired for solutions containing a single sugar at varying concentrations, and the correlation between current and sugar concentration was calculated as a function of potential and electrode array element. The correlation plots identified potential regions and electrodes that scaled most linearly with sugar concentration, and the number of electrodes used in building predictive models was reduced to 15. Partial least squares regression models relating electrochemical response to sugar concentration were constructed using data from single electrodes and multiple electrodes within the array, and the predictive abilities of these models were rigorously compared using a non-parametric Wilcoxon test. Models using single electrodes (Pt:Rh (90:10) for fructose, Au:Ni (82:18) for galactose, and Au for glucose) were judged to be statistically superior or indistinguishable from those built with multiple electrodes. Additionally, for each sugar, interval partial least squares regression successfully identified a subset of potentials within a given electrode that generated a model of statistically equivalent predictive ability relative to the full potential model. While including data from multiple electrodes offered no benefit in predicting sugar concentration, use of the array afforded the versatility and flexibility of selecting the best single electrode for each sugar.

  9. Development of sourdough fermented date seed for improving the quality and shelf life of flat bread: study with univariate and multivariate analyses.

    PubMed

    Habibi Najafi, Mohammad B; Pourfarzad, Amir; Zahedi, Hoda; Ahmadian-Kouchaksaraie, Zahra; Haddad Khodaparast, Mohammad H

    2016-01-01

    The aim of this work was to study the effects of a novel sourdough system prepared by wheat flour supplemented by combination of pulverized date seed, Lactobacillus plantarum, and/or Lactobacillus brevis as well as Saccharomyces cerevisiae on the sourdough characteristics, quality, sensory, texture, shelf life and image properties of Barbari flat bread. The highest sourdough acidity and bread specific volume was obtained with co-culture of Lb. plantarum + Lb. brevis + S. cerevisiae. The results suggest that fermentation is a potential bioprocessing technology for improving sensory aspects of bread supplemented with pulverized date seed, as a dietary fiber resource. Texture analysis of bread samples during 7 days of storage indicated that the presence of pulverized date seed in sourdough was able to diminish bread staling. The interaction of baker's yeast and lactic acid bacteria (LAB) has led to increase the particle average size of bread crumb and decrease the area fraction than the LAB samples. It was observed that all treatments of sourdough Barbari breads had higher cell wall thickness than the control Barbari bread. Avrami non-linear regression equation was chosen as useful mathematical model to properly study bread hardening kinetics. In addition, principal component analysis (PCA) allowed discriminating among sourdough and bread specialties. Partial least squares regression (PLSR) models were applied to determine the relationships between sensory and instrumental data. PMID:26787943

  10. Multivariate Analyses of Social and Religious Attitudes.

    ERIC Educational Resources Information Center

    Meyer, Roger A.

    The relationship between religious attitudes or orientations and prejudice was studied in a sample of 337 adults: college males, lay persons and ministers from local congregations, and seminary students in Louisiana and Texas. The study is based on Gordon Allport's theory concerning intrinsic and extrinsic religiousity. Ten religious groups were…

  11. Three-dimensional geological modelling and multivariate statistical analysis of water chemistry data to analyse and visualise aquifer structure and groundwater composition in the Wairau Plain, Marlborough District, New Zealand

    NASA Astrophysics Data System (ADS)

    Raiber, Matthias; White, Paul A.; Daughney, Christopher J.; Tschritter, Constanze; Davidson, Peter; Bainbridge, Sophie E.

    2012-05-01

    SummaryConcerns regarding groundwater contamination with nitrate and the long-term sustainability of groundwater resources have prompted the development of a multi-layered three-dimensional (3D) geological model to characterise the aquifer geometry of the Wairau Plain, Marlborough District, New Zealand. The 3D geological model which consists of eight litho-stratigraphic units has been subsequently used to synthesise hydrogeological and hydrogeochemical data for different aquifers in an approach that aims to demonstrate how integration of water chemistry data within the physical framework of a 3D geological model can help to better understand and conceptualise groundwater systems in complex geological settings. Multivariate statistical techniques (e.g. Principal Component Analysis and Hierarchical Cluster Analysis) were applied to groundwater chemistry data to identify hydrochemical facies which are characteristic of distinct evolutionary pathways and a common hydrologic history of groundwaters. Principal Component Analysis on hydrochemical data demonstrated that natural water-rock interactions, redox potential and human agricultural impact are the key controls of groundwater quality in the Wairau Plain. Hierarchical Cluster Analysis revealed distinct hydrochemical water quality groups in the Wairau Plain groundwater system. Visualisation of the results of the multivariate statistical analyses and distribution of groundwater nitrate concentrations in the context of aquifer lithology highlighted the link between groundwater chemistry and the lithology of host aquifers. The methodology followed in this study can be applied in a variety of hydrogeological settings to synthesise geological, hydrogeological and hydrochemical data and present them in a format readily understood by a wide range of stakeholders. This enables a more efficient communication of the results of scientific studies to the wider community.

  12. Analyses of microbial community within a composter operated using household garbage with special reference to the addition of soybean oil.

    PubMed

    Aoshima, M; Pedro, M S; Haruta, S; Ding, L; Fukada, T; Kigawa, A; Kodama, T; Ishii, M; Igarashi, Y

    2001-01-01

    A commercially available composter was operated using fixed composition of garbage with or without the addition of soybean oil. The composter was operated without adding seed microorganisms or bulking materials. Microflora within the composter were analyzed by denaturing gradient gel electrophoresis (DGGE) in the case of oil addition, or by 16/18 S rRNA gene sequencing of the isolated microorganisms in the case of no oil addition. The results showed that, irrespective of the addition of oil, the bacteria identified were all gram positive, and that lactobacilli seemed to be the key microorganisms. Based on the results, suitable microflora for use in a household composter are discussed.

  13. Second-order multivariate models for the processing of standard-addition synchronous fluorescence-pH data. Application to the analysis of salicylic acid and its major metabolite in human urine.

    PubMed

    Pagani, Ariana P; Ibañez, Gabriela A

    2014-05-01

    In the present work, we describe the determination of salicylic acid and its major metabolite, salicyluric acid, in spiked human urine samples, using synchronous fluorescence spectra measured in a flow-injection system with a double pH gradient. Because the fluorescent urine background constitutes a potentially interfering signal, it becomes necessary to achieve the second-order advantage. Moreover, due to significant changes in the signal of the analytes in the presence of the urine matrix, mainly for salicyluric acid, standard addition was required in order to obtain appropriate quantifications. Several second-order multivariate calibration models were evaluated for this purpose: PARAFAC and MCR-ALS in two different modes, and PLS/RBL.

  14. Bivariate and multivariate analyses of the influence of blood variables of patients submitted to Roux-en-Y gastric bypass on the stability of erythrocyte membrane against the chaotropic action of ethanol.

    PubMed

    de Arvelos, Leticia Ramos; Rocha, Vanessa Custódio Afonso; Felix, Gabriela Pereira; da Cunha, Cleine Chagas; Bernardino Neto, Morun; da Silva Garrote Filho, Mario; de Fátima Pinheiro, Conceição; Resende, Elmiro Santos; Penha-Silva, Nilson

    2013-03-01

    The stability of the erythrocyte membrane, which is essential for the maintenance of cell functions, occurs in a critical region of fluidity, which depends largely on its composition and the composition and characteristics of the medium. As the composition of the erythrocyte membrane is influenced by several blood variables, the stability of the erythrocyte membrane must have relations with them. The present study aimed to evaluate, by bivariate and multivariate statistical analyses, the correlations and causal relationships between hematologic and biochemical variables and the stability of the erythrocyte membrane against the chaotropic action of ethanol. The validity of this type of analysis depends on the homogeneity of the population and on the variability of the studied parameters, conditions that can be filled by patients who undergo bariatric surgery by the technique of Roux-en-Y gastric bypass since they will suffer feeding restrictions that have great impact on their blood composition. Pathway analysis revealed that an increase in hemoglobin leads to decreased stability of the cell, probably through a process mediated by an increase in mean corpuscular volume. Furthermore, an increase in the mean corpuscular hemoglobin (MCH) leads to an increase in erythrocyte membrane stability, probably because higher values of MCH are associated with smaller quantities of red blood cells and a larger contact area between the cell membrane and ethanol present in the medium.

  15. Biochemical analyses of the antioxidative activity and chemical ingredients in eight different Allium alien monosomic addition lines.

    PubMed

    Yaguchi, Shigenori; Matsumoto, Misato; Date, Rie; Harada, Kazuki; Maeda, Toshimichi; Yamauchi, Naoki; Shigyo, Masayoshi

    2013-01-01

    We measured the antioxidant contents and antioxidative activities in eight Allium fistulosum-shallot monosomic addition lines (MAL; FF+1A-FF+8A). The high antioxidative activity lines (FF+2A and FF+6A) showed high polyphenol accumulation. These additional chromosomes (2A and 6A) would therefore have anonymous genes related to the upregulation of polyphenol production, the antioxidative activities consequently being increased in these MALs. PMID:24317054

  16. MULTIVARIATE ANALYSES (CONONICAL CORRELATION AND PARTIAL LEAST SQUARE, PLS) TO MODEL AND ASSESS THE ASSOCIATION OF LANDSCAPE METRICS TO SURFACE WATER CHEMICAL AND BIOLOGICAL PROPERTIES USING SAVANNAH RIVER BASIN DATA.

    EPA Science Inventory

    Many multivariate methods are used in describing and predicting relation; each has its unique usage of categorical and non-categorical data. In multivariate analysis of variance (MANOVA), many response variables (y's) are related to many independent variables that are categorical...

  17. Detecting Molecular Features of Spectra Mainly Associated with Structural and Non-Structural Carbohydrates in Co-Products from BioEthanol Production Using DRIFT with Uni- and Multivariate Molecular Spectral Analyses

    PubMed Central

    Yu, Peiqiang; Damiran, Daalkhaijav; Azarfar, Arash; Niu, Zhiyuan

    2011-01-01

    The objective of this study was to use DRIFT spectroscopy with uni- and multivariate molecular spectral analyses as a novel approach to detect molecular features of spectra mainly associated with carbohydrate in the co-products (wheat DDGS, corn DDGS, blend DDGS) from bioethanol processing in comparison with original feedstock (wheat (Triticum), corn (Zea mays)). The carbohydrates related molecular spectral bands included: A_Cell (structural carbohydrates, peaks area region and baseline: ca. 1485–1188 cm−1), A_1240 (structural carbohydrates, peak area centered at ca. 1240 cm−1 with region and baseline: ca. 1292–1198 cm−1), A_CHO (total carbohydrates, peaks region and baseline: ca. 1187–950 cm−1), A_928 (non-structural carbohydrates, peak area centered at ca. 928 cm−1 with region and baseline: ca. 952–910 cm−1), A_860 (non-structural carbohydrates, peak area centered at ca. 860 cm−1 with region and baseline: ca. 880–827 cm−1), H_1415 (structural carbohydrate, peak height centered at ca. 1415 cm−1 with baseline: ca. 1485–1188 cm−1), H_1370 (structural carbohydrate, peak height at ca. 1370 cm−1 with a baseline: ca. 1485–1188 cm−1). The study shows that the grains had lower spectral intensity (KM Unit) of the cellulosic compounds of A_1240 (8.5 vs. 36.6, P < 0.05), higher (P < 0.05) intensities of the non-structural carbohydrate of A_928 (17.3 vs. 2.0) and A_860 (20.7 vs. 7.6) than their co-products from bioethanol processing. There were no differences (P > 0.05) in the peak area intensities of A_Cell (structural CHO) at 1292–1198 cm−1 and A_CHO (total CHO) at 1187–950 cm−1 with average molecular infrared intensity KM unit of 226.8 and 508.1, respectively. There were no differences (P > 0.05) in the peak height intensities of H_1415 and H_1370 (structural CHOs) with average intensities 1.35 and 1.15, respectively. The multivariate molecular spectral analyses were able to discriminate and classify between the corn and corn

  18. Detecting molecular features of spectra mainly associated with structural and non-structural carbohydrates in co-products from bioEthanol production using DRIFT with uni- and multivariate molecular spectral analyses.

    PubMed

    Yu, Peiqiang; Damiran, Daalkhaijav; Azarfar, Arash; Niu, Zhiyuan

    2011-01-01

    The objective of this study was to use DRIFT spectroscopy with uni- and multivariate molecular spectral analyses as a novel approach to detect molecular features of spectra mainly associated with carbohydrate in the co-products (wheat DDGS, corn DDGS, blend DDGS) from bioethanol processing in comparison with original feedstock (wheat (Triticum), corn (Zea mays)). The carbohydrates related molecular spectral bands included: A_Cell (structural carbohydrates, peaks area region and baseline: ca. 1485-1188 cm(-1)), A_1240 (structural carbohydrates, peak area centered at ca. 1240 cm(-1) with region and baseline: ca. 1292-1198 cm(-1)), A_CHO (total carbohydrates, peaks region and baseline: ca. 1187-950 cm(-1)), A_928 (non-structural carbohydrates, peak area centered at ca. 928 cm(-1) with region and baseline: ca. 952-910 cm(-1)), A_860 (non-structural carbohydrates, peak area centered at ca. 860 cm(-1) with region and baseline: ca. 880-827 cm(-1)), H_1415 (structural carbohydrate, peak height centered at ca. 1415 cm(-1) with baseline: ca. 1485-1188 cm(-1)), H_1370 (structural carbohydrate, peak height at ca. 1370 cm(-1) with a baseline: ca. 1485-1188 cm(-1)). The study shows that the grains had lower spectral intensity (KM Unit) of the cellulosic compounds of A_1240 (8.5 vs. 36.6, P < 0.05), higher (P < 0.05) intensities of the non-structural carbohydrate of A_928 (17.3 vs. 2.0) and A_860 (20.7 vs. 7.6) than their co-products from bioethanol processing. There were no differences (P > 0.05) in the peak area intensities of A_Cell (structural CHO) at 1292-1198 cm(-1) and A_CHO (total CHO) at 1187-950 cm(-1) with average molecular infrared intensity KM unit of 226.8 and 508.1, respectively. There were no differences (P > 0.05) in the peak height intensities of H_1415 and H_1370 (structural CHOs) with average intensities 1.35 and 1.15, respectively. The multivariate molecular spectral analyses were able to discriminate and classify between the corn and corn DDGS molecular

  19. A multivariate prediction model for microarray cross-hybridization

    PubMed Central

    Chen, Yian A; Chou, Cheng-Chung; Lu, Xinghua; Slate, Elizabeth H; Peck, Konan; Xu, Wenying; Voit, Eberhard O; Almeida, Jonas S

    2006-01-01

    Background Expression microarray analysis is one of the most popular molecular diagnostic techniques in the post-genomic era. However, this technique faces the fundamental problem of potential cross-hybridization. This is a pervasive problem for both oligonucleotide and cDNA microarrays; it is considered particularly problematic for the latter. No comprehensive multivariate predictive modeling has been performed to understand how multiple variables contribute to (cross-) hybridization. Results We propose a systematic search strategy using multiple multivariate models [multiple linear regressions, regression trees, and artificial neural network analyses (ANNs)] to select an effective set of predictors for hybridization. We validate this approach on a set of DNA microarrays with cytochrome p450 family genes. The performance of our multiple multivariate models is compared with that of a recently proposed third-order polynomial regression method that uses percent identity as the sole predictor. All multivariate models agree that the 'most contiguous base pairs between probe and target sequences,' rather than percent identity, is the best univariate predictor. The predictive power is improved by inclusion of additional nonlinear effects, in particular target GC content, when regression trees or ANNs are used. Conclusion A systematic multivariate approach is provided to assess the importance of multiple sequence features for hybridization and of relationships among these features. This approach can easily be applied to larger datasets. This will allow future developments of generalized hybridization models that will be able to correct for false-positive cross-hybridization signals in expression experiments. PMID:16509965

  20. Multivariate Models for Normal and Binary Responses in Intervention Studies

    ERIC Educational Resources Information Center

    Pituch, Keenan A.; Whittaker, Tiffany A.; Chang, Wanchen

    2016-01-01

    Use of multivariate analysis (e.g., multivariate analysis of variance) is common when normally distributed outcomes are collected in intervention research. However, when mixed responses--a set of normal and binary outcomes--are collected, standard multivariate analyses are no longer suitable. While mixed responses are often obtained in…

  1. Multivariate meta-analysis: potential and promise.

    PubMed

    Jackson, Dan; Riley, Richard; White, Ian R

    2011-09-10

    The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day 'Multivariate meta-analysis' event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice.

  2. Problems with Multivariate Normality: Can the Multivariate Bootstrap Help?

    ERIC Educational Resources Information Center

    Thompson, Bruce

    Multivariate normality is required for some statistical tests. This paper explores the implications of violating the assumption of multivariate normality and illustrates a graphical procedure for evaluating multivariate normality. The logic for using the multivariate bootstrap is presented. The multivariate bootstrap can be used when distribution…

  3. Determination of bromhexine in cough-cold syrups by absorption spectrophotometry and multivariate calibration using partial least-squares and hybrid linear analyses. Application of a novel method of wavelength selection.

    PubMed

    Goicoechea, H C; Olivieri, A C

    1999-07-12

    The mucolitic bromhexine [N-(2-amino-3,5-dibromobenzyl)-N-methylcyclohexylamine] has been determined in cough suppressant syrups by multivariate spectrophotometric calibration, together with partial least-squares (PLS-1) and hybrid linear analysis (HLA). Notwithstanding the spectral overlapping between bromhexine and syrup excipients, as well as the intrinsic variability of the latter in unknown samples, the recoveries are excellent. A novel method of wavelength selection was also applied, based on the concept of net analyte signal regression, as adapted to the HLA methodology. This method allows one to improve the performance of both PLS-1 and HLA in samples containing nonmodeled interferences. PMID:18967655

  4. Multivariate Regression with Calibration*

    PubMed Central

    Liu, Han; Wang, Lie; Zhao, Tuo

    2014-01-01

    We propose a new method named calibrated multivariate regression (CMR) for fitting high dimensional multivariate regression models. Compared to existing methods, CMR calibrates the regularization for each regression task with respect to its noise level so that it is simultaneously tuning insensitive and achieves an improved finite-sample performance. Computationally, we develop an efficient smoothed proximal gradient algorithm which has a worst-case iteration complexity O(1/ε), where ε is a pre-specified numerical accuracy. Theoretically, we prove that CMR achieves the optimal rate of convergence in parameter estimation. We illustrate the usefulness of CMR by thorough numerical simulations and show that CMR consistently outperforms other high dimensional multivariate regression methods. We also apply CMR on a brain activity prediction problem and find that CMR is as competitive as the handcrafted model created by human experts. PMID:25620861

  5. Multivariate bubbles and antibubbles

    NASA Astrophysics Data System (ADS)

    Fry, John

    2014-08-01

    In this paper we develop models for multivariate financial bubbles and antibubbles based on statistical physics. In particular, we extend a rich set of univariate models to higher dimensions. Changes in market regime can be explicitly shown to represent a phase transition from random to deterministic behaviour in prices. Moreover, our multivariate models are able to capture some of the contagious effects that occur during such episodes. We are able to show that declining lending quality helped fuel a bubble in the US stock market prior to 2008. Further, our approach offers interesting insights into the spatial development of UK house prices.

  6. Multivariate Data EXplorer (MDX)

    SciTech Connect

    Steed, Chad Allen

    2012-08-01

    The MDX toolkit facilitates exploratory data analysis and visualization of multivariate datasets. MDX provides and interactive graphical user interface to load, explore, and modify multivariate datasets stored in tabular forms. MDX uses an extended version of the parallel coordinates plot and scatterplots to represent the data. The user can perform rapid visual queries using mouse gestures in the visualization panels to select rows or columns of interest. The visualization panel provides coordinated multiple views whereby selections made in one plot are propagated to the other plots. Users can also export selected data or reconfigure the visualization panel to explore relationships between columns and rows in the data.

  7. Evaluation of the efficiency of continuous wavelet transform as processing and preprocessing algorithm for resolution of overlapped signals in univariate and multivariate regression analyses; an application to ternary and quaternary mixtures

    NASA Astrophysics Data System (ADS)

    Hegazy, Maha A.; Lotfy, Hayam M.; Mowaka, Shereen; Mohamed, Ekram Hany

    2016-07-01

    Wavelets have been adapted for a vast number of signal-processing applications due to the amount of information that can be extracted from a signal. In this work, a comparative study on the efficiency of continuous wavelet transform (CWT) as a signal processing tool in univariate regression and a pre-processing tool in multivariate analysis using partial least square (CWT-PLS) was conducted. These were applied to complex spectral signals of ternary and quaternary mixtures. CWT-PLS method succeeded in the simultaneous determination of a quaternary mixture of drotaverine (DRO), caffeine (CAF), paracetamol (PAR) and p-aminophenol (PAP, the major impurity of paracetamol). While, the univariate CWT failed to simultaneously determine the quaternary mixture components and was able to determine only PAR and PAP, the ternary mixtures of DRO, CAF, and PAR and CAF, PAR, and PAP. During the calculations of CWT, different wavelet families were tested. The univariate CWT method was validated according to the ICH guidelines. While for the development of the CWT-PLS model a calibration set was prepared by means of an orthogonal experimental design and their absorption spectra were recorded and processed by CWT. The CWT-PLS model was constructed by regression between the wavelet coefficients and concentration matrices and validation was performed by both cross validation and external validation sets. Both methods were successfully applied for determination of the studied drugs in pharmaceutical formulations.

  8. Multivariate Data EXplorer (MDX)

    2012-08-01

    The MDX toolkit facilitates exploratory data analysis and visualization of multivariate datasets. MDX provides and interactive graphical user interface to load, explore, and modify multivariate datasets stored in tabular forms. MDX uses an extended version of the parallel coordinates plot and scatterplots to represent the data. The user can perform rapid visual queries using mouse gestures in the visualization panels to select rows or columns of interest. The visualization panel provides coordinated multiple views wherebymore » selections made in one plot are propagated to the other plots. Users can also export selected data or reconfigure the visualization panel to explore relationships between columns and rows in the data.« less

  9. Analysing spatio-temporal patterns of the global NO2-distribution retrieved from GOME satellite observations using a generalized additive model

    NASA Astrophysics Data System (ADS)

    Hayn, M.; Beirle, S.; Hamprecht, F. A.; Platt, U.; Menze, B. H.; Wagner, T.

    2009-09-01

    With the increasing availability of observational data from different sources at a global level, joint analysis of these data is becoming especially attractive. For such an analysis - oftentimes with little prior knowledge about local and global interactions between the different observational variables at hand - an exploratory, data-driven analysis of the data may be of particular relevance. In the present work we used generalized additive models (GAM) in an exemplary study of spatio-temporal patterns in the tropospheric NO2-distribution derived from GOME satellite observations (1996 to 2001) at global scale. We focused on identifying correlations between NO2 and local wind fields, a quantity which is of particular interest in the analysis of spatio-temporal interactions. Formulating general functional, parametric relationships between the observed NO2 distribution and local wind fields, however, is difficult - if not impossible. So, rather than following a model-based analysis testing the data for predefined hypotheses (assuming, for example, sinusoidal seasonal trends), we used a GAM with non-parametric model terms to learn this functional relationship between NO2 and wind directly from the data. The NO2 observations showed to be affected by wind-dominated processes over large areas. We estimated the extent of areas affected by specific NO2 emission sources, and were able to highlight likely atmospheric transport "pathways". General temporal trends which were also part of our model - weekly, seasonal and linear changes - showed to be in good agreement with previous studies and alternative ways of analysing the time series. Overall, using a non-parametric model provided favorable means for a rapid inspection of this large spatio-temporal NO2 data set, with less bias than parametric approaches, and allowing to visualize dynamical processes of the NO2 distribution at a global scale.

  10. Impact of enzalutamide on quality of life in men with metastatic castration-resistant prostate cancer after chemotherapy: additional analyses from the AFFIRM randomized clinical trial

    PubMed Central

    Cella, D.; Ivanescu, C.; Holmstrom, S.; Bui, C. N.; Spalding, J.; Fizazi, K.

    2015-01-01

    Background To present longitudinal changes in Functional Assessment of Cancer Therapy-Prostate (FACT-P) scores during 25-week treatment with enzalutamide or placebo in men with progressive metastatic castration-resistant prostate cancer (mCRPC) after chemotherapy in the AFFIRM trial. Patients and methods Patients were randomly assigned to enzalutamide 160 mg/day or placebo. FACT-P was completed before randomization, at weeks 13, 17, 21, and 25, and every 12 weeks thereafter while on study treatment. Longitudinal changes in FACT-P scores from baseline to 25 weeks were analyzed using a mixed effects model for repeated measures (MMRM), with a pattern mixture model (PMM) applied as secondary analysis to address non-ignorable missing data. Cumulative distribution function (CDF) plots were generated and different methodological approaches and models for handling missing data were applied. Due to the exploratory nature of the analyses, adjustments for multiple comparisons were not made. AFFIRM is registered with ClinicalTrials.gov, number NCT00974311. Results The intention-to-treat FACT-P population included 938 patients (enzalutamide, n = 674; placebo n = 264) with evaluable FACT-P assessments at baseline and ≥1 post-baseline assessment. After 25 weeks, the mean FACT-P total score decreased by 1.52 points with enzalutamide compared with 13.73 points with placebo (P < 0.001). In addition, significant treatment differences at week 25 favoring enzalutamide were evident for all FACT-P subscales and indices, whether analyzed by MMRM or PMM. CDF plots revealed differences favoring enzalutamide compared with placebo across the full range of possible response levels for FACT-P total and all disease- and symptom-specific subscales/indices. Conclusion In men with progressive mCRPC after docetaxel-based chemotherapy, enzalutamide is superior to placebo in health-related quality-of-life outcomes, regardless of analysis model or threshold selected for meaningful response. Clinical

  11. Causal diagrams and multivariate analysis II: precision work.

    PubMed

    Jupiter, Daniel C

    2014-01-01

    In this Investigators' Corner, I continue my discussion of when and why we researchers should include variables in multivariate regression. My examination focuses on studies comparing treatment groups and situations for which we can either exclude variables from multivariate analyses or include them for reasons of precision.

  12. Multivariate Analysis of Ipsative Data: Problems and Solutions.

    ERIC Educational Resources Information Center

    McLean, James E.; Chissom, Brad S.

    The term "ipsative" refers to measurement based on intra-individual comparisons. The research literature in the social sciences contains many cautions about using ipsative data in multivariate analysis. The purpose of this paper is to identify the problems associated with the multivariate and regression analyses of ipsative data and to provide…

  13. Relationship between Multiple Regression and Selected Multivariable Methods.

    ERIC Educational Resources Information Center

    Schumacker, Randall E.

    The relationship of multiple linear regression to various multivariate statistical techniques is discussed. The importance of the standardized partial regression coefficient (beta weight) in multiple linear regression as it is applied in path, factor, LISREL, and discriminant analyses is emphasized. The multivariate methods discussed in this paper…

  14. Introduction to multivariate discrimination

    NASA Astrophysics Data System (ADS)

    Kégl, Balázs

    2013-07-01

    Multivariate discrimination or classification is one of the best-studied problem in machine learning, with a plethora of well-tested and well-performing algorithms. There are also several good general textbooks [1-9] on the subject written to an average engineering, computer science, or statistics graduate student; most of them are also accessible for an average physics student with some background on computer science and statistics. Hence, instead of writing a generic introduction, we concentrate here on relating the subject to a practitioner experimental physicist. After a short introduction on the basic setup (Section 1) we delve into the practical issues of complexity regularization, model selection, and hyperparameter optimization (Section 2), since it is this step that makes high-complexity non-parametric fitting so different from low-dimensional parametric fitting. To emphasize that this issue is not restricted to classification, we illustrate the concept on a low-dimensional but non-parametric regression example (Section 2.1). Section 3 describes the common algorithmic-statistical formal framework that unifies the main families of multivariate classification algorithms. We explain here the large-margin principle that partly explains why these algorithms work. Section 4 is devoted to the description of the three main (families of) classification algorithms, neural networks, the support vector machine, and AdaBoost. We do not go into the algorithmic details; the goal is to give an overview on the form of the functions these methods learn and on the objective functions they optimize. Besides their technical description, we also make an attempt to put these algorithm into a socio-historical context. We then briefly describe some rather heterogeneous applications to illustrate the pattern recognition pipeline and to show how widespread the use of these methods is (Section 5). We conclude the chapter with three essentially open research problems that are either

  15. Multivariate respiratory motion prediction

    NASA Astrophysics Data System (ADS)

    Dürichen, R.; Wissel, T.; Ernst, F.; Schlaefer, A.; Schweikard, A.

    2014-10-01

    In extracranial robotic radiotherapy, tumour motion is compensated by tracking external and internal surrogates. To compensate system specific time delays, time series prediction of the external optical surrogates is used. We investigate whether the prediction accuracy can be increased by expanding the current clinical setup by an accelerometer, a strain belt and a flow sensor. Four previously published prediction algorithms are adapted to multivariate inputs—normalized least mean squares (nLMS), wavelet-based least mean squares (wLMS), support vector regression (SVR) and relevance vector machines (RVM)—and evaluated for three different prediction horizons. The measurement involves 18 subjects and consists of two phases, focusing on long term trends (M1) and breathing artefacts (M2). To select the most relevant and least redundant sensors, a sequential forward selection (SFS) method is proposed. Using a multivariate setting, the results show that the clinically used nLMS algorithm is susceptible to large outliers. In the case of irregular breathing (M2), the mean root mean square error (RMSE) of a univariate nLMS algorithm is 0.66 mm and can be decreased to 0.46 mm by a multivariate RVM model (best algorithm on average). To investigate the full potential of this approach, the optimal sensor combination was also estimated on the complete test set. The results indicate that a further decrease in RMSE is possible for RVM (to 0.42 mm). This motivates further research about sensor selection methods. Besides the optical surrogates, the sensors most frequently selected by the algorithms are the accelerometer and the strain belt. These sensors could be easily integrated in the current clinical setup and would allow a more precise motion compensation.

  16. Collision prediction models using multivariate Poisson-lognormal regression.

    PubMed

    El-Basyouny, Karim; Sayed, Tarek

    2009-07-01

    This paper advocates the use of multivariate Poisson-lognormal (MVPLN) regression to develop models for collision count data. The MVPLN approach presents an opportunity to incorporate the correlations across collision severity levels and their influence on safety analyses. The paper introduces a new multivariate hazardous location identification technique, which generalizes the univariate posterior probability of excess that has been commonly proposed and applied in the literature. In addition, the paper presents an alternative approach for quantifying the effect of the multivariate structure on the precision of expected collision frequency. The MVPLN approach is compared with the independent (separate) univariate Poisson-lognormal (PLN) models with respect to model inference, goodness-of-fit, identification of hot spots and precision of expected collision frequency. The MVPLN is modeled using the WinBUGS platform which facilitates computation of posterior distributions as well as providing a goodness-of-fit measure for model comparisons. The results indicate that the estimates of the extra Poisson variation parameters were considerably smaller under MVPLN leading to higher precision. The improvement in precision is due mainly to the fact that MVPLN accounts for the correlation between the latent variables representing property damage only (PDO) and injuries plus fatalities (I+F). This correlation was estimated at 0.758, which is highly significant, suggesting that higher PDO rates are associated with higher I+F rates, as the collision likelihood for both types is likely to rise due to similar deficiencies in roadway design and/or other unobserved factors. In terms of goodness-of-fit, the MVPLN model provided a superior fit than the independent univariate models. The multivariate hazardous location identification results demonstrated that some hazardous locations could be overlooked if the analysis was restricted to the univariate models. PMID:19540972

  17. Multivariate Hypergeometric Similarity Measure

    PubMed Central

    Kaddi, Chanchala D.; Parry, R. Mitchell; Wang, May D.

    2016-01-01

    We propose a similarity measure based on the multivariate hypergeometric distribution for the pairwise comparison of images and data vectors. The formulation and performance of the proposed measure are compared with other similarity measures using synthetic data. A method of piecewise approximation is also implemented to facilitate application of the proposed measure to large samples. Example applications of the proposed similarity measure are presented using mass spectrometry imaging data and gene expression microarray data. Results from synthetic and biological data indicate that the proposed measure is capable of providing meaningful discrimination between samples, and that it can be a useful tool for identifying potentially related samples in large-scale biological data sets. PMID:24407308

  18. Multivariate heredity of melanin-based coloration, body mass and immunity.

    PubMed

    Kim, S-Y; Fargallo, J A; Vergara, P; Martínez-Padilla, J

    2013-08-01

    The genetic covariation among different traits may cause the appearance of correlated response to selection on multivariate phenotypes. Genes responsible for the expression of melanin-based color traits are also involved in other important physiological functions such as immunity and metabolism by pleiotropy, suggesting the possibility of multivariate evolution. However, little is known about the relationship between melanin coloration and these functions at the additive genetic level in wild vertebrates. From a multivariate perspective, we simultaneously explored inheritance and selection of melanin coloration, body mass and phytohemagglutinin (PHA)-mediated immune response by using long-term data over an 18-year period collected in a wild population of the common kestrel Falco tinnunculus. Pedigree-based quantitative genetic analyses showed negative genetic covariance between melanin-based coloration and body mass in male adults and positive genetic covariance between body mass and PHA-mediated immune response in fledglings as predicted by pleiotropic effects of melanocortin receptor activity. Multiple selection analyses showed an increased fitness in male adults with intermediate phenotypic values for melanin color and body mass. In male fledglings, there was evidence for a disruptive selection on rump gray color, but a stabilizing selection on PHA-mediated immune response. Our results provide an insight into the evolution of multivariate traits genetically related with melanin-based coloration. The differences in multivariate inheritance and selection between male and female kestrels might have resulted in sexual dimorphism in size and color. When pleiotropic effects are present, coloration can evolve through a complex pathway involving correlated response to selection on multivariate traits.

  19. Genetic basis of adult migration timing in anadromous steelhead discovered through multivariate association testing.

    PubMed

    Hess, Jon E; Zendt, Joseph S; Matala, Amanda R; Narum, Shawn R

    2016-05-11

    Migration traits are presumed to be complex and to involve interaction among multiple genes. We used both univariate analyses and a multivariate random forest (RF) machine learning algorithm to conduct association mapping of 15 239 single nucleotide polymorphisms (SNPs) for adult migration-timing phenotype in steelhead (Oncorhynchus mykiss). Our study focused on a model natural population of steelhead that exhibits two distinct migration-timing life histories with high levels of admixture in nature. Neutral divergence was limited between fish exhibiting summer- and winter-run migration owing to high levels of interbreeding, but a univariate mixed linear model found three SNPs from a major effect gene to be significantly associated with migration timing (p < 0.000005) that explained 46% of trait variation. Alignment to the annotated Salmo salar genome provided evidence that all three SNPs localize within a 46 kb region overlapping GREB1-like (an oestrogen target gene) on chromosome Ssa03. Additionally, multivariate analyses with RF identified that these three SNPs plus 15 additional SNPs explained up to 60% of trait variation. These candidate SNPs may provide the ability to predict adult migration timing of steelhead to facilitate conservation management of this species, and this study demonstrates the benefit of multivariate analyses for association studies. PMID:27170720

  20. Additive-dominance genetic model analyses for late-maturity alpha-amylase activity in a bread wheat factorial crossing population.

    PubMed

    Rasul, Golam; Glover, Karl D; Krishnan, Padmanaban G; Wu, Jixiang; Berzonsky, William A; Ibrahim, Amir M H

    2015-12-01

    Elevated level of late maturity α-amylase activity (LMAA) can result in low falling number scores, reduced grain quality, and downgrade of wheat (Triticum aestivum L.) class. A mating population was developed by crossing parents with different levels of LMAA. The F2 and F3 hybrids and their parents were evaluated for LMAA, and data were analyzed using the R software package 'qgtools' integrated with an additive-dominance genetic model and a mixed linear model approach. Simulated results showed high testing powers for additive and additive × environment variances, and comparatively low powers for dominance and dominance × environment variances. All variance components and their proportions to the phenotypic variance for the parents and hybrids were significant except for the dominance × environment variance. The estimated narrow-sense heritability and broad-sense heritability for LMAA were 14 and 54%, respectively. High significant negative additive effects for parents suggest that spring wheat cultivars 'Lancer' and 'Chester' can serve as good general combiners, and that 'Kinsman' and 'Seri-82' had negative specific combining ability in some hybrids despite of their own significant positive additive effects, suggesting they can be used as parents to reduce LMAA levels. Seri-82 showed very good general combining ability effect when used as a male parent, indicating the importance of reciprocal effects. High significant negative dominance effects and high-parent heterosis for hybrids demonstrated that the specific hybrid combinations; Chester × Kinsman, 'Lerma52' × Lancer, Lerma52 × 'LoSprout' and 'Janz' × Seri-82 could be generated to produce cultivars with significantly reduced LMAA level.

  1. Multivariate Analyses of Selected Mechanical Properties of Dry Bean Grain

    NASA Astrophysics Data System (ADS)

    Kibar, Hakan

    2015-04-01

    The direct shear test are widely used to measure the bulk material properties for economical design of bulk handling equipment and to estimate wall pressure inside storage structures, namely their bulk density, the angle of internal friction, shear strength, Poisson ratio, and lateral pressure ratios are required. Tests were conducted at thirty six different shear speeds (between 0.30-1.00 mm min-1) and three different normal stresses were applied (60, 120 and 180 kPa). The angle of internal friction, Poisson ratio, and lateral pressure ratios demonstrated fluctuations depending on the shear speeds. The results of the principal component analysis indicated that the first three principal components accounted for 97.40% of the total variability among the thirty six different shear speeds for all the traits investigated. The first principal component was the most important. In the result of principal component analysis, the shear speeds were divided into seven clusters. The pressures were decreased and increased with the change of the angle of internal friction and the lateral pressure ratio. The data obtained from the study will be useful in the structural design of dry bean bins to calculate loads on bins from the stored material and grain handling equipment.

  2. Sampling effort affects multivariate comparisons of stream assemblages

    USGS Publications Warehouse

    Cao, Y.; Larsen, D.P.; Hughes, R.M.; Angermeier, P.L.; Patton, T.M.

    2002-01-01

    Multivariate analyses are used widely for determining patterns of assemblage structure, inferring species-environment relationships and assessing human impacts on ecosystems. The estimation of ecological patterns often depends on sampling effort, so the degree to which sampling effort affects the outcome of multivariate analyses is a concern. We examined the effect of sampling effort on site and group separation, which was measured using a mean similarity method. Two similarity measures, the Jaccard Coefficient and Bray-Curtis Index were investigated with 1 benthic macroinvertebrate and 2 fish data sets. Site separation was significantly improved with increased sampling effort because the similarity between replicate samples of a site increased more rapidly than between sites. Similarly, the faster increase in similarity between sites of the same group than between sites of different groups caused clearer separation between groups. The strength of site and group separation completely stabilized only when the mean similarity between replicates reached 1. These results are applicable to commonly used multivariate techniques such as cluster analysis and ordination because these multivariate techniques start with a similarity matrix. Completely stable outcomes of multivariate analyses are not feasible. Instead, we suggest 2 criteria for estimating the stability of multivariate analyses of assemblage data: 1) mean within-site similarity across all sites compared, indicating sample representativeness, and 2) the SD of within-site similarity across sites, measuring sample comparability.

  3. Novel Flow Cytometry Analyses of Boar Sperm Viability: Can the Addition of Whole Sperm-Rich Fraction Seminal Plasma to Frozen-Thawed Boar Sperm Affect It?

    PubMed Central

    Díaz, Rommy; Boguen, Rodrigo; Martins, Simone Maria Massami Kitamura; Ravagnani, Gisele Mouro; Leal, Diego Feitosa; Oliveira, Melissa de Lima; Muro, Bruno Bracco Donatelli; Parra, Beatriz Martins; Meirelles, Flávio Vieira; Papa, Frederico Ozanan; Dell’Aqua, José Antônio; Alvarenga, Marco Antônio; Moretti, Aníbal de Sant’Anna; Sepúlveda, Néstor

    2016-01-01

    Boar semen cryopreservation remains a challenge due to the extension of cold shock damage. Thus, many alternatives have emerged to improve the quality of frozen-thawed boar sperm. Although the use of seminal plasma arising from boar sperm-rich fraction (SP-SRF) has shown good efficacy; however, the majority of actual sperm evaluation techniques include a single or dual sperm parameter analysis, which overrates the real sperm viability. Within this context, this work was performed to introduce a sperm flow cytometry fourfold stain technique for simultaneous evaluation of plasma and acrosomal membrane integrity and mitochondrial membrane potential. We then used the sperm flow cytometry fourfold stain technique to study the effect of SP-SRF on frozen-thawed boar sperm and further evaluated the effect of this treatment on sperm movement, tyrosine phosphorylation and fertility rate (FR). The sperm fourfold stain technique is accurate (R2 = 0.9356, p > 0.01) for simultaneous evaluation of plasma and acrosomal membrane integrity and mitochondrial membrane potential (IPIAH cells). Centrifugation pre-cryopreservation was not deleterious (p > 0.05) for any analyzed variables. Addition of SP-SRF after cryopreservation was able to improve total and progressive motility (p < 0.05) when boar semen was cryopreserved without SP-SRF; however, it was not able to decrease tyrosine phosphorylation (p > 0.05) or improve IPIAH cells (p > 0.05). FR was not (p > 0.05) statistically increased by the addition of seminal plasma, though females inseminated with frozen-thawed boar semen plus SP-SRF did perform better than those inseminated with sperm lacking seminal plasma. Thus, we conclude that sperm fourfold stain can be used to simultaneously evaluate plasma and acrosomal membrane integrity and mitochondrial membrane potential, and the addition of SP-SRF at thawed boar semen cryopreserved in absence of SP-SRF improve its total and progressive motility. PMID:27529819

  4. Multivariate Approaches to Classification in Extragalactic Astronomy

    NASA Astrophysics Data System (ADS)

    Fraix-Burnet, Didier; Thuillard, Marc; Chattopadhyay, Asis Kumar

    2015-08-01

    Clustering objects into synthetic groups is a natural activity of any science. Astrophysics is not an exception and is now facing a deluge of data. For galaxies, the one-century old Hubble classification and the Hubble tuning fork are still largely in use, together with numerous mono- or bivariate classifications most often made by eye. However, a classification must be driven by the data, and sophisticated multivariate statistical tools are used more and more often. In this paper we review these different approaches in order to situate them in the general context of unsupervised and supervised learning. We insist on the astrophysical outcomes of these studies to show that multivariate analyses provide an obvious path toward a renewal of our classification of galaxies and are invaluable tools to investigate the physics and evolution of galaxies.

  5. Cytometric fingerprinting: quantitative characterization of multivariate distributions.

    PubMed

    Rogers, Wade T; Moser, Allan R; Holyst, Herbert A; Bantly, Andrew; Mohler, Emile R; Scangas, George; Moore, Jonni S

    2008-05-01

    Recent technological advances in flow cytometry instrumentation provide the basis for high-dimensionality and high-throughput biological experimentation in a heterogeneous cellular context. Concomitant advances in scalable computational algorithms are necessary to better utilize the information that is contained in these high-complexity experiments. The development of such tools has the potential to expand the utility of flow cytometric analysis from a predominantly hypothesis-driven mode to one of discovery, or hypothesis-generating research. A new method of analysis of flow cytometric data called Cytometric Fingerprinting (CF) has been developed. CF captures the set of multivariate probability distribution functions corresponding to list-mode data and then "flattens" them into a computationally efficient fingerprint representation that facilitates quantitative comparisons of samples. An experimental and synthetic data were generated to act as reference sets for evaluating CF. Without the introduction of prior knowledge, CF was able to "discover" the location and concentration of spiked cells in ungated analyses over a concentration range covering four orders of magnitude, to a lower limit on the order of 10 spiked events in a background of 100,000 events. We describe a new method for quantitative analysis of list-mode cytometric data. CF includes a novel algorithm for space subdivision that improves estimation of the probability density function by dividing space into nonrectangular polytopes. Additionally it renders a multidimensional distribution in the form of a one-dimensional multiresolution hierarchical fingerprint that creates a computationally efficient representation of high dimensionality distribution functions. CF supports both the generation and testing of hypotheses, eliminates sources of operator bias, and provides an increased level of automation of data analysis.

  6. Multivariate linear recurrences and power series division

    PubMed Central

    Hauser, Herwig; Koutschan, Christoph

    2012-01-01

    Bousquet-Mélou and Petkovšek investigated the generating functions of multivariate linear recurrences with constant coefficients. We will give a reinterpretation of their results by means of division theorems for formal power series, which clarifies the structural background and provides short, conceptual proofs. In addition, extending the division to the context of differential operators, the case of recurrences with polynomial coefficients can be treated in an analogous way. PMID:23482936

  7. Analysing spatio-temporal patterns of the global NO2-distribution retrieved from GOME satellite observations using a generalized additive model

    NASA Astrophysics Data System (ADS)

    Hayn, M.; Beirle, S.; Hamprecht, F. A.; Platt, U.; Menze, B. H.; Wagner, T.

    2009-04-01

    With the increasing availability of observations from different space-borne sensors, the joint analysis of observational data from multiple sources becomes more and more attractive. For such an analysis - oftentimes with little prior knowledge about local and global interactions between the different observational variables available - an explorative data-driven analysis of the remote sensing data may be of particular relevance. In the present work we used generalized additive models (GAM) in this task, in an exemplary study of spatio-temporal patterns in the tropospheric NO2-distribution derived from GOME satellite observations (1996 to 2001) at global scale. We modelled different temporal trends in the time series of the observed NO2, but focused on identifying correlations between NO2 and local wind fields. Here, our nonparametric modelling approach had several advantages over standard parametric models: While the model-based analysis allowed to test predefined hypotheses (assuming, for example, sinusoidal seasonal trends) only, the GAM allowed to learn functional relations between different observational variables directly from the data. This was of particular interest in the present task, as little was known about relations between the observed NO2 distribution and transport processes by local wind fields, and the formulation of general functional relationships to be tested remained difficult. We found the observed temporal trends - weekly, seasonal and linear changes - to be in overall good agreement with previous studies and alternative ways of data analysis. However, NO2 observations showed to be affected by wind-dominated processes over several areas, world wide. Here we were able to estimate the extent of areas affected by specific NO2 emission sources, and to highlight likely atmospheric transport pathways. Overall, using a nonparametric model provided favourable means for a rapid inspection of this large spatio-temporal data set,with less bias than

  8. Water quality change detection: multivariate algorithms

    NASA Astrophysics Data System (ADS)

    Klise, Katherine A.; McKenna, Sean A.

    2006-05-01

    In light of growing concern over the safety and security of our nation's drinking water, increased attention has been focused on advanced monitoring of water distribution systems. The key to these advanced monitoring systems lies in the combination of real time data and robust statistical analysis. Currently available data streams from sensors provide near real time information on water quality. Combining these data streams with change detection algorithms, this project aims to develop automated monitoring techniques that will classify real time data and denote anomalous water types. Here, water quality data in 1 hour increments over 3000 hours at 4 locations are used to test multivariate algorithms to detect anomalous water quality events. The algorithms use all available water quality sensors to measure deviation from expected water quality. Simulated anomalous water quality events are added to the measured data to test three approaches to measure this deviation. These approaches include multivariate distance measures to 1) the previous observation, 2) the closest observation in multivariate space, and 3) the closest cluster of previous water quality observations. Clusters are established using kmeans classification. Each approach uses a moving window of previous water quality measurements to classify the current measurement as normal or anomalous. Receiver Operating Characteristic (ROC) curves test the ability of each approach to discriminate between normal and anomalous water quality using a variety of thresholds and simulated anomalous events. These analyses result in a better understanding of the deviation from normal water quality that is necessary to sound an alarm.

  9. Correlates of Communalities as Matching Variables in Differential Item Functioning Analyses

    ERIC Educational Resources Information Center

    Yildirim, Huseyin H.; Yildirim, Selda

    2011-01-01

    Multivariate matching in Differential Item Functioning (DIF) analyses may contribute to understand the sources of DIF. In this context, detecting appropriate additional matching variables is a crucial issue. This present article argues that the variables which are correlated with communalities in item difficulties can be used as an additional…

  10. MULTIVARIATE KERNEL PARTITION PROCESS MIXTURES

    PubMed Central

    Dunson, David B.

    2013-01-01

    Mixtures provide a useful approach for relaxing parametric assumptions. Discrete mixture models induce clusters, typically with the same cluster allocation for each parameter in multivariate cases. As a more flexible approach that facilitates sparse nonparametric modeling of multivariate random effects distributions, this article proposes a kernel partition process (KPP) in which the cluster allocation varies for different parameters. The KPP is shown to be the driving measure for a multivariate ordered Chinese restaurant process that induces a highly-flexible dependence structure in local clustering. This structure allows the relative locations of the random effects to inform the clustering process, with spatially-proximal random effects likely to be assigned the same cluster index. An exact block Gibbs sampler is developed for posterior computation, avoiding truncation of the infinite measure. The methods are applied to hormone curve data, and a dependent KPP is proposed for classification from functional predictors. PMID:24478563

  11. The evolution of multivariate maternal effects.

    PubMed

    Kuijper, Bram; Johnstone, Rufus A; Townley, Stuart

    2014-04-01

    There is a growing interest in predicting the social and ecological contexts that favor the evolution of maternal effects. Most predictions focus, however, on maternal effects that affect only a single character, whereas the evolution of maternal effects is poorly understood in the presence of suites of interacting traits. To overcome this, we simulate the evolution of multivariate maternal effects (captured by the matrix M) in a fluctuating environment. We find that the rate of environmental fluctuations has a substantial effect on the properties of M: in slowly changing environments, offspring are selected to have a multivariate phenotype roughly similar to the maternal phenotype, so that M is characterized by positive dominant eigenvalues; by contrast, rapidly changing environments favor Ms with dominant eigenvalues that are negative, as offspring favor a phenotype which substantially differs from the maternal phenotype. Moreover, when fluctuating selection on one maternal character is temporally delayed relative to selection on other traits, we find a striking pattern of cross-trait maternal effects in which maternal characters influence not only the same character in offspring, but also other offspring characters. Additionally, when selection on one character contains more stochastic noise relative to selection on other traits, large cross-trait maternal effects evolve from those maternal traits that experience the smallest amounts of noise. The presence of these cross-trait maternal effects shows that individual maternal effects cannot be studied in isolation, and that their study in a multivariate context may provide important insights about the nature of past selection. Our results call for more studies that measure multivariate maternal effects in wild populations.

  12. Multivariate Model of Infant Competence.

    ERIC Educational Resources Information Center

    Kierscht, Marcia Selland; Vietze, Peter M.

    This paper describes a multivariate model of early infant competence formulated from variables representing infant-environment transaction including: birthweight, habituation index, personality ratings of infant social orientation and task orientation, ratings of maternal responsiveness to infant distress and social signals, and observational…

  13. Parameter Sensitivity in Multivariate Methods

    ERIC Educational Resources Information Center

    Green, Bert F., Jr.

    1977-01-01

    Interpretation of multivariate models requires knowing how much the fit of the model is impaired by changes in the parameters. The relation of parameter change to loss of goodness of fit can be called parameter sensitivity. Formulas are presented for assessing the sensitivity of multiple regression and principal component weights. (Author/JKS)

  14. Causal diagrams and multivariate analysis III: confound it!

    PubMed

    Jupiter, Daniel C

    2015-01-01

    This commentary concludes my series concerning inclusion of variables in multivariate analyses. We take up the issues of confounding and effect modification and summarize the work we have thus far done. Finally, we provide a rough algorithm to help guide us through the maze of possibilities that we have outlined.

  15. Multivariate calibration applied to the quantitative analysis of infrared spectra

    SciTech Connect

    Haaland, D.M.

    1991-01-01

    Multivariate calibration methods are very useful for improving the precision, accuracy, and reliability of quantitative spectral analyses. Spectroscopists can more effectively use these sophisticated statistical tools if they have a qualitative understanding of the techniques involved. A qualitative picture of the factor analysis multivariate calibration methods of partial least squares (PLS) and principal component regression (PCR) is presented using infrared calibrations based upon spectra of phosphosilicate glass thin films on silicon wafers. Comparisons of the relative prediction abilities of four different multivariate calibration methods are given based on Monte Carlo simulations of spectral calibration and prediction data. The success of multivariate spectral calibrations is demonstrated for several quantitative infrared studies. The infrared absorption and emission spectra of thin-film dielectrics used in the manufacture of microelectronic devices demonstrate rapid, nondestructive at-line and in-situ analyses using PLS calibrations. Finally, the application of multivariate spectral calibrations to reagentless analysis of blood is presented. We have found that the determination of glucose in whole blood taken from diabetics can be precisely monitored from the PLS calibration of either mind- or near-infrared spectra of the blood. Progress toward the non-invasive determination of glucose levels in diabetics is an ultimate goal of this research. 13 refs., 4 figs.

  16. Network structure of multivariate time series.

    PubMed

    Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

    2015-10-21

    Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

  17. Network structure of multivariate time series

    NASA Astrophysics Data System (ADS)

    Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

    2015-10-01

    Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

  18. Network structure of multivariate time series

    PubMed Central

    Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

    2015-01-01

    Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail. PMID:26487040

  19. Multivariable PID control by decoupling

    NASA Astrophysics Data System (ADS)

    Garrido, Juan; Vázquez, Francisco; Morilla, Fernando

    2016-04-01

    This paper presents a new methodology to design multivariable proportional-integral-derivative (PID) controllers based on decoupling control. The method is presented for general n × n processes. In the design procedure, an ideal decoupling control with integral action is designed to minimise interactions. It depends on the desired open-loop processes that are specified according to realisability conditions and desired closed-loop performance specifications. These realisability conditions are stated and three common cases to define the open-loop processes are studied and proposed. Then, controller elements are approximated to PID structure. From a practical point of view, the wind-up problem is also considered and a new anti-wind-up scheme for multivariable PID controller is proposed. Comparisons with other works demonstrate the effectiveness of the methodology through the use of several simulation examples and an experimental lab process.

  20. Investigation of intervertebral disc degeneration using multivariate FTIR spectroscopic imaging.

    PubMed

    Mader, Kerstin T; Peeters, Mirte; Detiger, Suzanne E L; Helder, Marco N; Smit, Theo H; Le Maitre, Christine L; Sammon, Chris

    2016-06-23

    Traditionally tissue samples are analysed using protein or enzyme specific stains on serial sections to build up a picture of the distribution of components contained within them. In this study we investigated the potential of multivariate curve resolution-alternating least squares (MCR-ALS) to deconvolute 2nd derivative spectra of Fourier transform infrared (FTIR) microscopic images measured in transflectance mode of goat and human paraffin embedded intervertebral disc (IVD) tissue sections, to see if this methodology can provide analogous information to that provided by immunohistochemical stains and bioassays but from a single section. MCR-ALS analysis of non-degenerate and enzymatically in vivo degenerated goat IVDs reveals five matrix components displaying distribution maps matching histological stains for collagen, elastin and proteoglycan (PG), as well as immunohistochemical stains for collagen type I and II. Interestingly, two components exhibiting characteristic spectral and distribution profiles of proteoglycans were found, and relative component/tissue maps of these components (labelled PG1 and PG2) showed distinct distributions in non-degenerate versus mildly degenerate goat samples. MCR-ALS analysis of human IVD sections resulted in comparable spectral profiles to those observed in the goat samples, highlighting the inter species transferability of the presented methodology. Multivariate FTIR image analysis of a set of 43 goat IVD sections allowed the extraction of semi-quantitative information from component/tissue gradients taken across the IVD width of collagen type I, collagen type II, PG1 and PG2. Regional component/tissue parameters were calculated and significant correlations were found between histological grades of degeneration and PG parameters (PG1: p = 0.0003, PG2: p < 0.0001); glycosaminoglycan (GAG) content and PGs (PG1: p = 0.0055, PG2: p = 0.0001); and MRI T2* measurements and PGs (PG1: p = 0.0021, PG2: p < 0.0001). Additionally

  1. Multivariate geometry as an approach to algal community analysis

    USGS Publications Warehouse

    Allen, T.F.H.; Skagen, S.

    1973-01-01

    Multivariate analyses are put in the context of more usual approaches to phycological investigations. The intuitive common-sense involved in methods of ordination, classification and discrimination are emphasised by simple geometric accounts which avoid jargon and matrix algebra. Warnings are given that artifacts result from technique abuses by the naive or over-enthusiastic. An analysis of a simple periphyton data set is presented as an example of the approach. Suggestions are made as to situations in phycological investigations, where the techniques could be appropriate. The discipline is reprimanded for its neglect of the multivariate approach.

  2. Multivariate Statistical Analysis of MSL APXS Bulk Geochemical Data

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

  3. Multivariate Analysis of Genotype-Phenotype Association.

    PubMed

    Mitteroecker, Philipp; Cheverud, James M; Pavlicev, Mihaela

    2016-04-01

    With the advent of modern imaging and measurement technology, complex phenotypes are increasingly represented by large numbers of measurements, which may not bear biological meaning one by one. For such multivariate phenotypes, studying the pairwise associations between all measurements and all alleles is highly inefficient and prevents insight into the genetic pattern underlying the observed phenotypes. We present a new method for identifying patterns of allelic variation (genetic latent variables) that are maximally associated-in terms of effect size-with patterns of phenotypic variation (phenotypic latent variables). This multivariate genotype-phenotype mapping (MGP) separates phenotypic features under strong genetic control from less genetically determined features and thus permits an analysis of the multivariate structure of genotype-phenotype association, including its dimensionality and the clustering of genetic and phenotypic variables within this association. Different variants of MGP maximize different measures of genotype-phenotype association: genetic effect, genetic variance, or heritability. In an application to a mouse sample, scored for 353 SNPs and 11 phenotypic traits, the first dimension of genetic and phenotypic latent variables accounted for >70% of genetic variation present in all 11 measurements; 43% of variation in this phenotypic pattern was explained by the corresponding genetic latent variable. The first three dimensions together sufficed to account for almost 90% of genetic variation in the measurements and for all the interpretable genotype-phenotype association. Each dimension can be tested as a whole against the hypothesis of no association, thereby reducing the number of statistical tests from 7766 to 3-the maximal number of meaningful independent tests. Important alleles can be selected based on their effect size (additive or nonadditive effect on the phenotypic latent variable). This low dimensionality of the genotype-phenotype map

  4. Generalized Enhanced Multivariance Product Representation for Data Partitioning: Constancy Level

    SciTech Connect

    Tunga, M. Alper; Demiralp, Metin

    2011-09-14

    Enhanced Multivariance Product Representation (EMPR) method is used to represent multivariate functions in terms of less-variate structures. The EMPR method extends the HDMR expansion by inserting some additional support functions to increase the quality of the approximants obtained for dominantly or purely multiplicative analytical structures. This work aims to develop the generalized form of the EMPR method to be used in multivariate data partitioning approaches. For this purpose, the Generalized HDMR philosophy is taken into consideration to construct the details of the Generalized EMPR at constancy level as the introductory steps and encouraging results are obtained in data partitioning problems by using our new method. In addition, to examine this performance, a number of numerical implementations with concluding remarks are given at the end of this paper.

  5. Multivariate Strategies in Functional Magnetic Resonance Imaging

    ERIC Educational Resources Information Center

    Hansen, Lars Kai

    2007-01-01

    We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a "mind reading" predictive multivariate fMRI model.

  6. The statistical analysis of multivariate serological frequency data.

    PubMed

    Reyment, Richard A

    2005-11-01

    Data occurring in the form of frequencies are common in genetics-for example, in serology. Examples are provided by the AB0 group, the Rhesus group, and also DNA data. The statistical analysis of tables of frequencies is carried out using the available methods of multivariate analysis with usually three principal aims. One of these is to seek meaningful relationships between the components of a data set, the second is to examine relationships between populations from which the data have been obtained, the third is to bring about a reduction in dimensionality. This latter aim is usually realized by means of bivariate scatter diagrams using scores computed from a multivariate analysis. The multivariate statistical analysis of tables of frequencies cannot safely be carried out by standard multivariate procedures because they represent compositions and are therefore embedded in simplex space, a subspace of full space. Appropriate procedures for simplex space are compared and contrasted with simple standard methods of multivariate analysis ("raw" principal component analysis). The study shows that the differences between a log-ratio model and a simple logarithmic transformation of proportions may not be very great, particularly as regards graphical ordinations, but important discrepancies do occur. The divergencies between logarithmically based analyses and raw data are, however, great. Published data on Rhesus alleles observed for Italian populations are used to exemplify the subject. PMID:16024067

  7. The statistical analysis of multivariate serological frequency data.

    PubMed

    Reyment, Richard A

    2005-11-01

    Data occurring in the form of frequencies are common in genetics-for example, in serology. Examples are provided by the AB0 group, the Rhesus group, and also DNA data. The statistical analysis of tables of frequencies is carried out using the available methods of multivariate analysis with usually three principal aims. One of these is to seek meaningful relationships between the components of a data set, the second is to examine relationships between populations from which the data have been obtained, the third is to bring about a reduction in dimensionality. This latter aim is usually realized by means of bivariate scatter diagrams using scores computed from a multivariate analysis. The multivariate statistical analysis of tables of frequencies cannot safely be carried out by standard multivariate procedures because they represent compositions and are therefore embedded in simplex space, a subspace of full space. Appropriate procedures for simplex space are compared and contrasted with simple standard methods of multivariate analysis ("raw" principal component analysis). The study shows that the differences between a log-ratio model and a simple logarithmic transformation of proportions may not be very great, particularly as regards graphical ordinations, but important discrepancies do occur. The divergencies between logarithmically based analyses and raw data are, however, great. Published data on Rhesus alleles observed for Italian populations are used to exemplify the subject.

  8. Multivariate residues and maximal unitarity

    NASA Astrophysics Data System (ADS)

    Søgaard, Mads; Zhang, Yang

    2013-12-01

    We extend the maximal unitarity method to amplitude contributions whose cuts define multidimensional algebraic varieties. The technique is valid to all orders and is explicitly demonstrated at three loops in gauge theories with any number of fermions and scalars in the adjoint representation. Deca-cuts realized by replacement of real slice integration contours by higher-dimensional tori encircling the global poles are used to factorize the planar triple box onto a product of trees. We apply computational algebraic geometry and multivariate complex analysis to derive unique projectors for all master integral coefficients and obtain compact analytic formulae in terms of tree-level data.

  9. Software For Multivariate Bayesian Classification

    NASA Technical Reports Server (NTRS)

    Saul, Ronald; Laird, Philip; Shelton, Robert

    1996-01-01

    PHD general-purpose classifier computer program. Uses Bayesian methods to classify vectors of real numbers, based on combination of statistical techniques that include multivariate density estimation, Parzen density kernels, and EM (Expectation Maximization) algorithm. By means of simple graphical interface, user trains classifier to recognize two or more classes of data and then use it to identify new data. Written in ANSI C for Unix systems and optimized for online classification applications. Embedded in another program, or runs by itself using simple graphical-user-interface. Online help files makes program easy to use.

  10. A Multivariate Kruskal-Wallis Test with Post Hoc Procedures.

    ERIC Educational Resources Information Center

    Katz, Barry M.; McSweeney, Maryellen

    1980-01-01

    An explicit statement of a statistic which is a nonparametric analog to one-way MANOVA is presented. The statistic is a multivariate extension of the nonparametric Kruskal-Wallis test (1952). In addition two post hoc procedures are developed and compared. (Author/JKS)

  11. Maximum Likelihood Estimation of Multivariate Polyserial and Polychoric Correlation Coefficients.

    ERIC Educational Resources Information Center

    Poon, Wai-Yin; Lee, Sik-Yum

    1987-01-01

    Reparameterization is used to find the maximum likelihood estimates of parameters in a multivariate model having some component variable observable only in polychotomous form. Maximum likelihood estimates are found by a Fletcher Powell algorithm. In addition, the partition maximum likelihood method is proposed and illustrated. (Author/GDC)

  12. Exploration of new multivariate spectral calibration algorithms.

    SciTech Connect

    Van Benthem, Mark Hilary; Haaland, David Michael; Melgaard, David Kennett; Martin, Laura Elizabeth; Wehlburg, Christine Marie; Pell, Randy J.; Guenard, Robert D.

    2004-03-01

    A variety of multivariate calibration algorithms for quantitative spectral analyses were investigated and compared, and new algorithms were developed in the course of this Laboratory Directed Research and Development project. We were able to demonstrate the ability of the hybrid classical least squares/partial least squares (CLSIPLS) calibration algorithms to maintain calibrations in the presence of spectrometer drift and to transfer calibrations between spectrometers from the same or different manufacturers. These methods were found to be as good or better in prediction ability as the commonly used partial least squares (PLS) method. We also present the theory for an entirely new class of algorithms labeled augmented classical least squares (ACLS) methods. New factor selection methods are developed and described for the ACLS algorithms. These factor selection methods are demonstrated using near-infrared spectra collected from a system of dilute aqueous solutions. The ACLS algorithm is also shown to provide improved ease of use and better prediction ability than PLS when transferring calibrations between near-infrared calibrations from the same manufacturer. Finally, simulations incorporating either ideal or realistic errors in the spectra were used to compare the prediction abilities of the new ACLS algorithm with that of PLS. We found that in the presence of realistic errors with non-uniform spectral error variance across spectral channels or with spectral errors correlated between frequency channels, ACLS methods generally out-performed the more commonly used PLS method. These results demonstrate the need for realistic error structure in simulations when the prediction abilities of various algorithms are compared. The combination of equal or superior prediction ability and the ease of use of the ACLS algorithms make the new ACLS methods the preferred algorithms to use for multivariate spectral calibrations.

  13. Multivariate Chemical Image Fusion of Vibrational Spectroscopic Imaging Modalities.

    PubMed

    Gowen, Aoife A; Dorrepaal, Ronan M

    2016-01-01

    Chemical image fusion refers to the combination of chemical images from different modalities for improved characterisation of a sample. Challenges associated with existing approaches include: difficulties with imaging the same sample area or having identical pixels across microscopic modalities, lack of prior knowledge of sample composition and lack of knowledge regarding correlation between modalities for a given sample. In addition, the multivariate structure of chemical images is often overlooked when fusion is carried out. We address these challenges by proposing a framework for multivariate chemical image fusion of vibrational spectroscopic imaging modalities, demonstrating the approach for image registration, fusion and resolution enhancement of chemical images obtained with IR and Raman microscopy. PMID:27384549

  14. Method of multivariate spectral analysis

    DOEpatents

    Keenan, Michael R.; Kotula, Paul G.

    2004-01-06

    A method of determining the properties of a sample from measured spectral data collected from the sample by performing a multivariate spectral analysis. The method can include: generating a two-dimensional matrix A containing measured spectral data; providing a weighted spectral data matrix D by performing a weighting operation on matrix A; factoring D into the product of two matrices, C and S.sup.T, by performing a constrained alternating least-squares analysis of D=CS.sup.T, where C is a concentration intensity matrix and S is a spectral shapes matrix; unweighting C and S by applying the inverse of the weighting used previously; and determining the properties of the sample by inspecting C and S. This method can be used to analyze X-ray spectral data generated by operating a Scanning Electron Microscope (SEM) with an attached Energy Dispersive Spectrometer (EDS).

  15. Brushing of attribute clouds for the visualization of multivariate data.

    PubMed

    Jänicke, Heike; Böttinger, Michael; Scheuermann, Gerik

    2008-01-01

    The visualization and exploration of multivariate data is still a challenging task. Methods either try to visualize all variables simultaneously at each position using glyph-based approaches or use linked views for the interaction between attribute space and physical domain such as brushing of scatterplots. Most visualizations of the attribute space are either difficult to understand or suffer from visual clutter. We propose a transformation of the high-dimensional data in attribute space to 2D that results in a point cloud, called attribute cloud, such that points with similar multivariate attributes are located close to each other. The transformation is based on ideas from multivariate density estimation and manifold learning. The resulting attribute cloud is an easy to understand visualization of multivariate data in two dimensions. We explain several techniques to incorporate additional information into the attribute cloud, that help the user get a better understanding of multivariate data. Using different examples from fluid dynamics and climate simulation, we show how brushing can be used to explore the attribute cloud and find interesting structures in physical space.

  16. In situ sulfur isotopes (δ(34)S and δ(33)S) analyses in sulfides and elemental sulfur using high sensitivity cones combined with the addition of nitrogen by laser ablation MC-ICP-MS.

    PubMed

    Fu, Jiali; Hu, Zhaochu; Zhang, Wen; Yang, Lu; Liu, Yongsheng; Li, Ming; Zong, Keqing; Gao, Shan; Hu, Shenghong

    2016-03-10

    The sulfur isotope is an important geochemical tracer in diverse fields of geosciences. In this study, the effects of three different cone combinations with the addition of N2 on the performance of in situ S isotope analyses were investigated in detail. The signal intensities of S isotopes were improved by a factor of 2.3 and 3.6 using the X skimmer cone combined with the standard sample cone or the Jet sample cone, respectively, compared with the standard arrangement (H skimmer cone combined with the standard sample cone). This signal enhancement is important for the improvement of the precision and accuracy of in situ S isotope analysis at high spatial resolution. Different cone combinations have a significant effect on the mass bias and mass bias stability for S isotopes. Poor precisions of S isotope ratios were obtained using the Jet and X cones combination at their corresponding optimum makeup gas flow when using Ar plasma only. The addition of 4-8 ml min(-1) nitrogen to the central gas flow in laser ablation MC-ICP-MS was found to significantly enlarge the mass bias stability zone at their corresponding optimum makeup gas flow in these three different cone combinations. The polyatomic interferences of OO, SH, OOH were also significantly reduced, and the interference free plateaus of sulfur isotopes became broader and flatter in the nitrogen mode (N2 = 4 ml min(-1)). However, the signal intensity of S was not increased by the addition of nitrogen in this study. The laser fluence and ablation mode had significant effects on sulfur isotope fractionation during the analysis of sulfides and elemental sulfur by laser ablation MC-ICP-MS. The matrix effect among different sulfides and elemental sulfur was observed, but could be significantly reduced by line scan ablation in preference to single spot ablation under the optimized fluence. It is recommended that the d90 values of the particles in pressed powder pellets for accurate and precise S isotope analysis

  17. In situ sulfur isotopes (δ(34)S and δ(33)S) analyses in sulfides and elemental sulfur using high sensitivity cones combined with the addition of nitrogen by laser ablation MC-ICP-MS.

    PubMed

    Fu, Jiali; Hu, Zhaochu; Zhang, Wen; Yang, Lu; Liu, Yongsheng; Li, Ming; Zong, Keqing; Gao, Shan; Hu, Shenghong

    2016-03-10

    The sulfur isotope is an important geochemical tracer in diverse fields of geosciences. In this study, the effects of three different cone combinations with the addition of N2 on the performance of in situ S isotope analyses were investigated in detail. The signal intensities of S isotopes were improved by a factor of 2.3 and 3.6 using the X skimmer cone combined with the standard sample cone or the Jet sample cone, respectively, compared with the standard arrangement (H skimmer cone combined with the standard sample cone). This signal enhancement is important for the improvement of the precision and accuracy of in situ S isotope analysis at high spatial resolution. Different cone combinations have a significant effect on the mass bias and mass bias stability for S isotopes. Poor precisions of S isotope ratios were obtained using the Jet and X cones combination at their corresponding optimum makeup gas flow when using Ar plasma only. The addition of 4-8 ml min(-1) nitrogen to the central gas flow in laser ablation MC-ICP-MS was found to significantly enlarge the mass bias stability zone at their corresponding optimum makeup gas flow in these three different cone combinations. The polyatomic interferences of OO, SH, OOH were also significantly reduced, and the interference free plateaus of sulfur isotopes became broader and flatter in the nitrogen mode (N2 = 4 ml min(-1)). However, the signal intensity of S was not increased by the addition of nitrogen in this study. The laser fluence and ablation mode had significant effects on sulfur isotope fractionation during the analysis of sulfides and elemental sulfur by laser ablation MC-ICP-MS. The matrix effect among different sulfides and elemental sulfur was observed, but could be significantly reduced by line scan ablation in preference to single spot ablation under the optimized fluence. It is recommended that the d90 values of the particles in pressed powder pellets for accurate and precise S isotope analysis

  18. A Meta-View of Multivariate Statistical Inference Methods in European Psychology Journals.

    PubMed

    Harlow, Lisa L; Korendijk, Elly; Hamaker, Ellen L; Hox, Joop; Duerr, Sunny R

    2013-09-01

    We investigated the extent and nature of multivariate statistical inferential procedures used in eight European psychology journals covering a range of content (i.e., clinical, social, health, personality, organizational, developmental, educational, and cognitive). Multivariate methods included those found in popular texts that focused on prediction, group difference, and advanced modeling: multiple regression, logistic regression, analysis of covariance, multivariate analysis of variance, factor or principal component analysis, structural equation modeling, multilevel modeling, and other methods. Results revealed that an average of 57% of the articles from these eight journals involved multivariate analyses with a third using multiple regression, 17% using structural modeling, and the remaining methods collectively comprising about 50% of the analyses. The most frequently occurring inferential procedures involved prediction weights, dichotomous p values, figures with data, and significance tests with very few articles involving confidence intervals, statistical mediation, longitudinal analyses, power analysis, or meta-analysis. Contributions, limitations and future directions are discussed.

  19. Selection Indices and Multivariate Analysis Show Similar Results in the Evaluation of Growth and Carcass Traits in Beef Cattle

    PubMed Central

    Brito Lopes, Fernando; da Silva, Marcelo Corrêa; Magnabosco, Cláudio Ulhôa; Goncalves Narciso, Marcelo; Sainz, Roberto Daniel

    2016-01-01

    This research evaluated a multivariate approach as an alternative tool for the purpose of selection regarding expected progeny differences (EPDs). Data were fitted using a multi-trait model and consisted of growth traits (birth weight and weights at 120, 210, 365 and 450 days of age) and carcass traits (longissimus muscle area (LMA), back-fat thickness (BF), and rump fat thickness (RF)), registered over 21 years in extensive breeding systems of Polled Nellore cattle in Brazil. Multivariate analyses were performed using standardized (zero mean and unit variance) EPDs. The k mean method revealed that the best fit of data occurred using three clusters (k = 3) (P < 0.001). Estimates of genetic correlation among growth and carcass traits and the estimates of heritability were moderate to high, suggesting that a correlated response approach is suitable for practical decision making. Estimates of correlation between selection indices and the multivariate index (LD1) were moderate to high, ranging from 0.48 to 0.97. This reveals that both types of indices give similar results and that the multivariate approach is reliable for the purpose of selection. The alternative tool seems very handy when economic weights are not available or in cases where more rapid identification of the best animals is desired. Interestingly, multivariate analysis allowed forecasting information based on the relationships among breeding values (EPDs). Also, it enabled fine discrimination, rapid data summarization after genetic evaluation, and permitted accounting for maternal ability and the genetic direct potential of the animals. In addition, we recommend the use of longissimus muscle area and subcutaneous fat thickness as selection criteria, to allow estimation of breeding values before the first mating season in order to accelerate the response to individual selection. PMID:26789008

  20. Multivariate Time Series Similarity Searching

    PubMed Central

    Wang, Jimin; Zhu, Yuelong; Li, Shijin; Wan, Dingsheng; Zhang, Pengcheng

    2014-01-01

    Multivariate time series (MTS) datasets are very common in various financial, multimedia, and hydrological fields. In this paper, a dimension-combination method is proposed to search similar sequences for MTS. Firstly, the similarity of single-dimension series is calculated; then the overall similarity of the MTS is obtained by synthesizing each of the single-dimension similarity based on weighted BORDA voting method. The dimension-combination method could use the existing similarity searching method. Several experiments, which used the classification accuracy as a measure, were performed on six datasets from the UCI KDD Archive to validate the method. The results show the advantage of the approach compared to the traditional similarity measures, such as Euclidean distance (ED), cynamic time warping (DTW), point distribution (PD), PCA similarity factor (SPCA), and extended Frobenius norm (Eros), for MTS datasets in some ways. Our experiments also demonstrate that no measure can fit all datasets, and the proposed measure is a choice for similarity searches. PMID:24895665

  1. Multivariate time series similarity searching.

    PubMed

    Wang, Jimin; Zhu, Yuelong; Li, Shijin; Wan, Dingsheng; Zhang, Pengcheng

    2014-01-01

    Multivariate time series (MTS) datasets are very common in various financial, multimedia, and hydrological fields. In this paper, a dimension-combination method is proposed to search similar sequences for MTS. Firstly, the similarity of single-dimension series is calculated; then the overall similarity of the MTS is obtained by synthesizing each of the single-dimension similarity based on weighted BORDA voting method. The dimension-combination method could use the existing similarity searching method. Several experiments, which used the classification accuracy as a measure, were performed on six datasets from the UCI KDD Archive to validate the method. The results show the advantage of the approach compared to the traditional similarity measures, such as Euclidean distance (ED), cynamic time warping (DTW), point distribution (PD), PCA similarity factor (SPCA), and extended Frobenius norm (Eros), for MTS datasets in some ways. Our experiments also demonstrate that no measure can fit all datasets, and the proposed measure is a choice for similarity searches. PMID:24895665

  2. Inclusion of Dominance Effects in the Multivariate GBLUP Model.

    PubMed

    dos Santos, Jhonathan Pedroso Rigal; Vasconcellos, Renato Coelho de Castro; Pires, Luiz Paulo Miranda; Balestre, Marcio; Von Pinho, Renzo Garcia

    2016-01-01

    New proposals for models and applications of prediction processes with data on molecular markers may help reduce the financial costs of and identify superior genotypes in maize breeding programs. Studies evaluating Genomic Best Linear Unbiased Prediction (GBLUP) models including dominance effects have not been performed in the univariate and multivariate context in the data analysis of this crop. A single cross hybrid construction procedure was performed in this study using phenotypic data and actual molecular markers of 4,091 maize lines from the public database Panzea. A total of 400 simple hybrids resulting from this process were analyzed using the univariate and multivariate GBLUP model considering only additive effects additive plus dominance effects. Historic heritability scenarios of five traits and other genetic architecture settings were used to compare models, evaluating the predictive ability and estimation of variance components. Marginal differences were detected between the multivariate and univariate models. The main explanation for the small discrepancy between models is the low- to moderate-magnitude correlations between the traits studied and moderate heritabilities. These conditions do not favor the advantages of multivariate analysis. The inclusion of dominance effects in the models was an efficient strategy to improve the predictive ability and estimation quality of variance components. PMID:27074056

  3. Addition of docetaxel or bisphosphonates to standard of care in men with localised or metastatic, hormone-sensitive prostate cancer: a systematic review and meta-analyses of aggregate data

    PubMed Central

    Vale, Claire L; Burdett, Sarah; Rydzewska, Larysa H M; Albiges, Laurence; Clarke, Noel W; Fisher, David; Fizazi, Karim; Gravis, Gwenaelle; James, Nicholas D; Mason, Malcolm D; Parmar, Mahesh K B; Sweeney, Christopher J; Sydes, Matthew R; Tombal, Bertrand; Tierney, Jayne F

    2016-01-01

    docetaxel for men with locally advanced disease (M0). Survival results from three (GETUG-12, RTOG 0521, STAMPEDE) of these trials (2121 [53%] of 3978 men) showed no evidence of a benefit from the addition of docetaxel (HR 0·87 [95% CI 0·69–1·09]; p=0·218), whereas failure-free survival data from four (GETUG-12, RTOG 0521, STAMPEDE, TAX 3501) of these trials (2348 [59%] of 3978 men) showed that docetaxel improved failure-free survival (0·70 [0·61–0·81]; p<0·0001), which translates into a reduced absolute 4-year failure rate of 8% (5–10). We identified seven eligible randomised controlled trials of bisphosphonates for men with M1 disease. Survival results from three of these trials (2740 [88%] of 3109 men) showed that addition of bisphosphonates improved survival (0·88 [0·79–0·98]; p=0·025), which translates to 5% (1–8) absolute improvement, but this result was influenced by the positive result of one trial of sodium clodronate, and we found no evidence of a benefit from the addition of zoledronic acid (0·94 [0·83–1·07]; p=0·323), which translates to an absolute improvement in survival of 2% (−3 to 7). Of 17 trials of bisphosphonates for men with M0 disease, survival results from four trials (4079 [66%] of 6220 men) showed no evidence of benefit from the addition of bisphosphonates (1·03 [0·89–1·18]; p=0·724) or zoledronic acid (0·98 [0·82–1·16]; p=0·782). Failure-free survival definitions were too inconsistent for formal meta-analyses for the bisphosphonate trials. Interpretation The addition of docetaxel to standard of care should be considered standard care for men with M1 hormone-sensitive prostate cancer who are starting treatment for the first time. More evidence on the effects of docetaxel on survival is needed in the M0 disease setting. No evidence exists to suggest that zoledronic acid improves survival in men with

  4. Addition of docetaxel or bisphosphonates to standard of care in men with localised or metastatic, hormone-sensitive prostate cancer: a systematic review and meta-analyses of aggregate data

    PubMed Central

    Vale, Claire L; Burdett, Sarah; Rydzewska, Larysa H M; Albiges, Laurence; Clarke, Noel W; Fisher, David; Fizazi, Karim; Gravis, Gwenaelle; James, Nicholas D; Mason, Malcolm D; Parmar, Mahesh K B; Sweeney, Christopher J; Sydes, Matthew R; Tombal, Bertrand; Tierney, Jayne F

    2016-01-01

    docetaxel for men with locally advanced disease (M0). Survival results from three (GETUG-12, RTOG 0521, STAMPEDE) of these trials (2121 [53%] of 3978 men) showed no evidence of a benefit from the addition of docetaxel (HR 0·87 [95% CI 0·69–1·09]; p=0·218), whereas failure-free survival data from four (GETUG-12, RTOG 0521, STAMPEDE, TAX 3501) of these trials (2348 [59%] of 3978 men) showed that docetaxel improved failure-free survival (0·70 [0·61–0·81]; p<0·0001), which translates into a reduced absolute 4-year failure rate of 8% (5–10). We identified seven eligible randomised controlled trials of bisphosphonates for men with M1 disease. Survival results from three of these trials (2740 [88%] of 3109 men) showed that addition of bisphosphonates improved survival (0·88 [0·79–0·98]; p=0·025), which translates to 5% (1–8) absolute improvement, but this result was influenced by the positive result of one trial of sodium clodronate, and we found no evidence of a benefit from the addition of zoledronic acid (0·94 [0·83–1·07]; p=0·323), which translates to an absolute improvement in survival of 2% (−3 to 7). Of 17 trials of bisphosphonates for men with M0 disease, survival results from four trials (4079 [66%] of 6220 men) showed no evidence of benefit from the addition of bisphosphonates (1·03 [0·89–1·18]; p=0·724) or zoledronic acid (0·98 [0·82–1·16]; p=0·782). Failure-free survival definitions were too inconsistent for formal meta-analyses for the bisphosphonate trials. Interpretation The addition of docetaxel to standard of care should be considered standard care for men with M1 hormone-sensitive prostate cancer who are starting treatment for the first time. More evidence on the effects of docetaxel on survival is needed in the M0 disease setting. No evidence exists to suggest that zoledronic acid improves survival in men with M1 or M0 disease, and any potential benefit is probably small. Funding Medical Research Council UK. PMID

  5. Mardia's Multivariate Kurtosis with Missing Data

    ERIC Educational Resources Information Center

    Yuan, Ke-Hai; Lambert, Paul L.; Fouladi, Rachel T.

    2004-01-01

    Mardia's measure of multivariate kurtosis has been implemented in many statistical packages commonly used by social scientists. It provides important information on whether a commonly used multivariate procedure is appropriate for inference. Many statistical packages also have options for missing data. However, there is no procedure for applying…

  6. Application of multivariate statistical techniques in microbial ecology.

    PubMed

    Paliy, O; Shankar, V

    2016-03-01

    Recent advances in high-throughput methods of molecular analyses have led to an explosion of studies generating large-scale ecological data sets. In particular, noticeable effect has been attained in the field of microbial ecology, where new experimental approaches provided in-depth assessments of the composition, functions and dynamic changes of complex microbial communities. Because even a single high-throughput experiment produces large amount of data, powerful statistical techniques of multivariate analysis are well suited to analyse and interpret these data sets. Many different multivariate techniques are available, and often it is not clear which method should be applied to a particular data set. In this review, we describe and compare the most widely used multivariate statistical techniques including exploratory, interpretive and discriminatory procedures. We consider several important limitations and assumptions of these methods, and we present examples of how these approaches have been utilized in recent studies to provide insight into the ecology of the microbial world. Finally, we offer suggestions for the selection of appropriate methods based on the research question and data set structure.

  7. Application of multivariate statistical techniques in microbial ecology.

    PubMed

    Paliy, O; Shankar, V

    2016-03-01

    Recent advances in high-throughput methods of molecular analyses have led to an explosion of studies generating large-scale ecological data sets. In particular, noticeable effect has been attained in the field of microbial ecology, where new experimental approaches provided in-depth assessments of the composition, functions and dynamic changes of complex microbial communities. Because even a single high-throughput experiment produces large amount of data, powerful statistical techniques of multivariate analysis are well suited to analyse and interpret these data sets. Many different multivariate techniques are available, and often it is not clear which method should be applied to a particular data set. In this review, we describe and compare the most widely used multivariate statistical techniques including exploratory, interpretive and discriminatory procedures. We consider several important limitations and assumptions of these methods, and we present examples of how these approaches have been utilized in recent studies to provide insight into the ecology of the microbial world. Finally, we offer suggestions for the selection of appropriate methods based on the research question and data set structure. PMID:26786791

  8. Multivariate pluvial flood damage models

    SciTech Connect

    Van Ootegem, Luc; Verhofstadt, Elsy; Van Herck, Kristine; Creten, Tom

    2015-09-15

    Depth–damage-functions, relating the monetary flood damage to the depth of the inundation, are commonly used in the case of fluvial floods (floods caused by a river overflowing). We construct four multivariate damage models for pluvial floods (caused by extreme rainfall) by differentiating on the one hand between ground floor floods and basement floods and on the other hand between damage to residential buildings and damage to housing contents. We do not only take into account the effect of flood-depth on damage, but also incorporate the effects of non-hazard indicators (building characteristics, behavioural indicators and socio-economic variables). By using a Tobit-estimation technique on identified victims of pluvial floods in Flanders (Belgium), we take into account the effect of cases of reported zero damage. Our results show that the flood depth is an important predictor of damage, but with a diverging impact between ground floor floods and basement floods. Also non-hazard indicators are important. For example being aware of the risk just before the water enters the building reduces content damage considerably, underlining the importance of warning systems and policy in this case of pluvial floods. - Highlights: • Prediction of damage of pluvial floods using also non-hazard information • We include ‘no damage cases’ using a Tobit model. • The damage of flood depth is stronger for ground floor than for basement floods. • Non-hazard indicators are especially important for content damage. • Potential gain of policies that increase awareness of flood risks.

  9. A multivariate CAR model for mismatched lattices.

    PubMed

    Porter, Aaron T; Oleson, Jacob J

    2014-10-01

    In this paper, we develop a multivariate Gaussian conditional autoregressive model for use on mismatched lattices. Most current multivariate CAR models are designed for each multivariate outcome to utilize the same lattice structure. In many applications, a change of basis will allow different lattices to be utilized, but this is not always the case, because a change of basis is not always desirable or even possible. Our multivariate CAR model allows each outcome to have a different neighborhood structure which can utilize different lattices for each structure. The model is applied in two real data analysis. The first is a Bayesian learning example in mapping the 2006 Iowa Mumps epidemic, which demonstrates the importance of utilizing multiple channels of infection flow in mapping infectious diseases. The second is a multivariate analysis of poverty levels and educational attainment in the American Community Survey. PMID:25457598

  10. Sociopolitical Analyses.

    ERIC Educational Resources Information Center

    Van Galen, Jane, Ed.; And Others

    1992-01-01

    This theme issue of the serial "Educational Foundations" contains four articles devoted to the topic of "Sociopolitical Analyses." In "An Interview with Peter L. McLaren," Mary Leach presented the views of Peter L. McLaren on topics of local and national discourses, values, and the politics of difference. Landon E. Beyer's "Educational Studies and…

  11. Multivariate Analysis of Functional Metagenomes

    PubMed Central

    Dinsdale, Elizabeth A.; Edwards, Robert A.; Bailey, Barbara A.; Tuba, Imre; Akhter, Sajia; McNair, Katelyn; Schmieder, Robert; Apkarian, Naneh; Creek, Michelle; Guan, Eric; Hernandez, Mayra; Isaacs, Katherine; Peterson, Chris; Regh, Todd; Ponomarenko, Vadim

    2013-01-01

    Metagenomics is a primary tool for the description of microbial and viral communities. The sheer magnitude of the data generated in each metagenome makes identifying key differences in the function and taxonomy between communities difficult to elucidate. Here we discuss the application of seven different data mining and statistical analyses by comparing and contrasting the metabolic functions of 212 microbial metagenomes within and between 10 environments. Not all approaches are appropriate for all questions, and researchers should decide which approach addresses their questions. This work demonstrated the use of each approach: for example, random forests provided a robust and enlightening description of both the clustering of metagenomes and the metabolic processes that were important in separating microbial communities from different environments. All analyses identified that the presence of phage genes within the microbial community was a predictor of whether the microbial community was host-associated or free-living. Several analyses identified the subtle differences that occur with environments, such as those seen in different regions of the marine environment. PMID:23579547

  12. Generalising Calculus Ideas from Two Dimensions to Three: How Multivariable Calculus Students Think about Domain and Range

    ERIC Educational Resources Information Center

    Dorko, Allison; Weber, Eric

    2014-01-01

    We analysed multivariable calculus students' meanings for domain and range and their generalisation of that meaning as they reasoned about the domain and range of multivariable functions. We found that students' thinking about domain and range fell into three broad categories: input/output, independence/dependence, and/or as attached to specific…

  13. A Gibbs sampler for multivariate linear regression

    NASA Astrophysics Data System (ADS)

    Mantz, Adam B.

    2016-04-01

    Kelly described an efficient algorithm, using Gibbs sampling, for performing linear regression in the fairly general case where non-zero measurement errors exist for both the covariates and response variables, where these measurements may be correlated (for the same data point), where the response variable is affected by intrinsic scatter in addition to measurement error, and where the prior distribution of covariates is modelled by a flexible mixture of Gaussians rather than assumed to be uniform. Here, I extend the Kelly algorithm in two ways. First, the procedure is generalized to the case of multiple response variables. Secondly, I describe how to model the prior distribution of covariates using a Dirichlet process, which can be thought of as a Gaussian mixture where the number of mixture components is learned from the data. I present an example of multivariate regression using the extended algorithm, namely fitting scaling relations of the gas mass, temperature, and luminosity of dynamically relaxed galaxy clusters as a function of their mass and redshift. An implementation of the Gibbs sampler in the R language, called LRGS, is provided.

  14. Multivariate Models of Adult Pacific Salmon Returns

    PubMed Central

    Burke, Brian J.; Peterson, William T.; Beckman, Brian R.; Morgan, Cheryl; Daly, Elizabeth A.; Litz, Marisa

    2013-01-01

    Most modeling and statistical approaches encourage simplicity, yet ecological processes are often complex, as they are influenced by numerous dynamic environmental and biological factors. Pacific salmon abundance has been highly variable over the last few decades and most forecasting models have proven inadequate, primarily because of a lack of understanding of the processes affecting variability in survival. Better methods and data for predicting the abundance of returning adults are therefore required to effectively manage the species. We combined 31 distinct indicators of the marine environment collected over an 11-year period into a multivariate analysis to summarize and predict adult spring Chinook salmon returns to the Columbia River in 2012. In addition to forecasts, this tool quantifies the strength of the relationship between various ecological indicators and salmon returns, allowing interpretation of ecosystem processes. The relative importance of indicators varied, but a few trends emerged. Adult returns of spring Chinook salmon were best described using indicators of bottom-up ecological processes such as composition and abundance of zooplankton and fish prey as well as measures of individual fish, such as growth and condition. Local indicators of temperature or coastal upwelling did not contribute as much as large-scale indicators of temperature variability, matching the spatial scale over which salmon spend the majority of their ocean residence. Results suggest that effective management of Pacific salmon requires multiple types of data and that no single indicator can represent the complex early-ocean ecology of salmon. PMID:23326586

  15. Multivariable disturbance observer-based H2 analytical decoupling control design for multivariable systems

    NASA Astrophysics Data System (ADS)

    Zhang, Wei; Wang, Yagang; Liu, Yurong; Zhang, Weidong

    2016-01-01

    In this paper, an H2 analytical decoupling control scheme with multivariable disturbance observer for both stable and unstable multi-input/multi-output (MIMO) systems with multiple time delays is proposed. Compared with conventional control strategies, the main merit is that the proposed control scheme can improve the system performances effectively when the MIMO processes with severe model mismatches and strong external disturbances. Besides, the design method has three additional advantages. First, the derived controller and observer are given in analytical forms, the design procedure is simple. Second, the orders of the designed controller and observer are low, they can be implemented easily in practice. Finally, the performance and robustness can be adjusted easily by tuning the parameters in the designed controller and observer. It is useful for practical application. Simulations are provided to illustrate the effectiveness of the proposed control scheme.

  16. Multivariate statistical analysis of wildfires in Portugal

    NASA Astrophysics Data System (ADS)

    Costa, Ricardo; Caramelo, Liliana; Pereira, Mário

    2013-04-01

    Several studies demonstrate that wildfires in Portugal present high temporal and spatial variability as well as cluster behavior (Pereira et al., 2005, 2011). This study aims to contribute to the characterization of the fire regime in Portugal with the multivariate statistical analysis of the time series of number of fires and area burned in Portugal during the 1980 - 2009 period. The data used in the analysis is an extended version of the Rural Fire Portuguese Database (PRFD) (Pereira et al, 2011), provided by the National Forest Authority (Autoridade Florestal Nacional, AFN), the Portuguese Forest Service, which includes information for more than 500,000 fire records. There are many multiple advanced techniques for examining the relationships among multiple time series at the same time (e.g., canonical correlation analysis, principal components analysis, factor analysis, path analysis, multiple analyses of variance, clustering systems). This study compares and discusses the results obtained with these different techniques. Pereira, M.G., Trigo, R.M., DaCamara, C.C., Pereira, J.M.C., Leite, S.M., 2005: "Synoptic patterns associated with large summer forest fires in Portugal". Agricultural and Forest Meteorology. 129, 11-25. Pereira, M. G., Malamud, B. D., Trigo, R. M., and Alves, P. I.: The history and characteristics of the 1980-2005 Portuguese rural fire database, Nat. Hazards Earth Syst. Sci., 11, 3343-3358, doi:10.5194/nhess-11-3343-2011, 2011 This work is supported by European Union Funds (FEDER/COMPETE - Operational Competitiveness Programme) and by national funds (FCT - Portuguese Foundation for Science and Technology) under the project FCOMP-01-0124-FEDER-022692, the project FLAIR (PTDC/AAC-AMB/104702/2008) and the EU 7th Framework Program through FUME (contract number 243888).

  17. Enhancing scientific reasoning by refining students' models of multivariable causality

    NASA Astrophysics Data System (ADS)

    Keselman, Alla

    Inquiry learning as an educational method is gaining increasing support among elementary and middle school educators. In inquiry activities at the middle school level, students are typically asked to conduct investigations and infer causal relationships about multivariable causal systems. In these activities, students usually demonstrate significant strategic weaknesses and insufficient metastrategic understanding of task demands. Present work suggests that these weaknesses arise from students' deficient mental models of multivariable causality, in which effects of individual features are neither additive, nor constant. This study is an attempt to develop an intervention aimed at enhancing scientific reasoning by refining students' models of multivariable causality. Three groups of students engaged in a scientific investigation activity over seven weekly sessions. By creating unique combinations of five features potentially involved in earthquake mechanism and observing associated risk meter readings, students had to find out which of the features were causal, and to learn to predict earthquake risk. Additionally, students in the instructional and practice groups engaged in self-directed practice in making scientific predictions. The instructional group also participated in weekly instructional sessions on making predictions based on multivariable causality. Students in the practice and instructional conditions showed small to moderate improvement in their attention to the evidence and in their metastrategic ability to recognize effective investigative strategies in the work of other students. They also demonstrated a trend towards making a greater number of valid inferences than the control group students. Additionally, students in the instructional condition showed significant improvement in their ability to draw inferences based on multiple records. They also developed more accurate knowledge about non-causal features of the system. These gains were maintained

  18. Identification of Reliable Components in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS): a Data-Driven Approach across Metabolic Processes

    PubMed Central

    Motegi, Hiromi; Tsuboi, Yuuri; Saga, Ayako; Kagami, Tomoko; Inoue, Maki; Toki, Hideaki; Minowa, Osamu; Noda, Tetsuo; Kikuchi, Jun

    2015-01-01

    There is an increasing need to use multivariate statistical methods for understanding biological functions, identifying the mechanisms of diseases, and exploring biomarkers. In addition to classical analyses such as hierarchical cluster analysis, principal component analysis, and partial least squares discriminant analysis, various multivariate strategies, including independent component analysis, non-negative matrix factorization, and multivariate curve resolution, have recently been proposed. However, determining the number of components is problematic. Despite the proposal of several different methods, no satisfactory approach has yet been reported. To resolve this problem, we implemented a new idea: classifying a component as “reliable” or “unreliable” based on the reproducibility of its appearance, regardless of the number of components in the calculation. Using the clustering method for classification, we applied this idea to multivariate curve resolution-alternating least squares (MCR-ALS). Comparisons between conventional and modified methods applied to proton nuclear magnetic resonance (1H-NMR) spectral datasets derived from known standard mixtures and biological mixtures (urine and feces of mice) revealed that more plausible results are obtained by the modified method. In particular, clusters containing little information were detected with reliability. This strategy, named “cluster-aided MCR-ALS,” will facilitate the attainment of more reliable results in the metabolomics datasets. PMID:26531245

  19. Food additives

    PubMed Central

    Spencer, Michael

    1974-01-01

    Food additives are discussed from the food technology point of view. The reasons for their use are summarized: (1) to protect food from chemical and microbiological attack; (2) to even out seasonal supplies; (3) to improve their eating quality; (4) to improve their nutritional value. The various types of food additives are considered, e.g. colours, flavours, emulsifiers, bread and flour additives, preservatives, and nutritional additives. The paper concludes with consideration of those circumstances in which the use of additives is (a) justified and (b) unjustified. PMID:4467857

  20. Multivariate Longitudinal Analysis with Bivariate Correlation Test.

    PubMed

    Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

    2016-01-01

    In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model's parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated. PMID:27537692

  1. Multivariate Longitudinal Analysis with Bivariate Correlation Test

    PubMed Central

    Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

    2016-01-01

    In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model’s parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated. PMID:27537692

  2. Multivariate gene-set testing based on graphical models.

    PubMed

    Städler, Nicolas; Mukherjee, Sach

    2015-01-01

    The identification of predefined groups of genes ("gene-sets") which are differentially expressed between two conditions ("gene-set analysis", or GSA) is a very popular analysis in bioinformatics. GSA incorporates biological knowledge by aggregating over genes that are believed to be functionally related. This can enhance statistical power over analyses that consider only one gene at a time. However, currently available GSA approaches are based on univariate two-sample comparison of single genes. This means that they cannot test for multivariate hypotheses such as differences in covariance structure between the two conditions. Yet interplay between genes is a central aspect of biological investigation and it is likely that such interplay may differ between conditions. This paper proposes a novel approach for gene-set analysis that allows for truly multivariate hypotheses, in particular differences in gene-gene networks between conditions. Testing hypotheses concerning networks is challenging due the nature of the underlying estimation problem. Our starting point is a recent, general approach for high-dimensional two-sample testing. We refine the approach and show how it can be used to perform multivariate, network-based gene-set testing. We validate the approach in simulated examples and show results using high-throughput data from several studies in cancer biology.

  3. Multivariate statistical analysis of low-voltage EDS spectrum images

    SciTech Connect

    Anderson, I.M.

    1998-03-01

    Whereas energy-dispersive X-ray spectrometry (EDS) has been used for compositional analysis in the scanning electron microscope for 30 years, the benefits of using low operating voltages for such analyses have been explored only during the last few years. This paper couples low-voltage EDS with two other emerging areas of characterization: spectrum imaging and multivariate statistical analysis. The specimen analyzed for this study was a finished Intel Pentium processor, with the polyimide protective coating stripped off to expose the final active layers.

  4. Quantitative methods for analysing cumulative effects on fish migration success: a review.

    PubMed

    Johnson, J E; Patterson, D A; Martins, E G; Cooke, S J; Hinch, S G

    2012-07-01

    It is often recognized, but seldom addressed, that a quantitative assessment of the cumulative effects, both additive and non-additive, of multiple stressors on fish survival would provide a more realistic representation of the factors that influence fish migration. This review presents a compilation of analytical methods applied to a well-studied fish migration, a more general review of quantitative multivariable methods, and a synthesis on how to apply new analytical techniques in fish migration studies. A compilation of adult migration papers from Fraser River sockeye salmon Oncorhynchus nerka revealed a limited number of multivariable methods being applied and the sub-optimal reliance on univariable methods for multivariable problems. The literature review of fisheries science, general biology and medicine identified a large number of alternative methods for dealing with cumulative effects, with a limited number of techniques being used in fish migration studies. An evaluation of the different methods revealed that certain classes of multivariable analyses will probably prove useful in future assessments of cumulative effects on fish migration. This overview and evaluation of quantitative methods gathered from the disparate fields should serve as a primer for anyone seeking to quantify cumulative effects on fish migration survival.

  5. Application of multivariate outlier detection to fluid velocity measurements

    NASA Astrophysics Data System (ADS)

    Griffin, John; Schultz, Todd; Holman, Ryan; Ukeiley, Lawrence S.; Cattafesta, Louis N.

    2010-07-01

    A statistical-based approach to detect outliers in fluid-based velocity measurements is proposed. Outliers are effectively detected from experimental unimodal distributions with the application of an existing multivariate outlier detection algorithm for asymmetric distributions (Hubert and Van der Veeken, J Chemom 22:235-246, 2008). This approach is an extension of previous methods that only apply to symmetric distributions. For fluid velocity measurements, rejection of statistical outliers, meaning erroneous as well as low probability data, via multivariate outlier rejection is compared to a traditional method based on univariate statistics. For particle image velocimetry data, both tests are conducted after application of the current de facto standard spatial filter, the universal outlier detection test (Westerweel and Scarano, Exp Fluids 39:1096-1100, 2005). By doing so, the utility of statistical outlier detection in addition to spatial filters is demonstrated, and further, the differences between multivariate and univariate outlier detection are discussed. Since the proposed technique for outlier detection is an independent process, statistical outlier detection is complementary to spatial outlier detection and can be used as an additional validation tool.

  6. Multivariate analysis of eigenvalues and eigenvectors in tensor based morphometry

    NASA Astrophysics Data System (ADS)

    Rajagopalan, Vidya; Schwartzman, Armin; Hua, Xue; Leow, Alex; Thompson, Paul; Lepore, Natasha

    2015-01-01

    We develop a new algorithm to compute voxel-wise shape differences in tensor-based morphometry (TBM). As in standard TBM, we non-linearly register brain T1-weighed MRI data from a patient and control group to a template, and compute the Jacobian of the deformation fields. In standard TBM, the determinants of the Jacobian matrix at each voxel are statistically compared between the two groups. More recently, a multivariate extension of the statistical analysis involving the deformation tensors derived from the Jacobian matrices has been shown to improve statistical detection power.7 However, multivariate methods comprising large numbers of variables are computationally intensive and may be subject to noise. In addition, the anatomical interpretation of results is sometimes difficult. Here instead, we analyze the eigenvalues and the eigenvectors of the Jacobian matrices. Our method is validated on brain MRI data from Alzheimer's patients and healthy elderly controls from the Alzheimer's Disease Neuro Imaging Database.

  7. A multivariate rate equation for variable-interval performance.

    PubMed

    McDowell, J J; Kessel, R

    1979-03-01

    A value-like parameter is introduced into a rate equation for describing variable-interval performance. The equation, derived solely from formal considerations, expresses rate of responding as a joint function of rate of reinforcement and "reinforcer power." Preliminary tests of the rate equation show that it handles univariate data as well as Herrnstein's hyperbola. In addition, a form of Herrnstein's hyperbola can be derived from the equation, and it predicts forms of matching in concurrent situations. For the multivariate case, reinforcer values scaled in concurrent situations where matching is assumed to hold are taken as determinations of reinforcer power. The multivariate rate equation is fitted to an appropriate set of data and found to provide a good description of variable-interval performance when both rate and power of reinforcement are varied. Rate and power measures completely describe reinforcement. The effects of their joint variation are not predicted and cannot be described by Herrnstein's equation.

  8. A Multivariate Analysis of Galaxy Cluster Properties

    NASA Astrophysics Data System (ADS)

    Ogle, P. M.; Djorgovski, S.

    1993-05-01

    We have assembled from the literature a data base on on 394 clusters of galaxies, with up to 16 parameters per cluster. They include optical and x-ray luminosities, x-ray temperatures, galaxy velocity dispersions, central galaxy and particle densities, optical and x-ray core radii and ellipticities, etc. In addition, derived quantities, such as the mass-to-light ratios and x-ray gas masses are included. Doubtful measurements have been identified, and deleted from the data base. Our goal is to explore the correlations between these parameters, and interpret them in the framework of our understanding of evolution of clusters and large-scale structure, such as the Gott-Rees scaling hierarchy. Among the simple, monovariate correlations we found, the most significant include those between the optical and x-ray luminosities, x-ray temperatures, cluster velocity dispersions, and central galaxy densities, in various mutual combinations. While some of these correlations have been discussed previously in the literature, generally smaller samples of objects have been used. We will also present the results of a multivariate statistical analysis of the data, including a principal component analysis (PCA). Such an approach has not been used previously for studies of cluster properties, even though it is much more powerful and complete than the simple monovariate techniques which are commonly employed. The observed correlations may lead to powerful constraints for theoretical models of formation and evolution of galaxy clusters. P.M.O. was supported by a Caltech graduate fellowship. S.D. acknowledges a partial support from the NASA contract NAS5-31348 and the NSF PYI award AST-9157412.

  9. SNS shielding analyses overview

    SciTech Connect

    Popova, Irina; Gallmeier, Franz; Iverson, Erik B; Lu, Wei; Remec, Igor

    2015-01-01

    This paper gives an overview on on-going shielding analyses for Spallation Neutron Source. Presently, the most of the shielding work is concentrated on the beam lines and instrument enclosures to prepare for commissioning, save operation and adequate radiation background in the future. There is on-going work for the accelerator facility. This includes radiation-protection analyses for radiation monitors placement, designing shielding for additional facilities to test accelerator structures, redesigning some parts of the facility, and designing test facilities to the main accelerator structure for component testing. Neutronics analyses are required as well to support spent structure management, including waste characterisation analyses, choice of proper transport/storage package and shielding enhancement for the package if required.

  10. Classical least squares multivariate spectral analysis

    DOEpatents

    Haaland, David M.

    2002-01-01

    An improved classical least squares multivariate spectral analysis method that adds spectral shapes describing non-calibrated components and system effects (other than baseline corrections) present in the analyzed mixture to the prediction phase of the method. These improvements decrease or eliminate many of the restrictions to the CLS-type methods and greatly extend their capabilities, accuracy, and precision. One new application of PACLS includes the ability to accurately predict unknown sample concentrations when new unmodeled spectral components are present in the unknown samples. Other applications of PACLS include the incorporation of spectrometer drift into the quantitative multivariate model and the maintenance of a calibration on a drifting spectrometer. Finally, the ability of PACLS to transfer a multivariate model between spectrometers is demonstrated.

  11. Multivariate analysis: A statistical approach for computations

    NASA Astrophysics Data System (ADS)

    Michu, Sachin; Kaushik, Vandana

    2014-10-01

    Multivariate analysis is a type of multivariate statistical approach commonly used in, automotive diagnosis, education evaluating clusters in finance etc and more recently in the health-related professions. The objective of the paper is to provide a detailed exploratory discussion about factor analysis (FA) in image retrieval method and correlation analysis (CA) of network traffic. Image retrieval methods aim to retrieve relevant images from a collected database, based on their content. The problem is made more difficult due to the high dimension of the variable space in which the images are represented. Multivariate correlation analysis proposes an anomaly detection and analysis method based on the correlation coefficient matrix. Anomaly behaviors in the network include the various attacks on the network like DDOs attacks and network scanning.

  12. Lidar Analyses

    NASA Technical Reports Server (NTRS)

    Spiers, Gary D.

    1995-01-01

    A brief description of enhancements made to the NASA MSFC coherent lidar model is provided. Notable improvements are the addition of routines to automatically determine the 3 dB misalignment loss angle and the backscatter value at which the probability of a good estimate (for a maximum likelihood estimator) falls to 50%. The ability to automatically generate energy/aperture parametrization (EAP) plots which include the effects of angular misalignment has been added. These EAP plots make it very easy to see that for any practical system where there is some degree of misalignment then there is an optimum telescope diameter for which the laser pulse energy required to achieve a particular sensitivity is minimized. Increasing the telescope diameter above this will result in a reduction of sensitivity. These parameterizations also clearly show that the alignment tolerances at shorter wavelengths are much stricter than those at longer wavelengths. A brief outline of the NASA MSFC AEOLUS program is given and a summary of the lidar designs considered during the program is presented. A discussion of some of the design trades is performed both in the text and in a conference publication attached as an appendix.

  13. Multivariate moment closure techniques for stochastic kinetic models

    SciTech Connect

    Lakatos, Eszter Ale, Angelique; Kirk, Paul D. W.; Stumpf, Michael P. H.

    2015-09-07

    Stochastic effects dominate many chemical and biochemical processes. Their analysis, however, can be computationally prohibitively expensive and a range of approximation schemes have been proposed to lighten the computational burden. These, notably the increasingly popular linear noise approximation and the more general moment expansion methods, perform well for many dynamical regimes, especially linear systems. At higher levels of nonlinearity, it comes to an interplay between the nonlinearities and the stochastic dynamics, which is much harder to capture correctly by such approximations to the true stochastic processes. Moment-closure approaches promise to address this problem by capturing higher-order terms of the temporally evolving probability distribution. Here, we develop a set of multivariate moment-closures that allows us to describe the stochastic dynamics of nonlinear systems. Multivariate closure captures the way that correlations between different molecular species, induced by the reaction dynamics, interact with stochastic effects. We use multivariate Gaussian, gamma, and lognormal closure and illustrate their use in the context of two models that have proved challenging to the previous attempts at approximating stochastic dynamics: oscillations in p53 and Hes1. In addition, we consider a larger system, Erk-mediated mitogen-activated protein kinases signalling, where conventional stochastic simulation approaches incur unacceptably high computational costs.

  14. Multivariate moment closure techniques for stochastic kinetic models

    NASA Astrophysics Data System (ADS)

    Lakatos, Eszter; Ale, Angelique; Kirk, Paul D. W.; Stumpf, Michael P. H.

    2015-09-01

    Stochastic effects dominate many chemical and biochemical processes. Their analysis, however, can be computationally prohibitively expensive and a range of approximation schemes have been proposed to lighten the computational burden. These, notably the increasingly popular linear noise approximation and the more general moment expansion methods, perform well for many dynamical regimes, especially linear systems. At higher levels of nonlinearity, it comes to an interplay between the nonlinearities and the stochastic dynamics, which is much harder to capture correctly by such approximations to the true stochastic processes. Moment-closure approaches promise to address this problem by capturing higher-order terms of the temporally evolving probability distribution. Here, we develop a set of multivariate moment-closures that allows us to describe the stochastic dynamics of nonlinear systems. Multivariate closure captures the way that correlations between different molecular species, induced by the reaction dynamics, interact with stochastic effects. We use multivariate Gaussian, gamma, and lognormal closure and illustrate their use in the context of two models that have proved challenging to the previous attempts at approximating stochastic dynamics: oscillations in p53 and Hes1. In addition, we consider a larger system, Erk-mediated mitogen-activated protein kinases signalling, where conventional stochastic simulation approaches incur unacceptably high computational costs.

  15. Multivariate data analysis for outcome studies.

    PubMed

    Spector, P E

    1981-02-01

    The use of multivariate statistical techniques for analyzing the complex data often gathered in outcome studies is discussed. The multivariate analysis of variance (MANOVA) is suggested for multiple group studies common to outcome studies. This technique can be utilized for a large number of specific research designs whenever multiple outcome measures are collected. MANOVA offers two specific advantages over more familiar univariate approaches: it presents better control over Type 1 error rates while preserving statistical power, and it allows more thorough analysis of complex data. PMID:7223728

  16. Multivariate analysis of adaptive capacity for upper thermal limits in Drosophila simulans.

    PubMed

    van Heerwaarden, B; Sgrò, C M

    2013-04-01

    Thermal tolerance is an important factor influencing the distribution of ectotherms, but our understanding of the ability of species to evolve different thermal limits is limited. Based on univariate measures of adaptive capacity, it has recently been suggested that species may have limited evolutionary potential to extend their upper thermal limits under ramping temperature conditions that better reflect heat stress in nature. To test these findings more broadly, we used a paternal half-sibling breeding design to estimate the multivariate evolutionary potential for upper thermal limits in Drosophila simulans. We assessed heat tolerance using static (basal and hardened) and ramping assays. Our analyses revealed significant evolutionary potential for all three measures of heat tolerance. Additive genetic variances were significantly different from zero for all three traits. Our G matrix analysis revealed that all three traits would contribute to a response to selection for increased heat tolerance. Significant additive genetic covariances and additive genetic correlations between static basal and hardened heat-knockdown time, marginally nonsignificant between static basal and ramping heat-knockdown time, indicate that direct and correlated responses to selection for increased upper thermal limits are possible. Thus, combinations of all three traits will contribute to the evolution of upper thermal limits in response to selection imposed by a warming climate. Reliance on univariate estimates of evolutionary potential may not provide accurate insight into the ability of organisms to evolve upper thermal limits in nature.

  17. Up-scaling of multi-variable flood loss models from objects to land use units at the meso-scale

    NASA Astrophysics Data System (ADS)

    Kreibich, Heidi; Schröter, Kai; Merz, Bruno

    2016-05-01

    Flood risk management increasingly relies on risk analyses, including loss modelling. Most of the flood loss models usually applied in standard practice have in common that complex damaging processes are described by simple approaches like stage-damage functions. Novel multi-variable models significantly improve loss estimation on the micro-scale and may also be advantageous for large-scale applications. However, more input parameters also reveal additional uncertainty, even more in upscaling procedures for meso-scale applications, where the parameters need to be estimated on a regional area-wide basis. To gain more knowledge about challenges associated with the up-scaling of multi-variable flood loss models the following approach is applied: Single- and multi-variable micro-scale flood loss models are up-scaled and applied on the meso-scale, namely on basis of ATKIS land-use units. Application and validation is undertaken in 19 municipalities, which were affected during the 2002 flood by the River Mulde in Saxony, Germany by comparison to official loss data provided by the Saxon Relief Bank (SAB).In the meso-scale case study based model validation, most multi-variable models show smaller errors than the uni-variable stage-damage functions. The results show the suitability of the up-scaling approach, and, in accordance with micro-scale validation studies, that multi-variable models are an improvement in flood loss modelling also on the meso-scale. However, uncertainties remain high, stressing the importance of uncertainty quantification. Thus, the development of probabilistic loss models, like BT-FLEMO used in this study, which inherently provide uncertainty information are the way forward.

  18. Spacelab Charcoal Analyses

    NASA Technical Reports Server (NTRS)

    Slivon, L. E.; Hernon-Kenny, L. A.; Katona, V. R.; Dejarme, L. E.

    1995-01-01

    This report describes analytical methods and results obtained from chemical analysis of 31 charcoal samples in five sets. Each set was obtained from a single scrubber used to filter ambient air on board a Spacelab mission. Analysis of the charcoal samples was conducted by thermal desorption followed by gas chromatography/mass spectrometry (GC/MS). All samples were analyzed using identical methods. The method used for these analyses was able to detect compounds independent of their polarity or volatility. In addition to the charcoal samples, analyses of three Environmental Control and Life Support System (ECLSS) water samples were conducted specifically for trimethylamine.

  19. DUALITY IN MULTIVARIATE RECEPTOR MODEL. (R831078)

    EPA Science Inventory

    Multivariate receptor models are used for source apportionment of multiple observations of compositional data of air pollutants that obey mass conservation. Singular value decomposition of the data leads to two sets of eigenvectors. One set of eigenvectors spans a space in whi...

  20. Using Matlab in a Multivariable Calculus Course.

    ERIC Educational Resources Information Center

    Schlatter, Mark D.

    The benefits of high-level mathematics packages such as Matlab include both a computer algebra system and the ability to provide students with concrete visual examples. This paper discusses how both capabilities of Matlab were used in a multivariate calculus class. Graphical user interfaces which display three-dimensional surfaces, contour plots,…

  1. MBIS: multivariate Bayesian image segmentation tool.

    PubMed

    Esteban, Oscar; Wollny, Gert; Gorthi, Subrahmanyam; Ledesma-Carbayo, María-J; Thiran, Jean-Philippe; Santos, Andrés; Bach-Cuadra, Meritxell

    2014-07-01

    We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering tool based on the mixture of multivariate normal distributions model. MBIS supports multichannel bias field correction based on a B-spline model. A second methodological novelty is the inclusion of graph-cuts optimization for the stationary anisotropic hidden Markov random field model. Along with MBIS, we release an evaluation framework that contains three different experiments on multi-site data. We first validate the accuracy of segmentation and the estimated bias field for each channel. MBIS outperforms a widely used segmentation tool in a cross-comparison evaluation. The second experiment demonstrates the robustness of results on atlas-free segmentation of two image sets from scan-rescan protocols on 21 healthy subjects. Multivariate segmentation is more replicable than the monospectral counterpart on T1-weighted images. Finally, we provide a third experiment to illustrate how MBIS can be used in a large-scale study of tissue volume change with increasing age in 584 healthy subjects. This last result is meaningful as multivariate segmentation performs robustly without the need for prior knowledge.

  2. A Bayesian Multivariate Receptor Model for Estimating Source Contributions to Particulate Matter Pollution using National Databases

    PubMed Central

    Hackstadt, Amber J.; Peng, Roger D.

    2014-01-01

    Summary Time series studies have suggested that air pollution can negatively impact health. These studies have typically focused on the total mass of fine particulate matter air pollution or the individual chemical constituents that contribute to it, and not source-specific contributions to air pollution. Source-specific contribution estimates are useful from a regulatory standpoint by allowing regulators to focus limited resources on reducing emissions from sources that are major contributors to air pollution and are also desired when estimating source-specific health effects. However, researchers often lack direct observations of the emissions at the source level. We propose a Bayesian multivariate receptor model to infer information about source contributions from ambient air pollution measurements. The proposed model incorporates information from national databases containing data on both the composition of source emissions and the amount of emissions from known sources of air pollution. The proposed model is used to perform source apportionment analyses for two distinct locations in the United States (Boston, Massachusetts and Phoenix, Arizona). Our results mirror previous source apportionment analyses that did not utilize the information from national databases and provide additional information about uncertainty that is relevant to the estimation of health effects. PMID:25309119

  3. Multivariable Parametric Cost Model for Ground Optical: Telescope Assembly

    NASA Technical Reports Server (NTRS)

    Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia

    2004-01-01

    A parametric cost model for ground-based telescopes is developed using multi-variable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature were examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e. multi-telescope phased-array systems). Additionally, single variable models based on aperture diameter were derived.

  4. Multivariable Parametric Cost Model for Ground Optical Telescope Assembly

    NASA Technical Reports Server (NTRS)

    Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia

    2005-01-01

    A parametric cost model for ground-based telescopes is developed using multivariable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction-limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature are examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e., multi-telescope phased-array systems). Additionally, single variable models Based on aperture diameter are derived.

  5. Phosphazene additives

    DOEpatents

    Harrup, Mason K; Rollins, Harry W

    2013-11-26

    An additive comprising a phosphazene compound that has at least two reactive functional groups and at least one capping functional group bonded to phosphorus atoms of the phosphazene compound. One of the at least two reactive functional groups is configured to react with cellulose and the other of the at least two reactive functional groups is configured to react with a resin, such as an amine resin of a polycarboxylic acid resin. The at least one capping functional group is selected from the group consisting of a short chain ether group, an alkoxy group, or an aryloxy group. Also disclosed are an additive-resin admixture, a method of treating a wood product, and a wood product.

  6. Potlining Additives

    SciTech Connect

    Rudolf Keller

    2004-08-10

    In this project, a concept to improve the performance of aluminum production cells by introducing potlining additives was examined and tested. Boron oxide was added to cathode blocks, and titanium was dissolved in the metal pool; this resulted in the formation of titanium diboride and caused the molten aluminum to wet the carbonaceous cathode surface. Such wetting reportedly leads to operational improvements and extended cell life. In addition, boron oxide suppresses cyanide formation. This final report presents and discusses the results of this project. Substantial economic benefits for the practical implementation of the technology are projected, especially for modern cells with graphitized blocks. For example, with an energy savings of about 5% and an increase in pot life from 1500 to 2500 days, a cost savings of $ 0.023 per pound of aluminum produced is projected for a 200 kA pot.

  7. Multivariate distributions of soil hydraulic parameters

    NASA Astrophysics Data System (ADS)

    Qu, Wei; Pachepsky, Yakov; Huisman, Johan Alexander; Martinez, Gonzalo; Bogena, Heye; Vereecken, Harry

    2014-05-01

    on pedotransfer relationships not only within a given textural class but also on pedotransfer relationships within other textural classes since the pedotransfer relationships are developed across the database containing data for several textural classes. Therefore, joint multivariate parameter distributions for a specific class may not be sufficiently accurate. Currently PTF may give the best prediction of the parameter itself, but they are not designed to estimate correlations between parameters. Covariance matrices for soil hydraulic parameters present an additional type of pedotransfer information that needs to be acquired and used whenever random sets of those parameters are to be generated.

  8. Experimental evidence for multivariate stabilizing sexual selection.

    PubMed

    Brooks, Robert; Hunt, John; Blows, Mark W; Smith, Michael J; Bussière, Luc F; Jennions, Michael D

    2005-04-01

    Stabilizing selection is a fundamental concept in evolutionary biology. In the presence of a single intermediate optimum phenotype (fitness peak) on the fitness surface, stabilizing selection should cause the population to evolve toward such a peak. This prediction has seldom been tested, particularly for suites of correlated traits. The lack of tests for an evolutionary match between population means and adaptive peaks may be due, at least in part, to problems associated with empirically detecting multivariate stabilizing selection and with testing whether population means are at the peak of multivariate fitness surfaces. Here we show how canonical analysis of the fitness surface, combined with the estimation of confidence regions for stationary points on quadratic response surfaces, may be used to define multivariate stabilizing selection on a suite of traits and to establish whether natural populations reside on the multivariate peak. We manufactured artificial advertisement calls of the male cricket Teleogryllus commodus and played them back to females in laboratory phonotaxis trials to estimate the linear and nonlinear sexual selection that female phonotactic choice imposes on male call structure. Significant nonlinear selection on the major axes of the fitness surface was convex in nature and displayed an intermediate optimum, indicating multivariate stabilizing selection. The mean phenotypes of four independent samples of males, from the same population as the females used in phonotaxis trials, were within the 95% confidence region for the fitness peak. These experiments indicate that stabilizing sexual selection may play an important role in the evolution of male call properties in natural populations of T. commodus.

  9. Multivariate linkage analysis of specific language impairment (SLI).

    PubMed

    Monaco, Anthony P

    2007-09-01

    Specific language impairment (SLI) is defined as an inability to develop appropriate language skills without explanatory medical conditions, low intelligence or lack of opportunity. Previously, a genome scan of 98 families affected by SLI was completed by the SLI Consortium, resulting in the identification of two quantitative trait loci (QTL) on chromosomes 16q (SLI1) and 19q (SLI2). This was followed by a replication of both regions in an additional 86 families. Both these studies applied linkage methods to one phenotypic trait at a time. However, investigations have suggested that simultaneous analysis of several traits may offer more power. The current study therefore applied a multivariate variance-components approach to the SLI Consortium dataset using additional phenotypic data. A multivariate genome scan was completed and supported the importance of the SLI1 and SLI2 loci, whilst highlighting a possible novel QTL on chromosome 10. Further investigation implied that the effect of SLI1 on non-word repetition was equally as strong on reading and spelling phenotypes. In contrast, SLI2 appeared to have influences on a selection of expressive and receptive language phenotypes in addition to non-word repetition, but did not show linkage to literacy phenotypes.

  10. Causal diagrams and multivariate analysis I: a quiver full of arrows.

    PubMed

    Jupiter, Daniel C

    2014-01-01

    How do we know which variables we should include in our multivariate analyses? What role does each variable play in our understanding of the analysis? In this article I begin a discussion of these issues and describe 2 different types of studies for which this problem must be handled in different ways.

  11. A Multivariate Genetic Analysis of Specific Phobia, Separation Anxiety and Social Phobia in Early Childhood

    ERIC Educational Resources Information Center

    Eley, Thalia C.; Rijsdijk, Fruhling V.; Perrin, Sean; O'Connor, Thomas G.; Bolton, Derek

    2008-01-01

    Background: Comorbidity amongst anxiety disorders is very common in children as in adults and leads to considerable distress and impairment, yet is poorly understood. Multivariate genetic analyses can shed light on the origins of this comorbidity by revealing whether genetic or environmental risks for one disorder also influence another. We…

  12. Is the Library Important? Multivariate Studies at the National and International Level

    ERIC Educational Resources Information Center

    Krashen, Stephen; Lee, Syying; McQuillan, Jeff

    2012-01-01

    Three multivariate analyses, all controlling for the effects of poverty, confirm the importance of the library. Replicating McQuillan's analysis of 1992 NAEP scores, this study finds that access to books in school and public libraries was a significant predictor of 2007 fourth grade NAEP reading scores, as well as the difference between grade 4…

  13. Multivariate Statistical Modelling of Drought and Heat Wave Events

    NASA Astrophysics Data System (ADS)

    Manning, Colin; Widmann, Martin; Vrac, Mathieu; Maraun, Douglas; Bevaqua, Emanuele

    2016-04-01

    copula is a multivariate distribution function which allows one to model the dependence structure of given variables separately from the marginal behaviour. We firstly look at the structure of soil moisture drought over the entire of France using the SAFRAN dataset between 1959 and 2009. Soil moisture is represented using the Standardised Precipitation Evapotranspiration Index (SPEI). Drought characteristics are computed at grid point scale where drought conditions are identified as those with an SPEI value below -1.0. We model the multivariate dependence structure of drought events defined by certain characteristics and compute return levels of these events. We initially find that drought characteristics such as duration, mean SPEI and the maximum contiguous area to a grid point all have positive correlations, though the degree to which they are correlated can vary considerably spatially. A spatial representation of return levels then may provide insight into the areas most prone to drought conditions. As a next step, we analyse the dependence structure between soil moisture conditions preceding the onset of a heat wave and the heat wave itself.

  14. Overview of multivariate methods and their application to studies of wildlife habitat

    SciTech Connect

    Shugart, Jr., H. H.

    1980-01-01

    Multivariate statistical techniques as methods of choice in analyzing habitat relations among animals have distinct advantages over competitive methodologies. These considerations, joined with a reduction in the cost of computer time, the increased availability of multivariate statistical packages, and an increased willingness on the part of ecologists to use mathematics and statistics as tools, have created an exponentially increasing interest in multivariate statistical methods over the past decade. It is important to note that the earliest multivariate statistical analyses in ecology did more than introduce a set of appropriate and needed methodologies to ecology. The studies emphasized different spatial and organizational scales from those typically emphasized in habitat studies. The new studies, that used multivariate methods, emphasized individual organisms' responses in a heterogeneous environment. This philosophical (and to some degree, methodological) emphasis on heterogeneity has led to a potential to predict the consequences of disturbances and management on wildlife habitat. One recent development in this regard has been the coupling of forest succession simulators with multivariate analysis of habitat to predict habitat availability under different timber management procedures.

  15. Epidemiology of Type 1 Diabetes Mellitus in Korea through an Investigation of the National Registration Project of Type 1 Diabetes for the Reimbursement of Glucometer Strips with Additional Analyses Using Claims Data

    PubMed Central

    Song, Sun Ok; Nam, Joo Young; Park, Kyeong Hye; Yoon, Ji-Hae; Son, Kyung-Mi; Ko, Young; Lim, Dong-Ha

    2016-01-01

    Background The aim of this study was to estimate the prevalence and incidence of type 1 diabetes mellitus (T1DM) in Korea. In addition, we planned to do a performance analysis of the Registration Project of Type 1 diabetes for the reimbursement of consumable materials. Methods To obtain nationwide data on the incidence and prevalence of T1DM, we extracted claims data from July 2011 to August 2013 from the Registration Project of Type 1 diabetes on the reimbursement of consumable materials in the National Health Insurance (NHI) Database. For a more detailed analysis of the T1DM population in Korea, stratification by gender, age, and area was performed, and prevalence and incidence were calculated. Results Of the 8,256 subjects enrolled over the 26 months, the male to female ratio was 1 to 1.12, the median age was 37.1 years, and an average of 136 new T1DM patients were registered to the T1DM registry each month, resulting in 1,632 newly diagnosed T1DM patients each year. We found that the incidence rate of new T1DM cases was 3.28 per 100,000 people. The average proportion of T1DM patients compared with each region's population was 0.0125%. The total number of insurance subscribers under the universal compulsory NHI in Korea was 49,662,097, and the total number of diabetes patients, excluding duplication, was 3,762,332. Conclusion The prevalence of T1DM over the course of the study was approximately 0.017% to 0.021% of the entire population of Korea, and the annual incidence of T1DM was 3.28:100,000 overall and 3.25:100,000 for Koreans under 20 years old. PMID:26912154

  16. Multivariate, non-linear trend analysis of heterogeneous water quality monitoring data

    NASA Astrophysics Data System (ADS)

    Lischeid, Gunnar; Kalettka, Thomas; Steidl, Jörg; Merz, Christoph; Lehr, Christian

    2014-05-01

    Comprehensive water quality monitoring is considered a necessary prerequisite for sound water resources management and a valuable source for science. In practice, however, use of large monitoring data sets is often limited due to heterogeneous data sources, spatially and temporally variable monitoring schemes, non-equidistant sampling, large natural variability, and, last but not least, by the sheer size of the data sets that makes identification of unexpected peculiarities a tedious task. As a consequence, any initiation of gradual long-term system shifts can hardly be detected, especially as long as it is restricted to a small fraction of sampling sites. In addition, trends might be limited to a rather small subset of sampling sites or to certain periods of time and might thus escape attention. Usually, numerous solutes are monitored in parallel, but trend analyses are performed for each solute separately. However, in water quality samples trends are hardly restricted to single solutes, but affect various solutes synchronously in a characteristic way. Thus performing joint multivariate trend analyses would not only save effort and time, but would yield more robust assessments of system shifts. We present a non-linear multivariate data visualization approach that allows a rapid assessment of non-linear, possibly local trends and unexpected behaviour in large water quality monitoring data sets. It consists of a combination of Self-Organizing Maps and Sammon's Mapping (SOM-SM). The approach was applied to a data set of 2900 water samples, each comprising 13 solutes, compiled from various monitoring programs in the Federal State of Brandenburg (Germany). In total, 128 stream water, groundwater and small pond sites had been sampled between 1994 and 2012 at different and irregular time intervals. The SOM-SM product is a graph where every sample is represented by a symbol. Location of the symbols in the graph is optimized such that the distance between any two symbols

  17. Usual Dietary Intakes: SAS Macros for Fitting Multivariate Measurement Error Models & Estimating Multivariate Usual Intake Distributions

    Cancer.gov

    The following SAS macros can be used to create a multivariate usual intake distribution for multiple dietary components that are consumed nearly every day or episodically. A SAS macro for performing balanced repeated replication (BRR) variance estimation is also included.

  18. A Multivariate Granger Causality Concept towards Full Brain Functional Connectivity.

    PubMed

    Schmidt, Christoph; Pester, Britta; Schmid-Hertel, Nicole; Witte, Herbert; Wismüller, Axel; Leistritz, Lutz

    2016-01-01

    Detecting changes of spatially high-resolution functional connectivity patterns in the brain is crucial for improving the fundamental understanding of brain function in both health and disease, yet still poses one of the biggest challenges in computational neuroscience. Currently, classical multivariate Granger Causality analyses of directed interactions between single process components in coupled systems are commonly restricted to spatially low- dimensional data, which requires a pre-selection or aggregation of time series as a preprocessing step. In this paper we propose a new fully multivariate Granger Causality approach with embedded dimension reduction that makes it possible to obtain a representation of functional connectivity for spatially high-dimensional data. The resulting functional connectivity networks may consist of several thousand vertices and thus contain more detailed information compared to connectivity networks obtained from approaches based on particular regions of interest. Our large scale Granger Causality approach is applied to synthetic and resting state fMRI data with a focus on how well network community structure, which represents a functional segmentation of the network, is preserved. It is demonstrated that a number of different community detection algorithms, which utilize a variety of algorithmic strategies and exploit topological features differently, reveal meaningful information on the underlying network module structure.

  19. A Multivariate Granger Causality Concept towards Full Brain Functional Connectivity

    PubMed Central

    Schmid-Hertel, Nicole; Witte, Herbert; Wismüller, Axel; Leistritz, Lutz

    2016-01-01

    Detecting changes of spatially high-resolution functional connectivity patterns in the brain is crucial for improving the fundamental understanding of brain function in both health and disease, yet still poses one of the biggest challenges in computational neuroscience. Currently, classical multivariate Granger Causality analyses of directed interactions between single process components in coupled systems are commonly restricted to spatially low- dimensional data, which requires a pre-selection or aggregation of time series as a preprocessing step. In this paper we propose a new fully multivariate Granger Causality approach with embedded dimension reduction that makes it possible to obtain a representation of functional connectivity for spatially high-dimensional data. The resulting functional connectivity networks may consist of several thousand vertices and thus contain more detailed information compared to connectivity networks obtained from approaches based on particular regions of interest. Our large scale Granger Causality approach is applied to synthetic and resting state fMRI data with a focus on how well network community structure, which represents a functional segmentation of the network, is preserved. It is demonstrated that a number of different community detection algorithms, which utilize a variety of algorithmic strategies and exploit topological features differently, reveal meaningful information on the underlying network module structure. PMID:27064897

  20. Stellar populations in ω Centauri: a multivariate analysis

    NASA Astrophysics Data System (ADS)

    Fraix-Burnet, D.; Davoust, E.

    2015-07-01

    We have performed multivariate statistical analyses of photometric and chemical abundance parameters of three large samples of stars in the globular cluster ω Centauri. The statistical analysis of a sample of 735 stars based on seven chemical abundances with the method of Maximum Parsimony (cladistics) yields the most promising results: seven groups are found, distributed along three branches with distinct chemical, spatial and kinematical properties. A progressive chemical evolution can be traced from one group to the next, but also within groups, suggestive of an inhomogeneous chemical enrichment of the initial interstellar matter. The adjustment of stellar evolution models shows that the groups with metallicities [Fe/H] > -1.5 are Helium enriched, thus presumably of second generation. The spatial concentration of the groups increases with chemical evolution, except for two groups, which stand out in their other properties as well. The amplitude of rotation decreases with chemical evolution, except for two of the three metal-rich groups, which rotate fastest, as predicted by recent hydrodynamical simulations. The properties of the groups are interpreted in terms of star formation in gas clouds of different origins. In conclusion, our multivariate analysis has shown that metallicity alone cannot segregate the different populations of ω Centauri.

  1. Multivariate analysis of the impacts of the turbine fuel JP-4 in a microcosm toxicity test with implications for the evaluation of ecosystem dynamics and risk assessment.

    PubMed

    Landis, W G; Matthews, R A; Markiewicz, A J; Matthews, G B

    1993-12-01

    Turbine fuels are often the only aviation fuel available in most of the world. Turbine fuels consist of numerous constituents with varying water solubilities, volatilities and toxicities. This study investigates the toxicity of the water soluble fraction (WSF) of JP-4 using the Standard Aquatic Microcosm (SAM). Multivariate analysis of the complex data, including the relatively new method of nonmetric clustering, was used and compared to more traditional analyses. Particular emphasis is placed on ecosystem dynamics in multivariate space.The WSF is prepared by vigorously mixing the fuel and the SAM microcosm media in a separatory funnel. The water phase, which contains the water-soluble fraction of JP-4 is then collected. The SAM experiment was conducted using concentrations of 0.0, 1.5 and 15% WSF. The WSF is added on day 7 of the experiments by removing 450 ml from each microcosm including the controls, then adding the appropriate amount of toxicant solution and finally bringing the final volume to 3 L with microcosm media. Analysis of the WSF was performed by purge and trap gas chromatography. The organic constituents of the WSF were not recoverable from the water column within several days of the addition of the toxicant. However, the impact of the WSF on the microcosm was apparent. In the highest initial concentration treatment group an algal bloom ensued, generated by the apparent toxicity of the WSF of JP-4 to the daphnids. As the daphnid populations recovered the algal populations decreased to control values. Multivariate methods clearly demonstrated this initial impact along with an additional oscillation seperating the four treatment groups in the latter segment of the experiment. Apparent recovery may be an artifact of the projections used to describe the multivariate data. The variables that were most important in distinguishing the four groups shifted during the course of the 63 day experiment. Even this simple microcosm exhibited a variety of dynamics

  2. Nonlinear aerodynamic modeling using multivariate orthogonal functions

    NASA Technical Reports Server (NTRS)

    Morelli, Eugene A.

    1993-01-01

    A technique was developed for global modeling of nonlinear aerodynamic coefficients using multivariate orthogonal functions based on the data. Each orthogonal function retained in the model was decomposed into an expansion of ordinary polynomials in the independent variables, so that the final model could be interpreted as selectively retained terms from a multivariable power series expansion. A predicted squared-error metric was used to determine the orthogonal functions to be retained in the model; analytical derivatives were easily computed. The approach was demonstrated on the Z-body axis aerodynamic force coefficient (Cz) wind tunnel data for an F-18 research vehicle which came from a tabular wind tunnel and covered the entire subsonic flight envelope. For a realistic case, the analytical model predicted experimental values of Cz very well. The modeling technique is shown to be capable of generating a compact, global analytical representation of nonlinear aerodynamics. The polynomial model has good predictive capability, global validity, and analytical differentiability.

  3. Hybrid least squares multivariate spectral analysis methods

    DOEpatents

    Haaland, David M.

    2004-03-23

    A set of hybrid least squares multivariate spectral analysis methods in which spectral shapes of components or effects not present in the original calibration step are added in a following prediction or calibration step to improve the accuracy of the estimation of the amount of the original components in the sampled mixture. The hybrid method herein means a combination of an initial calibration step with subsequent analysis by an inverse multivariate analysis method. A spectral shape herein means normally the spectral shape of a non-calibrated chemical component in the sample mixture but can also mean the spectral shapes of other sources of spectral variation, including temperature drift, shifts between spectrometers, spectrometer drift, etc. The shape can be continuous, discontinuous, or even discrete points illustrative of the particular effect.

  4. Hybrid least squares multivariate spectral analysis methods

    DOEpatents

    Haaland, David M.

    2002-01-01

    A set of hybrid least squares multivariate spectral analysis methods in which spectral shapes of components or effects not present in the original calibration step are added in a following estimation or calibration step to improve the accuracy of the estimation of the amount of the original components in the sampled mixture. The "hybrid" method herein means a combination of an initial classical least squares analysis calibration step with subsequent analysis by an inverse multivariate analysis method. A "spectral shape" herein means normally the spectral shape of a non-calibrated chemical component in the sample mixture but can also mean the spectral shapes of other sources of spectral variation, including temperature drift, shifts between spectrometers, spectrometer drift, etc. The "shape" can be continuous, discontinuous, or even discrete points illustrative of the particular effect.

  5. Multivariate temporal dictionary learning for EEG.

    PubMed

    Barthélemy, Q; Gouy-Pailler, C; Isaac, Y; Souloumiac, A; Larue, A; Mars, J I

    2013-04-30

    This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG signals, this article proposes a data-driven method to obtain an adapted dictionary. To reach an efficient dictionary learning, appropriate spatial and temporal modeling is required. Inter-channels links are taken into account in the spatial multivariate model, and shift-invariance is used for the temporal model. Multivariate learned kernels are informative (a few atoms code plentiful energy) and interpretable (the atoms can have a physiological meaning). Using real EEG data, the proposed method is shown to outperform the classical multichannel matching pursuit used with a Gabor dictionary, as measured by the representative power of the learned dictionary and its spatial flexibility. Moreover, dictionary learning can capture interpretable patterns: this ability is illustrated on real data, learning a P300 evoked potential.

  6. Supporting inquiry learning by promoting normative understanding of multivariable causality

    NASA Astrophysics Data System (ADS)

    Keselman, Alla

    2003-11-01

    Early adolescents may lack the cognitive and metacognitive skills necessary for effective inquiry learning. In particular, they are likely to have a nonnormative mental model of multivariable causality in which effects of individual variables are neither additive nor consistent. Described here is a software-based intervention designed to facilitate students' metalevel and performance-level inquiry skills by enhancing their understanding of multivariable causality. Relative to an exploration-only group, sixth graders who practiced predicting an outcome (earthquake risk) based on multiple factors demonstrated increased attention to evidence, improved metalevel appreciation of effective strategies, and a trend toward consistent use of a controlled comparison strategy. Sixth graders who also received explicit instruction in making predictions based on multiple factors showed additional improvement in their ability to compare multiple instances as a basis for inferences and constructed the most accurate knowledge of the system. Gains were maintained in transfer tasks. The cognitive skills and metalevel understanding examined here are essential to inquiry learning.

  7. Regional dissociated heterochrony in multivariate analysis.

    PubMed

    Mitteroecker, P; Gunz, P; Weber, G W; Bookstein, F L

    2004-12-01

    Heterochrony, the classic framework to study ontogeny and phylogeny, in essence relies on a univariate concept of shape. Though principal component plots of multivariate shape data seem to resemble classical bivariate allometric plots, the language of heterochrony cannot be translated directly into general multivariate methodology. We simulate idealized multivariate ontogenetic trajectories and demonstrate their behavior in principal component plots in shape space and in size-shape space. The concept of "dissociation", which is conventionally regarded as a change in the relationship between shape change and size change, appears to be algebraically the same as regional dissociation - the variation of apparent heterochrony by region. Only if the trajectories of two related species lie along exactly the same path in shape space can the classic terminology of heterochrony apply so that pure dissociation of size change against shape change can be detected. We demonstrate a geometric morphometric approach to these issues using adult and subadult crania of 48 Pan paniscus and 47 P. troglodytes. On each specimen we digitized 47 landmarks and 144 semilandmarks on ridge curves and the external neurocranial surface. The relation between these two species' growth trajectories is too complex for a simple summary in terms of global heterochrony.

  8. Assessing causality in multivariate accident models.

    PubMed

    Elvik, Rune

    2011-01-01

    This paper discusses the application of operational criteria of causality to multivariate statistical models developed to identify sources of systematic variation in accident counts, in particular the effects of variables representing safety treatments. Nine criteria of causality serving as the basis for the discussion have been developed. The criteria resemble criteria that have been widely used in epidemiology. To assess whether the coefficients estimated in a multivariate accident prediction model represent causal relationships or are non-causal statistical associations, all criteria of causality are relevant, but the most important criterion is how well a model controls for potentially confounding factors. Examples are given to show how the criteria of causality can be applied to multivariate accident prediction models in order to assess the relationships included in these models. It will often be the case that some of the relationships included in a model can reasonably be treated as causal, whereas for others such an interpretation is less supported. The criteria of causality are indicative only and cannot provide a basis for stringent logical proof of causality.

  9. A three-dimensional multivariate representation of atmospheric variability

    NASA Astrophysics Data System (ADS)

    Žagar, Nedjeljka; Jelić, Damjan; Blaauw, Marten; Jesenko, Blaž

    2016-04-01

    A recently developed MODES software has been applied to the ECMWF analyses and forecasts and to several reanalysis datasets to describe the global variability of the balanced and inertio-gravity (IG) circulation across many scales by considering both mass and wind field and the whole model depth. In particular, the IG spectrum, which has only recently become observable in global datasets, can be studied simultaneously in the mass field and wind field and considering the whole model depth. MODES is open-access software that performs the normal-mode function decomposition of the 3D global datasets. Its application to the ERA Interim dataset reveals several aspects of the large-scale circulation after it has been partitioned into the linearly balanced and IG components. The global energy distribution is dominated by the balanced energy while the IG modes contribute around 8% of the total wave energy. However, on subsynoptic scales IG energy dominates and it is associated with the main features of tropical variability on all scales. The presented energy distribution and features of the zonally-averaged and equatorial circulation provide a reference for the intercomparison of several reanalysis datasets and for the validation of climate models. Features of the global IG circulation are compared in ERA Interim, MERRA and JRA reanalysis datasets and in several CMIP5 models. Since October 2014 the operational medium-range forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) have been analyzed by MODES daily and an online archive of all the outputs is available at http://meteo.fmf.uni-lj.si/MODES. New outputs are made available daily based on the 00 UTC run and subsequent 12-hour forecasts up to 240-hour forecast. In addition to the energy spectra and horizontal circulation on selected levels for the balanced and IG components, the equatorial Kelvin waves are presented in time and space as the most energetic tropical IG modes propagating vertically

  10. Optimizing functional network representation of multivariate time series.

    PubMed

    Zanin, Massimiliano; Sousa, Pedro; Papo, David; Bajo, Ricardo; García-Prieto, Juan; del Pozo, Francisco; Menasalvas, Ernestina; Boccaletti, Stefano

    2012-01-01

    By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes. We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia. We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks.

  11. Optimizing Functional Network Representation of Multivariate Time Series

    NASA Astrophysics Data System (ADS)

    Zanin, Massimiliano; Sousa, Pedro; Papo, David; Bajo, Ricardo; García-Prieto, Juan; Pozo, Francisco Del; Menasalvas, Ernestina; Boccaletti, Stefano

    2012-09-01

    By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes. We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia. We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks.

  12. Information Omitted From Analyses.

    PubMed

    2015-08-01

    In the Original Article titled “Higher- Order Genetic and Environmental Structure of Prevalent Forms of Child and Adolescent Psychopathology” published in the February 2011 issue of JAMA Psychiatry (then Archives of General Psychiatry) (2011;68[2]:181-189), there were 2 errors. Although the article stated that the dimensions of psychopathology were measured using parent informants for inattention, hyperactivity-impulsivity, and oppositional defiant disorder, and a combination of parent and youth informants for conduct disorder, major depression, generalized anxiety disorder, separation anxiety disorder, social phobia, specific phobia, agoraphobia, and obsessive-compulsive disorder, all dimensional scores used in the reported analyses were actually based on parent reports of symptoms; youth reports were not used. In addition, whereas the article stated that each symptom dimension was residualized on age, sex, age-squared, and age by sex, the dimensions actually were only residualized on age, sex, and age-squared. All analyses were repeated using parent informants for inattention, hyperactivity-impulsivity, and oppositional defiant disorder, and a combination of parent and youth informants for conduct disorder,major depression, generalized anxiety disorder, separation anxiety disorder, social phobia, specific phobia, agoraphobia, and obsessive-compulsive disorder; these dimensional scores were residualized on age, age-squared, sex, sex by age, and sex by age-squared. The results of the new analyses were qualitatively the same as those reported in the article, with no substantial changes in conclusions. The only notable small difference was that major depression and generalized anxiety disorder dimensions had small but significant loadings on the internalizing factor in addition to their substantial loadings on the general factor in the analyses of both genetic and non-shared covariances in the selected models in the new analyses. Corrections were made to the

  13. Using multivariate generalizability theory to assess the effect of content stratification on the reliability of a performance assessment.

    PubMed

    Keller, Lisa A; Clauser, Brian E; Swanson, David B

    2010-12-01

    In recent years, demand for performance assessments has continued to grow. However, performance assessments are notorious for lower reliability, and in particular, low reliability resulting from task specificity. Since reliability analyses typically treat the performance tasks as randomly sampled from an infinite universe of tasks, these estimates of reliability may not be accurate. For tests built according to a table of specifications, tasks are randomly sampled from different strata (content domains, skill areas, etc.). If these strata remain fixed in the test construction process, ignoring this stratification in the reliability analysis results in an underestimate of "parallel forms" reliability, and an overestimate of the person-by-task component. This research explores the effect of representing and misrepresenting the stratification appropriately in estimation of reliability and the standard error of measurement. Both multivariate and univariate generalizability studies are reported. Results indicate that the proper specification of the analytic design is essential in yielding the proper information both about the generalizability of the assessment and the standard error of measurement. Further, illustrative D studies present the effect under a variety of situations and test designs. Additional benefits of multivariate generalizability theory in test design and evaluation are also discussed.

  14. Piecewise aggregate representations and lower-bound distance functions for multivariate time series

    NASA Astrophysics Data System (ADS)

    Li, Hailin

    2015-06-01

    Dimensionality reduction is one of the most important methods to improve the efficiency of the techniques that are applied to the field of multivariate time series data mining. Due to multivariate time series with the variable-based and time-based dimensions, the reduction techniques must take both of them into consideration. To achieve this goal, we use a center sequence to represent a multivariate time series so that the new sequence can be seen as a univariate time series. Thus two sophisticated piecewise aggregate representations, including piecewise aggregate approximation and symbolization applied to univariate time series, are used to further represent the extended sequence that is derived from the center one. Furthermore, some distance functions are designed to measure the similarity between two representations. Through being proven by some related mathematical analysis, the proposed functions are lower bound on Euclidean distance and dynamic time warping. In this way, false dismissals can be avoided when they are used to index the time series. In addition, multivariate time series with different lengths can be transformed into the extended sequences with equal length, and their corresponding distance functions can measure the similarity between two unequal-length multivariate time series. The experimental results demonstrate that the proposed methods can reduce the dimensionality, and their corresponding distance functions satisfy the lower-bound condition, which can speed up the calculation of similarity search and indexing in the multivariate time series datasets.

  15. An Effective Method to Identify Heritable Components from Multivariate Phenotypes.

    PubMed

    Sun, Jiangwen; Kranzler, Henry R; Bi, Jinbo

    2015-01-01

    Multivariate phenotypes may be characterized collectively by a variety of low level traits, such as in the diagnosis of a disease that relies on multiple disease indicators. Such multivariate phenotypes are often used in genetic association studies. If highly heritable components of a multivariate phenotype can be identified, it can maximize the likelihood of finding genetic associations. Existing methods for phenotype refinement perform unsupervised cluster analysis on low-level traits and hence do not assess heritability. Existing heritable component analytics either cannot utilize general pedigrees or have to estimate the entire covariance matrix of low-level traits from limited samples, which leads to inaccurate estimates and is often computationally prohibitive. It is also difficult for these methods to exclude fixed effects from other covariates such as age, sex and race, in order to identify truly heritable components. We propose to search for a combination of low-level traits and directly maximize the heritability of this combined trait. A quadratic optimization problem is thus derived where the objective function is formulated by decomposing the traditional maximum likelihood method for estimating the heritability of a quantitative trait. The proposed approach can generate linearly-combined traits of high heritability that has been corrected for the fixed effects of covariates. The effectiveness of the proposed approach is demonstrated in simulations and by a case study of cocaine dependence. Our approach was computationally efficient and derived traits of higher heritability than those by other methods. Additional association analysis with the derived cocaine-use trait identified genetic markers that were replicated in an independent sample, further confirming the utility and advantage of the proposed approach. PMID:26658140

  16. Multivariate 3D modelling of Scottish soil properties

    NASA Astrophysics Data System (ADS)

    Poggio, Laura; Gimona, Alessandro

    2015-04-01

    Information regarding soil properties across landscapes at national or continental scales is critical for better soil and environmental management and for climate regulation and adaptation policy. The prediction of soil properties variation in space and time and their uncertainty is an important part of environmental modelling. Soil properties, and in particular the 3 fractions of soil texture, exhibit strong co-variation among themselves and therefore taking into account this correlation leads to spatially more accurate results. In this study the continuous vertical and lateral distributions of relevant soil properties in Scottish soils were modelled with a multivariate 3D-GAM+GS approach. The approach used involves 1) modelling the multivariate trend with full 3D spatial correlation, i.e., exploiting the values of the neighbouring pixels in 3D-space, and 2) 3D kriging to interpolate the residuals. The values at each cell for each of the considered depth layers were defined using a hybrid GAM-geostatistical 3D model, combining the fitting of a GAM (generalised Additive Models) to estimate multivariate trend of the variables, using a 3D smoother with related covariates. Gaussian simulations of the model residuals were used as spatial component to account for local details. A dataset of about 26,000 horizons (7,800 profiles) was used for this study. A validation set was randomly selected as 25% of the full dataset. Numerous covariates derived from globally available data, such as MODIS and SRTM, are considered. The results of the 3D-GAM+kriging showed low RMSE values, good R squared and an accurate reproduction of the spatial structure of the data for a range of soil properties. The results have an out-of-sample RMSE between 10 to 15% of the observed range when taking into account the whole profile. The approach followed allows the assessment of the uncertainty of both the trend and the residuals.

  17. An Effective Method to Identify Heritable Components from Multivariate Phenotypes.

    PubMed

    Sun, Jiangwen; Kranzler, Henry R; Bi, Jinbo

    2015-01-01

    Multivariate phenotypes may be characterized collectively by a variety of low level traits, such as in the diagnosis of a disease that relies on multiple disease indicators. Such multivariate phenotypes are often used in genetic association studies. If highly heritable components of a multivariate phenotype can be identified, it can maximize the likelihood of finding genetic associations. Existing methods for phenotype refinement perform unsupervised cluster analysis on low-level traits and hence do not assess heritability. Existing heritable component analytics either cannot utilize general pedigrees or have to estimate the entire covariance matrix of low-level traits from limited samples, which leads to inaccurate estimates and is often computationally prohibitive. It is also difficult for these methods to exclude fixed effects from other covariates such as age, sex and race, in order to identify truly heritable components. We propose to search for a combination of low-level traits and directly maximize the heritability of this combined trait. A quadratic optimization problem is thus derived where the objective function is formulated by decomposing the traditional maximum likelihood method for estimating the heritability of a quantitative trait. The proposed approach can generate linearly-combined traits of high heritability that has been corrected for the fixed effects of covariates. The effectiveness of the proposed approach is demonstrated in simulations and by a case study of cocaine dependence. Our approach was computationally efficient and derived traits of higher heritability than those by other methods. Additional association analysis with the derived cocaine-use trait identified genetic markers that were replicated in an independent sample, further confirming the utility and advantage of the proposed approach.

  18. An Effective Method to Identify Heritable Components from Multivariate Phenotypes

    PubMed Central

    Sun, Jiangwen; Kranzler, Henry R.; Bi, Jinbo

    2015-01-01

    Multivariate phenotypes may be characterized collectively by a variety of low level traits, such as in the diagnosis of a disease that relies on multiple disease indicators. Such multivariate phenotypes are often used in genetic association studies. If highly heritable components of a multivariate phenotype can be identified, it can maximize the likelihood of finding genetic associations. Existing methods for phenotype refinement perform unsupervised cluster analysis on low-level traits and hence do not assess heritability. Existing heritable component analytics either cannot utilize general pedigrees or have to estimate the entire covariance matrix of low-level traits from limited samples, which leads to inaccurate estimates and is often computationally prohibitive. It is also difficult for these methods to exclude fixed effects from other covariates such as age, sex and race, in order to identify truly heritable components. We propose to search for a combination of low-level traits and directly maximize the heritability of this combined trait. A quadratic optimization problem is thus derived where the objective function is formulated by decomposing the traditional maximum likelihood method for estimating the heritability of a quantitative trait. The proposed approach can generate linearly-combined traits of high heritability that has been corrected for the fixed effects of covariates. The effectiveness of the proposed approach is demonstrated in simulations and by a case study of cocaine dependence. Our approach was computationally efficient and derived traits of higher heritability than those by other methods. Additional association analysis with the derived cocaine-use trait identified genetic markers that were replicated in an independent sample, further confirming the utility and advantage of the proposed approach. PMID:26658140

  19. On a Family of Multivariate Modified Humbert Polynomials

    PubMed Central

    Aktaş, Rabia; Erkuş-Duman, Esra

    2013-01-01

    This paper attempts to present a multivariable extension of generalized Humbert polynomials. The results obtained here include various families of multilinear and multilateral generating functions, miscellaneous properties, and also some special cases for these multivariable polynomials. PMID:23935411

  20. Analyzing Multivariate Repeated Measures Designs When Covariance Matrices Are Heterogeneous.

    ERIC Educational Resources Information Center

    Lix, Lisa M.; And Others

    Methods for the analysis of within-subjects effects in multivariate groups by trials repeated measures designs are considered in the presence of heteroscedasticity of the group variance-covariance matrices and multivariate nonnormality. Under a doubly multivariate model approach to hypothesis testing, within-subjects main and interaction effect…

  1. Time varying, multivariate volume data reduction

    SciTech Connect

    Ahrens, James P; Fout, Nathaniel; Ma, Kwan - Liu

    2010-01-01

    Large-scale supercomputing is revolutionizing the way science is conducted. A growing challenge, however, is understanding the massive quantities of data produced by large-scale simulations. The data, typically time-varying, multivariate, and volumetric, can occupy from hundreds of gigabytes to several terabytes of storage space. Transferring and processing volume data of such sizes is prohibitively expensive and resource intensive. Although it may not be possible to entirely alleviate these problems, data compression should be considered as part of a viable solution, especially when the primary means of data analysis is volume rendering. In this paper we present our study of multivariate compression, which exploits correlations among related variables, for volume rendering. Two configurations for multidimensional compression based on vector quantization are examined. We emphasize quality reconstruction and interactive rendering, which leads us to a solution using graphics hardware to perform on-the-fly decompression during rendering. In this paper we present a solution which addresses the need for data reduction in large supercomputing environments where data resulting from simulations occupies tremendous amounts of storage. Our solution employs a lossy encoding scheme to acrueve data reduction with several options in terms of rate-distortion behavior. We focus on encoding of multiple variables together, with optional compression in space and time. The compressed volumes can be rendered directly with commodity graphics cards at interactive frame rates and rendering quality similar to that of static volume renderers. Compression results using a multivariate time-varying data set indicate that encoding multiple variables results in acceptable performance in the case of spatial and temporal encoding as compared to independent compression of variables. The relative performance of spatial vs. temporal compression is data dependent, although temporal compression has the

  2. Internet Addiction in High School Students in Turkey and Multivariate Analyses of the Underlying Factors.

    PubMed

    Kilic, Mahmut; Avci, Dilek; Uzuncakmak, Tugba

    2016-01-01

    The aim of this study is to examine the Internet addiction among adolescents in relation to their sociodemographic characteristics, communication skills, and perceived familial social support. This cross-sectional research is conducted in the high schools in some city centers, in Turkey, in 2013. In this study, cluster sampling was used. In each school, a class for each grade level was randomly selected, and all the students in the selected classes were included in the sample. One thousand seven hundred forty-two students aged between 14 and 20 years were included in the sample.The mean Internet Addiction Scale (IAS) score of the students was found to be 27.9 ± 21.2. According to the scores obtained from IAS, 81.8% of the students were found to display no symptoms (<50 points), 16.9% were found to display borderline symptoms (50-79 points), and 1.3% were found to be Internet addicts (≥80 points). According to the results of the binary logistic regression, male students and the students in single sex vocational schools were found to report higher levels of borderline Internet addiction. It was also observed that the IAS score increases when the father's educational level increases and when the students' school performance is worse. On the other hand, the IAS score decreases when the student grade level, perceived family social support, and communication skills scores increase.The risk factors for Internet addiction are being a male, low academic achievement, inadequate social support and communication skills, and father's high educational level. PMID:26950841

  3. [Number needed to treat: Interpretation and estimation in multivariable analyses and censored data].

    PubMed

    Gómez-Acebo, Inés; Dierssen-Sotos, Trinidad; Llorca, Javier

    2014-05-20

    Number needed to treat has been recommended as an easy way to transmit results from a trial, especially controlled clinical trials. Most articles estimate it from a 2×2 table, as the inverse of the absolute risk reduction. However, some limitations have been pointed out: The interpretation is not as easy as claimed, confidence intervals are frequently not estimated, and the estimation from 2×2 tables is inadequate when the main effect measure has been estimated adjusting for confounding factors. In this paper, we revise how to obtain point estimations and confidence intervals of number needed to treat in 4 situations: 2×2tables, logistic regression, Kaplan-Meier method, and Cox regression. PMID:23850150

  4. Flexible multivariate hemodynamics fMRI data analyses and simulations with PyHRF

    PubMed Central

    Vincent, Thomas; Badillo, Solveig; Risser, Laurent; Chaari, Lotfi; Bakhous, Christine; Forbes, Florence; Ciuciu, Philippe

    2014-01-01

    As part of fMRI data analysis, the pyhrf package provides a set of tools for addressing the two main issues involved in intra-subject fMRI data analysis: (1) the localization of cerebral regions that elicit evoked activity and (2) the estimation of activation dynamics also known as Hemodynamic Response Function (HRF) recovery. To tackle these two problems, pyhrf implements the Joint Detection-Estimation framework (JDE) which recovers parcel-level HRFs and embeds an adaptive spatio-temporal regularization scheme of activation maps. With respect to the sole detection issue (1), the classical voxelwise GLM procedure is also available through nipy, whereas Finite Impulse Response (FIR) and temporally regularized FIR models are concerned with HRF estimation (2) and are specifically implemented in pyhrf. Several parcellation tools are also integrated such as spatial and functional clustering. Parcellations may be used for spatial averaging prior to FIR/RFIR analysis or to specify the spatial support of the HRF estimates in the JDE approach. These analysis procedures can be applied either to volume-based data sets or to data projected onto the cortical surface. For validation purpose, this package is shipped with artificial and real fMRI data sets, which are used in this paper to compare the outcome of the different available approaches. The artificial fMRI data generator is also described to illustrate how to simulate different activation configurations, HRF shapes or nuisance components. To cope with the high computational needs for inference, pyhrf handles distributing computing by exploiting cluster units as well as multi-core machines. Finally, a dedicated viewer is presented, which handles n-dimensional images and provides suitable features to explore whole brain hemodynamics (time series, maps, ROI mask overlay). PMID:24782699

  5. [Number needed to treat: Interpretation and estimation in multivariable analyses and censored data].

    PubMed

    Gómez-Acebo, Inés; Dierssen-Sotos, Trinidad; Llorca, Javier

    2014-05-20

    Number needed to treat has been recommended as an easy way to transmit results from a trial, especially controlled clinical trials. Most articles estimate it from a 2×2 table, as the inverse of the absolute risk reduction. However, some limitations have been pointed out: The interpretation is not as easy as claimed, confidence intervals are frequently not estimated, and the estimation from 2×2 tables is inadequate when the main effect measure has been estimated adjusting for confounding factors. In this paper, we revise how to obtain point estimations and confidence intervals of number needed to treat in 4 situations: 2×2tables, logistic regression, Kaplan-Meier method, and Cox regression.

  6. Parsimonious Use of Indicators for Evaluating Sustainability Systems with Multivariate Statistical Analyses

    EPA Science Inventory

    Indicators are commonly used for evaluating relative sustainability for competing products and processes. When a set of indicators is chosen for a particular system of study, it is important to ensure that they are variable independently of each other. Often the number of indicat...

  7. Univariate and multivariate analyses of risk factors predisposing to auditory toxicity in patients receiving aminoglycosides.

    PubMed Central

    Gatell, J M; Ferran, F; Araujo, V; Bonet, M; Soriano, E; Traserra, J; SanMiguel, J G

    1987-01-01

    Risk factors predisposing to auditory toxicity of aminoglycosides were analyzed from records of 187 patients enrolled in three prospective randomized trials comparing the toxicity of netilmicin, tobramycin, and amikacin. Patients were eligible if they received three or more days of therapy and at least two serial audiograms were available. The overall auditory toxicity rate was 9.6% (18 of 187). Auditory toxicity was detected in 4.4, 10.8, and 23.5% of patients given netilmicin, tobramycin, and amikacin, respectively (P = 0.05). In the univariate analysis, patients who developed auditory toxicity were significantly older (P = 0.01) and had a significantly higher (P = 0.04) percentage of trough levels of netilmicin or tobramycin above 2 mg/liter or amikacin above 5 mg/liter. In the final logistic regression model, only age was retained as independently influencing the development of auditory toxicity (P less than 0.00001). Conversely, factors that did not add significantly to the prediction of auditory toxicity were aminoglycoside serum levels, total aminoglycoside dose, duration of therapy, sex, peak temperature, presence of bacteremia, shock, liver cirrhosis, dehydration, previous otic pathology or renal failure, and development of renal toxicity. At least in certain populations, age is the most important predisposing factor for the development of auditory toxicity in patients receiving aminoglycosides. PMID:3674849

  8. Quantitative Analyses in a Multivariate Study of Language Attrition: The Impact of Extralinguistic Factors

    ERIC Educational Resources Information Center

    Schmid, Monika S.; Dusseldorp, Elise

    2010-01-01

    Most linguistic processes--acquisition, change, deterioration--take place in and are determined by a complex and multifactorial web of language internal and language external influences. This implies that the impact of each individual factor can only be determined on the basis of a careful consideration of its interplay with all other factors. The…

  9. Quality by design case study: an integrated multivariate approach to drug product and process development.

    PubMed

    Huang, Jun; Kaul, Goldi; Cai, Chunsheng; Chatlapalli, Ramarao; Hernandez-Abad, Pedro; Ghosh, Krishnendu; Nagi, Arwinder

    2009-12-01

    To facilitate an in-depth process understanding, and offer opportunities for developing control strategies to ensure product quality, a combination of experimental design, optimization and multivariate techniques was integrated into the process development of a drug product. A process DOE was used to evaluate effects of the design factors on manufacturability and final product CQAs, and establish design space to ensure desired CQAs. Two types of analyses were performed to extract maximal information, DOE effect & response surface analysis and multivariate analysis (PCA and PLS). The DOE effect analysis was used to evaluate the interactions and effects of three design factors (water amount, wet massing time and lubrication time), on response variables (blend flow, compressibility and tablet dissolution). The design space was established by the combined use of DOE, optimization and multivariate analysis to ensure desired CQAs. Multivariate analysis of all variables from the DOE batches was conducted to study relationships between the variables and to evaluate the impact of material attributes/process parameters on manufacturability and final product CQAs. The integrated multivariate approach exemplifies application of QbD principles and tools to drug product and process development.

  10. Structural Laplacian Eigenmaps for modeling sets of multivariate sequences.

    PubMed

    Lewandowski, Michal; Makris, Dimitrios; Velastin, Sergio A; Nebel, Jean-Christophe

    2014-06-01

    A novel embedding-based dimensionality reduction approach, called structural Laplacian Eigenmaps, is proposed to learn models representing any concept that can be defined by a set of multivariate sequences. This approach relies on the expression of the intrinsic structure of the multivariate sequences in the form of structural constraints, which are imposed on dimensionality reduction process to generate a compact and data-driven manifold in a low dimensional space. This manifold is a mathematical representation of the intrinsic nature of the concept of interest regardless of the stylistic variability found in its instances. In addition, this approach is extended to model jointly several related concepts within a unified representation creating a continuous space between concept manifolds. Since a generated manifold encodes the unique characteristic of the concept of interest, it can be employed for classification of unknown instances of concepts. Exhaustive experimental evaluation on different datasets confirms the superiority of the proposed methodology to other state-of-the-art dimensionality reduction methods. Finally, the practical value of this novel dimensionality reduction method is demonstrated in three challenging computer vision applications, i.e., view-dependent and view-independent action recognition as well as human-human interaction classification. PMID:24144690

  11. Analytical advantages of multivariate data processing. One, two, three, infinity?

    PubMed

    Olivieri, Alejandro C

    2008-08-01

    Multidimensional data are being abundantly produced by modern analytical instrumentation, calling for new and powerful data-processing techniques. Research in the last two decades has resulted in the development of a multitude of different processing algorithms, each equipped with its own sophisticated artillery. Analysts have slowly discovered that this body of knowledge can be appropriately classified, and that common aspects pervade all these seemingly different ways of analyzing data. As a result, going from univariate data (a single datum per sample, employed in the well-known classical univariate calibration) to multivariate data (data arrays per sample of increasingly complex structure and number of dimensions) is known to provide a gain in sensitivity and selectivity, combined with analytical advantages which cannot be overestimated. The first-order advantage, achieved using vector sample data, allows analysts to flag new samples which cannot be adequately modeled with the current calibration set. The second-order advantage, achieved with second- (or higher-) order sample data, allows one not only to mark new samples containing components which do not occur in the calibration phase but also to model their contribution to the overall signal, and most importantly, to accurately quantitate the calibrated analyte(s). No additional analytical advantages appear to be known for third-order data processing. Future research may permit, among other interesting issues, to assess if this "1, 2, 3, infinity" situation of multivariate calibration is really true. PMID:18613646

  12. Linear, multivariable robust control with a mu perspective

    NASA Technical Reports Server (NTRS)

    Packard, Andy; Doyle, John; Balas, Gary

    1993-01-01

    The structured singular value is a linear algebra tool developed to study a particular class of matrix perturbation problems arising in robust feedback control of multivariable systems. These perturbations are called linear fractional, and are a natural way to model many types of uncertainty in linear systems, including state-space parameter uncertainty, multiplicative and additive unmodeled dynamics uncertainty, and coprime factor and gap metric uncertainty. The structured singular value theory provides a natural extension of classical SISO robustness measures and concepts to MIMO systems. The structured singular value analysis, coupled with approximate synthesis methods, make it possible to study the tradeoff between performance and uncertainty that occurs in all feedback systems. In MIMO systems, the complexity of the spatial interactions in the loop gains make it difficult to heuristically quantify the tradeoffs that must occur. This paper examines the role played by the structured singular value (and its computable bounds) in answering these questions, as well as its role in the general robust, multivariable control analysis and design problem.

  13. Multivariate curve-fitting in GAUSS

    USGS Publications Warehouse

    Bunck, C.M.; Pendleton, G.W.

    1988-01-01

    Multivariate curve-fitting techniques for repeated measures have been developed and an interactive program has been written in GAUSS. The program implements not only the one-factor design described in Morrison (1967) but also includes pairwise comparisons of curves and rates, a two-factor design, and other options. Strategies for selecting the appropriate degree for the polynomial are provided. The methods and program are illustrated with data from studies of the effects of environmental contaminants on ducklings, nesting kestrels and quail.

  14. Multivariate postprocessing techniques for probabilistic hydrological forecasting

    NASA Astrophysics Data System (ADS)

    Hemri, Stephan; Lisniak, Dmytro; Klein, Bastian

    2016-04-01

    Hydrologic ensemble forecasts driven by atmospheric ensemble prediction systems need statistical postprocessing in order to account for systematic errors in terms of both mean and spread. Runoff is an inherently multivariate process with typical events lasting from hours in case of floods to weeks or even months in case of droughts. This calls for multivariate postprocessing techniques that yield well calibrated forecasts in univariate terms and ensure a realistic temporal dependence structure at the same time. To this end, the univariate ensemble model output statistics (EMOS; Gneiting et al., 2005) postprocessing method is combined with two different copula approaches that ensure multivariate calibration throughout the entire forecast horizon. These approaches comprise ensemble copula coupling (ECC; Schefzik et al., 2013), which preserves the dependence structure of the raw ensemble, and a Gaussian copula approach (GCA; Pinson and Girard, 2012), which estimates the temporal correlations from training observations. Both methods are tested in a case study covering three subcatchments of the river Rhine that represent different sizes and hydrological regimes: the Upper Rhine up to the gauge Maxau, the river Moselle up to the gauge Trier, and the river Lahn up to the gauge Kalkofen. The results indicate that both ECC and GCA are suitable for modelling the temporal dependences of probabilistic hydrologic forecasts (Hemri et al., 2015). References Gneiting, T., A. E. Raftery, A. H. Westveld, and T. Goldman (2005), Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation, Monthly Weather Review, 133(5), 1098-1118, DOI: 10.1175/MWR2904.1. Hemri, S., D. Lisniak, and B. Klein, Multivariate postprocessing techniques for probabilistic hydrological forecasting, Water Resources Research, 51(9), 7436-7451, DOI: 10.1002/2014WR016473. Pinson, P., and R. Girard (2012), Evaluating the quality of scenarios of short-term wind power

  15. Multivariate Lipschitz optimization: Survey and computational comparison

    SciTech Connect

    Hansen, P.; Gourdin, E.; Jaumard, B.

    1994-12-31

    Many methods have been proposed to minimize a multivariate Lipschitz function on a box. They pertain the three approaches: (i) reduction to the univariate case by projection (Pijavskii) or by using a space-filling curve (Strongin); (ii) construction and refinement of a single upper bounding function (Pijavskii, Mladineo, Mayne and Polak, Jaumard Hermann and Ribault, Wood...); (iii) branch and bound with local upper bounding functions (Galperin, Pint{acute e}r, Meewella and Mayne, the present authors). A survey is made, stressing similarities of algorithms, expressed when possible within a unified framework. Moreover, an extensive computational comparison is reported on.

  16. Algorithms for computing the multivariable stability margin

    NASA Technical Reports Server (NTRS)

    Tekawy, Jonathan A.; Safonov, Michael G.; Chiang, Richard Y.

    1989-01-01

    Stability margin for multiloop flight control systems has become a critical issue, especially in highly maneuverable aircraft designs where there are inherent strong cross-couplings between the various feedback control loops. To cope with this issue, we have developed computer algorithms based on non-differentiable optimization theory. These algorithms have been developed for computing the Multivariable Stability Margin (MSM). The MSM of a dynamical system is the size of the smallest structured perturbation in component dynamics that will destabilize the system. These algorithms have been coded and appear to be reliable. As illustrated by examples, they provide the basis for evaluating the robustness and performance of flight control systems.

  17. F100 Multivariable Control Synthesis Program. Computer Implementation of the F100 Multivariable Control Algorithm

    NASA Technical Reports Server (NTRS)

    Soeder, J. F.

    1983-01-01

    As turbofan engines become more complex, the development of controls necessitate the use of multivariable control techniques. A control developed for the F100-PW-100(3) turbofan engine by using linear quadratic regulator theory and other modern multivariable control synthesis techniques is described. The assembly language implementation of this control on an SEL 810B minicomputer is described. This implementation was then evaluated by using a real-time hybrid simulation of the engine. The control software was modified to run with a real engine. These modifications, in the form of sensor and actuator failure checks and control executive sequencing, are discussed. Finally recommendations for control software implementations are presented.

  18. Somatic and vicarious pain are represented by dissociable multivariate brain patterns

    PubMed Central

    Krishnan, Anjali; Woo, Choong-Wan; Chang, Luke J; Ruzic, Luka; Gu, Xiaosi; López-Solà, Marina; Jackson, Philip L; Pujol, Jesús; Fan, Jin; Wager, Tor D

    2016-01-01

    Understanding how humans represent others’ pain is critical for understanding pro-social behavior. ‘Shared experience’ theories propose common brain representations for somatic and vicarious pain, but other evidence suggests that specialized circuits are required to experience others’ suffering. Combining functional neuroimaging with multivariate pattern analyses, we identified dissociable patterns that predicted somatic (high versus low: 100%) and vicarious (high versus low: 100%) pain intensity in out-of-sample individuals. Critically, each pattern was at chance in predicting the other experience, demonstrating separate modifiability of both patterns. Somatotopy (upper versus lower limb: 93% accuracy for both conditions) was also distinct, located in somatosensory versus mentalizing-related circuits for somatic and vicarious pain, respectively. Two additional studies demonstrated the generalizability of the somatic pain pattern (which was originally developed on thermal pain) to mechanical and electrical pain, and also demonstrated the replicability of the somatic/vicarious dissociation. These findings suggest possible mechanisms underlying limitations in feeling others’ pain, and present new, more specific, brain targets for studying pain empathy. DOI: http://dx.doi.org/10.7554/eLife.15166.001 PMID:27296895

  19. Somatic and vicarious pain are represented by dissociable multivariate brain patterns.

    PubMed

    Krishnan, Anjali; Woo, Choong-Wan; Chang, Luke J; Ruzic, Luka; Gu, Xiaosi; López-Solà, Marina; Jackson, Philip L; Pujol, Jesús; Fan, Jin; Wager, Tor D

    2016-01-01

    Understanding how humans represent others' pain is critical for understanding pro-social behavior. 'Shared experience' theories propose common brain representations for somatic and vicarious pain, but other evidence suggests that specialized circuits are required to experience others' suffering. Combining functional neuroimaging with multivariate pattern analyses, we identified dissociable patterns that predicted somatic (high versus low: 100%) and vicarious (high versus low: 100%) pain intensity in out-of-sample individuals. Critically, each pattern was at chance in predicting the other experience, demonstrating separate modifiability of both patterns. Somatotopy (upper versus lower limb: 93% accuracy for both conditions) was also distinct, located in somatosensory versus mentalizing-related circuits for somatic and vicarious pain, respectively. Two additional studies demonstrated the generalizability of the somatic pain pattern (which was originally developed on thermal pain) to mechanical and electrical pain, and also demonstrated the replicability of the somatic/vicarious dissociation. These findings suggest possible mechanisms underlying limitations in feeling others' pain, and present new, more specific, brain targets for studying pain empathy. PMID:27296895

  20. Multivariate intralocus sexual conflict in seed beetles.

    PubMed

    Berger, David; Berg, Elena C; Widegren, William; Arnqvist, Göran; Maklakov, Alexei A

    2014-12-01

    Intralocus sexual conflict (IaSC) is pervasive because males and females experience differences in selection but share much of the same genome. Traits with integrated genetic architecture should be reservoirs of sexually antagonistic genetic variation for fitness, but explorations of multivariate IaSC are scarce. Previously, we showed that upward artificial selection on male life span decreased male fitness but increased female fitness compared with downward selection in the seed beetle Callosobruchus maculatus. Here, we use these selection lines to investigate sex-specific evolution of four functionally integrated traits (metabolic rate, locomotor activity, body mass, and life span) that collectively define a sexually dimorphic life-history syndrome in many species. Male-limited selection for short life span led to correlated evolution in females toward a more male-like multivariate phenotype. Conversely, males selected for long life span became more female-like, implying that IaSC results from genetic integration of this suite of traits. However, while life span, metabolism, and body mass showed correlated evolution in the sexes, activity did not evolve in males but, surprisingly, did so in females. This led to sexual monomorphism in locomotor activity in short-life lines associated with detrimental effects in females. Our results thus support the general tenet that widespread pleiotropy generates IaSC despite sex-specific genetic architecture.

  1. Adaptable Multivariate Calibration Models for Spectral Applications

    SciTech Connect

    THOMAS,EDWARD V.

    1999-12-20

    Multivariate calibration techniques have been used in a wide variety of spectroscopic situations. In many of these situations spectral variation can be partitioned into meaningful classes. For example, suppose that multiple spectra are obtained from each of a number of different objects wherein the level of the analyte of interest varies within each object over time. In such situations the total spectral variation observed across all measurements has two distinct general sources of variation: intra-object and inter-object. One might want to develop a global multivariate calibration model that predicts the analyte of interest accurately both within and across objects, including new objects not involved in developing the calibration model. However, this goal might be hard to realize if the inter-object spectral variation is complex and difficult to model. If the intra-object spectral variation is consistent across objects, an effective alternative approach might be to develop a generic intra-object model that can be adapted to each object separately. This paper contains recommendations for experimental protocols and data analysis in such situations. The approach is illustrated with an example involving the noninvasive measurement of glucose using near-infrared reflectance spectroscopy. Extensions to calibration maintenance and calibration transfer are discussed.

  2. A multivariate Baltic Sea environmental index.

    PubMed

    Dippner, Joachim W; Kornilovs, Georgs; Junker, Karin

    2012-11-01

    Since 2001/2002, the correlation between North Atlantic Oscillation index and biological variables in the North Sea and Baltic Sea fails, which might be addressed to a global climate regime shift. To understand inter-annual and inter-decadal variability in environmental variables, a new multivariate index for the Baltic Sea is developed and presented here. The multivariate Baltic Sea Environmental (BSE) index is defined as the 1st principal component score of four z-transformed time series: the Arctic Oscillation index, the salinity between 120 and 200 m in the Gotland Sea, the integrated river runoff of all rivers draining into the Baltic Sea, and the relative vorticity of geostrophic wind over the Baltic Sea area. A statistical downscaling technique has been applied to project different climate indices to the sea surface temperature in the Gotland, to the Landsort gauge, and the sea ice extent. The new BSE index shows a better performance than all other climate indices and is equivalent to the Chen index for physical properties. An application of the new index to zooplankton time series from the central Baltic Sea (Latvian EEZ) shows an excellent skill in potential predictability of environmental time series.

  3. Fast Multivariate Search on Large Aviation Datasets

    NASA Technical Reports Server (NTRS)

    Bhaduri, Kanishka; Zhu, Qiang; Oza, Nikunj C.; Srivastava, Ashok N.

    2010-01-01

    Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which can contain up to several gigabytes of data. Surprisingly, research on MTS search is very limited. Most existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two provably correct algorithms to solve this problem (1) an R-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences, and (2) a List Based Search (LBS) algorithm which uses sorted lists for indexing. We demonstrate the performance of these algorithms using two large MTS databases from the aviation domain, each containing several millions of observations Both these tests show that our algorithms have very high prune rates (>95%) thus needing actual

  4. Multichannel hierarchical image classification using multivariate copulas

    NASA Astrophysics Data System (ADS)

    Voisin, Aurélie; Krylov, Vladimir A.; Moser, Gabriele; Serpico, Sebastiano B.; Zerubia, Josiane

    2012-03-01

    This paper focuses on the classification of multichannel images. The proposed supervised Bayesian classification method applied to histological (medical) optical images and to remote sensing (optical and synthetic aperture radar) imagery consists of two steps. The first step introduces the joint statistical modeling of the coregistered input images. For each class and each input channel, the class-conditional marginal probability density functions are estimated by finite mixtures of well-chosen parametric families. For optical imagery, the normal distribution is a well-known model. For radar imagery, we have selected generalized gamma, log-normal, Nakagami and Weibull distributions. Next, the multivariate d-dimensional Clayton copula, where d can be interpreted as the number of input channels, is applied to estimate multivariate joint class-conditional statistics. As a second step, we plug the estimated joint probability density functions into a hierarchical Markovian model based on a quadtree structure. Multiscale features are extracted by discrete wavelet transforms, or by using input multiresolution data. To obtain the classification map, we integrate an exact estimator of the marginal posterior mode.

  5. Multivariate statistical analysis of environmental monitoring data

    SciTech Connect

    Ross, D.L.

    1997-11-01

    EPA requires statistical procedures to determine whether soil or ground water adjacent to or below waste units is contaminated. These statistical procedures are often based on comparisons between two sets of data: one representing background conditions, and one representing site conditions. Since statistical requirements were originally promulgated in the 1980s, EPA has made several improvements and modifications. There are, however, problems which remain. One problem is that the regulations do not require a minimum probability that contaminated sites will be correctly identified. Another problems is that the effect of testing several correlated constituents on the probable outcome of the statistical tests has not been quantified. Results from computer simulations to determine power functions for realistic monitoring situations are presented here. Power functions for two different statistical procedures: the Student`s t-test, and the multivariate Hotelling`s T{sup 2} test, are compared. The comparisons indicate that the multivariate test is often more powerful when the tests are applied with significance levels to control the probability of falsely identifying clean sites as contaminated. This program could also be used to verify that statistical procedures achieve some minimum power standard at a regulated waste unit.

  6. Augmented Classical Least Squares Multivariate Spectral Analysis

    DOEpatents

    Haaland, David M.; Melgaard, David K.

    2005-01-11

    A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

  7. Augmented Classical Least Squares Multivariate Spectral Analysis

    DOEpatents

    Haaland, David M.; Melgaard, David K.

    2005-07-26

    A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

  8. Augmented classical least squares multivariate spectral analysis

    DOEpatents

    Haaland, David M.; Melgaard, David K.

    2004-02-03

    A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

  9. Benchmarking a reduced multivariate polynomial pattern classifier.

    PubMed

    Toh, Kar-Ann; Tran, Quoc-Long; Srinivasan, Dipti

    2004-06-01

    A novel method using a reduced multivariate polynomial model has been developed for biometric decision fusion where simplicity and ease of use could be a concern. However, much to our surprise, the reduced model was found to have good classification accuracy for several commonly used data sets from the Web. In this paper, we extend the single output model to a multiple outputs model to handle multiple class problems. The method is particularly suitable for problems with small number of features and large number of examples. Basic component of this polynomial model boils down to construction of new pattern features which are sums of the original features and combination of these new and original features using power and product terms. A linear regularized least-squares predictor is then built using these constructed features. The number of constructed feature terms varies linearly with the order of the polynomial, instead of having a power law in the case of full multivariate polynomials. The method is simple as it amounts to only a few lines of Matlab code. We perform extensive experiments on this reduced model using 42 data sets. Our results compared remarkably well with best reported results of several commonly used algorithms from the literature. Both the classification accuracy and efficiency aspects are reported for this reduced model.

  10. An Integrated Multivariable Artificial Pancreas Control System

    PubMed Central

    Turksoy, Kamuran; Quinn, Lauretta T.; Littlejohn, Elizabeth

    2014-01-01

    The objective was to develop a closed-loop (CL) artificial pancreas (AP) control system that uses continuous measurements of glucose concentration and physiological variables, integrated with a hypoglycemia early alarm module to regulate glucose concentration and prevent hypoglycemia. Eleven open-loop (OL) and 9 CL experiments were performed. A multivariable adaptive artificial pancreas (MAAP) system was used for the first 6 CL experiments. An integrated multivariable adaptive artificial pancreas (IMAAP) system consisting of MAAP augmented with a hypoglycemia early alarm system was used during the last 3 CL experiments. Glucose values and physical activity information were measured and transferred to the controller every 10 minutes and insulin suggestions were entered to the pump manually. All experiments were designed to be close to real-life conditions. Severe hypoglycemic episodes were seen several times during the OL experiments. With the MAAP system, the occurrence of severe hypoglycemia was decreased significantly (P < .01). No hypoglycemia was seen with the IMAAP system. There was also a significant difference (P < .01) between OL and CL experiments with regard to percentage of glucose concentration (54% vs 58%) that remained within target range (70-180 mg/dl). Integration of an adaptive control and hypoglycemia early alarm system was able to keep glucose concentration values in target range in patients with type 1 diabetes. Postprandial hypoglycemia and exercise-induced hypoglycemia did not occur when this system was used. Physical activity information improved estimation of the blood glucose concentration and effectiveness of the control system. PMID:24876613

  11. An integrated multivariable artificial pancreas control system.

    PubMed

    Turksoy, Kamuran; Quinn, Lauretta T; Littlejohn, Elizabeth; Cinar, Ali

    2014-05-01

    The objective was to develop a closed-loop (CL) artificial pancreas (AP) control system that uses continuous measurements of glucose concentration and physiological variables, integrated with a hypoglycemia early alarm module to regulate glucose concentration and prevent hypoglycemia. Eleven open-loop (OL) and 9 CL experiments were performed. A multivariable adaptive artificial pancreas (MAAP) system was used for the first 6 CL experiments. An integrated multivariable adaptive artificial pancreas (IMAAP) system consisting of MAAP augmented with a hypoglycemia early alarm system was used during the last 3 CL experiments. Glucose values and physical activity information were measured and transferred to the controller every 10 minutes and insulin suggestions were entered to the pump manually. All experiments were designed to be close to real-life conditions. Severe hypoglycemic episodes were seen several times during the OL experiments. With the MAAP system, the occurrence of severe hypoglycemia was decreased significantly (P < .01). No hypoglycemia was seen with the IMAAP system. There was also a significant difference (P < .01) between OL and CL experiments with regard to percentage of glucose concentration (54% vs 58%) that remained within target range (70-180 mg/dl). Integration of an adaptive control and hypoglycemia early alarm system was able to keep glucose concentration values in target range in patients with type 1 diabetes. Postprandial hypoglycemia and exercise-induced hypoglycemia did not occur when this system was used. Physical activity information improved estimation of the blood glucose concentration and effectiveness of the control system.

  12. Characterization of Lavandula spp. Honey Using Multivariate Techniques

    PubMed Central

    2016-01-01

    Traditionally, melissopalynological and physicochemical analyses have been the most used to determine the botanical origin of honey. However, when performed individually, these analyses may provide less unambiguous results, making it difficult to discriminate between mono and multifloral honeys. In this context, with the aim of better characterizing this beehive product, a selection of 112 Lavandula spp. monofloral honey samples from several regions were evaluated by association of multivariate statistical techniques with physicochemical, melissopalynological and phenolic compounds analysis. All honey samples fulfilled the quality standards recommended by international legislation, except regarding sucrose content and diastase activity. The content of sucrose and the percentage of Lavandula spp. pollen have a strong positive association. In fact, it was found that higher amounts of sucrose in honey are related with highest percentage of pollen of Lavandula spp.. The samples were very similar for most of the physicochemical parameters, except for proline, flavonoids and phenols (bioactive factors). Concerning the pollen spectrum, the variation of Lavandula spp. pollen percentage in honey had little contribution to the formation of samples groups. The formation of two groups regarding the physicochemical parameters suggests that the presence of other pollen types in small percentages influences the factor termed as “bioactive”, which has been linked to diverse beneficial health effects. PMID:27588420

  13. Characterization of Lavandula spp. Honey Using Multivariate Techniques.

    PubMed

    Estevinho, Leticia M; Chambó, Emerson Dechechi; Pereira, Ana Paula Rodrigues; Carvalho, Carlos Alfredo Lopes de; Toledo, Vagner de Alencar Arnaut de

    2016-01-01

    Traditionally, melissopalynological and physicochemical analyses have been the most used to determine the botanical origin of honey. However, when performed individually, these analyses may provide less unambiguous results, making it difficult to discriminate between mono and multifloral honeys. In this context, with the aim of better characterizing this beehive product, a selection of 112 Lavandula spp. monofloral honey samples from several regions were evaluated by association of multivariate statistical techniques with physicochemical, melissopalynological and phenolic compounds analysis. All honey samples fulfilled the quality standards recommended by international legislation, except regarding sucrose content and diastase activity. The content of sucrose and the percentage of Lavandula spp. pollen have a strong positive association. In fact, it was found that higher amounts of sucrose in honey are related with highest percentage of pollen of Lavandula spp.. The samples were very similar for most of the physicochemical parameters, except for proline, flavonoids and phenols (bioactive factors). Concerning the pollen spectrum, the variation of Lavandula spp. pollen percentage in honey had little contribution to the formation of samples groups. The formation of two groups regarding the physicochemical parameters suggests that the presence of other pollen types in small percentages influences the factor termed as "bioactive", which has been linked to diverse beneficial health effects. PMID:27588420

  14. Comparing Predictors in Multivariate Regression Models: An Extension of Dominance Analysis

    ERIC Educational Resources Information Center

    Azen, Razia; Budescu, David V.

    2006-01-01

    Dominance analysis (DA) is a method used to compare the relative importance of predictors in multiple regression. DA determines the dominance of one predictor over another by comparing their additional R[squared] contributions across all subset models. In this article DA is extended to multivariate models by identifying a minimal set of criteria…

  15. An Empirical Bayes Method for Multivariate Meta-analysis with an Application in Clinical Trials

    PubMed Central

    Chen, Yong; Luo, Sheng; Chu, Haitao; Su, Xiao; Nie, Lei

    2013-01-01

    We propose an empirical Bayes method for evaluating overall and study-specific treatment effects in multivariate meta-analysis with binary outcome. Instead of modeling transformed proportions or risks via commonly used multivariate general or generalized linear models, we directly model the risks without any transformation. The exact posterior distribution of the study-specific relative risk is derived. The hyperparameters in the posterior distribution can be inferred through an empirical Bayes procedure. As our method does not rely on the choice of transformation, it provides a flexible alternative to the existing methods and in addition, the correlation parameter can be intuitively interpreted as the correlation coefficient between risks. PMID:25089070

  16. Multivariate analysis applied to tomato hybrid production.

    PubMed

    Balasch, S; Nuez, F; Palomares, G; Cuartero, J

    1984-11-01

    Twenty characters were measured on 60 tomato varieties cultivated in the open-air and in polyethylene plastic-house. Data were analyzed by means of principal components, factorial discriminant methods, Mahalanobis D(2) distances and principal coordinate techniques. Factorial discriminant and Mahalanobis D(2) distances methods, both of which require collecting data plant by plant, lead to similar conclusions as the principal components method that only requires taking data by plots. Characters that make up the principal components in both environments studied are the same, although the relative importance of each one of them varies within the principal components. By combining information supplied by multivariate analysis with the inheritance mode of characters, crossings among cultivars can be experimented with that will produce heterotic hybrids showing characters within previously established limits.

  17. Bayesian Local Contamination Models for Multivariate Outliers

    PubMed Central

    Page, Garritt L.; Dunson, David B.

    2013-01-01

    In studies where data are generated from multiple locations or sources it is common for there to exist observations that are quite unlike the majority. Motivated by the application of establishing a reference value in an inter-laboratory setting when outlying labs are present, we propose a local contamination model that is able to accommodate unusual multivariate realizations in a flexible way. The proposed method models the process level of a hierarchical model using a mixture with a parametric component and a possibly nonparametric contamination. Much of the flexibility in the methodology is achieved by allowing varying random subsets of the elements in the lab-specific mean vectors to be allocated to the contamination component. Computational methods are developed and the methodology is compared to three other possible approaches using a simulation study. We apply the proposed method to a NIST/NOAA sponsored inter-laboratory study which motivated the methodological development. PMID:24363465

  18. Response Surface Modeling Using Multivariate Orthogonal Functions

    NASA Technical Reports Server (NTRS)

    Morelli, Eugene A.; DeLoach, Richard

    2001-01-01

    A nonlinear modeling technique was used to characterize response surfaces for non-dimensional longitudinal aerodynamic force and moment coefficients, based on wind tunnel data from a commercial jet transport model. Data were collected using two experimental procedures - one based on modem design of experiments (MDOE), and one using a classical one factor at a time (OFAT) approach. The nonlinear modeling technique used multivariate orthogonal functions generated from the independent variable data as modeling functions in a least squares context to characterize the response surfaces. Model terms were selected automatically using a prediction error metric. Prediction error bounds computed from the modeling data alone were found to be- a good measure of actual prediction error for prediction points within the inference space. Root-mean-square model fit error and prediction error were less than 4 percent of the mean response value in all cases. Efficacy and prediction performance of the response surface models identified from both MDOE and OFAT experiments were investigated.

  19. Acute proliferative retrolental fibroplasia: multivariate risk analysis.

    PubMed Central

    Flynn, J T

    1983-01-01

    This study has presented a two-way analysis of a data set consisting of demographic, diagnostic, and therapeutic variables against the risk of occurrence of APRLF and its location in the retina in a population of 639 infants in birthweights ranging from 600 to 1500 gm. Univariate and multivariate risk analysis techniques were employed to analyze the data. As established from previous studies, birthweight was a powerful predictor of the outcome variable. Oxygen therapy as defined and quantified in this study was not. Duration of ventilatory assistance did seem associated. The population was not uniform. Infants below 1000 gm birthweight had such a high incidence of APRLF that no other exogenous risk factors seemed of significance. Above 1000 gm birthweight, certain factors, particularly duration of ventilation, seemed of predictive strength and significance. Images FIGURE 5 A FIGURE 5 B FIGURE 4 A FIGURE 4 B PMID:6689564

  20. Inferring phase equations from multivariate time series.

    PubMed

    Tokuda, Isao T; Jain, Swati; Kiss, István Z; Hudson, John L

    2007-08-10

    An approach is presented for extracting phase equations from multivariate time series data recorded from a network of weakly coupled limit cycle oscillators. Our aim is to estimate important properties of the phase equations including natural frequencies and interaction functions between the oscillators. Our approach requires the measurement of an experimental observable of the oscillators; in contrast with previous methods it does not require measurements in isolated single or two-oscillator setups. This noninvasive technique can be advantageous in biological systems, where extraction of few oscillators may be a difficult task. The method is most efficient when data are taken from the nonsynchronized regime. Applicability to experimental systems is demonstrated by using a network of electrochemical oscillators; the obtained phase model is utilized to predict the synchronization diagram of the system.

  1. A new subgrid-scale representation of hydrometeor fields using a multivariate PDF

    NASA Astrophysics Data System (ADS)

    Griffin, Brian M.; Larson, Vincent E.

    2016-06-01

    The subgrid-scale representation of hydrometeor fields is important for calculating microphysical process rates. In order to represent subgrid-scale variability, the Cloud Layers Unified By Binormals (CLUBB) parameterization uses a multivariate probability density function (PDF). In addition to vertical velocity, temperature, and moisture fields, the PDF includes hydrometeor fields. Previously, hydrometeor fields were assumed to follow a multivariate single lognormal distribution. Now, in order to better represent the distribution of hydrometeors, two new multivariate PDFs are formulated and introduced.The new PDFs represent hydrometeors using either a delta-lognormal or a delta-double-lognormal shape. The two new PDF distributions, plus the previous single lognormal shape, are compared to histograms of data taken from large-eddy simulations (LESs) of a precipitating cumulus case, a drizzling stratocumulus case, and a deep convective case. Finally, the warm microphysical process rates produced by the different hydrometeor PDFs are compared to the same process rates produced by the LES.

  2. Multivariate spatial models of excess crash frequency at area level: case of Costa Rica.

    PubMed

    Aguero-Valverde, Jonathan

    2013-10-01

    Recently, areal models of crash frequency have being used in the analysis of various area-wide factors affecting road crashes. On the other hand, disease mapping methods are commonly used in epidemiology to assess the relative risk of the population at different spatial units. A natural next step is to combine these two approaches to estimate the excess crash frequency at area level as a measure of absolute crash risk. Furthermore, multivariate spatial models of crash severity are explored in order to account for both frequency and severity of crashes and control for the spatial correlation frequently found in crash data. This paper aims to extent the concept of safety performance functions to be used in areal models of crash frequency. A multivariate spatial model is used for that purpose and compared to its univariate counterpart. Full Bayes hierarchical approach is used to estimate the models of crash frequency at canton level for Costa Rica. An intrinsic multivariate conditional autoregressive model is used for modeling spatial random effects. The results show that the multivariate spatial model performs better than its univariate counterpart in terms of the penalized goodness-of-fit measure Deviance Information Criteria. Additionally, the effects of the spatial smoothing due to the multivariate spatial random effects are evident in the estimation of excess equivalent property damage only crashes.

  3. On the use of multivariate statistical methods for combining in-stream monitoring data and spatial analysis to characterize water quality conditions in the White River basin, Indiana, USA.

    PubMed

    Gamble, Andrew; Babbar-Sebens, Meghna

    2012-01-01

    Mechanistic hydrologic and water quality models provide useful alternatives for estimating water quality in unmonitored streams. However, developing these elaborate models for large watersheds can be time-consuming and expensive, in addition to challenges that arise during calibration when there is limited spatial and/or temporal monitored in-stream water quality data. The main objective of this research was to investigate different approaches for developing multivariate analysis models as alternative methods for rapidly assessing relationships between spatio-temporal physical attributes of the watershed and water quality conditions in monitored streams, and then using the developed relationships for estimating water quality conditions in unmonitored streams. The study compares the use of various statistical estimates (mean, geometric mean, trimmed mean, and median) of monitored water quality variables to represent annual and seasonal water quality conditions. The relationship between these estimates and the spatial data is then modeled via linear and non-linear multivariate methods. Overall, the non-linear techniques for classification outperformed the linear techniques with an average cross-validation accuracy of 79.7%. Additionally, the geometric mean based models outperformed models based on other statistical indicators with an average cross-validation accuracy of 80.2%. Dividing the data into annual and quarterly datasets also offered important insights into the behavior of certain water quality variables impacted by seasonal variations. The research provides useful guidance on the use and interpretation of the various statistical estimates and statistical models for multivariate water quality analyses.

  4. Multivariate mixtures of Erlangs for density estimation under censoring.

    PubMed

    Verbelen, Roel; Antonio, Katrien; Claeskens, Gerda

    2016-07-01

    Multivariate mixtures of Erlang distributions form a versatile, yet analytically tractable, class of distributions making them suitable for multivariate density estimation. We present a flexible and effective fitting procedure for multivariate mixtures of Erlangs, which iteratively uses the EM algorithm, by introducing a computationally efficient initialization and adjustment strategy for the shape parameter vectors. We furthermore extend the EM algorithm for multivariate mixtures of Erlangs to be able to deal with randomly censored and fixed truncated data. The effectiveness of the proposed algorithm is demonstrated on simulated as well as real data sets.

  5. Partial Least Square Analyses of Landscape and Surface Water Biota Associations in the Savannah River Basin

    EPA Science Inventory

    Ecologists are often faced with problem of small sample size, correlated and large number of predictors, and high noise-to-signal relationships. This necessitates excluding important variables from the model when applying standard multiple or multivariate regression analyses. In ...

  6. Multivariate approach for studying interactions between environmental variables and microbial communities.

    PubMed

    Wang, Xinhui; Eijkemans, Marinus J C; Wallinga, Jacco; Biesbroek, Giske; Trzciński, Krzysztof; Sanders, Elisabeth A M; Bogaert, Debby

    2012-01-01

    To understand the role of human microbiota in health and disease, we need to study effects of environmental and other epidemiological variables on the composition of microbial communities. The composition of a microbial community may depend on multiple factors simultaneously. Therefore we need multivariate methods for detecting, analyzing and visualizing the interactions between environmental variables and microbial communities. We provide two different approaches for multivariate analysis of these complex combined datasets: (i) We select variables that correlate with overall microbiota composition and microbiota members that correlate with the metadata using canonical correlation analysis, determine independency of the observed correlations in a multivariate regression analysis, and visualize the effect size and direction of the observed correlations using heatmaps; (ii) We select variables and microbiota members using univariate or bivariate regression analysis, followed by multivariate regression analysis, and visualize the effect size and direction of the observed correlations using heatmaps. We illustrate the results of both approaches using a dataset containing respiratory microbiota composition and accompanying metadata. The two different approaches provide slightly different results; with approach (i) using canonical correlation analysis to select determinants and microbiota members detecting fewer and stronger correlations only and approach (ii) using univariate or bivariate analyses to select determinants and microbiota members detecting a similar but broader pattern of correlations. The proposed approaches both detect and visualize independent correlations between multiple environmental variables and members of the microbial community. Depending on the size of the datasets and the hypothesis tested one can select the method of preference.

  7. Multivariate crash modeling for motor vehicle and non-motorized modes at the macroscopic level.

    PubMed

    Lee, Jaeyoung; Abdel-Aty, Mohamed; Jiang, Ximiao

    2015-05-01

    Macroscopic traffic crash analyses have been conducted to incorporate traffic safety into long-term transportation planning. This study aims at developing a multivariate Poisson lognormal conditional autoregressive model at the macroscopic level for crashes by different transportation modes such as motor vehicle, bicycle, and pedestrian crashes. Many previous studies have shown the presence of common unobserved factors across different crash types. Thus, it was expected that adopting multivariate model structure would show a better modeling performance since it can capture shared unobserved features across various types. The multivariate model and univariate model were estimated based on traffic analysis zones (TAZs) and compared. It was found that the multivariate model significantly outperforms the univariate model. It is expected that the findings from this study can contribute to more reliable traffic crash modeling, especially when focusing on different modes. Also, variables that are found significant for each mode can be used to guide traffic safety policy decision makers to allocate resources more efficiently for the zones with higher risk of a particular transportation mode.

  8. The Multivariate Largest Lyapunov Exponent as an Age-Related Metric of Quiet Standing Balance

    PubMed Central

    Liu, Kun; Wang, Hongrui; Xiao, Jinzhuang

    2015-01-01

    The largest Lyapunov exponent has been researched as a metric of the balance ability during human quiet standing. However, the sensitivity and accuracy of this measurement method are not good enough for clinical use. The present research proposes a metric of the human body's standing balance ability based on the multivariate largest Lyapunov exponent which can quantify the human standing balance. The dynamic multivariate time series of ankle, knee, and hip were measured by multiple electrical goniometers. Thirty-six normal people of different ages participated in the test. With acquired data, the multivariate largest Lyapunov exponent was calculated. Finally, the results of the proposed approach were analysed and compared with the traditional method, for which the largest Lyapunov exponent and power spectral density from the centre of pressure were also calculated. The following conclusions can be obtained. The multivariate largest Lyapunov exponent has a higher degree of differentiation in differentiating balance in eyes-closed conditions. The MLLE value reflects the overall coordination between multisegment movements. Individuals of different ages can be distinguished by their MLLE values. The standing stability of human is reduced with the increment of age. PMID:26064182

  9. Flexible Linked Axes for multivariate data visualization.

    PubMed

    Claessen, Jarry H T; van Wijk, Jarke J

    2011-12-01

    Multivariate data visualization is a classic topic, for which many solutions have been proposed, each with its own strengths and weaknesses. In standard solutions the structure of the visualization is fixed, we explore how to give the user more freedom to define visualizations. Our new approach is based on the usage of Flexible Linked Axes: The user is enabled to define a visualization by drawing and linking axes on a canvas. Each axis has an associated attribute and range, which can be adapted. Links between pairs of axes are used to show data in either scatter plot- or Parallel Coordinates Plot-style. Flexible Linked Axes enable users to define a wide variety of different visualizations. These include standard methods, such as scatter plot matrices, radar charts, and PCPs [11]; less well known approaches, such as Hyperboxes [1], TimeWheels [17], and many-to-many relational parallel coordinate displays [14]; and also custom visualizations, consisting of combinations of scatter plots and PCPs. Furthermore, our method allows users to define composite visualizations that automatically support brushing and linking. We have discussed our approach with ten prospective users, who found the concept easy to understand and highly promising.

  10. Composite density maps for multivariate trajectories.

    PubMed

    Scheepens, Roeland; Willems, Niels; van de Wetering, Huub; Andrienko, Gennady; Andrienko, Natalia; van Wijk, Jarke J

    2011-12-01

    We consider moving objects as multivariate time-series. By visually analyzing the attributes, patterns may appear that explain why certain movements have occurred. Density maps as proposed by Scheepens et al. [25] are a way to reveal these patterns by means of aggregations of filtered subsets of trajectories. Since filtering is often not sufficient for analysts to express their domain knowledge, we propose to use expressions instead. We present a flexible architecture for density maps to enable custom, versatile exploration using multiple density fields. The flexibility comes from a script, depicted in this paper as a block diagram, which defines an advanced computation of a density field. We define six different types of blocks to create, compose, and enhance trajectories or density fields. Blocks are customized by means of expressions that allow the analyst to model domain knowledge. The versatility of our architecture is demonstrated with several maritime use cases developed with domain experts. Our approach is expected to be useful for the analysis of objects in other domains. PMID:22034373

  11. Apparatus and system for multivariate spectral analysis

    DOEpatents

    Keenan, Michael R.; Kotula, Paul G.

    2003-06-24

    An apparatus and system for determining the properties of a sample from measured spectral data collected from the sample by performing a method of multivariate spectral analysis. The method can include: generating a two-dimensional matrix A containing measured spectral data; providing a weighted spectral data matrix D by performing a weighting operation on matrix A; factoring D into the product of two matrices, C and S.sup.T, by performing a constrained alternating least-squares analysis of D=CS.sup.T, where C is a concentration intensity matrix and S is a spectral shapes matrix; unweighting C and S by applying the inverse of the weighting used previously; and determining the properties of the sample by inspecting C and S. This method can be used by a spectrum analyzer to process X-ray spectral data generated by a spectral analysis system that can include a Scanning Electron Microscope (SEM) with an Energy Dispersive Detector and Pulse Height Analyzer.

  12. Multivariate volume visualization through dynamic projections

    SciTech Connect

    Liu, Shusen; Wang, Bei; Thiagarajan, Jayaraman J.; Bremer, Peer -Timo; Pascucci, Valerio

    2014-11-01

    We propose a multivariate volume visualization framework that tightly couples dynamic projections with a high-dimensional transfer function design for interactive volume visualization. We assume that the complex, high-dimensional data in the attribute space can be well-represented through a collection of low-dimensional linear subspaces, and embed the data points in a variety of 2D views created as projections onto these subspaces. Through dynamic projections, we present animated transitions between different views to help the user navigate and explore the attribute space for effective transfer function design. Our framework not only provides a more intuitive understanding of the attribute space but also allows the design of the transfer function under multiple dynamic views, which is more flexible than being restricted to a single static view of the data. For large volumetric datasets, we maintain interactivity during the transfer function design via intelligent sampling and scalable clustering. As a result, using examples in combustion and climate simulations, we demonstrate how our framework can be used to visualize interesting structures in the volumetric space.

  13. Multivariate sensitivity to voice during auditory categorization

    PubMed Central

    Peelle, Jonathan E.; Kraemer, David; Lloyd, Samuel; Granger, Richard

    2015-01-01

    Past neuroimaging studies have documented discrete regions of human temporal cortex that are more strongly activated by conspecific voice sounds than by nonvoice sounds. However, the mechanisms underlying this voice sensitivity remain unclear. In the present functional MRI study, we took a novel approach to examining voice sensitivity, in which we applied a signal detection paradigm to the assessment of multivariate pattern classification among several living and nonliving categories of auditory stimuli. Within this framework, voice sensitivity can be interpreted as a distinct neural representation of brain activity that correctly distinguishes human vocalizations from other auditory object categories. Across a series of auditory categorization tests, we found that bilateral superior and middle temporal cortex consistently exhibited robust sensitivity to human vocal sounds. Although the strongest categorization was in distinguishing human voice from other categories, subsets of these regions were also able to distinguish reliably between nonhuman categories, suggesting a general role in auditory object categorization. Our findings complement the current evidence of cortical sensitivity to human vocal sounds by revealing that the greatest sensitivity during categorization tasks is devoted to distinguishing voice from nonvoice categories within human temporal cortex. PMID:26245316

  14. Multivariate sensitivity to voice during auditory categorization.

    PubMed

    Lee, Yune Sang; Peelle, Jonathan E; Kraemer, David; Lloyd, Samuel; Granger, Richard

    2015-09-01

    Past neuroimaging studies have documented discrete regions of human temporal cortex that are more strongly activated by conspecific voice sounds than by nonvoice sounds. However, the mechanisms underlying this voice sensitivity remain unclear. In the present functional MRI study, we took a novel approach to examining voice sensitivity, in which we applied a signal detection paradigm to the assessment of multivariate pattern classification among several living and nonliving categories of auditory stimuli. Within this framework, voice sensitivity can be interpreted as a distinct neural representation of brain activity that correctly distinguishes human vocalizations from other auditory object categories. Across a series of auditory categorization tests, we found that bilateral superior and middle temporal cortex consistently exhibited robust sensitivity to human vocal sounds. Although the strongest categorization was in distinguishing human voice from other categories, subsets of these regions were also able to distinguish reliably between nonhuman categories, suggesting a general role in auditory object categorization. Our findings complement the current evidence of cortical sensitivity to human vocal sounds by revealing that the greatest sensitivity during categorization tasks is devoted to distinguishing voice from nonvoice categories within human temporal cortex. PMID:26245316

  15. Computational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learning.

    PubMed

    Davatzikos, Christos

    2016-10-01

    The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges. PMID:27514582

  16. Computational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learning.

    PubMed

    Davatzikos, Christos

    2016-10-01

    The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges.

  17. Comparison of multivariate calibration methods for quantitative spectral analysis

    SciTech Connect

    Thomas, E.V.; Haaland, D.M. )

    1990-05-15

    The quantitative prediction abilities of four multivariate calibration methods for spectral analyses are compared by using extensive Monte Carlo simulations. The calibration methods compared include inverse least-squares (ILS), classical least-squares (CLS), partial least-squares (PLS), and principal component regression (PCR) methods. ILS is a frequency-limited method while the latter three are capable of full-spectrum calibration. The simulations were performed assuming Beer's law holds and that spectral measurement errors and concentration errors associated with the reference method are normally distributed. Eight different factors that could affect the relative performance of the calibration methods were varied in a two-level, eight-factor experimental design in order to evaluate their effect on the prediction abilities of the four methods. It is found that each of the three full-spectrum methods has its range of superior performance. The frequency-limited ILS method was never the best method, although in the presence of relatively large concentration errors it sometimes yields comparable analysis precision to the full-spectrum methods for the major spectral component. The importance of each factor in the absolute and relative performances of the four methods is compared.

  18. Multivariable Techniques for High-Speed Research Flight Control Systems

    NASA Technical Reports Server (NTRS)

    Newman, Brett A.

    1999-01-01

    This report describes the activities and findings conducted under contract with NASA Langley Research Center. Subject matter is the investigation of suitable multivariable flight control design methodologies and solutions for large, flexible high-speed vehicles. Specifically, methodologies are to address the inner control loops used for stabilization and augmentation of a highly coupled airframe system possibly involving rigid-body motion, structural vibrations, unsteady aerodynamics, and actuator dynamics. Design and analysis techniques considered in this body of work are both conventional-based and contemporary-based, and the vehicle of interest is the High-Speed Civil Transport (HSCT). Major findings include: (1) control architectures based on aft tail only are not well suited for highly flexible, high-speed vehicles, (2) theoretical underpinnings of the Wykes structural mode control logic is based on several assumptions concerning vehicle dynamic characteristics, and if not satisfied, the control logic can break down leading to mode destabilization, (3) two-loop control architectures that utilize small forward vanes with the aft tail provide highly attractive and feasible solutions to the longitudinal axis control challenges, and (4) closed-loop simulation sizing analyses indicate the baseline vane model utilized in this report is most likely oversized for normal loading conditions.

  19. Optimal mapping of site-specific multivariate soil properties.

    PubMed

    Burrough, P A; Swindell, J

    1997-01-01

    This paper demonstrates how geostatistics and fuzzy k-means classification can be used together to improve our practical understanding of crop yield-site response. Two aspects of soil are important for precision farming: (a) sensible classes for a given crop, and (b) their spatial variation. Local site classifications are more sensitive than general taxonomies and can be provided by the method of fuzzy k-means to transform a multivariate data set with i attributes measured at n sites into k overlapping classes; each site has a membership value mk for each class in the range 0-1. Soil variation is of interest when conditions vary over patches manageable by agricultural machinery. The spatial variation of each of the k classes can be analysed by computing the variograms of mk over the n sites. Memberships for each of the k classes can be mapped by ordinary kriging. Areas of class dominance and the transition zones between them can be identified by an inter-class confusion index; reducing the zones to boundaries gives crisp maps of dominant soil groups that can be used to guide precision farming equipment. Automation of the procedure is straightforward given sufficient data. Time variations in soil properties can be automatically incorporated in the computation of membership values. The procedures are illustrated with multi-year crop yield data collected from a 5 ha demonstration field at the Royal Agricultural College in Cirencester, UK. PMID:9573478

  20. Determining the Metabolic Footprints of Hydrocarbon Degradation Using Multivariate Analysis

    PubMed Central

    Smith, Renee. J.; Jeffries, Thomas C.; Adetutu, Eric M.; Fairweather, Peter G.; Mitchell, James G.

    2013-01-01

    The functional dynamics of microbial communities are largely responsible for the clean-up of hydrocarbons in the environment. However, knowledge of the distinguishing functional genes, known as the metabolic footprint, present in hydrocarbon-impacted sites is still scarcely understood. Here, we conducted several multivariate analyses to characterise the metabolic footprints present in a variety of hydrocarbon-impacted and non-impacted sediments. Non-metric multi-dimensional scaling (NMDS) and canonical analysis of principal coordinates (CAP) showed a clear distinction between the two groups. A high relative abundance of genes associated with cofactors, virulence, phages and fatty acids were present in the non-impacted sediments, accounting for 45.7 % of the overall dissimilarity. In the hydrocarbon-impacted sites, a high relative abundance of genes associated with iron acquisition and metabolism, dormancy and sporulation, motility, metabolism of aromatic compounds and cell signalling were observed, accounting for 22.3 % of the overall dissimilarity. These results suggest a major shift in functionality has occurred with pathways essential to the degradation of hydrocarbons becoming overrepresented at the expense of other, less essential metabolisms. PMID:24282619

  1. A general, multivariate definition of causal effects in epidemiology.

    PubMed

    Flanders, W Dana; Klein, Mitchel

    2015-07-01

    Population causal effects are often defined as contrasts of average individual-level counterfactual outcomes, comparing different exposure levels. Common examples include causal risk difference and risk ratios. These and most other examples emphasize effects on disease onset, a reflection of the usual epidemiological interest in disease occurrence. Exposure effects on other health characteristics, such as prevalence or conditional risk of a particular disability, can be important as well, but contrasts involving these other measures may often be dismissed as non-causal. For example, an observed prevalence ratio might often viewed as an estimator of a causal incidence ratio and hence subject to bias. In this manuscript, we provide and evaluate a definition of causal effects that generalizes those previously available. A key part of the generalization is that contrasts used in the definition can involve multivariate, counterfactual outcomes, rather than only univariate outcomes. An important consequence of our generalization is that, using it, one can properly define causal effects based on a wide variety of additional measures. Examples include causal prevalence ratios and differences and causal conditional risk ratios and differences. We illustrate how these additional measures can be useful, natural, easily estimated, and of public health importance. Furthermore, we discuss conditions for valid estimation of each type of causal effect, and how improper interpretation or inferences for the wrong target population can be sources of bias.

  2. Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

    ERIC Educational Resources Information Center

    Price, Larry R.

    2012-01-01

    The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…

  3. Evaluating Univariate, Bivariate, and Multivariate Normality Using Graphical Procedures.

    ERIC Educational Resources Information Center

    Burdenski, Thomas K., Jr.

    This paper reviews graphical and nongraphical procedures for evaluating multivariate normality by guiding the reader through univariate and bivariate procedures that are necessary, but insufficient, indications of a multivariate normal distribution. A data set using three dependent variables for two groups provided by D. George and P. Mallery…

  4. Simulating Multivariate Nonnormal Data Using an Iterative Algorithm

    ERIC Educational Resources Information Center

    Ruscio, John; Kaczetow, Walter

    2008-01-01

    Simulating multivariate nonnormal data with specified correlation matrices is difficult. One especially popular method is Vale and Maurelli's (1983) extension of Fleishman's (1978) polynomial transformation technique to multivariate applications. This requires the specification of distributional moments and the calculation of an intermediate…

  5. Exploratory Multivariate Analysis of Variance: Contrasts and Variables.

    ERIC Educational Resources Information Center

    Barcikowski, Robert S.; Elliott, Ronald S.

    The contribution of individual variables to overall multivariate significance in a multivariate analysis of variance (MANOVA) is investigated using a combination of canonical discriminant analysis and Roy-Bose simultaneous confidence intervals. Difficulties with this procedure are discussed, and its advantages are illustrated using examples based…

  6. Bioharness™ Multivariable Monitoring Device: Part. II: Reliability

    PubMed Central

    Johnstone, James A.; Ford, Paul A.; Hughes, Gerwyn; Watson, Tim; Garrett, Andrew T.

    2012-01-01

    The Bioharness™ monitoring system may provide physiological information on human performance but the reliability of this data is fundamental for confidence in the equipment being used. The objective of this study was to assess the reliability of each of the 5 Bioharness™ variables using a treadmill based protocol. 10 healthy males participated. A between and within subject design to assess the reliability of Heart rate (HR), Breathing Frequency (BF), Accelerometry (ACC) and Infra-red skin temperature (ST) was completed via a repeated, discontinuous, incremental treadmill protocol. Posture (P) was assessed by a tilt table, moved through 160°. Between subject data reported low Coefficient of Variation (CV) and strong correlations(r) for ACC and P (CV< 7.6; r = 0.99, p < 0.01). In contrast, HR and BF (CV~19.4; r~0.70, p < 0.01) and ST (CV 3.7; r = 0.61, p < 0.01), present more variable data. Intra and inter device data presented strong relationships (r > 0.89, p < 0.01) and low CV (<10.1) for HR, ACC, P and ST. BF produced weaker relationships (r < 0.72) and higher CV (<17.4). In comparison to the other variables BF variable consistently presents less reliability. Global results suggest that the Bioharness™ is a reliable multivariable monitoring device during laboratory testing within the limits presented. Key pointsHeart rate and breathing frequency data increased in variance at higher velocities (i.e. ≥ 10 km.h-1)In comparison to the between subject testing, the intra and inter reliability presented good reliability in data suggesting placement or position of device relative to performer could be important for data collectionUnderstanding a devices variability in measurement is important before it can be used within an exercise testing or monitoring setting PMID:24149347

  7. ibr: Iterative bias reduction multivariate smoothing

    SciTech Connect

    Hengartner, Nicholas W; Cornillon, Pierre-andre; Matzner - Lober, Eric

    2009-01-01

    Regression is a fundamental data analysis tool for relating a univariate response variable Y to a multivariate predictor X {element_of} E R{sup d} from the observations (X{sub i}, Y{sub i}), i = 1,...,n. Traditional nonparametric regression use the assumption that the regression function varies smoothly in the independent variable x to locally estimate the conditional expectation m(x) = E[Y|X = x]. The resulting vector of predicted values {cflx Y}{sub i} at the observed covariates X{sub i} is called a regression smoother, or simply a smoother, because the predicted values {cflx Y}{sub i} are less variable than the original observations Y{sub i}. Linear smoothers are linear in the response variable Y and are operationally written as {cflx m} = X{sub {lambda}}Y, where S{sub {lambda}} is a n x n smoothing matrix. The smoothing matrix S{sub {lambda}} typically depends on a tuning parameter which we denote by {lambda}, and that governs the tradeoff between the smoothness of the estimate and the goodness-of-fit of the smoother to the data by controlling the effective size of the local neighborhood over which the responses are averaged. We parameterize the smoothing matrix such that large values of {lambda} are associated to smoothers that averages over larger neighborhood and produce very smooth curves, while small {lambda} are associated to smoothers that average over smaller neighborhood to produce a more wiggly curve that wants to interpolate the data. The parameter {lambda} is the bandwidth for kernel smoother, the span size for running-mean smoother, bin smoother, and the penalty factor {lambda} for spline smoother.

  8. Sequential Linker Installation: Precise Placement of Functional Groups in Multivariate Metal-Organic Frameworks

    SciTech Connect

    Yuan, S; Lu, WG; Chen, YP; Zhang, Q; Liu, TF; Feng, DW; Wang, X; Qin, JS; Zhou, HC

    2015-03-11

    A unique strategy, sequential linker installation (SLI), has been developed to construct multivariate MOFs with functional groups precisely positioned. PCN-700, a Zr-MOF with eight-connected Zr6O4(OH)(8)(H2O)(4) clusters, has been judiciously designed; the Zr-6 clusters in this MOF are arranged in such a fashion that, by replacement of terminal OH-/H2O ligands, subsequent insertion of linear dicarboxylate linkers is achieved. We demonstrate that linkers with distinct lengths and functionalities can be sequentially installed into PCN-700. Single-crystal to single-crystal transformation is realized so that the positions of the subsequently installed linkers are pinpointed via single-crystal X-ray diffraction analyses. This methodology provides a powerful tool to construct multivariate MOFs with precisely positioned functionalities in the desired proximity, which would otherwise be difficult to achieve.

  9. The potential of circulating extracellular small RNAs (smexRNA) in veterinary diagnostics—Identifying biomarker signatures by multivariate data analysis

    PubMed Central

    Melanie, Spornraft; Benedikt, Kirchner; Pfaffl, Michael W.; Irmgard, Riedmaier

    2015-01-01

    Worldwide growth and performance-enhancing substances are used in cattle husbandry to increase productivity. In certain countries however e.g., in the EU, these practices are forbidden to prevent the consumers from potential health risks of substance residues in food. To maximize economic profit, ‘black sheep‘ among farmers might circumvent the detection methods used in routine controls, which highlights the need for an innovative and reliable detection method. Transcriptomics is a promising new approach in the discovery of veterinary medicine biomarkers and also a missing puzzle piece, as up to date, metabolomics and proteomics are paramount. Due to increased stability and easy sampling, circulating extracellular small RNAs (smexRNAs) in bovine plasma were small RNA-sequenced and their potential to serve as biomarker candidates was evaluated using multivariate data analysis tools. After running the data evaluation pipeline, the proportion of miRNAs (microRNAs) and piRNAs (PIWI-interacting small non-coding RNAs) on the total sequenced reads was calculated. Additionally, top 10 signatures were compared which revealed that the readcount data sets were highly affected by the most abundant miRNA and piRNA profiles. To evaluate the discriminative power of multivariate data analyses to identify animals after veterinary drug application on the basis of smexRNAs, OPLS-DA was performed. In summary, the quality of miRNA models using all mapped reads for both treatment groups (animals treated with steroid hormones or the β-agonist clenbuterol) is predominant to those generated with combined data sets or piRNAs alone. Using multivariate projection methodologies like OPLS-DA have proven the best potential to generate discriminative miRNA models, supported by small RNA-Seq data. Based on the presented comparative OPLS-DA, miRNAs are the favorable smexRNA biomarker candidates in the research field of veterinary drug abuse. PMID:27077039

  10. The potential of circulating extracellular small RNAs (smexRNA) in veterinary diagnostics-Identifying biomarker signatures by multivariate data analysis.

    PubMed

    Melanie, Spornraft; Benedikt, Kirchner; Pfaffl, Michael W; Irmgard, Riedmaier

    2015-09-01

    Worldwide growth and performance-enhancing substances are used in cattle husbandry to increase productivity. In certain countries however e.g., in the EU, these practices are forbidden to prevent the consumers from potential health risks of substance residues in food. To maximize economic profit, 'black sheep' among farmers might circumvent the detection methods used in routine controls, which highlights the need for an innovative and reliable detection method. Transcriptomics is a promising new approach in the discovery of veterinary medicine biomarkers and also a missing puzzle piece, as up to date, metabolomics and proteomics are paramount. Due to increased stability and easy sampling, circulating extracellular small RNAs (smexRNAs) in bovine plasma were small RNA-sequenced and their potential to serve as biomarker candidates was evaluated using multivariate data analysis tools. After running the data evaluation pipeline, the proportion of miRNAs (microRNAs) and piRNAs (PIWI-interacting small non-coding RNAs) on the total sequenced reads was calculated. Additionally, top 10 signatures were compared which revealed that the readcount data sets were highly affected by the most abundant miRNA and piRNA profiles. To evaluate the discriminative power of multivariate data analyses to identify animals after veterinary drug application on the basis of smexRNAs, OPLS-DA was performed. In summary, the quality of miRNA models using all mapped reads for both treatment groups (animals treated with steroid hormones or the β-agonist clenbuterol) is predominant to those generated with combined data sets or piRNAs alone. Using multivariate projection methodologies like OPLS-DA have proven the best potential to generate discriminative miRNA models, supported by small RNA-Seq data. Based on the presented comparative OPLS-DA, miRNAs are the favorable smexRNA biomarker candidates in the research field of veterinary drug abuse.

  11. Conventional univariate versus multivariate spectrophotometric assisted techniques for simultaneous determination of perindopril arginin and amlodipine besylate in presence of their degradation products.

    PubMed

    Hegazy, Maha A; Abbas, Samah S; Zaazaa, Hala E; Essam, Hebatallah M

    2015-01-01

    The resolving power of spectrophotometric assisted mathematical techniques were demonstrated for the simultaneous determination of perindopril arginin (PER) and amlodipine besylate (AML) in presence of their degradation products. The conventional univariate methods include the absorptivity factor method (AFM) and absorption correction method (ACM), which were able to determine the two drugs, simultaneously, but not in the presence of their degradation products. In both methods, amlodipine was determined directly at 360 nm in the concentration range of 8-28 μg mL(-1), on the other hand perindopril was determined by AFM at 222.2 nm and by ACM at 208 nm in the concentration range of 10-70 μg mL(-1). Moreover, the applied multivariate calibration methods were able for the determination of perindopril and amlodipine in presence of their degradation products using concentration residuals augmented classical least squares (CRACLS) and partial least squares (PLS). The proposed multivariate methods were applied to 19 synthetic samples in the concentration ranges of 60-100 μg mL(-1) perindopril and 20-40 μg mL(-1) amlodipine. Commercially available tablet formulations were successfully analysed using the developed methods without interference from other dosage form additives except PLS model, which failed to determine both drugs in their pharmaceutical dosage form. PMID:26123511

  12. Design of reduced-order state estimators for linear time-varying multivariable systems

    NASA Technical Reports Server (NTRS)

    Nguyen, Charles C.

    1987-01-01

    The design of reduced-order state estimators for linear time-varying multivariable systems is considered. Employing the concepts of matrix operators and the method of canonical transformations, this paper shows that there exists a reduced-order state estimator for linear time-varying systems that are 'lexicography-fixedly observable'. In addition, the eigenvalues of the estimator can be arbitrarily assigned. A simple algorithm is proposed for the design of the state estimator.

  13. Measuring and comparing evolvability and constraint in multivariate characters.

    PubMed

    Hansen, T F; Houle, D

    2008-09-01

    The Lande equation forms the basis for our understanding of the short-term evolution of quantitative traits in a multivariate context. It predicts the response to selection as the product of an additive genetic variance matrix and a selection gradient. The selection gradient approximates the force and direction of selection, and the genetic variance matrix quantifies the role of the genetic system in evolution. Attempts to understand the evolutionary significance of the genetic variance matrix are hampered by the fact that the majority of the methods used to characterize and compare variance matrices have not been derived in an explicit theoretical context. We use the Lande equation to derive new measures of the ability of a variance matrix to allow or constrain evolution in any direction in phenotype space. Evolvability captures the ability of a population to evolve in the direction of selection when stabilizing selection is absent. Conditional evolvability captures the ability of a population to respond to directional selection in the presence of stabilizing selection on other trait combinations. We then derive measures of character autonomy and integration from these evolvabilities. We study the properties of these measures and show how they can be used to interpret and compare variance matrices. As an illustration, we show that divergence of wing shape in the dipteran family Drosophilidae has proceeded in directions that have relatively high evolvabilities.

  14. Insights into multivariate calibration using errors-in-variables modeling

    SciTech Connect

    Thomas, E.V.

    1996-09-01

    A {ital q}-vector of responses, y, is related to a {ital p}-vector of explanatory variables, x, through a causal linear model. In analytical chemistry, y and x might represent the spectrum and associated set of constituent concentrations of a multicomponent sample which are related through Beer`s law. The model parameters are estimated during a calibration process in which both x and y are available for a number of observations (samples/specimens) which are collectively referred to as the calibration set. For new observations, the fitted calibration model is then used as the basis for predicting the unknown values of the new x`s (concentrations) form the associated new y`s (spectra) in the prediction set. This prediction procedure can be viewed as parameter estimation in an errors-in-variables (EIV) framework. In addition to providing a basis for simultaneous inference about the new x`s, consideration of the EIV framework yields a number of insights relating to the design and execution of calibration studies. A particularly interesting result is that predictions of the new x`s for individual samples can be improved by using seemingly unrelated information contained in the y`s from the other members of the prediction set. Furthermore, motivated by this EIV analysis, this result can be extended beyond the causal modeling context to a broader range of applications of multivariate calibration which involve the use of principal components regression.

  15. A general framework for multivariate multi-index drought prediction based on Multivariate Ensemble Streamflow Prediction (MESP)

    NASA Astrophysics Data System (ADS)

    Hao, Zengchao; Hao, Fanghua; Singh, Vijay P.

    2016-08-01

    Drought is among the costliest natural hazards worldwide and extreme drought events in recent years have caused huge losses to various sectors. Drought prediction is therefore critically important for providing early warning information to aid decision making to cope with drought. Due to the complicated nature of drought, it has been recognized that the univariate drought indicator may not be sufficient for drought characterization and hence multivariate drought indices have been developed for drought monitoring. Alongside the substantial effort in drought monitoring with multivariate drought indices, it is of equal importance to develop a drought prediction method with multivariate drought indices to integrate drought information from various sources. This study proposes a general framework for multivariate multi-index drought prediction that is capable of integrating complementary prediction skills from multiple drought indices. The Multivariate Ensemble Streamflow Prediction (MESP) is employed to sample from historical records for obtaining statistical prediction of multiple variables, which is then used as inputs to achieve multivariate prediction. The framework is illustrated with a linearly combined drought index (LDI), which is a commonly used multivariate drought index, based on climate division data in California and New York in the United States with different seasonality of precipitation. The predictive skill of LDI (represented with persistence) is assessed by comparison with the univariate drought index and results show that the LDI prediction skill is less affected by seasonality than the meteorological drought prediction based on SPI. Prediction results from the case study show that the proposed multivariate drought prediction outperforms the persistence prediction, implying a satisfactory performance of multivariate drought prediction. The proposed method would be useful for drought prediction to integrate drought information from various sources

  16. Describing the Elephant: Structure and Function in Multivariate Data.

    ERIC Educational Resources Information Center

    McDonald, Roderick P.

    1986-01-01

    There is a unity underlying the diversity of models for the analysis of multivariate data. Essentially, they constitute a family of models, most generally nonlinear, for structural/functional relations between variables drawn from a behavior domain. (Author)

  17. A unifying modeling framework for highly multivariate disease mapping.

    PubMed

    Botella-Rocamora, P; Martinez-Beneito, M A; Banerjee, S

    2015-04-30

    Multivariate disease mapping refers to the joint mapping of multiple diseases from regionally aggregated data and continues to be the subject of considerable attention for biostatisticians and spatial epidemiologists. The key issue is to map multiple diseases accounting for any correlations among themselves. Recently, Martinez-Beneito (2013) provided a unifying framework for multivariate disease mapping. While attractive in that it colligates a variety of existing statistical models for mapping multiple diseases, this and other existing approaches are computationally burdensome and preclude the multivariate analysis of moderate to large numbers of diseases. Here, we propose an alternative reformulation that accrues substantial computational benefits enabling the joint mapping of tens of diseases. Furthermore, the approach subsumes almost all existing classes of multivariate disease mapping models and offers substantial insight into the properties of statistical disease mapping models. PMID:25645551

  18. Multivariate Generalizability Models for Tests Developed from Tables of Specifications.

    ERIC Educational Resources Information Center

    Jarjoura, David; Brennan, Robert L.

    1983-01-01

    Multivariate generalizability techniques are used to bridge the gap between psychometric constraints and the tables of specifications needed in test development. Techniques are illustrated with results from the American College Testing Assessment Program. (Author/PN)

  19. A unifying modeling framework for highly multivariate disease mapping.

    PubMed

    Botella-Rocamora, P; Martinez-Beneito, M A; Banerjee, S

    2015-04-30

    Multivariate disease mapping refers to the joint mapping of multiple diseases from regionally aggregated data and continues to be the subject of considerable attention for biostatisticians and spatial epidemiologists. The key issue is to map multiple diseases accounting for any correlations among themselves. Recently, Martinez-Beneito (2013) provided a unifying framework for multivariate disease mapping. While attractive in that it colligates a variety of existing statistical models for mapping multiple diseases, this and other existing approaches are computationally burdensome and preclude the multivariate analysis of moderate to large numbers of diseases. Here, we propose an alternative reformulation that accrues substantial computational benefits enabling the joint mapping of tens of diseases. Furthermore, the approach subsumes almost all existing classes of multivariate disease mapping models and offers substantial insight into the properties of statistical disease mapping models.

  20. Constructing multivariate distributions with generalized marginals and t-copulas

    NASA Astrophysics Data System (ADS)

    Dass, Sarat C.; Huang, Wenmei; Muthuvalu, Mohana S.

    2014-10-01

    Generalized distributions are probability distributions that have both discrete and continuous components. In this paper, a method is proposed for constructing flexible multivariate distributions based on arbitrarily pre-specified generalized marginals and t-copulas. We give theoretical results establishing identifiability of the parameters of the multivariate distribution. These distributions are useful for modeling real data that show non-Gaussian characteristics such as disease trajectories (i.e., malaria and dengue) over time and space.

  1. Pattern recognition used to investigate multivariate data in analytical chemistry

    SciTech Connect

    Jurs, P.C.

    1986-06-06

    Pattern recognition and allied multivariate methods provide an approach to the interpretation of the multivariate data often encountered in analytical chemistry. Widely used methods include mapping and display, discriminant development, clustering, and modeling. Each has been applied to a variety of chemical problems, and examples are given. The results of two recent studies are shown, a classification of subjects as normal or cystic fibrosis heterozygotes and simulation of chemical shifts of carbon-13 nuclear magnetic resonance spectra by linear model equations.

  2. Multivariate analysis of environmental data for two hydrographic basins

    SciTech Connect

    Andrade, J.M.; Prada, D.; Muniategui, S.; Gonzalez, E.; Alonso, E. )

    1992-02-01

    A multivariate study (PCA Analysis and Cluster analysis) of two Spanish hydrographic basins (The Mandeo and Mero basins) was made to achieve reliable conclusions about their actual physico-chemical environmental situation. Two police-samples' are defined, their effects explained, and are introduced in Cluster analysis as a way to examine sample quality. The multivariate analysis shows different qualities in the two hydrographic basins.

  3. Classification of worldwide drainage basins through the multivariate analysis of variables controlling their hydrosedimentary response

    NASA Astrophysics Data System (ADS)

    Raux, Julie; Copard, Yoann; Laignel, Benoît; Fournier, Matthieu; Masseï, Nicolas

    2011-04-01

    Quality and amount of waters and sediments conveyed within large drainage basins are crucial for human societies and biodiversity concerns. This work aims to determine the factors controlling the hydrosedimentary response (water discharge and sediment load) of 24 worldwide large drainage basins. In this respect, eleven geomorphologic and climatic variables routinely used in the literature were considered and others as fractal dimension, elongation and mean channel slope are novel for such an issue. In addition, two variables, land cover and lithology indexes, somewhat different from the literature in terms of calculation principles, were also included. All these variables were then subjected to multivariate statistical analyses (CA and PCA) and confronted in a matrix correlation. On the whole, our results display that water discharge is controlled by runoff, precipitation, basin area, elongation and fractal dimension while sediment load is governed by runoff, precipitation and maximum elevation. Mean channel slope and land-use have a minor role while other parameters (hypsometry, lithology, length, slope, mean elevation and temperature) do not play a significant role in the hydrosedimentary response. Such statistical analyses also bring out a classification of these drainage basins, comprising five to six main clusters which are ranged according to the main variables ruling their hydrosedimentary response. Two clusters are essentially governed by geomorphometric parameters (area, elongation, fractal dimension, mean elevation and hypsometry) while one cluster is rather controlled by transfer processes (runoff) and by active tectonic (maximum elevation). Hydrosedimentary response of arctic and continental rivers is controlled by low temperature while two drainage basins show any trend. A comparison of our results with other previous works dealing with this same issue points to some significant disagreements essentially based on the number of drainage basins

  4. Retrieval of tea polyphenol at leaf level using spectral transformation and multi-variate statistical approach

    NASA Astrophysics Data System (ADS)

    Dutta, Dibyendu; Das, Prabir Kumar; Bhunia, Uttam Kumar; Singh, Upasana; Singh, Shalini; Sharma, Jaswant Raj; Dadhwal, Vinay Kumar

    2015-04-01

    In the present study, field based hyperspectral data was used to estimate the tea (Camellia sinensis L.) polyphenol at Deha Tea garden of Assam state, India. Leaf reflectance spectra were first filtered for noise and then transformed into normalized and first derivative reflectance for further analysis. Stepwise discriminant analysis was carried out to select sensitive bands for a range of polyphenol concentration by minimizing the effects of other factors such as age of the bushes and management practices. The wavelengths at 358, 369, 484, 845, 916, 1387, 1420, 1435, 1621 and 2294 nm were identified as sensitive to tea polyphenol, among which 2294 nm was found to be the most recurring band. The noise removed selected bands, their transformed derivatives and principal components were regressed with the tea polyphenol using univariate and multi-variate analysis. In univariate analysis the correlation was very poor with RMSE more than 3.0. A significant improvement in R2 values were observed when multivariate analyses like stepwise multiple linear regression (SMLR) and partial least square regression (PLSR) was carried out. The PLSR of first derivative reflectance was most accurate (R2 = 0.81 and RMSE = 1.39 mg g-1) among all the uni- and multivariate analysis for predicting the polyphenol of fresh tea leaves.

  5. Epithelial cells in bone marrow of oesophageal cancer patients: a significant prognostic factor in multivariate analysis

    PubMed Central

    Thorban, S; Rosenberg, R; Busch, R; Roder, R J

    2000-01-01

    The detection of epithelial cells in bone marrow, blood or lymph nodes indicates a disseminatory potential of solid tumours. 225 patients with squamous cell carcinoma of the oesophagus were prospectively studied. Prior to any therapy, cytokeratin-positive (CK) cells in bone marrow were immunocytochemically detected in 75 patients with the monoclonal anti-epithelial-cell antibody A45-B/B3 and correlated with established histopathologic and patient-specific prognosis factors. The prognosis factors were assessed by multivariate analysis. Twenty-nine of 75 (38.7%) patients with oesophageal cancer showed CK-positive cells in bone marrow. The analyses of the mean and median overall survival time showed a significant difference between patients with and without epithelial cells in bone marrow (P< 0.001). Multivariate analysis in the total patient population and in patients with curative resection of the primary tumour confirmed the curative resection rate and the bone marrow status as the strongest independent prognostic factors, besides the T-category. The detection of epithelial cells in bone marrow of oesophageal cancer patients is a substantial prognostic factor proved by multivariate analysis and is helpful for exact preoperative staging, as well as monitoring of neoadjuvant therapy. © 2000 Cancer Research Campaign PMID:10883665

  6. On set-valued functionals: Multivariate risk measures and Aumann integrals

    NASA Astrophysics Data System (ADS)

    Ararat, Cagin

    particular, it is shown that a shortfall risk measure can be written as an intersection over a family of divergence risk measures indexed by a scalarization parameter. Examples include the multivariate versions of the entropic risk measure and the average value at risk. In the second part, Aumann integrals of set-valued functions on a measurable space are viewed as set-valued functionals and a Daniell-Stone type characterization theorem is proved for such functionals. More precisely, it is shown that a functional that maps measurable set-valued functions into a certain complete lattice of subsets of Rm can be written as the Aumann integral with respect to a measure if and only if the functional is (1) additive and (2) positively homogeneous, (3) it preserves decreasing limits, (4) it maps halfspace-valued functions to halfspaces, and (5) it maps shifted cone-valued functions to shifted cones. While the first three properties already exist in the classical Daniell-Stone theorem for the Lebesgue integral, the last two properties are peculiar to the set-valued framework and they suffice to complement the first three properties to identify a set-valued functional as the Aumann integral with respect to a measure.

  7. A parameter-tuned genetic algorithm for statistically constrained economic design of multivariate CUSUM control charts: a Taguchi loss approach

    NASA Astrophysics Data System (ADS)

    Niaki, Seyed Taghi Akhavan; Javad Ershadi, Mohammad

    2012-12-01

    In this research, the main parameters of the multivariate cumulative sum (CUSUM) control chart (the reference value k, the control limit H, the sample size n and the sampling interval h) are determined by minimising the Lorenzen-Vance cost function [Lorenzen, T.J., and Vance, L.C. (1986), 'The Economic Design of Control Charts: A Unified Approach', Technometrics, 28, 3-10], in which the external costs of employing the chart are added. In addition, the model is statistically constrained to achieve desired in-control and out-of-control average run lengths. The Taguchi loss approach is used to model the problem and a genetic algorithm, for which its main parameters are tuned using the response surface methodology (RSM), is proposed to solve it. At the end, sensitivity analyses on the main parameters of the cost function are presented and their practical conclusions are drawn. The results show that RSM significantly improves the performance of the proposed algorithm and the external costs of applying the chart, which are due to real-world constraints, do not increase the average total loss very much.

  8. Predictive analysis of beer quality by correlating sensory evaluation with higher alcohol and ester production using multivariate statistics methods.

    PubMed

    Dong, Jian-Jun; Li, Qing-Liang; Yin, Hua; Zhong, Cheng; Hao, Jun-Guang; Yang, Pan-Fei; Tian, Yu-Hong; Jia, Shi-Ru

    2014-10-15

    Sensory evaluation is regarded as a necessary procedure to ensure a reproducible quality of beer. Meanwhile, high-throughput analytical methods provide a powerful tool to analyse various flavour compounds, such as higher alcohol and ester. In this study, the relationship between flavour compounds and sensory evaluation was established by non-linear models such as partial least squares (PLS), genetic algorithm back-propagation neural network (GA-BP), support vector machine (SVM). It was shown that SVM with a Radial Basis Function (RBF) had a better performance of prediction accuracy for both calibration set (94.3%) and validation set (96.2%) than other models. Relatively lower prediction abilities were observed for GA-BP (52.1%) and PLS (31.7%). In addition, the kernel function of SVM played an essential role of model training when the prediction accuracy of SVM with polynomial kernel function was 32.9%. As a powerful multivariate statistics method, SVM holds great potential to assess beer quality. PMID:24837965

  9. Predictive analysis of beer quality by correlating sensory evaluation with higher alcohol and ester production using multivariate statistics methods.

    PubMed

    Dong, Jian-Jun; Li, Qing-Liang; Yin, Hua; Zhong, Cheng; Hao, Jun-Guang; Yang, Pan-Fei; Tian, Yu-Hong; Jia, Shi-Ru

    2014-10-15

    Sensory evaluation is regarded as a necessary procedure to ensure a reproducible quality of beer. Meanwhile, high-throughput analytical methods provide a powerful tool to analyse various flavour compounds, such as higher alcohol and ester. In this study, the relationship between flavour compounds and sensory evaluation was established by non-linear models such as partial least squares (PLS), genetic algorithm back-propagation neural network (GA-BP), support vector machine (SVM). It was shown that SVM with a Radial Basis Function (RBF) had a better performance of prediction accuracy for both calibration set (94.3%) and validation set (96.2%) than other models. Relatively lower prediction abilities were observed for GA-BP (52.1%) and PLS (31.7%). In addition, the kernel function of SVM played an essential role of model training when the prediction accuracy of SVM with polynomial kernel function was 32.9%. As a powerful multivariate statistics method, SVM holds great potential to assess beer quality.

  10. Multivariate analysis of progressive thermal desorption coupled gas chromatography-mass spectrometry.

    SciTech Connect

    Van Benthem, Mark Hilary; Mowry, Curtis Dale; Kotula, Paul Gabriel; Borek, Theodore Thaddeus, III

    2010-09-01

    Thermal decomposition of poly dimethyl siloxane compounds, Sylgard{reg_sign} 184 and 186, were examined using thermal desorption coupled gas chromatography-mass spectrometry (TD/GC-MS) and multivariate analysis. This work describes a method of producing multiway data using a stepped thermal desorption. The technique involves sequentially heating a sample of the material of interest with subsequent analysis in a commercial GC/MS system. The decomposition chromatograms were analyzed using multivariate analysis tools including principal component analysis (PCA), factor rotation employing the varimax criterion, and multivariate curve resolution. The results of the analysis show seven components related to offgassing of various fractions of siloxanes that vary as a function of temperature. Thermal desorption coupled with gas chromatography-mass spectrometry (TD/GC-MS) is a powerful analytical technique for analyzing chemical mixtures. It has great potential in numerous analytic areas including materials analysis, sports medicine, in the detection of designer drugs; and biological research for metabolomics. Data analysis is complicated, far from automated and can result in high false positive or false negative rates. We have demonstrated a step-wise TD/GC-MS technique that removes more volatile compounds from a sample before extracting the less volatile compounds. This creates an additional dimension of separation before the GC column, while simultaneously generating three-way data. Sandia's proven multivariate analysis methods, when applied to these data, have several advantages over current commercial options. It also has demonstrated potential for success in finding and enabling identification of trace compounds. Several challenges remain, however, including understanding the sources of noise in the data, outlier detection, improving the data pretreatment and analysis methods, developing a software tool for ease of use by the chemist, and demonstrating our belief that

  11. F100 multivariable control synthesis program: Evaluation of a multivariable control using a real-time engine simulation

    NASA Technical Reports Server (NTRS)

    Szuch, J. R.; Soeder, J. F.; Seldner, K.; Cwynar, D. S.

    1977-01-01

    The design, evaluation, and testing of a practical, multivariable, linear quadratic regulator control for the F100 turbofan engine were accomplished. NASA evaluation of the multivariable control logic and implementation are covered. The evaluation utilized a real time, hybrid computer simulation of the engine. Results of the evaluation are presented, and recommendations concerning future engine testing of the control are made. Results indicated that the engine testing of the control should be conducted as planned.

  12. Multivariate Analysis, Retrieval, and Storage System (MARS). Volume 1: MARS System and Analysis Techniques

    NASA Technical Reports Server (NTRS)

    Hague, D. S.; Vanderberg, J. D.; Woodbury, N. W.

    1974-01-01

    A method for rapidly examining the probable applicability of weight estimating formulae to a specific aerospace vehicle design is presented. The Multivariate Analysis Retrieval and Storage System (MARS) is comprised of three computer programs which sequentially operate on the weight and geometry characteristics of past aerospace vehicles designs. Weight and geometric characteristics are stored in a set of data bases which are fully computerized. Additional data bases are readily added to the MARS system and/or the existing data bases may be easily expanded to include additional vehicles or vehicle characteristics.

  13. Fluorescence measurements for evaluating the application of multivariate analysis techniques to optically thick environments.

    SciTech Connect

    Reichardt, Thomas A.; Timlin, Jerilyn Ann; Jones, Howland D. T.; Sickafoose, Shane M.; Schmitt, Randal L.

    2010-09-01

    Laser-induced fluorescence measurements of cuvette-contained laser dye mixtures are made for evaluation of multivariate analysis techniques to optically thick environments. Nine mixtures of Coumarin 500 and Rhodamine 610 are analyzed, as well as the pure dyes. For each sample, the cuvette is positioned on a two-axis translation stage to allow the interrogation at different spatial locations, allowing the examination of both primary (absorption of the laser light) and secondary (absorption of the fluorescence) inner filter effects. In addition to these expected inner filter effects, we find evidence that a portion of the absorbed fluorescence is re-emitted. A total of 688 spectra are acquired for the evaluation of multivariate analysis approaches to account for nonlinear effects.

  14. Simple and Multivariate Relationships Between Spiritual Intelligence with General Health and Happiness.

    PubMed

    Amirian, Mohammad-Elyas; Fazilat-Pour, Masoud

    2016-08-01

    The present study examined simple and multivariate relationships of spiritual intelligence with general health and happiness. The employed method was descriptive and correlational. King's Spiritual Quotient scales, GHQ-28 and Oxford Happiness Inventory, are filled out by a sample consisted of 384 students, which were selected using stratified random sampling from the students of Shahid Bahonar University of Kerman. Data are subjected to descriptive and inferential statistics including correlations and multivariate regressions. Bivariate correlations support positive and significant predictive value of spiritual intelligence toward general health and happiness. Further analysis showed that among the Spiritual Intelligence' subscales, Existential Critical Thinking Predicted General Health and Happiness, reversely. In addition, happiness was positively predicted by generation of personal meaning and transcendental awareness. The findings are discussed in line with the previous studies and the relevant theoretical background.

  15. Simple and Multivariate Relationships Between Spiritual Intelligence with General Health and Happiness.

    PubMed

    Amirian, Mohammad-Elyas; Fazilat-Pour, Masoud

    2016-08-01

    The present study examined simple and multivariate relationships of spiritual intelligence with general health and happiness. The employed method was descriptive and correlational. King's Spiritual Quotient scales, GHQ-28 and Oxford Happiness Inventory, are filled out by a sample consisted of 384 students, which were selected using stratified random sampling from the students of Shahid Bahonar University of Kerman. Data are subjected to descriptive and inferential statistics including correlations and multivariate regressions. Bivariate correlations support positive and significant predictive value of spiritual intelligence toward general health and happiness. Further analysis showed that among the Spiritual Intelligence' subscales, Existential Critical Thinking Predicted General Health and Happiness, reversely. In addition, happiness was positively predicted by generation of personal meaning and transcendental awareness. The findings are discussed in line with the previous studies and the relevant theoretical background. PMID:25616864

  16. Statistical Analyses of Raw Material Data for MTM45-1/CF7442A-36% RW: CMH Cure Cycle

    NASA Technical Reports Server (NTRS)

    Coroneos, Rula; Pai, Shantaram, S.; Murthy, Pappu

    2013-01-01

    This report describes statistical characterization of physical properties of the composite material system MTM45-1/CF7442A, which has been tested and is currently being considered for use on spacecraft structures. This composite system is made of 6K plain weave graphite fibers in a highly toughened resin system. This report summarizes the distribution types and statistical details of the tests and the conditions for the experimental data generated. These distributions will be used in multivariate regression analyses to help determine material and design allowables for similar material systems and to establish a procedure for other material systems. Additionally, these distributions will be used in future probabilistic analyses of spacecraft structures. The specific properties that are characterized are the ultimate strength, modulus, and Poisson??s ratio by using a commercially available statistical package. Results are displayed using graphical and semigraphical methods and are included in the accompanying appendixes.

  17. Gene‐set and multivariate genome‐wide association analysis of oppositional defiant behavior subtypes in attention‐deficit/hyperactivity disorder

    PubMed Central

    van Donkelaar, Marjolein M. J.; Poelmans, Geert; Buitelaar, Jan K.; Sonuga‐Barke, Edmund J. S.; Stringaris, Argyris; consortium, IMAGE; Faraone, Stephen V.; Franke, Barbara; Steinhausen, Hans‐Christoph; van Hulzen, Kimm J. E.

    2015-01-01

    Oppositional defiant disorder (ODD) is a frequent psychiatric disorder seen in children and adolescents with attention‐deficit‐hyperactivity disorder (ADHD). ODD is also a common antecedent to both affective disorders and aggressive behaviors. Although the heritability of ODD has been estimated to be around 0.60, there has been little research into the molecular genetics of ODD. The present study examined the association of irritable and defiant/vindictive dimensions and categorical subtypes of ODD (based on latent class analyses) with previously described specific polymorphisms (DRD4 exon3 VNTR, 5‐HTTLPR, and seven OXTR SNPs) as well as with dopamine, serotonin, and oxytocin genes and pathways in a clinical sample of children and adolescents with ADHD. In addition, we performed a multivariate genome‐wide association study (GWAS) of the aforementioned ODD dimensions and subtypes. Apart from adjusting the analyses for age and sex, we controlled for “parental ability to cope with disruptive behavior.” None of the hypothesis‐driven analyses revealed a significant association with ODD dimensions and subtypes. Inadequate parenting behavior was significantly associated with all ODD dimensions and subtypes, most strongly with defiant/vindictive behaviors. In addition, the GWAS did not result in genome‐wide significant findings but bioinformatics and literature analyses revealed that the proteins encoded by 28 of the 53 top‐ranked genes functionally interact in a molecular landscape centered around Beta‐catenin signaling and involved in the regulation of neurite outgrowth. Our findings provide new insights into the molecular basis of ODD and inform future genetic studies of oppositional behavior. © 2015 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc. PMID:26184070

  18. Gene-set and multivariate genome-wide association analysis of oppositional defiant behavior subtypes in attention-deficit/hyperactivity disorder.

    PubMed

    Aebi, Marcel; van Donkelaar, Marjolein M J; Poelmans, Geert; Buitelaar, Jan K; Sonuga-Barke, Edmund J S; Stringaris, Argyris; Consortium, Image; Faraone, Stephen V; Franke, Barbara; Steinhausen, Hans-Christoph; van Hulzen, Kimm J E

    2016-07-01

    Oppositional defiant disorder (ODD) is a frequent psychiatric disorder seen in children and adolescents with attention-deficit-hyperactivity disorder (ADHD). ODD is also a common antecedent to both affective disorders and aggressive behaviors. Although the heritability of ODD has been estimated to be around 0.60, there has been little research into the molecular genetics of ODD. The present study examined the association of irritable and defiant/vindictive dimensions and categorical subtypes of ODD (based on latent class analyses) with previously described specific polymorphisms (DRD4 exon3 VNTR, 5-HTTLPR, and seven OXTR SNPs) as well as with dopamine, serotonin, and oxytocin genes and pathways in a clinical sample of children and adolescents with ADHD. In addition, we performed a multivariate genome-wide association study (GWAS) of the aforementioned ODD dimensions and subtypes. Apart from adjusting the analyses for age and sex, we controlled for "parental ability to cope with disruptive behavior." None of the hypothesis-driven analyses revealed a significant association with ODD dimensions and subtypes. Inadequate parenting behavior was significantly associated with all ODD dimensions and subtypes, most strongly with defiant/vindictive behaviors. In addition, the GWAS did not result in genome-wide significant findings but bioinformatics and literature analyses revealed that the proteins encoded by 28 of the 53 top-ranked genes functionally interact in a molecular landscape centered around Beta-catenin signaling and involved in the regulation of neurite outgrowth. Our findings provide new insights into the molecular basis of ODD and inform future genetic studies of oppositional behavior. © 2015 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc.

  19. Multivariate Analysis for Animal Selection in Experimental Research

    PubMed Central

    Pinto, Renan Mercuri; de Campos, Dijon Henrique Salomé; Tomasi, Loreta Casquel; Cicogna, Antonio Carlos; Okoshi, Katashi; Padovani, Carlos Roberto

    2015-01-01

    Background Several researchers seek methods for the selection of homogeneous groups of animals in experimental studies, a fact justified because homogeneity is an indispensable prerequisite for casualization of treatments. The lack of robust methods that comply with statistical and biological principles is the reason why researchers use empirical or subjective methods, influencing their results. Objective To develop a multivariate statistical model for the selection of a homogeneous group of animals for experimental research and to elaborate a computational package to use it. Methods The set of echocardiographic data of 115 male Wistar rats with supravalvular aortic stenosis (AoS) was used as an example of model development. Initially, the data were standardized, and became dimensionless. Then, the variance matrix of the set was submitted to principal components analysis (PCA), aiming at reducing the parametric space and at retaining the relevant variability. That technique established a new Cartesian system into which the animals were allocated, and finally the confidence region (ellipsoid) was built for the profile of the animals’ homogeneous responses. The animals located inside the ellipsoid were considered as belonging to the homogeneous batch; those outside the ellipsoid were considered spurious. Results The PCA established eight descriptive axes that represented the accumulated variance of the data set in 88.71%. The allocation of the animals in the new system and the construction of the confidence region revealed six spurious animals as compared to the homogeneous batch of 109 animals. Conclusion The biometric criterion presented proved to be effective, because it considers the animal as a whole, analyzing jointly all parameters measured, in addition to having a small discard rate. PMID:25651342

  20. Multivariate refutation of aetiological hypotheses in non-experimental epidemiology.

    PubMed

    Maclure, M

    1990-12-01

    Extension of Karl Popper's logic of refutation from the realm of contingency tables to multivariate modelling leads to the conclusion that rigorously scientific multivariate analysis in non-experimental epidemiology differs from the traditional quasi-scientific approach. Instead of aiming for high sensitivity in detecting aetiological agents, the goal in refutation is high specificity--to give the best defence of the 'innocence' of every exposure hypothesized as being a cause. Instead of 'forward selection' or 'backward elimination', multivariate refutation uses the method of 'forward elimination'. This entails a likelihood approach (which may be complemented by, but should be demarcated from, Bayesian methods) not only for statistical inference but also, by analogy, for study design and conduct: one starts with the conclusion (the estimate or hypothesis) and works backwards to the observations (the likelihood of the data or the design of the study). Differences in practice can sometimes be large, as illustrated by a study of hypothesized triggers of myocardial infarction. Multivariate refutation should replace the concept of multivariate modelling in non-experimental epidemiology.

  1. Multicomponent seismic noise attenuation with multivariate order statistic filters

    NASA Astrophysics Data System (ADS)

    Wang, Chao; Wang, Yun; Wang, Xiaokai; Xun, Chao

    2016-10-01

    The vector relationship between multicomponent seismic data is highly important for multicomponent processing and interpretation, but this vector relationship could be damaged when each component is processed individually. To overcome the drawback of standard component-by-component filtering, multivariate order statistic filters are introduced and extended to attenuate the noise of multicomponent seismic data by treating such dataset as a vector wavefield rather than a set of scalar fields. According to the characteristics of seismic signals, we implement this type of multivariate filtering along local events. First, the optimal local events are recognized according to the similarity between the vector signals which are windowed from neighbouring seismic traces with a sliding time window along each trial trajectory. An efficient strategy is used to reduce the computational cost of similarity measurement for vector signals. Next, one vector sample each from the neighbouring traces are extracted along the optimal local event as the input data for a multivariate filter. Different multivariate filters are optimal for different noise. The multichannel modified trimmed mean (MTM) filter, as one of the multivariate order statistic filters, is applied to synthetic and field multicomponent seismic data to test its performance for attenuating white Gaussian noise. The results indicate that the multichannel MTM filter can attenuate noise while preserving the relative amplitude information of multicomponent seismic data more effectively than a single-channel filter.

  2. 10 CFR 436.24 - Uncertainty analyses.

    Code of Federal Regulations, 2012 CFR

    2012-01-01

    ... DEPARTMENT OF ENERGY ENERGY CONSERVATION FEDERAL ENERGY MANAGEMENT AND PLANNING PROGRAMS Methodology and... by conducting additional analyses using any standard engineering economics method such as sensitivity... energy or water system alternative....

  3. 10 CFR 436.24 - Uncertainty analyses.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

    ... DEPARTMENT OF ENERGY ENERGY CONSERVATION FEDERAL ENERGY MANAGEMENT AND PLANNING PROGRAMS Methodology and... by conducting additional analyses using any standard engineering economics method such as sensitivity... energy or water system alternative....

  4. 10 CFR 436.24 - Uncertainty analyses.

    Code of Federal Regulations, 2013 CFR

    2013-01-01

    ... Procedures for Life Cycle Cost Analyses § 436.24 Uncertainty analyses. If particular items of cost data or... impact of uncertainty on the calculation of life cycle cost effectiveness or the assignment of rank order... and probabilistic analysis. If additional analysis casts substantial doubt on the life cycle...

  5. 10 CFR 436.24 - Uncertainty analyses.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... Procedures for Life Cycle Cost Analyses § 436.24 Uncertainty analyses. If particular items of cost data or... impact of uncertainty on the calculation of life cycle cost effectiveness or the assignment of rank order... and probabilistic analysis. If additional analysis casts substantial doubt on the life cycle...

  6. 10 CFR 436.24 - Uncertainty analyses.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... Procedures for Life Cycle Cost Analyses § 436.24 Uncertainty analyses. If particular items of cost data or... impact of uncertainty on the calculation of life cycle cost effectiveness or the assignment of rank order... and probabilistic analysis. If additional analysis casts substantial doubt on the life cycle...

  7. Multivariate Stable Isotope Analysis to Determine Linkages between Benzocaine Seizures

    NASA Astrophysics Data System (ADS)

    Kemp, H. F.; Meier-Augenstein, W.; Collins, M.; Salouros, H.; Cunningham, A.; Harrison, M.

    2012-04-01

    In July 2010, a woman was jailed for nine years in the UK after the prosecution successfully argued that attempting to import a cutting agent was proof of involvement in a conspiracy to supply Cocaine. That landmark ruling provided law enforcement agencies with much greater scope to tackle those involved in this aspect of the drug trade, specifically targeting those importing the likes of benzocaine or lidocaine. Huge quantities of these compounds are imported into the UK and between May and August 2010, four shipments of Benzocaine amounting to more then 4 tons had been seized as part of Operation Kitley, a joint initiative between the UK Border Agency and the Serious Organised Crime Agency (SOCA). By diluting cocaine, traffickers can make it go a lot further for very little cost, leading to huge profits. In recent years, dealers have moved away from inert substances, like sugar and baby milk powder, in favour of active pharmaceutical ingredients (APIs), including anaesthetics like Benzocaine and Lidocaine. Both these mimic the numbing effect of cocaine, and resemble it closely in colour, texture and some chemical behaviours, making it easier to conceal the fact that the drug has been diluted. API cutting agents have helped traffickers to maintain steady supplies in the face of successful interdiction and even expand the market in the UK, particularly to young people aged from their mid teens to early twenties. From importation to street-level, the purity of the drug can be reduced up to a factor of 80 and street level cocaine can have a cocaine content as low as 1%. In view of the increasing use of Benzocaine as cutting agent for Cocaine, a study was carried out to investigate if 2H, 13C, 15N and 18O stable isotope signatures could be used in conjunction with multivariate chemometric data analysis to determine potential linkage between benzocaine exhibits seized from different locations or individuals to assist with investigation and prosecution of drug

  8. Multivariate cluster analysis of forest fire events in Portugal

    NASA Astrophysics Data System (ADS)

    Tonini, Marj; Pereira, Mario; Vega Orozco, Carmen; Parente, Joana

    2015-04-01

    Portugal is one of the major fire-prone European countries, mainly due to its favourable climatic, topographic and vegetation conditions. Compared to the other Mediterranean countries, the number of events registered here from 1980 up to nowadays is the highest one; likewise, with respect to the burnt area, Portugal is the third most affected country. Portuguese mapped burnt areas are available from the website of the Institute for the Conservation of Nature and Forests (ICNF). This official geodatabase is the result of satellite measurements starting from the year 1990. The spatial information, delivered in shapefile format, provides a detailed description of the shape and the size of area burnt by each fire, while the date/time information relate to the ignition fire is restricted to the year of occurrence. In terms of a statistical formalism wildfires can be associated to a stochastic point process, where events are analysed as a set of geographical coordinates corresponding, for example, to the centroid of each burnt area. The spatio/temporal pattern of stochastic point processes, including the cluster analysis, is a basic procedure to discover predisposing factorsas well as for prevention and forecasting purposes. These kinds of studies are primarily focused on investigating the spatial cluster behaviour of environmental data sequences and/or mapping their distribution at different times. To include both the two dimensions (space and time) a comprehensive spatio-temporal analysis is needful. In the present study authors attempt to verify if, in the case of wildfires in Portugal, space and time act independently or if, conversely, neighbouring events are also closer in time. We present an application of the spatio-temporal K-function to a long dataset (1990-2012) of mapped burnt areas. Moreover, the multivariate K-function allowed checking for an eventual different distribution between small and large fires. The final objective is to elaborate a 3D

  9. Probabilistic, multi-variate flood damage modelling using random forests and Bayesian networks

    NASA Astrophysics Data System (ADS)

    Kreibich, Heidi; Schröter, Kai

    2015-04-01

    Decisions on flood risk management and adaptation are increasingly based on risk analyses. Such analyses are associated with considerable uncertainty, even more if changes in risk due to global change are expected. Although uncertainty analysis and probabilistic approaches have received increased attention recently, they are hardly applied in flood damage assessments. Most of the damage models usually applied in standard practice have in common that complex damaging processes are described by simple, deterministic approaches like stage-damage functions. This presentation will show approaches for probabilistic, multi-variate flood damage modelling on the micro- and meso-scale and discuss their potential and limitations. Reference: Merz, B.; Kreibich, H.; Lall, U. (2013): Multi-variate flood damage assessment: a tree-based data-mining approach. NHESS, 13(1), 53-64. Schröter, K., Kreibich, H., Vogel, K., Riggelsen, C., Scherbaum, F., Merz, B. (2014): How useful are complex flood damage models? - Water Resources Research, 50, 4, p. 3378-3395.

  10. PyMVPA: A python toolbox for multivariate pattern analysis of fMRI data.

    PubMed

    Hanke, Michael; Halchenko, Yaroslav O; Sederberg, Per B; Hanson, Stephen José; Haxby, James V; Pollmann, Stefan

    2009-01-01

    Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability. PMID:19184561

  11. PyMVPA: A python toolbox for multivariate pattern analysis of fMRI data.

    PubMed

    Hanke, Michael; Halchenko, Yaroslav O; Sederberg, Per B; Hanson, Stephen José; Haxby, James V; Pollmann, Stefan

    2009-01-01

    Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability.

  12. Combination of multivariate curve resolution and multivariate classification techniques for comprehensive high-performance liquid chromatography-diode array absorbance detection fingerprints analysis of Salvia reuterana extracts.

    PubMed

    Hakimzadeh, Neda; Parastar, Hadi; Fattahi, Mohammad

    2014-01-24

    In this study, multivariate curve resolution (MCR) and multivariate classification methods are proposed to develop a new chemometric strategy for comprehensive analysis of high-performance liquid chromatography-diode array absorbance detection (HPLC-DAD) fingerprints of sixty Salvia reuterana samples from five different geographical regions. Different chromatographic problems occurred during HPLC-DAD analysis of S. reuterana samples, such as baseline/background contribution and noise, low signal-to-noise ratio (S/N), asymmetric peaks, elution time shifts, and peak overlap are handled using the proposed strategy. In this way, chromatographic fingerprints of sixty samples are properly segmented to ten common chromatographic regions using local rank analysis and then, the corresponding segments are column-wise augmented for subsequent MCR analysis. Extended multivariate curve resolution-alternating least squares (MCR-ALS) is used to obtain pure component profiles in each segment. In general, thirty-one chemical components were resolved using MCR-ALS in sixty S. reuterana samples and the lack of fit (LOF) values of MCR-ALS models were below 10.0% in all cases. Pure spectral profiles are considered for identification of chemical components by comparing their resolved spectra with the standard ones and twenty-four components out of thirty-one components were identified. Additionally, pure elution profiles are used to obtain relative concentrations of chemical components in different samples for multivariate classification analysis by principal component analysis (PCA) and k-nearest neighbors (kNN). Inspection of the PCA score plot (explaining 76.1% of variance accounted for three PCs) showed that S. reuterana samples belong to four clusters. The degree of class separation (DCS) which quantifies the distance separating clusters in relation to the scatter within each cluster is calculated for four clusters and it was in the range of 1.6-5.8. These results are then

  13. A multivariate model of parent-adolescent relationship variables in early adolescence.

    PubMed

    McKinney, Cliff; Renk, Kimberly

    2011-08-01

    Given the importance of predicting outcomes for early adolescents, this study examines a multivariate model of parent-adolescent relationship variables, including parenting, family environment, and conflict. Participants, who completed measures assessing these variables, included 710 culturally diverse 11-14-year-olds who were attending a middle school in a Southeastern state. The parents of a subset of these adolescents (i.e., 487 mother-father pairs) participated in this study as well. Correlational analyses indicate that authoritative and authoritarian parenting, family cohesion and adaptability, and conflict are significant predictors of early adolescents' internalizing and externalizing problems. Structural equation modeling analyses indicate that fathers' parenting may not predict directly externalizing problems in male and female adolescents but instead may act through conflict. More direct relationships exist when examining mothers' parenting. The impact of parenting, family environment, and conflict on early adolescents' internalizing and externalizing problems and the importance of both gender and cross-informant ratings are emphasized.

  14. Balance characteristics of multivariate background error covariance for rainy and dry seasons and their impact on precipitation forecasts of two rainfall events

    NASA Astrophysics Data System (ADS)

    Chen, Yaodeng; Xia, Xue; Min, Jinzhong; Huang, Xiang-Yu; Rizvi, Syed R. H.

    2016-10-01

    Atmospheric moisture content or humidity is an important analysis variable of any meteorological data assimilation system. The humidity analysis can be univariate, using humidity background (normally short-range numerical forecasts) and humidity observations. However, more and more data assimilation systems are multivariate, analyzing humidity together with wind, temperature and pressure. Background error covariances, with unbalanced velocity potential and humidity in the multivariate formulation, are generated from weather research and forecasting model forecasts, collected over a summer rainy season and a winter dry season. The unbalanced velocity potential and humidity related correlations are shown to be significantly larger, indicating more important roles unbalanced velocity potential and humidity play, in the rainy season than that in the dry season. Three cycling data assimilation experiments of two rainfall events in the middle and lower reaches of the Yangtze River are carried out. The experiments differ in the formulation of the background error covariances. Results indicate that only including unbalanced velocity potential in the multivariate background error covariance improves wind analyses, but has little impact on temperature and humidity analyses. In contrast, further including humidity in the multivariate background error covariance although has a slight negative effect on wind analyses and a neutral effect on temperature analyses, but significantly improves humidity analyses, leading to precipitation forecasts more consistent with China Hourly Merged Precipitation Analysis.

  15. An update on multivariate return periods in hydrology

    NASA Astrophysics Data System (ADS)

    Gräler, Benedikt; Petroselli, Andrea; Grimaldi, Salvatore; De Baets, Bernard; Verhoest, Niko

    2016-05-01

    Many hydrological studies are devoted to the identification of events that are expected to occur on average within a certain time span. While this topic is well established in the univariate case, recent advances focus on a multivariate characterization of events based on copulas. Following a previous study, we show how the definition of the survival Kendall return period fits into the set of multivariate return periods.Moreover, we preliminary investigate the ability of the multivariate return period definitions to select maximal events from a time series. Starting from a rich simulated data set, we show how similar the selection of events from a data set is. It can be deduced from the study and theoretically underpinned that the strength of correlation in the sample influences the differences between the selection of maximal events.

  16. Properties of multivariable root loci. M.S. Thesis

    NASA Technical Reports Server (NTRS)

    Yagle, A. E.

    1981-01-01

    Various properties of multivariable root loci are analyzed from a frequency domain point of view by using the technique of Newton polygons, and some generalizations of the SISO root locus rules to the multivariable case are pointed out. The behavior of the angles of arrival and departure is related to the Smith-MacMillan form of G(s) and explicit equations for these angles are obtained. After specializing to first order and a restricted class of higher order poles and zeros, some simple equations for these angles that are direct generalizations of the SISO equations are found. The unusual behavior of root loci on the real axis at branch points is studied. The SISO root locus rules for break-in and break-out points are shown to generalize directly to the multivariable case. Some methods for computing both types of points are presented.

  17. Anthropometric multivariate structure and dermatoglyphic peculiarities in biochemically and morphologically different heterozygous groups.

    PubMed

    Kobyliansky, E; Livshits, G

    1986-06-01

    Multivariate analysis of the relationship between degree of heterozygosity at four blood group loci and the morphological variability in a human population was carried out. Additionally, the possibility that dermatoglyphic patterns correlate with biochemical and anthropometric variables was also investigated. A strong and significant increase in the frequency of morphologically multimodal individuals was observed, which paralleled the heterozygosity level. Discriminant analysis, by quantitative characters, of the closest pair of biochemically different samples yielded a satisfactory discrimination. Multiple correlations of each variable with all the others (18 traits), the communality of characters, the index of integration, and the Mahalanobis distances of the factor scores for each individual (all extracted from principal component analysis) were all indicative of the different multivariate structures of homo- and heterozygous individuals and thus supported the hypothesis that heterozygotes tend to cluster near the center of the joint multivariate distribution. The dermatoglyphic patterns showed a certain relationship with the morphological makeup of individuals. Correlations between biochemical heterozygosity at blood group loci and patterns of digital dermatoglyphics were rather irregular.

  18. Inheritance of Nitrogen Use Efficiency in Inbred Progenies of Tropical Maize Based on Multivariate Diallel Analysis

    PubMed Central

    Guedes, Fernando Lisboa; Diniz, Rafael Parreira; Balestre, Marcio; Ribeiro, Camila Bastos; Camargos, Renato Barbosa; Souza, João Cândido

    2014-01-01

    The objective of our study was to characterize and determine the patterns of genetic control in relation to tolerance and efficiency of nitrogen use by means of a complete diallel cross involving contrasting inbred progenies of tropical maize based on a univariate approach within the perspective of a multivariate mixed model. Eleven progenies, previously classified regarding the tolerance and responsiveness to nitrogen, were crossed in a complete diallel cross. Fifty-five hybrids were obtained. The hybrids and the progenies were evaluated at two different nitrogen levels, in two locations. The grain yield was measured as well as its yield components. The heritability values between the higher and lower nitrogen input environment did not differ among themselves. It was observed that the general combining ability values were similar for both approaches univariate and multivariate, when it was analyzed within each location and nitrogen level. The estimate of variance of the specific combining ability was higher than general combining ability estimate and the ratio between them was 0.54. The univariate and multivariate approaches are equivalent in experiments with good precision and high heritability. The nonadditive genetic effects exhibit greater quantities than the additive genetic effects for the genetic control of nitrogen use efficiency. PMID:25587575

  19. A new subgrid-scale representation of hydrometeor fields using a multivariate PDF

    DOE PAGES

    Griffin, Brian M.; Larson, Vincent E.

    2016-06-03

    The subgrid-scale representation of hydrometeor fields is important for calculating microphysical process rates. In order to represent subgrid-scale variability, the Cloud Layers Unified By Binormals (CLUBB) parameterization uses a multivariate probability density function (PDF). In addition to vertical velocity, temperature, and moisture fields, the PDF includes hydrometeor fields. Previously, hydrometeor fields were assumed to follow a multivariate single lognormal distribution. Now, in order to better represent the distribution of hydrometeors, two new multivariate PDFs are formulated and introduced.The new PDFs represent hydrometeors using either a delta-lognormal or a delta-double-lognormal shape. The two new PDF distributions, plus the previous single lognormalmore » shape, are compared to histograms of data taken from large-eddy simulations (LESs) of a precipitating cumulus case, a drizzling stratocumulus case, and a deep convective case. Finally, the warm microphysical process rates produced by the different hydrometeor PDFs are compared to the same process rates produced by the LES.« less

  20. Multivariate concentration determination using principal component regression with residual analysis

    PubMed Central

    Keithley, Richard B.; Heien, Michael L.; Wightman, R. Mark

    2009-01-01

    Data analysis is an essential tenet of analytical chemistry, extending the possible information obtained from the measurement of chemical phenomena. Chemometric methods have grown considerably in recent years, but their wide use is hindered because some still consider them too complicated. The purpose of this review is to describe a multivariate chemometric method, principal component regression, in a simple manner from the point of view of an analytical chemist, to demonstrate the need for proper quality-control (QC) measures in multivariate analysis and to advocate the use of residuals as a proper QC method. PMID:20160977

  1. Multivariate optimization of capillary electrophoresis methods: a critical review.

    PubMed

    Orlandini, Serena; Gotti, Roberto; Furlanetto, Sandra

    2014-01-01

    In this article a review on the recent applications of multivariate techniques for optimization of electromigration methods, is presented. Papers published in the period from August 2007 to February 2013, have been taken into consideration. Upon a brief description of each of the involved CE operative modes, the characteristics of the chemometric strategies (type of design, factors and responses) applied to face a number of analytical challenges, are presented. Finally, a critical discussion, giving some practical advices and pointing out the most common issues involved in multivariate set-up of CE methods, is provided.

  2. Fixed order dynamic compensation for multivariable linear systems

    NASA Technical Reports Server (NTRS)

    Kramer, F. S.; Calise, A. J.

    1986-01-01

    This paper considers the design of fixed order dynamic compensators for multivariable time invariant linear systems, minimizing a linear quadratic performance cost functional. Attention is given to robustness issues in terms of multivariable frequency domain specifications. An output feedback formulation is adopted by suitably augmenting the system description to include the compensator states. Either a controller or observer canonical form is imposed on the compensator description to reduce the number of free parameters to its minimal number. The internal structure of the compensator is prespecified by assigning a set of ascending feedback invariant indices, thus forming a Brunovsky structure for the nominal compensator.

  3. Steady-state decoupling and design of linear multivariable systems

    NASA Technical Reports Server (NTRS)

    Thaler, G. J.

    1974-01-01

    A constructive criterion for decoupling the steady states of a linear time-invariant multivariable system is presented. This criterion consists of a set of inequalities which, when satisfied, will cause the steady states of a system to be decoupled. Stability analysis and a new design technique for such systems are given. A new and simple connection between single-loop and multivariable cases is found. These results are then applied to the compensation design for NASA STOL C-8A aircraft. Both steady-state decoupling and stability are justified through computer simulations.

  4. Statistical analysis of multivariate atmospheric variables. [cloud cover

    NASA Technical Reports Server (NTRS)

    Tubbs, J. D.

    1979-01-01

    Topics covered include: (1) estimation in discrete multivariate distributions; (2) a procedure to predict cloud cover frequencies in the bivariate case; (3) a program to compute conditional bivariate normal parameters; (4) the transformation of nonnormal multivariate to near-normal; (5) test of fit for the extreme value distribution based upon the generalized minimum chi-square; (6) test of fit for continuous distributions based upon the generalized minimum chi-square; (7) effect of correlated observations on confidence sets based upon chi-square statistics; and (8) generation of random variates from specified distributions.

  5. What matters? Assessing and developing inquiry and multivariable reasoning skills in high school chemistry

    NASA Astrophysics Data System (ADS)

    Daftedar Abdelhadi, Raghda Mohamed

    Although the Next Generation Science Standards (NGSS) present a detailed set of Science and Engineering Practices, a finer grained representation of the underlying skills is lacking in the standards document. Therefore, it has been reported that teachers are facing challenges deciphering and effectively implementing the standards, especially with regards to the Practices. This analytical study assessed the development of high school chemistry students' (N = 41) inquiry, multivariable causal reasoning skills, and metacognition as a mediator for their development. Inquiry tasks based on concepts of element properties of the periodic table as well as reaction kinetics required students to conduct controlled thought experiments, make inferences, and declare predictions of the level of the outcome variable by coordinating the effects of multiple variables. An embedded mixed methods design was utilized for depth and breadth of understanding. Various sources of data were collected including students' written artifacts, audio recordings of in-depth observational groups and interviews. Data analysis was informed by a conceptual framework formulated around the concepts of coordinating theory and evidence, metacognition, and mental models of multivariable causal reasoning. Results of the study indicated positive change towards conducting controlled experimentation, making valid inferences and justifications. Additionally, significant positive correlation between metastrategic and metacognitive competencies, and sophistication of experimental strategies, signified the central role metacognition played. Finally, lack of consistency in indicating effective variables during the multivariable prediction task pointed towards the fragile mental models of multivariable causal reasoning the students had. Implications for teacher education, science education policy as well as classroom research methods are discussed. Finally, recommendations for developing reform-based chemistry

  6. Assessing clinical outcomes of patients with acute calculous cholecystitis in addition to the Tokyo grading: a retrospective study.

    PubMed

    Cheng, Wei-Chun; Chiu, Yen-Cheng; Chuang, Chiao-Hsiung; Chen, Chiung-Yu

    2014-09-01

    The management of acute cholecystitis is still based on clinical expertise. This study aims to investigate whether the outcome of acute cholecystitis can be related to the severity criteria of the Tokyo guidelines and additional clinical comorbidities. A total of 103 patients with acute cholecystitis were retrospectively enrolled and their medical records were reviewed. They were all classified according to therapeutic modality, including early cholecystectomy and antibiotic treatment with or without percutaneous cholecystostomy. The impact of the Tokyo guidelines and the presence of comorbidities on clinical outcome were assessed by univariate and multivariate regression analyses. According to Tokyo severity grading, 48 patients were Grade I, 31 patients were Grade II, and 24 patients were Grade III. The Grade III patients had a longer hospital stay than Grade II and Grade I patients (15.2 days, 9.2 days, and 7.3 days, respectively, p < 0.05). According to multivariate analysis, patients with Grade III Tokyo severity, higher Charlson's Comorbidity Score, and encountering complications had a longer hospital stay. Based on treatment modality, surgeons selected the patients with less severity and fewer comorbidities for cholecystectomy, and these patients had a shorter hospital stay. In addition to the grading of the Tokyo guidelines, comorbidities had an additional impact on clinical outcomes and should be an important consideration when making therapeutic decisions.

  7. Multivariate expression analysis of the gene network underlying sexual development in turtle embryos with temperature-dependent and genotypic sex determination.

    PubMed

    Valenzuela, N

    2010-01-01

    Sexual development has long been the target of study and despite great advances in our understanding of the composition and regulation of the gene network underlying gonadogenesis, our knowledge remains incomplete. Of particular interest is the relative role that the environment and the genome play in directing gonadal formation, especially the effect of environmental temperature in directing this process in vertebrates. Comparative analyses in closely related taxa with contrasting sex-determining mechanisms should help fill this gap. Here I present a multivariate study of the regulation of the gene network underlying sexual development in turtles with temperature-dependent (TSD; Chrysemys picta) and genotypic sex determination (GSD; Apalone mutica). I combine novel data on SOX9 and DMRT1 from these species with contrasting sex-determining mechanisms for the first time with previously reported data on DAX1, SF-1 (NR5A1), WT1, and aromatase (CYP19A1) from these same taxa. Comparative expression analyses of SOX9 and DMRT1 from these and other species indicate additional elements whose expression has diverged among TSD taxa, further supporting the notion that significant evolutionary changes have accrued in the regulation of the TSD gene network in reptiles. A non-parametric MANOVA revealed that temperature had a significant effect in multivariate gene expression in C. picta that varied during embryonic development, whereas the covariation of gene expression in A. mutica was insensitive to temperature. A phenotypic trajectory analysis (PTA) of gene expression comparing both species directly indicated that the relative covariation in gene expression varied between temperatures in C. picta. Furthermore, the 25 degrees C trajectory of C. picta differed from that of A. mutica in the magnitude of gene expression change. Additional analyses revealed a stronger covariation in gene expression and a more interconnected regulatory network in A. mutica, consistent with the

  8. Optimal Multicomponent Analysis Using the Generalized Standard Addition Method.

    ERIC Educational Resources Information Center

    Raymond, Margaret; And Others

    1983-01-01

    Describes an experiment on the simultaneous determination of chromium and magnesium by spectophotometry modified to include the Generalized Standard Addition Method computer program, a multivariate calibration method that provides optimal multicomponent analysis in the presence of interference and matrix effects. Provides instructions for…

  9. Effects of Covariance Heterogeneity on Three Procedures for Analyzing Multivariate Repeated Measures Designs.

    ERIC Educational Resources Information Center

    Vallejo, Guillermo; Fidalgo, Angel; Fernandez, Paula

    2001-01-01

    Estimated empirical Type I error rate and power rate for three procedures for analyzing multivariate repeated measures designs: (1) the doubly multivariate model; (2) the Welch-James multivariate solution (H. Keselman, M. Carriere, a nd L. Lix, 1993); and (3) the multivariate version of the modified Brown-Forsythe procedure (M. Brown and A.…

  10. NOAA's National Snow Analyses

    NASA Astrophysics Data System (ADS)

    Carroll, T. R.; Cline, D. W.; Olheiser, C. M.; Rost, A. A.; Nilsson, A. O.; Fall, G. M.; Li, L.; Bovitz, C. T.

    2005-12-01

    NOAA's National Operational Hydrologic Remote Sensing Center (NOHRSC) routinely ingests all of the electronically available, real-time, ground-based, snow data; airborne snow water equivalent data; satellite areal extent of snow cover information; and numerical weather prediction (NWP) model forcings for the coterminous U.S. The NWP model forcings are physically downscaled from their native 13 km2 spatial resolution to a 1 km2 resolution for the CONUS. The downscaled NWP forcings drive an energy-and-mass-balance snow accumulation and ablation model at a 1 km2 spatial resolution and at a 1 hour temporal resolution for the country. The ground-based, airborne, and satellite snow observations are assimilated into the snow model's simulated state variables using a Newtonian nudging technique. The principle advantages of the assimilation technique are: (1) approximate balance is maintained in the snow model, (2) physical processes are easily accommodated in the model, and (3) asynoptic data are incorporated at the appropriate times. The snow model is reinitialized with the assimilated snow observations to generate a variety of snow products that combine to form NOAA's NOHRSC National Snow Analyses (NSA). The NOHRSC NSA incorporate all of the available information necessary and available to produce a "best estimate" of real-time snow cover conditions at 1 km2 spatial resolution and 1 hour temporal resolution for the country. The NOHRSC NSA consist of a variety of daily, operational, products that characterize real-time snowpack conditions including: snow water equivalent, snow depth, surface and internal snowpack temperatures, surface and blowing snow sublimation, and snowmelt for the CONUS. The products are generated and distributed in a variety of formats including: interactive maps, time-series, alphanumeric products (e.g., mean areal snow water equivalent on a hydrologic basin-by-basin basis), text and map discussions, map animations, and quantitative gridded products

  11. An Open Source Geovisual Analytics Toolbox for Multivariate Spatio-Temporal Data in Environmental Change Modelling

    NASA Astrophysics Data System (ADS)

    Bernasocchi, M.; Coltekin, A.; Gruber, S.

    2012-07-01

    In environmental change studies, often multiple variables are measured or modelled, and temporal information is essential for the task. These multivariate geographic time-series datasets are often big and difficult to analyse. While many established methods such as PCP (parallel coordinate plots), STC (space-time cubes), scatter-plots and multiple (linked) visualisations help provide more information, we observe that most of the common geovisual analytics suits do not include three-dimensional (3D) visualisations. However, in many environmental studies, we hypothesize that the addition of 3D terrain visualisations along with appropriate data plots and two-dimensional views can help improve the analysts' ability to interpret the spatial relevance better. To test our ideas, we conceptualize, develop, implement and evaluate a geovisual analytics toolbox in a user-centred manner. The conceptualization of the tool is based on concrete user needs that have been identified and collected during informal brainstorming sessions and in a structured focus group session prior to the development. The design process, therefore, is based on a combination of user-centred design with a requirement analysis and agile development. Based on the findings from this phase, the toolbox was designed to have a modular structure and was built on open source geographic information systems (GIS) program Quantum GIS (QGIS), thus benefiting from existing GIS functionality. The modules include a globe view for 3D terrain visualisation (OSGEarth), a scattergram, a time vs. value plot, and a 3D helix visualisation as well as the possibility to view the raw data. The visualisation frame allows real-time linking of these representations. After the design and development stage, a case study was created featuring data from Zermatt valley and the toolbox was evaluated based on expert interviews. Analysts performed multiple spatial and temporal tasks with the case study using the toolbox. The expert

  12. Potential shift correction in multivariate curve resolution of voltammetric data. General formulation and application to some experimental systems.

    PubMed

    Alberich, Arístides; Díaz-Cruz, José Manuel; Ariño, Cristina; Esteban, Miquel

    2008-01-01

    A new mathematical algorithm is proposed to correct the progressive potential shift of some voltammetric signals that decrease the linearity of the data. The corrected data matrix can be further analysed by Multivariate Curve Resolution by Alternating Least Squares (MCR-ALS) and the vector including the potential shift corrections can be fitted to specific equations such as that by DeFord-Hume. A detailed discussion is given on the different cases of potential shift correction, and, in some of them, mathematical simulation is made or experimental systems [Cd(ii)-glutathione and Zn(ii)-glycine] are analysed.

  13. ADVANCING THE UNDERSTANDING OF BEHAVIORS ASSOCIATED WITH BACILLE CALMETTE GUÉRIN INFECTION USING MULTIVARIATE ANALYSIS

    PubMed Central

    Rodriguez-Zas, Sandra L.; Nixon, Scott E.; Lawson, Marcus A.; Mccusker, Robert H.; Southey, Bruce R.; O’Connor, Jason C.; Dantzer, Robert; Kelley, Keith W.

    2014-01-01

    Behavioral indicators in the murine Bacille Calmette Guérin (BCG) model of inflammation have been studied individually; however, the variability of the behaviors across BCG levels and the mouse-to-mouse variation within BCG-treatment group are only partially understood. The objectives of this study were: 1) to gain a comprehensive understanding of sickness and depression-like behaviors in a BCG model of inflammation using multivariate approaches, and 2) to explore behavioral differences between BCG-treatment groups and among mice within group. Adult mice were challenged with either 0mg (saline), 5mg or 10mg of BCG (BCG-treatment groups: BCG0, BCG5, or BCG10, respectively) at Day 0 of the experiment. Sickness indicators included body weight changes between Day 0 and Day 2 and between Day 2 and Day 5, and horizontal locomotor activity and vertical activity (rearing) measured at Day 6. Depression-like indicators included duration of immobility in the forced swim test and in the tail suspension test at Day 6 and sucrose consumption in the sucrose preference test at Day 7. The simultaneous consideration of complementary sickness and depression-like indicators enabled a more precise characterization of behavioral changes associated with BCG-treatment and of mouse-to-mouse variation, relative to the analysis of indicators individually. Univariate and multivariate analyses confirmed differences between BCG-treatment groups in weight change early on the trial. Significant differences between BCG-treatment groups in depression-like behaviors were still measurable after Day 5. The potential for multivariate models to account for the correlation between behavioral indicators and to augment the analytical precision relative to univariate models was demonstrated both for sickness and for depression-like indicators. Unsupervised learning approaches revealed the complementary information provided by the sickness and depression-like indicators considered. Supervised learning

  14. Quality evaluation and prediction of Citrullus lanatus by 1H NMR-based metabolomics and multivariate analysis.

    PubMed

    Tarachiwin, Lucksanaporn; Masako, Osawa; Fukusaki, Eiichiro

    2008-07-23

    (1)H NMR spectrometry in combination with multivariate analysis was considered to provide greater information on quality assessment over an ordinary sensory testing method due to its high reliability and high accuracy. The sensory quality evaluation of watermelon (Citrullus lanatus (Thunb.) Matsum. & Nakai) was carried out by means of (1)H NMR-based metabolomics. Multivariate analyses by partial least-squares projections to latent structures-discrimination analysis (PLS-DA) and PLS-regression offered extensive information for quality differentiation and quality evaluation, respectively. The impact of watermelon and rootstock cultivars on the sensory qualities of watermelon was determined on the basis of (1)H NMR metabolic fingerprinting and profiling. The significant metabolites contributing to the discrimination were also identified. A multivariate calibration model was successfully constructed by PLS-regression with extremely high reliability and accuracy. Thus, (1)H NMR-based metabolomics with multivariate analysis was considered to be one of the most suitable complementary techniques that could be applied to assess and predict the sensory quality of watermelons and other horticultural plants.

  15. Wavelet Analyses and Applications

    ERIC Educational Resources Information Center

    Bordeianu, Cristian C.; Landau, Rubin H.; Paez, Manuel J.

    2009-01-01

    It is shown how a modern extension of Fourier analysis known as wavelet analysis is applied to signals containing multiscale information. First, a continuous wavelet transform is used to analyse the spectrum of a nonstationary signal (one whose form changes in time). The spectral analysis of such a signal gives the strength of the signal in each…

  16. Apollo 14 microbial analyses

    NASA Technical Reports Server (NTRS)

    Taylor, G. R.

    1972-01-01

    Extensive microbiological analyses that were performed on the Apollo 14 prime and backup crewmembers and ancillary personnel are discussed. The crewmembers were subjected to four separate and quite different environments during the 137-day monitoring period. The relation between each of these environments and observed changes in the microflora of each astronaut are presented.

  17. Multivariate Multinomial Logit Models for Dyadic Sequential Interaction Data

    ERIC Educational Resources Information Center

    de Rooij, Mark; Kroonenberg, Pieter M.

    2003-01-01

    The analysis of discrete dyadic sequential behavior and, in particular, the problem of forecasting future behavior from current and past behavior in such data is the main theme of the present article. We propose to use multivariate multinomial logit models and the potential of which will be demonstrated with data on Imagery play therapy. In such a…

  18. MULTIVARIATE LINEAR MIXED MODELS FOR MULTIPLE OUTCOMES. (R824757)

    EPA Science Inventory

    We propose a multivariate linear mixed (MLMM) for the analysis of multiple outcomes, which generalizes the latent variable model of Sammel and Ryan. The proposed model assumes a flexible correlation structure among the multiple outcomes, and allows a global test of the impact of ...

  19. FACTOR ANALYTIC MODELS OF CLUSTERED MULTIVARIATE DATA WITH INFORMATIVE CENSORING

    EPA Science Inventory

    This paper describes a general class of factor analytic models for the analysis of clustered multivariate data in the presence of informative missingness. We assume that there are distinct sets of cluster-level latent variables related to the primary outcomes and to the censorin...

  20. Performance of Four Multivariate Tests under Variance-Covariance Heteroscedasticity.

    ERIC Educational Resources Information Center

    Tang, K. Linda; Algina, James

    1993-01-01

    Type I error rates of four multivariate tests (Pilai-Bartlett trace, Johansen's test, James' first-order test, and James' second-order test) were compared for heterogeneous covariance matrices in 360 simulated experiments. The superior performance of Johansen's test and James' second-order test is discussed. (SLD)

  1. The Optimization of Multivariate Generalizability Studies with Budget Constraints.

    ERIC Educational Resources Information Center

    Marcoulides, George A.; Goldstein, Zvi

    1992-01-01

    A method is presented for determining the optimal number of conditions to use in multivariate-multifacet generalizability designs when resource constraints are imposed. A decision maker can determine the number of observations needed to obtain the largest possible generalizability coefficient. The procedure easily applies to the univariate case.…

  2. MULTIVARIATE RECEPTOR MODELS AND MODEL UNCERTAINTY. (R825173)

    EPA Science Inventory

    Abstract

    Estimation of the number of major pollution sources, the source composition profiles, and the source contributions are the main interests in multivariate receptor modeling. Due to lack of identifiability of the receptor model, however, the estimation cannot be...

  3. Multivariate Normal Integrals and Contingency Tables with Ordered Categories.

    ERIC Educational Resources Information Center

    Wang, Yuchung J.

    1997-01-01

    A k-dimensional multivariate normal distribution is made discrete by partitioning the k-dimensional Euclidean space with rectangular grids. The probability integrals over the partitioned cubes forms a k-dimensional contingency table with ordered categories. A loglinear model with main effects plus two-way interactions provides an approximation for…

  4. Use of Technology to Develop Student Intuition in Multivariable Calculus

    ERIC Educational Resources Information Center

    Kaur, Manmohan

    2006-01-01

    In order to get undergraduates interested in mathematics, it is essential to involve them in its "discovery". In this paper, we will explain how technology and the knowledge of lower dimensional calculus can be used to help them develop intuition leading to their discovering the first derivative rule in multivariable calculus. (Contains 7 figures.)

  5. A Multivariate Generalizability Analysis of the Multistate Bar Examination

    ERIC Educational Resources Information Center

    Yin, Ping

    2005-01-01

    The main purpose of this study is to examine the content structure of the Multistate Bar Examination (MBE) using the "table of specifications" model from the perspective of multivariate generalizability theory. Specifically, using MBE data collected over different years (six administrations: three from the February test and three from July test),…

  6. Remote Multivariable Control Design Using a Competition Game

    ERIC Educational Resources Information Center

    Atanasijevic-Kunc, M.; Logar, V.; Karba, R.; Papic, M.; Kos, A.

    2011-01-01

    In this paper, some approaches to teaching multivariable control design are discussed, with special attention being devoted to a step-by-step transition to e-learning. The approach put into practice and presented here is developed through design projects, from which one is chosen as a competition game and is realized using the E-CHO system,…

  7. Multivariate Meta-Analysis Using Individual Participant Data

    ERIC Educational Resources Information Center

    Riley, R. D.; Price, M. J.; Jackson, D.; Wardle, M.; Gueyffier, F.; Wang, J.; Staessen, J. A.; White, I. R.

    2015-01-01

    When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is…

  8. Generating Nonnormal Multivariate Data Using Copulas: Applications to SEM

    ERIC Educational Resources Information Center

    Mair, Patrick; Satorra, Albert; Bentler, Peter M.

    2012-01-01

    This article develops a procedure based on copulas to simulate multivariate nonnormal data that satisfy a prespecified variance-covariance matrix. The covariance matrix used can comply with a specific moment structure form (e.g., a factor analysis or a general structural equation model). Thus, the method is particularly useful for Monte Carlo…

  9. MULTIVARIATE CURVE RESOLUTION OF NMR SPECTROSCOPY METABONOMIC DATA

    EPA Science Inventory

    Sandia National Laboratories is working with the EPA to evaluate and develop mathematical tools for analysis of the collected NMR spectroscopy data. Initially, we have focused on the use of Multivariate Curve Resolution (MCR) also known as molecular factor analysis (MFA), a tech...

  10. Multivariate Tests for Correlated Data in Completely Randomized Designs.

    ERIC Educational Resources Information Center

    Mielke, Paul W., Jr.; Berry, Kenneth J.

    1999-01-01

    Provides power comparisons for three permutation tests and the Bartlett-Nanda-Pillai trace test (BNP) (M. Bartlett, 1939; D. Nanda, 1950; K. Pillai, 1955) in completely randomized experimental designs with correlated multivariate-dependent variables. The power of the BNP was generally found to be less than that of at least one of the permutation…

  11. Some Properties of Two Measures of Multivariate Association.

    ERIC Educational Resources Information Center

    van den Burg, Willem; Lewis, Charles

    1988-01-01

    Measures of multivariate association, based on Wilks'"lambda" or the Bartlett-Nanda-Pillai trace criterion "V", are compared in terms of properties of univariate R-squared, which they generalize. A unified set of derivations of properties is provided, which is self-contained and not restricted to decompositions in canonical variates. (Author/TJH)

  12. Bayesian Methods for Scalable Multivariate Value-Added Assessment

    ERIC Educational Resources Information Center

    Lockwood, J. R.; McCaffrey, Daniel F.; Mariano, Louis T.; Setodji, Claude

    2007-01-01

    There is increased interest in value-added models relying on longitudinal student-level test score data to isolate teachers' contributions to student achievement. The complex linkage of students to teachers as students progress through grades poses both substantive and computational challenges. This article introduces a multivariate Bayesian…

  13. Multivariable feedback design - Concepts for a classical/modern synthesis

    NASA Technical Reports Server (NTRS)

    Doyle, J. C.; Stein, G.

    1981-01-01

    This paper presents a practical design perspective on multivariable feedback control problems. It reviews the basic issue - feedback design in the face of uncertainties - and generalizes known single-input, single-output (SISO) statements and constraints of the design problem to multiinput, multioutput (MIMO) cases. Two major MIMO design approaches are then evaluated in the context of these results.

  14. Multivariate-normality goodness-of-fit tests

    NASA Technical Reports Server (NTRS)

    Falls, L. W.; Crutcher, H. L.

    1977-01-01

    Computer program applies chi-square Pearson test to multivariate statistics for application in any field in which data of two or more variables (dimensions) are sampled for statistical purposes. Program handles dimensions two through five, with up to thousand data sets.

  15. Mathematical Formulation of Multivariate Euclidean Models for Discrimination Methods.

    ERIC Educational Resources Information Center

    Mullen, Kenneth; Ennis, Daniel M.

    1987-01-01

    Multivariate models for the triangular and duo-trio methods are described, and theoretical methods are compared to a Monte Carlo simulation. Implications are discussed for a new theory of multidimensional scaling which challenges the traditional assumption that proximity measures and perceptual distances are monotonically related. (Author/GDC)

  16. Univariate Analysis of Multivariate Outcomes in Educational Psychology.

    ERIC Educational Resources Information Center

    Hubble, L. M.

    1984-01-01

    The author examined the prevalence of multiple operational definitions of outcome constructs and an estimate of the incidence of Type I error rates when univariate procedures were applied to multiple variables in educational psychology. Multiple operational definitions of constructs were advocated and wider use of multivariate analysis was…

  17. Estimating the decomposition of predictive information in multivariate systems

    NASA Astrophysics Data System (ADS)

    Faes, Luca; Kugiumtzis, Dimitris; Nollo, Giandomenico; Jurysta, Fabrice; Marinazzo, Daniele

    2015-03-01

    In the study of complex systems from observed multivariate time series, insight into the evolution of one system may be under investigation, which can be explained by the information storage of the system and the information transfer from other interacting systems. We present a framework for the model-free estimation of information storage and information transfer computed as the terms composing the predictive information about the target of a multivariate dynamical process. The approach tackles the curse of dimensionality employing a nonuniform embedding scheme that selects progressively, among the past components of the multivariate process, only those that contribute most, in terms of conditional mutual information, to the present target process. Moreover, it computes all information-theoretic quantities using a nearest-neighbor technique designed to compensate the bias due to the different dimensionality of individual entropy terms. The resulting estimators of prediction entropy, storage entropy, transfer entropy, and partial transfer entropy are tested on simulations of coupled linear stochastic and nonlinear deterministic dynamic processes, demonstrating the superiority of the proposed approach over the traditional estimators based on uniform embedding. The framework is then applied to multivariate physiologic time series, resulting in physiologically well-interpretable information decompositions of cardiovascular and cardiorespiratory interactions during head-up tilt and of joint brain-heart dynamics during sleep.

  18. SAMPLING EFFORT AFFECTS MULTIVARIATE COMPARISONS OF STREAM COMMUNITIES

    EPA Science Inventory

    The estimation of ecological trends and patterns is often dependent on the size of individual samples from each site (sample size) or spatial scale in general. Multivariate analysis is widely used for determining patterns of community structure, inferring species-environment rela...

  19. Learning Adaptive Forecasting Models from Irregularly Sampled Multivariate Clinical Data

    PubMed Central

    Liu, Zitao; Hauskrecht, Milos

    2016-01-01

    Building accurate predictive models of clinical multivariate time series is crucial for understanding of the patient condition, the dynamics of a disease, and clinical decision making. A challenging aspect of this process is that the model should be flexible and adaptive to reflect well patient-specific temporal behaviors and this also in the case when the available patient-specific data are sparse and short span. To address this problem we propose and develop an adaptive two-stage forecasting approach for modeling multivariate, irregularly sampled clinical time series of varying lengths. The proposed model (1) learns the population trend from a collection of time series for past patients; (2) captures individual-specific short-term multivariate variability; and (3) adapts by automatically adjusting its predictions based on new observations. The proposed forecasting model is evaluated on a real-world clinical time series dataset. The results demonstrate the benefits of our approach on the prediction tasks for multivariate, irregularly sampled clinical time series, and show that it can outperform both the population based and patient-specific time series prediction models in terms of prediction accuracy. PMID:27525189

  20. Design of multivariable feedback control systems via spectral assignment

    NASA Technical Reports Server (NTRS)

    Mielke, R. R.; Tung, L. J.; Marefat, M.

    1983-01-01

    The applicability of spectral assignment techniques to the design of multivariable feedback control systems was investigated. A fractional representation design procedure for unstable plants is presented and illustrated with an example. A computer aided design software package implementing eigenvalue/eigenvector design procedures is described. A design example which illustrates the use of the program is explained.

  1. Multivariate classification of infrared spectra of cell and tissue samples

    DOEpatents

    Haaland, David M.; Jones, Howland D. T.; Thomas, Edward V.

    1997-01-01

    Multivariate classification techniques are applied to spectra from cell and tissue samples irradiated with infrared radiation to determine if the samples are normal or abnormal (cancerous). Mid and near infrared radiation can be used for in vivo and in vitro classifications using at least different wavelengths.

  2. Introduction to Kernel Methods: Classification of Multivariate Data

    NASA Astrophysics Data System (ADS)

    Fauvel, M.

    2016-05-01

    In this chapter, kernel methods are presented for the classification of multivariate data. An introduction example is given to enlighten the main idea of kernel methods. Then emphasis is done on the Support Vector Machine. Structural risk minimization is presented, and linear and non-linear SVM are described. Finally, a full example of SVM classification is given on simulated hyperspectral data.

  3. Ways To Evaluate the Assumption of Multivariate Normality.

    ERIC Educational Resources Information Center

    Ashcraft, Alyce S.

    This paper reviews graphical and nongraphical methods for estimating multivariate normality. Prior to exploring this methodology, a foundation is established by presenting ways to assess univariate and bivariate normality. A data set of three variables used by J. Stevens (1986) is analyzed using Q-Q plots, stem and leaf plots, histograms,…

  4. Multivariate Models of Mothers' and Fathers' Aggression toward Their Children

    ERIC Educational Resources Information Center

    Smith Slep, Amy M.; O'Leary, Susan G.

    2007-01-01

    Multivariate, biopsychosocial, explanatory models of mothers' and fathers' psychological and physical aggression toward their 3- to 7-year-old children were fitted and cross-validated in 453 representatively sampled families. Models explaining mothers' and fathers' aggression were substantially similar. Surprisingly, many variables identified as…

  5. Preliminary Multi-Variable Parametric Cost Model for Space Telescopes

    NASA Technical Reports Server (NTRS)

    Stahl, H. Philip; Hendrichs, Todd

    2010-01-01

    This slide presentation reviews creating a preliminary multi-variable cost model for the contract costs of making a space telescope. There is discussion of the methodology for collecting the data, definition of the statistical analysis methodology, single variable model results, testing of historical models and an introduction of the multi variable models.

  6. SUGGESTIONS FOR OPTIMIZED PLANNING OF MULTIVARIATE MONITORING OF ATMOSPHERIC POLLUTION

    EPA Science Inventory

    Recent work in factor analysis of multivariate data sets has shown that variables with little signal should not be included in the factor analysis. Work also shows that rotational ambiguity is reduced if sources impacting a receptor have both large and small contributions. Thes...

  7. Bayesian Analysis of Multivariate Probit Models with Surrogate Outcome Data

    ERIC Educational Resources Information Center

    Poon, Wai-Yin; Wang, Hai-Bin

    2010-01-01

    A new class of parametric models that generalize the multivariate probit model and the errors-in-variables model is developed to model and analyze ordinal data. A general model structure is assumed to accommodate the information that is obtained via surrogate variables. A hybrid Gibbs sampler is developed to estimate the model parameters. To…

  8. A Multivariate Descriptive Model of Motivation for Orthodontic Treatment.

    ERIC Educational Resources Information Center

    Hackett, Paul M. W.; And Others

    1993-01-01

    Motivation for receiving orthodontic treatment was studied among 109 young adults, and a multivariate model of the process is proposed. The combination of smallest scale analysis and Partial Order Scalogram Analysis by base Coordinates (POSAC) illustrates an interesting methodology for health treatment studies and explores motivation for dental…

  9. Choosing the Greenest Synthesis: A Multivariate Metric Green Chemistry Exercise

    ERIC Educational Resources Information Center

    Mercer, Sean M.; Andraos, John; Jessop, Philip G.

    2012-01-01

    The ability to correctly identify the greenest of several syntheses is a particularly useful asset for young chemists in the growing green economy. The famous univariate metrics atom economy and environmental factor provide insufficient information to allow for a proper selection of a green process. Multivariate metrics, such as those used in…

  10. Multivariable feedback design: concepts for a classical/modern synthesis

    SciTech Connect

    Doyle, J C; Stein, G

    1980-01-01

    A practical design perspective on multivariable feedback control problems is presented. The basic issue - feedback design in the face of uncertainites - is reviewed and known SISO statements and constraints of the design problem to MIMO cases are generalized. Two major MIMO design approaches are then evaluated in the context of these results.

  11. Determining the geographical origin of Chinese cabbages using multielement composition and strontium isotope ratio analyses

    NASA Astrophysics Data System (ADS)

    BONG, Y.; Shin, W.; Gautam, M. K.; Jeong, Y.; Lee, A.; Jang, C.; Lim, Y.; Chung, G.; Lee, K.

    2012-12-01

    Recently, the Korean market has seen many cases of Chinese cabbage (Brassica rapa ssp. pekinensis) that have been imported from China, yet are sold as a Korean product to illegally benefit from the price difference between the two products. This study aims to establish a method of distinguishing the geographical origin of Chinese cabbage. One hundred Chinese cabbage heads from Korea and 60 cabbage heads from China were subjected to multielement composition and strontium isotope ratio (87Sr/86Sr) analyses. The 87Sr/86Sr ratio differed, based on the geological characteristics of their district of production. In addition, the content of many elements differed between cabbages from Korea and China. In particular, the difference in the content of Sr and Ti alone and the combination of Sr, Ca, and Mg allowed us to distinguish relatively well between Korea and China as the country of origin. The present study demonstrates that the chemical and Sr isotopic analyses exactly reflect the geology of the production areas of Chinese cabbage. Also, multivariate statistical analyses of multiple elements were found to be very effective in distinguishing the geographical origin of Chinese cabbages.

  12. Reciprocal Benefits of Mass-Univariate and Multivariate Modeling in Brain Mapping: Applications to Event-Related Functional MRI, H2 15O-, and FDG-PET

    PubMed Central

    Habeck, Christian G.

    2006-01-01

    In brain mapping studies of sensory, cognitive, and motor operations, specific waveforms of dynamic neural activity are predicted based on theoretical models of human information processing. For example in event-related functional MRI (fMRI), the general linear model (GLM) is employed in mass-univariate analyses to identify the regions whose dynamic activity closely matches the expected waveforms. By comparison multivariate analyses based on PCA or ICA provide greater flexibility in detecting spatiotemporal properties of experimental data that may strongly support alternative neuroscientific explanations. We investigated conjoint multivariate and mass-univariate analyses that combine the capabilities to (1) verify activation of neural machinery we already understand and (2) discover reliable signatures of new neural machinery. We examined combinations of GLM and PCA that recover latent neural signals (waveforms and footprints) with greater accuracy than either method alone. Comparative results are illustrated with analyses of real fMRI data, adding to Monte Carlo simulation support. PMID:23165047

  13. Targeting sources of drought tolerance within an Avena spp. collection through multivariate approaches.

    PubMed

    Sánchez-Martín, Javier; Mur, Luis A J; Rubiales, Diego; Prats, Elena

    2012-11-01

    In this study, we find and characterize the sources of tolerance to drought amongst an oat (Avena sativa L.) germplasm collection of 174 landraces and cultivars. We used multivariate analysis, non-supervised principal component analyses (PCA) and supervised discriminant function analyses (DFA) to suggest the key mechanism/s responsible for coping with drought stress. Following initial assessment of drought symptoms and area under the drought progress curve, a subset of 14 accessions were selected for further analysis. The collection was assessed for relative water content (RWC), cell membrane stability, stomatal conductance (g (1)), leaf temperature, water use efficiency (WUE), lipid peroxidation, lipoxygenase activity, chlorophyll levels and antioxidant capacity during a drought time course experiment. Without the use of multivariate approaches, it proved difficult to unequivocally link drought tolerance to specific physiological processes in the different resistant oat accessions. These approaches allowed the ranking of many supposed drought tolerance traits in the order of degree of importance within this crop, thereby highlighting those with a causal relationship to drought stress tolerance. Analyses of the loading vectors used to derive the PCA and DFA models indicated that two traits involved in water relations, temperature and RWC together with the area of drought curves, were important indicators of drought tolerance. However, other parameters involved in water use such as g (1) and WUE were less able to discriminate between the accessions. These observations validate our approach which should be seen as representing a cost-effective initial screen that could be subsequently employed to target drought tolerance in segregating populations.

  14. Multivariate analysis and chemometric characterisation of textile wastewater streams.

    PubMed

    Kavsek, Darja; Jeric, Tina; Le Marechal, Alenka Majcen; Vajnhandl, Simona; Bednárová, Adriána; Voncina, Darinka Brodnjak

    2013-01-01

    The aim of this work was to design a quick and reliable method for the evaluation and classification of wastewater streams into treatable and non-treatable effluents for reuse/recycling. Different chemometric methods were used for this purpose handling the enormous amount of data, and additionally to find any hidden information, which would increase our knowledge and improve the classification. The data obtained from the processes description, together with the analytical results of measured parameters' characterising the wastewater of a particular process, enabled us to build a fast-decision model for separating different textile wastewater outlets. Altogether 49 wastewater samples from the textile finishing company were analysed, and 19 different physical chemical measurements were performed for each of them. The resulting classification model was aimed at an automated decision about the choice of treatment technologies or a prediction about the reusability of wastewaters within any textile finishing or other company having similar characteristics of wastewater streams.

  15. Multivariate analysis and chemometric characterisation of textile wastewater streams.

    PubMed

    Kavsek, Darja; Jeric, Tina; Le Marechal, Alenka Majcen; Vajnhandl, Simona; Bednárová, Adriána; Voncina, Darinka Brodnjak

    2013-01-01

    The aim of this work was to design a quick and reliable method for the evaluation and classification of wastewater streams into treatable and non-treatable effluents for reuse/recycling. Different chemometric methods were used for this purpose handling the enormous amount of data, and additionally to find any hidden information, which would increase our knowledge and improve the classification. The data obtained from the processes description, together with the analytical results of measured parameters' characterising the wastewater of a particular process, enabled us to build a fast-decision model for separating different textile wastewater outlets. Altogether 49 wastewater samples from the textile finishing company were analysed, and 19 different physical chemical measurements were performed for each of them. The resulting classification model was aimed at an automated decision about the choice of treatment technologies or a prediction about the reusability of wastewaters within any textile finishing or other company having similar characteristics of wastewater streams. PMID:23878942

  16. Assessment of trace elements levels in patients with Type 2 diabetes using multivariate statistical analysis.

    PubMed

    Badran, M; Morsy, R; Soliman, H; Elnimr, T

    2016-01-01

    The trace elements metabolism has been reported to possess specific roles in the pathogenesis and progress of diabetes mellitus. Due to the continuous increase in the population of patients with Type 2 diabetes (T2D), this study aims to assess the levels and inter-relationships of fast blood glucose (FBG) and serum trace elements in Type 2 diabetic patients. This study was conducted on 40 Egyptian Type 2 diabetic patients and 36 healthy volunteers (Hospital of Tanta University, Tanta, Egypt). The blood serum was digested and then used to determine the levels of 24 trace elements using an inductive coupled plasma mass spectroscopy (ICP-MS). Multivariate statistical analysis depended on correlation coefficient, cluster analysis (CA) and principal component analysis (PCA), were used to analysis the data. The results exhibited significant changes in FBG and eight of trace elements, Zn, Cu, Se, Fe, Mn, Cr, Mg, and As, levels in the blood serum of Type 2 diabetic patients relative to those of healthy controls. The statistical analyses using multivariate statistical techniques were obvious in the reduction of the experimental variables, and grouping the trace elements in patients into three clusters. The application of PCA revealed a distinct difference in associations of trace elements and their clustering patterns in control and patients group in particular for Mg, Fe, Cu, and Zn that appeared to be the most crucial factors which related with Type 2 diabetes. Therefore, on the basis of this study, the contributors of trace elements content in Type 2 diabetic patients can be determine and specify with correlation relationship and multivariate statistical analysis, which confirm that the alteration of some essential trace metals may play a role in the development of diabetes mellitus.

  17. Assessment of trace elements levels in patients with Type 2 diabetes using multivariate statistical analysis.

    PubMed

    Badran, M; Morsy, R; Soliman, H; Elnimr, T

    2016-01-01

    The trace elements metabolism has been reported to possess specific roles in the pathogenesis and progress of diabetes mellitus. Due to the continuous increase in the population of patients with Type 2 diabetes (T2D), this study aims to assess the levels and inter-relationships of fast blood glucose (FBG) and serum trace elements in Type 2 diabetic patients. This study was conducted on 40 Egyptian Type 2 diabetic patients and 36 healthy volunteers (Hospital of Tanta University, Tanta, Egypt). The blood serum was digested and then used to determine the levels of 24 trace elements using an inductive coupled plasma mass spectroscopy (ICP-MS). Multivariate statistical analysis depended on correlation coefficient, cluster analysis (CA) and principal component analysis (PCA), were used to analysis the data. The results exhibited significant changes in FBG and eight of trace elements, Zn, Cu, Se, Fe, Mn, Cr, Mg, and As, levels in the blood serum of Type 2 diabetic patients relative to those of healthy controls. The statistical analyses using multivariate statistical techniques were obvious in the reduction of the experimental variables, and grouping the trace elements in patients into three clusters. The application of PCA revealed a distinct difference in associations of trace elements and their clustering patterns in control and patients group in particular for Mg, Fe, Cu, and Zn that appeared to be the most crucial factors which related with Type 2 diabetes. Therefore, on the basis of this study, the contributors of trace elements content in Type 2 diabetic patients can be determine and specify with correlation relationship and multivariate statistical analysis, which confirm that the alteration of some essential trace metals may play a role in the development of diabetes mellitus. PMID:26653752

  18. Characterization of Physical Controls on Stream Base-flow and the Flux of Surface Water and Groundwater Using Multivariate Analysis in the Northern Great Plains

    NASA Astrophysics Data System (ADS)

    Bednar, J. M.; Long, A. J.

    2015-12-01

    Stream base-flow estimation is commonly performed by using graphical or chemical hydrograph separation methods that have limitations due to the spatial and temporal availability of data. Current graphical separation methods are limited in that they rely solely on streamflow records, whereas chemical methods are expensive and involve intense data collection. Graphical hydrograph separation methods are applicable to perennial and gaining streams but result in large uncertainty when applied to ephemeral or losing streams that are typical of dry climates. A new method planned for development will consist of multivariate analysis to determine which spatial and temporal variables are the controlling factors for base flow. Data used in the development of this methodology will include geologic, hydrologic, climatic, land surface, and remotely sensed data that are widely available to the public. Factors considered will include geologic media, flow-duration curves, temporal variability of streamflow, stream type, precipitation, drought-severity index, land-surface slope, and vegetation. This research will examine differences in variables controlling base flow between dry and humid climates, perennial and ephemeral streams, and gaining and losing stream reaches. Although the accuracy of each variable will vary, the use of multivariate analyses will help compensate for those variables with low accuracy. Base-flow estimates were previously calculated for all streams with streamflow data located in the Williston and Powder River structural basins using the U.S. Geological Survey hydrograph separation software, PART; these streams, in addition to streams not previously analyzed, will be evaluated by using the method that is being developed. The study area for this research will include the Heart River basin in southwestern North Dakota, the White River basin in southwestern South Dakota, and the Niobrara River basin in northern Nebraska.

  19. Multivariate and Cladistic Analyses of Isolated Teeth Reveal Sympatry of Theropod Dinosaurs in the Late Jurassic of Northern Germany

    PubMed Central

    Gerke, Oliver; Wings, Oliver

    2016-01-01

    Remains of theropod dinosaurs are very rare in Northern Germany because the area was repeatedly submerged by a shallow epicontinental sea during the Mesozoic. Here, 80 Late Jurassic theropod teeth are described of which the majority were collected over decades from marine carbonates in nowadays abandoned and backfilled quarries of the 19th century. Eighteen different morphotypes (A—R) could be distinguished and 3D models based on micro-CT scans of the best examples of all morphotypes are included as supplements. The teeth were identified with the assistance of discriminant function analysis and cladistic analysis based on updated datamatrices. The results show that a large variety of theropod groups were present in the Late Jurassic of northern Germany. Identified specimens comprise basal Tyrannosauroidea, as well as Allosauroidea, Megalosauroidea cf. Marshosaurus, Megalosauridae cf. Torvosaurus and probably Ceratosauria. The formerly reported presence of Dromaeosauridae in the Late Jurassic of northern Germany could not be confirmed. Some teeth of this study resemble specimens described as pertaining to Carcharodontosauria (morphotype A) and Abelisauridae (morphotype K). This interpretation is however, not supported by discriminant function analysis and cladistic analysis. Two smaller morphotypes (N and Q) differ only in some probably size-related characteristics from larger morphotypes (B and C) and could well represent juveniles of adult specimens. The similarity of the northern German theropods with groups from contemporaneous localities suggests faunal exchange via land-connections in the Late Jurassic between Germany, Portugal and North America. PMID:27383054

  20. Multivariate Analyses and Classification of Inertial Sensor Data to Identify Aging Effects on the Timed-Up-and-Go Test.

    PubMed

    Vervoort, Danique; Vuillerme, Nicolas; Kosse, Nienke; Hortobágyi, Tibor; Lamoth, Claudine J C

    2016-01-01

    Many tests can crudely quantify age-related mobility decrease but instrumented versions of mobility tests could increase their specificity and sensitivity. The Timed-up-and-Go (TUG) test includes several elements that people use in daily life. The test has different transition phases: rise from a chair, walk, 180° turn, walk back, turn, and sit-down on a chair. For this reason the TUG is an often used test to evaluate in a standardized way possible decline in balance and walking ability due to age and or pathology. Using inertial sensors, qualitative information about the performance of the sub-phases can provide more specific information about a decline in balance and walking ability. The first aim of our study was to identify variables extracted from the instrumented timed-up-and-go (iTUG) that most effectively distinguished performance differences across age (age 18-75). Second, we determined the discriminative ability of those identified variables to classify a younger (age 18-45) and older age group (age 46-75). From healthy adults (n = 59), trunk accelerations and angular velocities were recorded during iTUG performance. iTUG phases were detected with wavelet-analysis. Using a Partial Least Square (PLS) model, from the 72-iTUG variables calculated across phases, those that explained most of the covariance between variables and age were extracted. Subsequently, a PLS-discriminant analysis (DA) assessed classification power of the identified iTUG variables to discriminate the age groups. 27 variables, related to turning, walking and the stand-to-sit movement explained 71% of the variation in age. The PLS-DA with these 27 variables showed a sensitivity and specificity of 90% and 85%. Based on this model, the iTUG can accurately distinguish young and older adults. Such data can serve as a reference for pathological aging with respect to a widely used mobility test. Mobility tests like the TUG supplemented with smart technology could be used in clinical practice. PMID:27271994

  1. Multivariate and Cladistic Analyses of Isolated Teeth Reveal Sympatry of Theropod Dinosaurs in the Late Jurassic of Northern Germany.

    PubMed

    Gerke, Oliver; Wings, Oliver

    2016-01-01

    Remains of theropod dinosaurs are very rare in Northern Germany because the area was repeatedly submerged by a shallow epicontinental sea during the Mesozoic. Here, 80 Late Jurassic theropod teeth are described of which the majority were collected over decades from marine carbonates in nowadays abandoned and backfilled quarries of the 19th century. Eighteen different morphotypes (A-R) could be distinguished and 3D models based on micro-CT scans of the best examples of all morphotypes are included as supplements. The teeth were identified with the assistance of discriminant function analysis and cladistic analysis based on updated datamatrices. The results show that a large variety of theropod groups were present in the Late Jurassic of northern Germany. Identified specimens comprise basal Tyrannosauroidea, as well as Allosauroidea, Megalosauroidea cf. Marshosaurus, Megalosauridae cf. Torvosaurus and probably Ceratosauria. The formerly reported presence of Dromaeosauridae in the Late Jurassic of northern Germany could not be confirmed. Some teeth of this study resemble specimens described as pertaining to Carcharodontosauria (morphotype A) and Abelisauridae (morphotype K). This interpretation is however, not supported by discriminant function analysis and cladistic analysis. Two smaller morphotypes (N and Q) differ only in some probably size-related characteristics from larger morphotypes (B and C) and could well represent juveniles of adult specimens. The similarity of the northern German theropods with groups from contemporaneous localities suggests faunal exchange via land-connections in the Late Jurassic between Germany, Portugal and North America.

  2. Multivariate Analyses and Classification of Inertial Sensor Data to Identify Aging Effects on the Timed-Up-and-Go Test

    PubMed Central

    Vervoort, Danique; Vuillerme, Nicolas; Kosse, Nienke; Hortobágyi, Tibor; Lamoth, Claudine J. C.

    2016-01-01

    Many tests can crudely quantify age-related mobility decrease but instrumented versions of mobility tests could increase their specificity and sensitivity. The Timed-up-and-Go (TUG) test includes several elements that people use in daily life. The test has different transition phases: rise from a chair, walk, 180° turn, walk back, turn, and sit-down on a chair. For this reason the TUG is an often used test to evaluate in a standardized way possible decline in balance and walking ability due to age and or pathology. Using inertial sensors, qualitative information about the performance of the sub-phases can provide more specific information about a decline in balance and walking ability. The first aim of our study was to identify variables extracted from the instrumented timed-up-and-go (iTUG) that most effectively distinguished performance differences across age (age 18–75). Second, we determined the discriminative ability of those identified variables to classify a younger (age 18–45) and older age group (age 46–75). From healthy adults (n = 59), trunk accelerations and angular velocities were recorded during iTUG performance. iTUG phases were detected with wavelet-analysis. Using a Partial Least Square (PLS) model, from the 72-iTUG variables calculated across phases, those that explained most of the covariance between variables and age were extracted. Subsequently, a PLS-discriminant analysis (DA) assessed classification power of the identified iTUG variables to discriminate the age groups. 27 variables, related to turning, walking and the stand-to-sit movement explained 71% of the variation in age. The PLS-DA with these 27 variables showed a sensitivity and specificity of 90% and 85%. Based on this model, the iTUG can accurately distinguish young and older adults. Such data can serve as a reference for pathological aging with respect to a widely used mobility test. Mobility tests like the TUG supplemented with smart technology could be used in clinical practice. PMID:27271994

  3. Multivariate analyses of NP-TLC chromatographic retention data for grouping of structurally-related plant secondary metabolites.

    PubMed

    Shawky, Eman

    2016-09-01

    The chromatographic behavior of 28 plant secondary metabolites belonging to four chemically similar classes (alkaloids, flavonoids, flavone glycosides and sesquiterpenes) was studied by normal-phase thin-layer chromatography (NP-TLC) under 5 different chromatographic systems commonly used in plant drug analysis with the aim to explore whether the retention properties of these metabolites can determine the chemical group they belong to. The use of RM values as the retention parameter is implemented as a relatively new approach in plant analysis. Principal component analysis (PCA), hierarchical clustering heat maps and discriminant analysis (DA), were used for statistical evaluation of the chromatographic data and extraction of similarities between chemically related compounds. The twenty eight metabolites were classified into four groups by principal component analysis. The heat map of hierarchical clustering revealed that all metabolites were clustered into four groups, except for caffeine, while linear discriminant analysis showed that 96.4% of metabolites are predicted correctly as the groupings identified by chemical class in original and cross-validated data. The main advantage of the approach described in current paper is its simplicity which can assist with preliminary identification of metabolites in complex plant extracts. PMID:27395422

  4. Multivariate and Cladistic Analyses of Isolated Teeth Reveal Sympatry of Theropod Dinosaurs in the Late Jurassic of Northern Germany.

    PubMed

    Gerke, Oliver; Wings, Oliver

    2016-01-01

    Remains of theropod dinosaurs are very rare in Northern Germany because the area was repeatedly submerged by a shallow epicontinental sea during the Mesozoic. Here, 80 Late Jurassic theropod teeth are described of which the majority were collected over decades from marine carbonates in nowadays abandoned and backfilled quarries of the 19th century. Eighteen different morphotypes (A-R) could be distinguished and 3D models based on micro-CT scans of the best examples of all morphotypes are included as supplements. The teeth were identified with the assistance of discriminant function analysis and cladistic analysis based on updated datamatrices. The results show that a large variety of theropod groups were present in the Late Jurassic of northern Germany. Identified specimens comprise basal Tyrannosauroidea, as well as Allosauroidea, Megalosauroidea cf. Marshosaurus, Megalosauridae cf. Torvosaurus and probably Ceratosauria. The formerly reported presence of Dromaeosauridae in the Late Jurassic of northern Germany could not be confirmed. Some teeth of this study resemble specimens described as pertaining to Carcharodontosauria (morphotype A) and Abelisauridae (morphotype K). This interpretation is however, not supported by discriminant function analysis and cladistic analysis. Two smaller morphotypes (N and Q) differ only in some probably size-related characteristics from larger morphotypes (B and C) and could well represent juveniles of adult specimens. The similarity of the northern German theropods with groups from contemporaneous localities suggests faunal exchange via land-connections in the Late Jurassic between Germany, Portugal and North America. PMID:27383054

  5. Multivariate analyses of NP-TLC chromatographic retention data for grouping of structurally-related plant secondary metabolites.

    PubMed

    Shawky, Eman

    2016-09-01

    The chromatographic behavior of 28 plant secondary metabolites belonging to four chemically similar classes (alkaloids, flavonoids, flavone glycosides and sesquiterpenes) was studied by normal-phase thin-layer chromatography (NP-TLC) under 5 different chromatographic systems commonly used in plant drug analysis with the aim to explore whether the retention properties of these metabolites can determine the chemical group they belong to. The use of RM values as the retention parameter is implemented as a relatively new approach in plant analysis. Principal component analysis (PCA), hierarchical clustering heat maps and discriminant analysis (DA), were used for statistical evaluation of the chromatographic data and extraction of similarities between chemically related compounds. The twenty eight metabolites were classified into four groups by principal component analysis. The heat map of hierarchical clustering revealed that all metabolites were clustered into four groups, except for caffeine, while linear discriminant analysis showed that 96.4% of metabolites are predicted correctly as the groupings identified by chemical class in original and cross-validated data. The main advantage of the approach described in current paper is its simplicity which can assist with preliminary identification of metabolites in complex plant extracts.

  6. A Versatile Cell Death Screening Assay Using Dye-Stained Cells and Multivariate Image Analysis

    PubMed Central

    Collins, Tony J.; Ylanko, Jarkko; Geng, Fei

    2015-01-01

    Abstract A novel dye-based method for measuring cell death in image-based screens is presented. Unlike conventional high- and medium-throughput cell death assays that measure only one form of cell death accurately, using multivariate analysis of micrographs of cells stained with the inexpensive mix, red dye nonyl acridine orange, and a nuclear stain, it was possible to quantify cell death induced by a variety of different agonists even without a positive control. Surprisingly, using a single known cytotoxic agent as a positive control for training a multivariate classifier allowed accurate quantification of cytotoxicity for mechanistically unrelated compounds enabling generation of dose–response curves. Comparison with low throughput biochemical methods suggested that cell death was accurately distinguished from cell stress induced by low concentrations of the bioactive compounds Tunicamycin and Brefeldin A. High-throughput image-based format analyses of more than 300 kinase inhibitors correctly identified 11 as cytotoxic with only 1 false positive. The simplicity and robustness of this dye-based assay makes it particularly suited to live cell screening for toxic compounds. PMID:26422066

  7. Multivariate model of female black bear habitat use for a Geographic Information System

    USGS Publications Warehouse

    Clark, Joseph D.; Dunn, James E.; Smith, Kimberly G.

    1993-01-01

    Simple univariate statistical techniques may not adequately assess the multidimensional nature of habitats used by wildlife. Thus, we developed a multivariate method to model habitat-use potential using a set of female black bear (Ursus americanus) radio locations and habitat data consisting of forest cover type, elevation, slope, aspect, distance to roads, distance to streams, and forest cover type diversity score in the Ozark Mountains of Arkansas. The model is based on the Mahalanobis distance statistic coupled with Geographic Information System (GIS) technology. That statistic is a measure of dissimilarity and represents a standardized squared distance between a set of sample variates and an ideal based on the mean of variates associated with animal observations. Calculations were made with the GIS to produce a map containing Mahalanobis distance values within each cell on a 60- × 60-m grid. The model identified areas of high habitat use potential that could not otherwise be identified by independent perusal of any single map layer. This technique avoids many pitfalls that commonly affect typical multivariate analyses of habitat use and is a useful tool for habitat manipulation or mitigation to favor terrestrial vertebrates that use habitats on a landscape scale.

  8. Visual classification of very fine-grained sediments: Evaluation through univariate and multivariate statistics

    USGS Publications Warehouse

    Hohn, M. Ed; Nuhfer, E.B.; Vinopal, R.J.; Klanderman, D.S.

    1980-01-01

    Classifying very fine-grained rocks through fabric elements provides information about depositional environments, but is subject to the biases of visual taxonomy. To evaluate the statistical significance of an empirical classification of very fine-grained rocks, samples from Devonian shales in four cored wells in West Virginia and Virginia were measured for 15 variables: quartz, illite, pyrite and expandable clays determined by X-ray diffraction; total sulfur, organic content, inorganic carbon, matrix density, bulk density, porosity, silt, as well as density, sonic travel time, resistivity, and ??-ray response measured from well logs. The four lithologic types comprised: (1) sharply banded shale, (2) thinly laminated shale, (3) lenticularly laminated shale, and (4) nonbanded shale. Univariate and multivariate analyses of variance showed that the lithologic classification reflects significant differences for the variables measured, difference that can be detected independently of stratigraphic effects. Little-known statistical methods found useful in this work included: the multivariate analysis of variance with more than one effect, simultaneous plotting of samples and variables on canonical variates, and the use of parametric ANOVA and MANOVA on ranked data. ?? 1980 Plenum Publishing Corporation.

  9. A Versatile Cell Death Screening Assay Using Dye-Stained Cells and Multivariate Image Analysis.

    PubMed

    Collins, Tony J; Ylanko, Jarkko; Geng, Fei; Andrews, David W

    2015-11-01

    A novel dye-based method for measuring cell death in image-based screens is presented. Unlike conventional high- and medium-throughput cell death assays that measure only one form of cell death accurately, using multivariate analysis of micrographs of cells stained with the inexpensive mix, red dye nonyl acridine orange, and a nuclear stain, it was possible to quantify cell death induced by a variety of different agonists even without a positive control. Surprisingly, using a single known cytotoxic agent as a positive control for training a multivariate classifier allowed accurate quantification of cytotoxicity for mechanistically unrelated compounds enabling generation of dose-response curves. Comparison with low throughput biochemical methods suggested that cell death was accurately distinguished from cell stress induced by low concentrations of the bioactive compounds Tunicamycin and Brefeldin A. High-throughput image-based format analyses of more than 300 kinase inhibitors correctly identified 11 as cytotoxic with only 1 false positive. The simplicity and robustness of this dye-based assay makes it particularly suited to live cell screening for toxic compounds.

  10. Visual classification of very fine-grained sediments: evaluation through univariate and multivariate statistics

    SciTech Connect

    Hohn, M.E.; Nuhfer, E.B.; Vinopal, R.J.; Klanderman, D.S.

    1980-01-01

    Classifying very fine-grained rocks through fabric elements provides information about depositional environments, but is subject to the biases of visual taxonomy. To evaluate the statistical significance of an empirical classification of very fine-grained rocks, samples from Devonian shales in four cored wells in West Virginia and Virginia were measured for 15 variables: quartz, illite, pyrite and expandable clays determined by X-ray diffraction; total sulfur, organic content, inorganic carbon, matrix density, bulk density, porosity, silt, as well as density, sonic travel time, resistivity, and ..gamma..-ray response measured from well logs. The four lithologic types comprised: (1) sharply banded shale, (2) thinly laminated shale, (3) lenticularly laminated shale, and (4) nonbanded shale. Univariate and multivariate analyses of variance showed that the lithologic classification reflects significant differences for the variables measured, difference that can be detected independently of stratigraphic effects. Little-known statistical methods found useful in this work included: the multivariate analysis of variance with more than one effect, simultaneous plotting of samples and variables on canonical variates, and the use of parametric ANOVA and MANOVA on ranked data.

  11. [Food additives and healthiness].

    PubMed

    Heinonen, Marina

    2014-01-01

    Additives are used for improving food structure or preventing its spoilage, for example. Many substances used as additives are also naturally present in food. The safety of additives is evaluated according to commonly agreed principles. If high concentrations of an additive cause adverse health effects for humans, a limit of acceptable daily intake (ADI) is set for it. An additive is a risk only when ADI is exceeded. The healthiness of food is measured on the basis of nutrient density and scientifically proven effects.

  12. LDEF Satellite Radiation Analyses

    NASA Technical Reports Server (NTRS)

    Armstrong, T. W.; Colborn, B. L.

    1996-01-01

    Model calculations and analyses have been carried out to compare with several sets of data (dose, induced radioactivity in various experiment samples and spacecraft components, fission foil measurements, and LET spectra) from passive radiation dosimetry on the Long Duration Exposure Facility (LDEF) satellite, which was recovered after almost six years in space. The calculations and data comparisons are used to estimate the accuracy of current models and methods for predicting the ionizing radiation environment in low earth orbit. The emphasis is on checking the accuracy of trapped proton flux and anisotropy models.

  13. Multivariate permutation test to compare survival curves for matched data

    PubMed Central

    2013-01-01

    Background In the absence of randomization, the comparison of an experimental treatment with respect to the standard may be done based on a matched design. When there is a limited set of cases receiving the experimental treatment, matching of a proper set of controls in a non fixed proportion is convenient. Methods In order to deal with the highly stratified survival data generated by multiple matching, we extend the multivariate permutation testing approach, since standard nonparametric methods for the comparison of survival curves cannot be applied in this setting. Results We demonstrate the validity of the proposed method with simulations, and we illustrate its application to data from an observational study for the comparison of bone marrow transplantation and chemotherapy in the treatment of paediatric leukaemia. Conclusions The use of the multivariate permutation testing approach is recommended in the highly stratified context of survival matched data, especially when the proportional hazards assumption does not hold. PMID:23399031

  14. Generalized error-dependent prediction uncertainty in multivariate calibration.

    PubMed

    Allegrini, Franco; Wentzell, Peter D; Olivieri, Alejandro C

    2016-01-15

    Most of the current expressions used to calculate figures of merit in multivariate calibration have been derived assuming independent and identically distributed (iid) measurement errors. However, it is well known that this condition is not always valid for real data sets, where the existence of many external factors can lead to correlated and/or heteroscedastic noise structures. In this report, the influence of the deviations from the classical iid paradigm is analyzed in the context of error propagation theory. New expressions have been derived to calculate sample dependent prediction standard errors under different scenarios. These expressions allow for a quantitative study of the influence of the different sources of instrumental error affecting the system under analysis. Significant differences are observed when the prediction error is estimated in each of the studied scenarios using the most popular first-order multivariate algorithms, under both simulated and experimental conditions.

  15. Empirical performance of the multivariate normal universal portfolio

    NASA Astrophysics Data System (ADS)

    Tan, Choon Peng; Pang, Sook Theng

    2013-09-01

    Universal portfolios generated by the multivariate normal distribution are studied with emphasis on the case where variables are dependent, namely, the covariance matrix is not diagonal. The moving-order multivariate normal universal portfolio requires very long implementation time and large computer memory in its implementation. With the objective of reducing memory and implementation time, the finite-order universal portfolio is introduced. Some stock-price data sets are selected from the local stock exchange and the finite-order universal portfolio is run on the data sets, for small finite order. Empirically, it is shown that the portfolio can outperform the moving-order Dirichlet universal portfolio of Cover and Ordentlich[2] for certain parameters in the selected data sets.

  16. A novel definition of the multivariate coefficient of variation.

    PubMed

    Albert, Adelin; Zhang, Lixin

    2010-10-01

    The coefficient of variation CV (%) is widely used to measure the relative variation of a random variable to its mean or to assess and compare the performance of analytical techniques/equipments. A review is made of the existing multivariate extensions of the univariate CV where, instead of a random variable, a random vector is considered, and a novel definition is proposed. The multivariate CV obtained only requires the calculation of the mean vector, the covariance matrix and simple quadratic forms. No matrix inversion is needed which makes the new approach equally attractive in high dimensional as in very small sample size problems. As an illustration, the method is applied to electrophoresis data from external quality assessment in laboratory medicine, to phenotypic characteristics of pocket gophers and to a microarray data set.

  17. Estimation of sparse directed acyclic graphs for multivariate counts data.

    PubMed

    Han, Sung Won; Zhong, Hua

    2016-09-01

    The next-generation sequencing data, called high-throughput sequencing data, are recorded as count data, which are generally far from normal distribution. Under the assumption that the count data follow the Poisson log-normal distribution, this article provides an L1-penalized likelihood framework and an efficient search algorithm to estimate the structure of sparse directed acyclic graphs (DAGs) for multivariate counts data. In searching for the solution, we use iterative optimization procedures to estimate the adjacency matrix and the variance matrix of the latent variables. The simulation result shows that our proposed method outperforms the approach which assumes multivariate normal distributions, and the log-transformation approach. It also shows that the proposed method outperforms the rank-based PC method under sparse network or hub network structures. As a real data example, we demonstrate the efficiency of the proposed method in estimating the gene regulatory networks of the ovarian cancer study. PMID:26849781

  18. Multivariate geostatistical simulation by minimising spatial cross-correlation

    NASA Astrophysics Data System (ADS)

    Sohrabian, Babak; Tercan, Abdullah Erhan

    2014-03-01

    Joint simulation of attributes in multivariate geostatistics can be achieved by transforming spatially correlated variables into independent factors. In this study, a new approach for this transformation, Minimum Spatial Cross-correlation (MSC) method, is suggested. The method is based on minimising the sum of squares of cross-variograms at different distances. In the approach, the problem in higher space (N × N) is reduced to N×N-1/2 problems in the two-dimensional space and the reduced problem is solved iteratively using Gradient Descent Algorithm. The method is applied to the joint simulation of a set of multivariate data in a marble quarry and the results are compared with Minimum/Maximum Autocorrelation Factors (MAF) method.

  19. Temporal pattern mining for multivariate clinical decision support.

    PubMed

    Saini, Sheetal; Dua, Sumeet

    2013-01-01

    Multivariate temporal data are collections of contiguous data values that reflect complex temporal changes over a given duration. Technological advances have resulted in significant amounts of such data in high-throughput disciplines, including EEG and iEEG data for effective and efficient healthcare informatics, and decision support. Most data analytics and data-mining algorithms are effective in capturing global trends, but fail to capture localized behavioral changes in large temporal data sets. We present a two-step algorithmic methodology to uncover temporal patterns and exploiting them for an efficient and accurate decision support system. This methodology aids the discovery of previously unknown, nontrivial, and potentially useful temporal patterns for enhanced patient-specific clinical decision support with high degrees of sensitivity and specificity. Classification results on multivariate time series iEEG data for epileptic seizure detection also demonstrate the efficacy and accuracy of the technique to uncover interesting and effective domain class-specific temporal patterns.

  20. Causality networks from multivariate time series and application to epilepsy.

    PubMed

    Siggiridou, Elsa; Koutlis, Christos; Tsimpiris, Alkiviadis; Kimiskidis, Vasilios K; Kugiumtzis, Dimitris

    2015-08-01

    Granger causality and variants of this concept allow the study of complex dynamical systems as networks constructed from multivariate time series. In this work, a large number of Granger causality measures used to form causality networks from multivariate time series are assessed. For this, realizations on high dimensional coupled dynamical systems are considered and the performance of the Granger causality measures is evaluated, seeking for the measures that form networks closest to the true network of the dynamical system. In particular, the comparison focuses on Granger causality measures that reduce the state space dimension when many variables are observed. Further, the linear and nonlinear Granger causality measures of dimension reduction are compared to a standard Granger causality measure on electroencephalographic (EEG) recordings containing episodes of epileptiform discharges.

  1. Multivariate data validation for investigating primary HCMV infection in pregnancy.

    PubMed

    Barberini, Luigi; Noto, Antonio; Saba, Luca; Palmas, Francesco; Fanos, Vassilios; Dessì, Angelica; Zavattoni, Maurizio; Fattuoni, Claudia; Mussap, Michele

    2016-12-01

    We reported data concerning the Gas Chromatography-Mass Spectrometry (GC-MS) based metabolomic analysis of amniotic fluid (AF) samples obtained from pregnant women infected with Human Cytomegalovirus (HCMV). These data support the publication "Primary HCMV Infection in Pregnancy from Classic Data towards Metabolomics: an Exploratory analysis" (C. Fattuoni, F. Palmas, A. Noto, L. Barberini, M. Mussap, et al., 2016) [2]. GC-MS and Multivariate analysis allow to recognize the molecular phenotype of HCMV infected fetuses (transmitters) and that of HCMV non-infected fetuses (non-transmitters); moreover, GC-MS and multivariate analysis allow to distinguish and to compare the molecular phenotype of these two groups with a control group consisting of AF samples obtained in HCMV non-infected pregnant women. The obtained data discriminate controls from transmitters as well as from non-transmitters; no statistically significant difference was found between transmitters and non-transmitters. PMID:27656676

  2. A method for designing robust multivariable feedback systems

    NASA Technical Reports Server (NTRS)

    Milich, David Albert; Athans, Michael; Valavani, Lena; Stein, Gunter

    1988-01-01

    A new methodology is developed for the synthesis of linear, time-invariant (LTI) controllers for multivariable LTI systems. The aim is to achieve stability and performance robustness of the feedback system in the presence of multiple unstructured uncertainty blocks; i.e., to satisfy a frequency-domain inequality in terms of the structured singular value. The design technique is referred to as the Causality Recovery Methodology (CRM). Starting with an initial (nominally) stabilizing compensator, the CRM produces a closed-loop system whose performance-robustness is at least as good as, and hopefully superior to, that of the original design. The robustness improvement is obtained by solving an infinite-dimensional, convex optimization program. A finite-dimensional implementation of the CRM was developed, and it was applied to a multivariate design example.

  3. Enhanced bio-manufacturing through advanced multivariate statistical technologies.

    PubMed

    Martin, E B; Morris, A J

    2002-11-13

    The paper describes the interrogation of data, from a reaction vessel producing an active pharmaceutical ingredient (API), using advanced multivariate statistical techniques. Due to the limited number of batches available, data augmentation was used to increase the number of batches thereby enabling the extraction of more subtle process behaviour from the data. A second methodology investigated was that of multi-group modelling. This allowed between cluster variability to be removed, thus allowing attention to focus on within process variability. The paper describes how the different approaches enabled the realisation of a better understanding of the factors causing the onset of an impurity formation to be obtained as well demonstrating the power of multivariate statistical data analysis techniques to provide an enhanced understanding of the process.

  4. Scalar and Multivariate Approaches for Optimal Network Design in Antarctica

    NASA Astrophysics Data System (ADS)

    Hryniw, Natalia

    Observations are crucial for weather and climate, not only for daily forecasts and logistical purposes, for but maintaining representative records and for tuning atmospheric models. Here scalar theory for optimal network design is expanded in a multivariate framework, to allow for optimal station siting for full field optimization. Ensemble sensitivity theory is expanded to produce the covariance trace approach, which optimizes for the trace of the covariance matrix. Relative entropy is also used for multivariate optimization as an information theory approach for finding optimal locations. Antarctic surface temperature data is used as a testbed for these methods. Both methods produce different results which are tied to the fundamental physical parameters of the Antarctic temperature field.

  5. Latent fingerprints analysis using tape-lift, Raman microscopy, and multivariate data analysis methods.

    PubMed

    Widjaja, Effendi

    2009-04-01

    This paper describes the use of combined techniques, i.e. Raman spectral mapping, tape-lift, and multivariate data analysis, to extract chemical information of latent fingerprint and/or trace amounts of materials deposited in fingerprints. The tape-lift method was employed to lift trace particles, extrinsic materials, or sebum deposited on the finger of an individual after recent handling of such materials. The analysis of the tape-lifted materials was performed by Raman spectral mapping at a specific area. The collected mixture Raman spectra containing signals from lifting media and lifted materials was then deconvoluted using a powerful multivariate technique, namely band-target entropy minimization (BTEM). Three cases, i.e. a sebum-rich fingerprint after touching the forehead, a drug model comprising ibuprofen, L-arginine, and sodium bicarbonate, and an additive model comprising sucrose and aspartame were investigated. BTEM could recover all pure component spectra of both lifting media and tape-lifted materials. As such, all these test substances can be correctly identified using their unique pure Raman spectral signatures. In addition, the spatial distributions of all these identified components could also be determined. These combined three techniques hold promise as a new tool in forensic applications.

  6. Studying Resist Stochastics with the Multivariate Poisson Propagation Model

    DOE PAGES

    Naulleau, Patrick; Anderson, Christopher; Chao, Weilun; Bhattarai, Suchit; Neureuther, Andrew

    2014-01-01

    Progress in the ultimate performance of extreme ultraviolet resist has arguably decelerated in recent years suggesting an approach to stochastic limits both in photon counts and material parameters. Here we report on the performance of a variety of leading extreme ultraviolet resist both with and without chemical amplification. The measured performance is compared to stochastic modeling results using the Multivariate Poisson Propagation Model. The results show that the best materials are indeed nearing modeled performance limits.

  7. Reconstructing embedding spaces of coupled dynamical systems from multivariate data.

    PubMed

    Boccaletti, S; Valladares, D L; Pecora, Louis M; Geffert, Hite P; Carroll, T

    2002-03-01

    A method for reconstructing dimensions of subspaces for weakly coupled dynamical systems is offered. The tool is able to extrapolate the subspace dimensions from the zero coupling limit, where the division of dimensions as per the algorithm is exact. Implementation of the proposed technique to multivariate data demonstrates its effectiveness in disentangling subspace dimensionalities also in the case of emergent synchronized motions, for both numerical and experimental systems.

  8. A direct-gradient multivariate index of biotic condition

    USGS Publications Warehouse

    Miranda, Leandro E.; Aycock, J.N.; Killgore, K. J.

    2012-01-01

    Multimetric indexes constructed by summing metric scores have been criticized despite many of their merits. A leading criticism is the potential for investigator bias involved in metric selection and scoring. Often there is a large number of competing metrics equally well correlated with environmental stressors, requiring a judgment call by the investigator to select the most suitable metrics to include in the index and how to score them. Data-driven procedures for multimetric index formulation published during the last decade have reduced this limitation, yet apprehension remains. Multivariate approaches that select metrics with statistical algorithms may reduce the level of investigator bias and alleviate a weakness of multimetric indexes. We investigated the suitability of a direct-gradient multivariate procedure to derive an index of biotic condition for fish assemblages in oxbow lakes in the Lower Mississippi Alluvial Valley. Although this multivariate procedure also requires that the investigator identify a set of suitable metrics potentially associated with a set of environmental stressors, it is different from multimetric procedures because it limits investigator judgment in selecting a subset of biotic metrics to include in the index and because it produces metric weights suitable for computation of index scores. The procedure, applied to a sample of 35 competing biotic metrics measured at 50 oxbow lakes distributed over a wide geographical region in the Lower Mississippi Alluvial Valley, selected 11 metrics that adequately indexed the biotic condition of five test lakes. Because the multivariate index includes only metrics that explain the maximum variability in the stressor variables rather than a balanced set of metrics chosen to reflect various fish assemblage attributes, it is fundamentally different from multimetric indexes of biotic integrity with advantages and disadvantages. As such, it provides an alternative to multimetric procedures.

  9. Analysis of Forest Foliage Using a Multivariate Mixture Model

    NASA Technical Reports Server (NTRS)

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

    1997-01-01

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

  10. Multivariable speed synchronisation for a parallel hybrid electric vehicle drivetrain

    NASA Astrophysics Data System (ADS)

    Alt, B.; Antritter, F.; Svaricek, F.; Schultalbers, M.

    2013-03-01

    In this article, a new drivetrain configuration of a parallel hybrid electric vehicle is considered and a novel model-based control design strategy is given. In particular, the control design covers the speed synchronisation task during a restart of the internal combustion engine. The proposed multivariable synchronisation strategy is based on feedforward and decoupled feedback controllers. The performance and the robustness properties of the closed-loop system are illustrated by nonlinear simulation results.

  11. Using Statistical Process Control for detecting anomalies in multivariate spatiotemporal Earth Observations

    NASA Astrophysics Data System (ADS)

    Flach, Milan; Mahecha, Miguel; Gans, Fabian; Rodner, Erik; Bodesheim, Paul; Guanche-Garcia, Yanira; Brenning, Alexander; Denzler, Joachim; Reichstein, Markus

    2016-04-01

    The number of available Earth observations (EOs) is currently substantially increasing. Detecting anomalous patterns in these multivariate time series is an important step in identifying changes in the underlying dynamical system. Likewise, data quality issues might result in anomalous multivariate data constellations and have to be identified before corrupting subsequent analyses. In industrial application a common strategy is to monitor production chains with several sensors coupled to some statistical process control (SPC) algorithm. The basic idea is to raise an alarm when these sensor data depict some anomalous pattern according to the SPC, i.e. the production chain is considered 'out of control'. In fact, the industrial applications are conceptually similar to the on-line monitoring of EOs. However, algorithms used in the context of SPC or process monitoring are rarely considered for supervising multivariate spatio-temporal Earth observations. The objective of this study is to exploit the potential and transferability of SPC concepts to Earth system applications. We compare a range of different algorithms typically applied by SPC systems and evaluate their capability to detect e.g. known extreme events in land surface processes. Specifically two main issues are addressed: (1) identifying the most suitable combination of data pre-processing and detection algorithm for a specific type of event and (2) analyzing the limits of the individual approaches with respect to the magnitude, spatio-temporal size of the event as well as the data's signal to noise ratio. Extensive artificial data sets that represent the typical properties of Earth observations are used in this study. Our results show that the majority of the algorithms used can be considered for the detection of multivariate spatiotemporal events and directly transferred to real Earth observation data as currently assembled in different projects at the European scale, e.g. http://baci-h2020.eu

  12. Multivariate Error Covariance Estimates by Monte-Carlo Simulation for Assimilation Studies in the Pacific Ocean

    NASA Technical Reports Server (NTRS)

    Borovikov, Anna; Rienecker, Michele M.; Keppenne, Christian; Johnson, Gregory C.

    2004-01-01

    UOI and MvOI is similar with respect to the temperature field, the salinity and velocity fields are greatly improved when multivariate correction is used, as evident from the analyses of the rms differences of these fields and independent observations. The MvOI assimilation is found to improve upon the control run in generating the water masses with properties close to the observed, while the UOI failed to maintain the temperature and salinity structure.

  13. Various forms of indexing HDMR for modelling multivariate classification problems

    SciTech Connect

    Aksu, Çağrı; Tunga, M. Alper

    2014-12-10

    The Indexing HDMR method was recently developed for modelling multivariate interpolation problems. The method uses the Plain HDMR philosophy in partitioning the given multivariate data set into less variate data sets and then constructing an analytical structure through these partitioned data sets to represent the given multidimensional problem. Indexing HDMR makes HDMR be applicable to classification problems having real world data. Mostly, we do not know all possible class values in the domain of the given problem, that is, we have a non-orthogonal data structure. However, Plain HDMR needs an orthogonal data structure in the given problem to be modelled. In this sense, the main idea of this work is to offer various forms of Indexing HDMR to successfully model these real life classification problems. To test these different forms, several well-known multivariate classification problems given in UCI Machine Learning Repository were used and it was observed that the accuracy results lie between 80% and 95% which are very satisfactory.

  14. Multivariate Fronthaul Quantization for Downlink C-RAN

    NASA Astrophysics Data System (ADS)

    Lee, Wonju; Simeone, Osvaldo; Kang, Joonhyuk; Shamai, Shlomo

    2016-10-01

    The Cloud-Radio Access Network (C-RAN) cellular architecture relies on the transfer of complex baseband signals to and from a central unit (CU) over digital fronthaul links to enable the virtualization of the baseband processing functionalities of distributed radio units (RUs). The standard design of digital fronthauling is based on either scalar quantization or on more sophisticated point to-point compression techniques operating on baseband signals. Motivated by network-information theoretic results, techniques for fronthaul quantization and compression that improve over point-to-point solutions by allowing for joint processing across multiple fronthaul links at the CU have been recently proposed for both the uplink and the downlink. For the downlink, a form of joint compression, known in network information theory as multivariate compression, was shown to be advantageous under a non-constructive asymptotic information-theoretic framework. In this paper, instead, the design of a practical symbol-by-symbol fronthaul quantization algorithm that implements the idea of multivariate compression is investigated for the C-RAN downlink. As compared to current standards, the proposed multivariate quantization (MQ) only requires changes in the CU processing while no modification is needed at the RUs. The algorithm is extended to enable the joint optimization of downlink precoding and quantization, reduced-complexity MQ via successive block quantization, and variable-length compression. Numerical results, which include performance evaluations over standard cellular models, demonstrate the advantages of MQ and the merits of a joint optimization with precoding.

  15. A System of Multivariable Krawtchouk Polynomials and a Probabilistic Application

    NASA Astrophysics Data System (ADS)

    Grünbaum, F. Alberto; Rahman, Mizan

    2011-12-01

    The one variable Krawtchouk polynomials, a special case of the 2F1 function did appear in the spectral representation of the transition kernel for a Markov chain studied a long time ago by M. Hoare and M. Rahman. A multivariable extension of this Markov chain was considered in a later paper by these authors where a certain two variable extension of the F1 Appel function shows up in the spectral analysis of the corresponding transition kernel. Independently of any probabilistic consideration a certain multivariable version of the Gelfand-Aomoto hypergeometric function was considered in papers by H. Mizukawa and H. Tanaka. These authors and others such as P. Iliev and P. Tertwilliger treat the two-dimensional version of the Hoare-Rahman work from a Lie-theoretic point of view. P. Iliev then treats the general n-dimensional case. All of these authors proved several properties of these functions. Here we show that these functions play a crucial role in the spectral analysis of the transition kernel that comes from pushing the work of Hoare-Rahman to the multivariable case. The methods employed here to prove this as well as several properties of these functions are completely different to those used by the authors mentioned above.

  16. MToS: A Tree of Shapes for Multivariate Images.

    PubMed

    Carlinet, Edwin; Géraud, Thierry

    2015-12-01

    The topographic map of a gray-level image, also called tree of shapes, provides a high-level hierarchical representation of the image contents. This representation, invariant to contrast changes and to contrast inversion, has been proved very useful to achieve many image processing and pattern recognition tasks. Its definition relies on the total ordering of pixel values, so this representation does not exist for color images, or more generally, multivariate images. Common workarounds, such as marginal processing, or imposing a total order on data, are not satisfactory and yield many problems. This paper presents a method to build a tree-based representation of multivariate images, which features marginally the same properties of the gray-level tree of shapes. Briefly put, we do not impose an arbitrary ordering on values, but we only rely on the inclusion relationship between shapes in the image definition domain. The interest of having a contrast invariant and self-dual representation of multivariate image is illustrated through several applications (filtering, segmentation, and object recognition) on different types of data: color natural images, document images, satellite hyperspectral imaging, multimodal medical imaging, and videos.

  17. Extracting bb Higgs Decay Signals using Multivariate Techniques

    SciTech Connect

    Smith, W Clarke; /George Washington U. /SLAC

    2012-08-28

    For low-mass Higgs boson production at ATLAS at {radical}s = 7 TeV, the hard subprocess gg {yields} h{sup 0} {yields} b{bar b} dominates but is in turn drowned out by background. We seek to exploit the intrinsic few-MeV mass width of the Higgs boson to observe it above the background in b{bar b}-dijet mass plots. The mass resolution of existing mass-reconstruction algorithms is insufficient for this purpose due to jet combinatorics, that is, the algorithms cannot identify every jet that results from b{bar b} Higgs decay. We combine these algorithms using the neural net (NN) and boosted regression tree (BDT) multivariate methods in attempt to improve the mass resolution. Events involving gg {yields} h{sup 0} {yields} b{bar b} are generated using Monte Carlo methods with Pythia and then the Toolkit for Multivariate Analysis (TMVA) is used to train and test NNs and BDTs. For a 120 GeV Standard Model Higgs boson, the m{sub h{sup 0}}-reconstruction width is reduced from 8.6 to 6.5 GeV. Most importantly, however, the methods used here allow for more advanced m{sub h{sup 0}}-reconstructions to be created in the future using multivariate methods.

  18. Poisson and Multinomial Mixture Models for Multivariate SIMS Image Segmentation

    SciTech Connect

    Willse, Alan R.; Tyler, Bonnie

    2002-11-08

    Multivariate statistical methods have been advocated for analysis of spectral images, such as those obtained with imaging time-of-flight secondary ion mass spectrometry (TOF-SIMS). TOF-SIMS images using total secondary ion counts or secondary ion counts at individual masses often fail to reveal all salient chemical patterns on the surface. Multivariate methods simultaneously analyze peak intensities at all masses. We propose multivariate methods based on Poisson and multinomial mixture models to segment SIMS images into chemically homogeneous regions. The Poisson mixture model is derived from the assumption that secondary ion counts at any mass in a chemically homogeneous region vary according to the Poisson distribution. The multinomial model is derived as a standardized Poisson mixture model, which is analogous to standardizing the data by dividing by total secondary ion counts. The methods are adapted for contextual image segmentation, allowing for spatial correlation of neighboring pixels. The methods are applied to 52 mass units of a SIMS image with known chemical components. The spectral profile and relative prevalence for each chemical phase are obtained from estimates of model parameters.

  19. Multivariate diagnostics and anomaly detection for nuclear safeguards

    SciTech Connect

    Burr, T.; Jones, J.; Wangen, L.

    1994-08-01

    For process control and other reasons, new and future nuclear reprocessing plants are expected to be increasingly more automated than older plants. As a consequence of this automation, the quantity of data potentially available for safeguards may be much greater in future reprocessing plants than in current plants. The authors first review recent literature that applies multivariate Shewhart and multivariate cumulative sum (Cusum) tests to detect anomalous data. These tests are used to evaluate residuals obtained from a simulated three-tank problem in which five variables (volume, density, and concentrations of uranium, plutonium, and nitric acid) in each tank are modeled and measured. They then present results from several simulations involving transfers between the tanks and between the tanks and the environment. Residuals from a no-fault problem in which the measurements and model predictions are both correct are used to develop Cusum test parameters which are then used to test for faults for several simulated anomalous situations, such as an unknown leak or diversion of material from one of the tanks. The leak can be detected by comparing measurements, which estimate the true state of the tank system, with the model predictions, which estimate the state of the tank system as it ``should`` be. The no-fault simulation compares false alarm behavior for the various tests, whereas the anomalous problems allow one to compare the power of the various tests to detect faults under possible diversion scenarios. For comparison with the multivariate tests, univariate tests are also applied to the residuals.

  20. Data-based transformations in multivariate analysis. Final report

    SciTech Connect

    Dunn, J.E.

    1980-04-15

    Univariate transformations are considered initially, because of the common practice of transforming separately the marginal distribution of each variable of a multivariate observation. Familiar examples include those based on a priori assumptions about the underlying sampling distribution, as well as several general classes of empirical transformations recommended in a recent text by Mosteller and Tukey. Multi-normal criteria are considered as a basis for obtaining and evaluating multivariate transformations, including the likelihood criterion and various transformations to uniform statistics. The extension of power and shifted-power transformations to multivariate analysis is reviewed in detail, including recently published work involving q-sample problems. Finally, applications of projective transformations are proposed in order to remove the effects of extraneous sources of variation, e.g., specimens of different ages, from different nutritional backgrounds, etc. It is shown that the actual values of these ancillary variables will not be required if the analysis is performed in a subspace which is orthogonal to the gradients attributable to these variables. Models are proposed for principal components analysis, canonical correlation, linear classification functions, and discriminant function analysis in the general MANOVA context.

  1. Placebo group improvement in trials of pharmacotherapies for alcohol use disorders: A multivariate meta-analysis examining change over time

    PubMed Central

    Del Re, AC; Maisel, Natalya; Blodgett, Janet; Wilbourne, Paula; Finney, John

    2014-01-01

    Objective Placebo group improvement in pharmacotherapy trials has been increasing over time across several pharmacological treatment areas. However, it is unknown to what degree increasing improvement has occurred in pharmacotherapy trials for alcohol use disorders or what factors may account for placebo group improvement. This meta-analysis of 47 alcohol pharmacotherapy trials evaluated (1) the magnitude of placebo group improvement, (2) the extent to which placebo group improvement has been increasing over time, and (3) several potential moderators that might account for variation in placebo group improvement. Method Random-effects univariate and multivariate analyses were conducted that examined the magnitude of placebo group improvement in the 47 studies and several potential moderators of improvement: (a) publication year, (b) country in which the study was conducted, (c) outcome data source/type, (d) number of placebo administrations, (e) overall severity of study participants, and (f) additional psychosocial treatment. Results Substantial placebo group improvement was found overall and improvement was larger in more recent studies. Greater improvement was found on moderately subjective outcomes, with more frequent administrations of the placebo, and in studies with greater participant severity of illness. However, even after controlling for these moderators, placebo group improvement remained significant, as did placebo group improvement over time. Conclusion Similar to previous pharmacotherapy placebo research, substantial pre- to post-test placebo group improvement has occurred in alcohol pharmacotherapy trials, an effect that has been increasing over time. However, several plausible moderator variables were not able to explain why placebo group improvement has been increasing over time. PMID:23857312

  2. Publishing nutrition research: a review of multivariate techniques--part 3: data reduction methods.

    PubMed

    Gleason, Philip M; Boushey, Carol J; Harris, Jeffrey E; Zoellner, Jamie

    2015-07-01

    This is the ninth in a series of monographs on research design and analysis, and the third in a set of these monographs devoted to multivariate methods. The purpose of this article is to provide an overview of data reduction methods, including principal components analysis, factor analysis, reduced rank regression, and cluster analysis. In the field of nutrition, data reduction methods can be used for three general purposes: for descriptive analysis in which large sets of variables are efficiently summarized, to create variables to be used in subsequent analysis and hypothesis testing, and in questionnaire development. The article describes the situations in which these data reduction methods can be most useful, briefly describes how the underlying statistical analyses are performed, and summarizes how the results of these data reduction methods should be interpreted.

  3. A multivariate approach for high throughput pectin profiling by combining glycan microarrays with monoclonal antibodies.

    PubMed

    Sousa, António G; Ahl, Louise I; Pedersen, Henriette L; Fangel, Jonatan U; Sørensen, Susanne O; Willats, William G T

    2015-05-29

    Pectin-one of the most complex biomacromolecules in nature has been extensively studied using various techniques. This has been done so in an attempt to understand the chemical composition and conformation of pectin, whilst discovering and optimising new industrial applications of the polymer. For the last decade the emergence of glycan microarray technology has led to a growing capacity of acquiring simultaneous measurements related to various carbohydrate characteristics while generating large collections of data. Here we used a multivariate analysis approach in order to analyse a set of 359 pectin samples probed with 14 different monoclonal antibodies (mAbs). Principal component analysis (PCA) and partial least squares (PLS) regression were utilised to obtain the most optimal qualitative and quantitative information from the spotted microarrays. The potential use of microarray technology combined with chemometrics for the accurate determination of degree of methyl-esterification (DM) and degree of blockiness (DB) was assessed. PMID:25950120

  4. Estimating the difference between structure-factor amplitudes using multivariate Bayesian inference.

    PubMed

    Katona, Gergely; Garcia-Bonete, Maria José; Lundholm, Ida V

    2016-05-01

    In experimental research referencing two or more measurements to one another is a powerful tool to reduce the effect of systematic errors between different sets of measurements. The interesting quantity is usually derived from two measurements on the same sample under different conditions. While an elaborate experimental design is essential for improving the estimate, the data analysis should also maximally exploit the covariance between the measurements. In X-ray crystallography the difference between structure-factor amplitudes carries important information to solve experimental phasing problems or to determine time-dependent structural changes in pump-probe experiments. Here a multivariate Bayesian method was used to analyse intensity measurement pairs to determine their underlying structure-factor amplitudes and their differences. The posterior distribution of the model parameter was approximated with a Markov chain Monte Carlo algorithm. The described merging method is shown to be especially advantageous when systematic and random errors result in recording negative intensity measurements. PMID:27126118

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

    PubMed

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

    2016-01-01

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

  6. Estimating the difference between structure-factor amplitudes using multivariate Bayesian inference

    PubMed Central

    Katona, Gergely; Garcia-Bonete, Maria-José; Lundholm, Ida V.

    2016-01-01

    In experimental research referencing two or more measurements to one another is a powerful tool to reduce the effect of systematic errors between different sets of measurements. The interesting quantity is usually derived from two measurements on the same sample under different conditions. While an elaborate experimental design is essential for improving the estimate, the data analysis should also maximally exploit the covariance between the measurements. In X-ray crystallography the difference between structure-factor amplitudes carries important information to solve experimental phasing problems or to determine time-dependent structural changes in pump–probe experiments. Here a multivariate Bayesian method was used to analyse intensity measurement pairs to determine their underlying structure-factor amplitudes and their differences. The posterior distribution of the model parameter was approximated with a Markov chain Monte Carlo algorithm. The described merging method is shown to be especially advantageous when systematic and random errors result in recording negative intensity measurements. PMID:27126118

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

    PubMed

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

    2016-01-01

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

  8. Multivariate analysis to discriminate yeast strains with technological applications in table olive processing.

    PubMed

    Rodríguez-Gómez, Francisco; Romero-Gil, Veronica; Bautista-Gallego, Joaquín; Garrido-Fernández, Antonio; Arroyo-López, Francisco Noé

    2012-04-01

    This survey uses a multivariate classification analysis to discriminate yeast strains with interesting biochemical activities for the processing of table olives among a collection of 32 isolates belonging to 16 different yeast species. Lipase, esterase and β-glucosidase activities (desirable characteristics) were quantitatively evaluated in both extracellular and cellular fractions for all isolates in different types of culture media. The study of the quantitative data by cluster and principal component analyses led to the identification of several Wickerhamomyces anomalus, Candida boidinii and Candida diddensiae isolates with promising characteristics (the best global activity levels), clearly differentiated from the rest of the yeasts. The results obtained in this work open up new alternatives to this methodology for the study, classification and selection of the most suitable yeasts to be used as starters, alone or in combination with lactic acid bacteria, during table olive processing.

  9. Conformational study of arbutin by quantum chemical calculations and multivariate analysis

    NASA Astrophysics Data System (ADS)

    Araujo-Andrade, Cuauhtémoc; Lopes, Susy; Fausto, Rui; Gómez-Zavaglia, Andrea

    2010-06-01

    A conformational study of the molecule of arbutin (4-hydroxyphenyl-β- D-glucopyranoside) has been undertaken. The molecule is composed by a glucopyranoside moiety bound to a phenol ring. It has eight conformationally relevant dihedral angles, five of them related with the orientation of the hydroxyl groups and the remaining three taking part in the skeleton of the molecule. A systematic search on the conformational space of arbutin was performed using molecular orbital methods, followed by the identification of structural similarities between the different conformers, using multivariate analyses. This approach allowed the grouping of conformers according to their structural affinity and the establishment of correlations between their structures and several properties. Intramolecular interactions involving OH groups were also investigated and correlations between spectroscopic, structural and thermodynamic properties established. The developed strategy might be useful to investigate the structure and structure/properties correlations in other conformationally flexible molecules.

  10. Web-based tools for modelling and analysis of multivariate data: California ozone pollution activity

    PubMed Central

    Dinov, Ivo D.; Christou, Nicolas

    2014-01-01

    This article presents a hands-on web-based activity motivated by the relation between human health and ozone pollution in California. This case study is based on multivariate data collected monthly at 20 locations in California between 1980 and 2006. Several strategies and tools for data interrogation and exploratory data analysis, model fitting and statistical inference on these data are presented. All components of this case study (data, tools, activity) are freely available online at: http://wiki.stat.ucla.edu/socr/index.php/SOCR_MotionCharts_CAOzoneData. Several types of exploratory (motion charts, box-and-whisker plots, spider charts) and quantitative (inference, regression, analysis of variance (ANOVA)) data analyses tools are demonstrated. Two specific human health related questions (temporal and geographic effects of ozone pollution) are discussed as motivational challenges. PMID:24465054

  11. Application of multivariate statistical methods to the analysis of ancient Turkish potsherds

    SciTech Connect

    Martin, R.C.

    1986-01-01

    Three hundred ancient Turkish potsherds were analyzed by instrumental neutron activation analysis, and the resulting data analyzed by several techniques of multivariate statistical analysis, some only recently developed. The programs AGCLUS, MASLOC, and SIMCA were sequentially employed to characterize and group the samples by type of pottery and site of excavation. Comparison of the statistical analyses by each method provided archaeological insight into the site/type relationships of the samples and ultimately evidence relevant to the commercial relations between the ancient communities and specialization of pottery production over time. The techniques used for statistical analysis were found to be of significant potential utility in the future analysis of other archaeometric data sets. 25 refs., 33 figs.

  12. Analysis of fatty acid composition of sea cucumber Apostichopus japonicus using multivariate statistics

    NASA Astrophysics Data System (ADS)

    Xu, Qinzeng; Gao, Fei; Xu, Qiang; Yang, Hongsheng

    2014-11-01

    Fatty acids (FAs) provide energy and also can be used to trace trophic relationships among organisms. Sea cucumber Apostichopus japonicus goes into a state of aestivation during warm summer months. We examined fatty acid profiles in aestivated and non-aestivated A. japonicus using multivariate analyses (PERMANOVA, MDS, ANOSIM, and SIMPER). The results indicate that the fatty acid profiles of aestivated and non-aestivated sea cucumbers differed significantly. The FAs that were produced by bacteria and brown kelp contributed the most to the differences in the fatty acid composition of aestivated and nonaestivated sea cucumbers. Aestivated sea cucumbers may synthesize FAs from heterotrophic bacteria during early aestivation, and long chain FAs such as eicosapentaenoic (EPA) and docosahexaenoic acid (DHA) that produced from intestinal degradation, are digested during deep aestivation. Specific changes in the fatty acid composition of A. japonicus during aestivation needs more detailed study in the future.

  13. Decomposing biodiversity data using the Latent Dirichlet Allocation model, a probabilistic multivariate statistical method.

    PubMed

    Valle, Denis; Baiser, Benjamin; Woodall, Christopher W; Chazdon, Robin

    2014-12-01

    We propose a novel multivariate method to analyse biodiversity data based on the Latent Dirichlet Allocation (LDA) model. LDA, a probabilistic model, reduces assemblages to sets of distinct component communities. It produces easily interpretable results, can represent abrupt and gradual changes in composition, accommodates missing data and allows for coherent estimates of uncertainty. We illustrate our method using tree data for the eastern United States and from a tropical successional chronosequence. The model is able to detect pervasive declines in the oak community in Minnesota and Indiana, potentially due to fire suppression, increased growing season precipitation and herbivory. The chronosequence analysis is able to delineate clear successional trends in species composition, while also revealing that site-specific factors significantly impact these successional trajectories. The proposed method provides a means to decompose and track the dynamics of species assemblages along temporal and spatial gradients, including effects of global change and forest disturbances.

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

    PubMed Central

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

    2016-01-01

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

  15. Integrating binary traits with quantitative phenotypes for association mapping of multivariate phenotypes.

    PubMed

    Mukhopadhyay, Indranil; Saha, Sujayam; Ghosh, Saurabh

    2011-01-01

    Clinical binary end-point traits are often governed by quantitative precursors. Hence it may be a prudent strategy to analyze a clinical end-point trait by considering a multivariate phenotype vector, possibly including both quantitative and qualitative phenotypes. A major statistical challenge lies in integrating the constituent phenotypes into a reduced univariate phenotype for association analyses. We assess the performances of certain reduced phenotypes using analysis of variance and a model-free quantile-based approach. We find that analysis of variance is more powerful than the quantile-based approach in detecting association, particularly for rare variants. We also find that using a principal component of the quantitative phenotypes and the residual of a logistic regression of the binary phenotype on the quantitative phenotypes may be an optimal method for integrating a binary phenotype with quantitative phenotypes to define a reduced univariate phenotype. PMID:22373144

  16. Multivariate statistical analysis of stream sediments for mineral resources from the Craig NTMS Quadrangle, Colorado

    SciTech Connect

    Beyth, M.; McInteer, C.; Broxton, D.E.; Bolivar, S.L.; Luke, M.E.

    1980-06-01

    Multivariate statistical analyses were carried out on Hydrogeochemical and Stream Sediment Reconnaissance data from the Craig quadrangle, Colorado, to support the National Uranium Resource Evaluation and to evaluate strategic or other important commercial mineral resources. A few areas for favorable uranium mineralization are suggested for parts of the Wyoming Basin, Park Range, and Gore Range. Six potential source rocks for uranium are postulated based on factor score mapping. Vanadium in stream sediments is suggested as a pathfinder for carnotite-type mineralization. A probable northwest trend of lead-zinc-copper mineralization associated with Tertiary intrusions is suggested. A few locations are mapped where copper is associated with cobalt. Concentrations of placer sands containing rare earth elements, probably of commercial value, are indicated for parts of the Sand Wash Basin.

  17. Assessment of water quality parameters using multivariate analysis for Klang River basin, Malaysia.

    PubMed

    Mohamed, Ibrahim; Othman, Faridah; Ibrahim, Adriana I N; Alaa-Eldin, M E; Yunus, Rossita M

    2015-01-01

    This case study uses several univariate and multivariate statistical techniques to evaluate and interpret a water quality data set obtained from the Klang River basin located within the state of Selangor and the Federal Territory of Kuala Lumpur, Malaysia. The river drains an area of 1,288 km(2), from the steep mountain rainforests of the main Central Range along Peninsular Malaysia to the river mouth in Port Klang, into the Straits of Malacca. Water quality was monitored at 20 stations, nine of which are situated along the main river and 11 along six tributaries. Data was collected from 1997 to 2007 for seven parameters used to evaluate the status of the water quality, namely dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, suspended solids, ammoniacal nitrogen, pH, and temperature. The data were first investigated using descriptive statistical tools, followed by two practical multivariate analyses that reduced the data dimensions for better interpretation. The analyses employed were factor analysis and principal component analysis, which explain 60 and 81.6% of the total variation in the data, respectively. We found that the resulting latent variables from the factor analysis are interpretable and beneficial for describing the water quality in the Klang River. This study presents the usefulness of several statistical methods in evaluating and interpreting water quality data for the purpose of monitoring the effectiveness of water resource management. The results should provide more straightforward data interpretation as well as valuable insight for managers to conceive optimum action plans for controlling pollution in river water. PMID:25433545

  18. LDEF Satellite Radiation Analyses

    NASA Technical Reports Server (NTRS)

    Armstrong, T. W.; Colborn, B. L.

    1996-01-01

    This report covers work performed by Science Applications International Corporation (SAIC) under contract NAS8-39386 from the NASA Marshall Space Flight Center entitled LDEF Satellite Radiation Analyses. The basic objective of the study was to evaluate the accuracy of present models and computational methods for defining the ionizing radiation environment for spacecraft in Low Earth Orbit (LEO) by making comparisons with radiation measurements made on the Long Duration Exposure Facility (LDEF) satellite, which was recovered after almost six years in space. The emphasis of the work here is on predictions and comparisons with LDEF measurements of induced radioactivity and Linear Energy Transfer (LET) measurements. These model/data comparisons have been used to evaluate the accuracy of current models for predicting the flux and directionality of trapped protons for LEO missions.

  19. EEG analyses with SOBI.

    SciTech Connect

    Glickman, Matthew R.; Tang, Akaysha

    2009-02-01

    The motivating vision behind Sandia's MENTOR/PAL LDRD project has been that of systems which use real-time psychophysiological data to support and enhance human performance, both individually and of groups. Relevant and significant psychophysiological data being a necessary prerequisite to such systems, this LDRD has focused on identifying and refining such signals. The project has focused in particular on EEG (electroencephalogram) data as a promising candidate signal because it (potentially) provides a broad window on brain activity with relatively low cost and logistical constraints. We report here on two analyses performed on EEG data collected in this project using the SOBI (Second Order Blind Identification) algorithm to identify two independent sources of brain activity: one in the frontal lobe and one in the occipital. The first study looks at directional influences between the two components, while the second study looks at inferring gender based upon the frontal component.

  20. Network Class Superposition Analyses

    PubMed Central

    Pearson, Carl A. B.; Zeng, Chen; Simha, Rahul

    2013-01-01

    Networks are often used to understand a whole system by modeling the interactions among its pieces. Examples include biomolecules in a cell interacting to provide some primary function, or species in an environment forming a stable community. However, these interactions are often unknown; instead, the pieces' dynamic states are known, and network structure must be inferred. Because observed function may be explained by many different networks (e.g., for the yeast cell cycle process [1]), considering dynamics beyond this primary function means picking a single network or suitable sample: measuring over all networks exhibiting the primary function is computationally infeasible. We circumvent that obstacle by calculating the network class ensemble. We represent the ensemble by a stochastic matrix , which is a transition-by-transition superposition of the system dynamics for each member of the class. We present concrete results for derived from Boolean time series dynamics on networks obeying the Strong Inhibition rule, by applying to several traditional questions about network dynamics. We show that the distribution of the number of point attractors can be accurately estimated with . We show how to generate Derrida plots based on . We show that -based Shannon entropy outperforms other methods at selecting experiments to further narrow the network structure. We also outline an experimental test of predictions based on . We motivate all of these results in terms of a popular molecular biology Boolean network model for the yeast cell cycle, but the methods and analyses we introduce are general. We conclude with open questions for , for example, application to other models, computational considerations when scaling up to larger systems, and other potential analyses. PMID:23565141

  1. Network class superposition analyses.

    PubMed

    Pearson, Carl A B; Zeng, Chen; Simha, Rahul

    2013-01-01

    Networks are often used to understand a whole system by modeling the interactions among its pieces. Examples include biomolecules in a cell interacting to provide some primary function, or species in an environment forming a stable community. However, these interactions are often unknown; instead, the pieces' dynamic states are known, and network structure must be inferred. Because observed function may be explained by many different networks (e.g., ≈ 10(30) for the yeast cell cycle process), considering dynamics beyond this primary function means picking a single network or suitable sample: measuring over all networks exhibiting the primary function is computationally infeasible. We circumvent that obstacle by calculating the network class ensemble. We represent the ensemble by a stochastic matrix T, which is a transition-by-transition superposition of the system dynamics for each member of the class. We present concrete results for T derived from boolean time series dynamics on networks obeying the Strong Inhibition rule, by applying T to several traditional questions about network dynamics. We show that the distribution of the number of point attractors can be accurately estimated with T. We show how to generate Derrida plots based on T. We show that T-based Shannon entropy outperforms other methods at selecting experiments to further narrow the network structure. We also outline an experimental test of predictions based on T. We motivate all of these results in terms of a popular molecular biology boolean network model for the yeast cell cycle, but the methods and analyses we introduce are general. We conclude with open questions for T, for example, application to other models, computational considerations when scaling up to larger systems, and other potential analyses. PMID:23565141

  2. Uncertainty and Sensitivity Analyses Plan

    SciTech Connect

    Simpson, J.C.; Ramsdell, J.V. Jr.

    1993-04-01

    Hanford Environmental Dose Reconstruction (HEDR) Project staff are developing mathematical models to be used to estimate the radiation dose that individuals may have received as a result of emissions since 1944 from the US Department of Energy's (DOE) Hanford Site near Richland, Washington. An uncertainty and sensitivity analyses plan is essential to understand and interpret the predictions from these mathematical models. This is especially true in the case of the HEDR models where the values of many parameters are unknown. This plan gives a thorough documentation of the uncertainty and hierarchical sensitivity analysis methods recommended for use on all HEDR mathematical models. The documentation includes both technical definitions and examples. In addition, an extensive demonstration of the uncertainty and sensitivity analysis process is provided using actual results from the Hanford Environmental Dose Reconstruction Integrated Codes (HEDRIC). This demonstration shows how the approaches used in the recommended plan can be adapted for all dose predictions in the HEDR Project.

  3. Hydrogeochemical characteristics of groundwater in Latvia using multivariate statistical analysis

    NASA Astrophysics Data System (ADS)

    Retike, Inga; Kalvans, Andis; Bikse, Janis; Popovs, Konrads; Babre, Alise

    2015-04-01

    product between the two previously named clusters. Groundwater in cluster 2, 6 and 7 is considered to be a result of carbonate weathering with some addition of sea salts or gypsum dissolution. As a conclusion, the highest or lowest concentrations of some trace elements in groundwater was found out to be strongly associated with certain clusters. For example, Cluster 9 represents gypsum dissolution and has the highest concentrations of F, Sr, Rb, Li and the lowest levels of Ba. It can be also concluded that multivariate statistical analysis of major components can be used as an exploratory and predictive tool to identify groundwater objects with high possibility of elevated or reduced concentrations of harmful or essential trace elements. The research is supported by the European Union through the ESF Mobilitas grant No MJD309 and the European Regional Development Fund project Nr.2013/0054/2DP/2.1.1.1.0/13/APIA/VIAA/007 and NRP project EVIDENnT project "Groundwater and climate scenarios" subproject "Groundwater Research".

  4. Ketamine induces anxiolytic effects in adult zebrafish: A multivariate statistics approach.

    PubMed

    De Campos, Eduardo Geraldo; Bruni, Aline Thais; De Martinis, Bruno Spinosa

    2015-10-01

    Ketamine inappropriate use has been associated with serious consequences for human health. Anesthetic properties of ketamine are well-known, but its side effects are poorly described, including the effects on anxiety. In this context, animal models are a safe way to conduct this neurobehavioral research and zebrafish (Danio rerio) is an interesting model which has several advantages. The validation and interpretation of results of behavioral assays requires a suitable statistical approach, and the use of multivariate statistical methods has been little explored, especially in zebrafish behavioral models. Here, we investigated the anxiolytic-induced effects of ketamine in adult zebrafish, using Light-Dark Test and proposing the Multivariate Statistics methods (PCA, HCA and SIMCA) to analyze the results. In addition, we compared the processing of data to the one carried out by analysis of variance (ANOVA) ketamine produced significant concentration of exposure-dependent anxiolytic effects, increasing time in white area and number of crossings and decreasing latency to first access to white area. Average entry duration behavior resulted in a slight decrease from control to treatment groups, with an observed concentration-dependent increase among the exposed groups. PCA results indicated that two principal components represent 88.74% of all the system information. HCA and PCA results showed a higher similarity among control and treatment groups exposed to lower concentrations of ketamine and among treatment groups exposed to concentrations of 40 and 60 mg L(-1). In SIMCA results, interclasses distances were concentration of exposure-dependent increased and misclassifications and interclasses residues results also support these findings. These findings confirm the anxiolytic potential of ketamine and zebrafish sensibility to this drug. In summary, our study confirms that zebrafish and multivariate statistics data validation are an appropriate and viable behavioral model

  5. Variations in GP nursing home patient workload: results of a multivariate analysis.

    PubMed

    O'Neill, C; Groom, L; Avery, A J; Boot, D; Thornhill, K

    2000-11-01

    The number of old people living in UK nursing homes has increased substantially over the past 15 y. There is evidence that such patients generate larger workloads for primary carers than do those of similar age and sex living in their own homes. Clearly, any extra workload involved in providing primary care services to nursing home patients, needs to be reflected in the resources afforded general practitioners (GPs) who are tasked with its provision. By the same token variations in workloads between patients need to be examined and explained for any insights these might provide on funding issues. To examine and explain variations in GP workload associated with nursing home patients and determine the implications of these for GP funding, a 12 month case control study of all nursing home residents over 65 y old registered with nine general practices was undertaken. A multivariate regression analysis was used to examine variations in GP workload associated with 270 nursing home patients. Multivariate regression models explaining the variation in workload cost per month in terms of the GP practice delivering care and patients age and sex had little explanatory power (R(2)=0.07). A fuller method including the patient's Barthel score and initial diagnosis as additional explanatory variables added little to the explanatory power of the model (R(2)=0.12). The ability of the multivariate models used here to explain the variation in GP workload was poor. GPs may require an allowance to compensate for differences in workload associated with nursing home patients but adjusting these payments for differences in age, sex, initial diagnosis or the other variables included in this analysis would not appear to be supported. PMID:11114754

  6. U.S. Truck Driver Anthropometric Study and Multivariate Anthropometric Models for Cab Designs

    PubMed Central

    Guan, Jinhua; Hsiao, Hongwei; Bradtmiller, Bruce; Kau, Tsui-Ying; Reed, Matthew R.; Jahns, Steven K.; Loczi, Josef; Hardee, H. Lenora; Piamonte, Dominic Paul T.

    2015-01-01

    Objective This study presents data from a large-scale anthropometric study of U.S. truck drivers and the multivariate anthropometric models developed for the design of next-generation truck cabs. Background Up-to-date anthropometric information of the U.S. truck driver population is needed for the design of safe and ergonomically efficient truck cabs. Method We collected 35 anthropometric dimensions for 1,950 truck drivers (1,779 males and 171 females) across the continental United States using a sampling plan designed to capture the appropriate ethnic, gender, and age distributions of the truck driver population. Results Truck drivers are heavier than the U.S. general population, with a difference in mean body weight of 13.5 kg for males and 15.4 kg for females. They are also different in physique from the U.S. general population. In addition, the current truck drivers are heavier and different in physique compared to their counterparts of 25 to 30 years ago. Conclusion The data obtained in this study provide more accurate anthropometric information for cab designs than do the current U.S. general population data or truck driver data collected 25 to 30 years ago. Multivariate anthropometric models, spanning 95% of the current truck driver population on the basis of a set of 12 anthropometric measurements, have been developed to facilitate future cab designs. Application The up-to-date truck driver anthropometric data and multivariate anthropometric models will benefit the design of future truck cabs which, in turn, will help promote the safety and health of the U.S. truck drivers. PMID:23156628

  7. Polyimide processing additives

    NASA Technical Reports Server (NTRS)

    Fletcher, James C. (Inventor); Pratt, J. Richard (Inventor); St.clair, Terry L. (Inventor); Stoakley, Diane M. (Inventor); Burks, Harold D. (Inventor)

    1992-01-01

    A process for preparing polyimides having enhanced melt flow properties is described. The process consists of heating a mixture of a high molecular weight poly-(amic acid) or polyimide with a low molecular weight amic acid or imide additive in the range of 0.05 to 15 percent by weight of additive. The polyimide powders so obtained show improved processability, as evidenced by lower melt viscosity by capillary rheometry. Likewise, films prepared from mixtures of polymers with additives show improved processability with earlier onset of stretching by TMA.

  8. Polyimide processing additives

    NASA Technical Reports Server (NTRS)

    Pratt, J. Richard (Inventor); St.clair, Terry L. (Inventor); Stoakley, Diane M. (Inventor); Burks, Harold D. (Inventor)

    1993-01-01

    A process for preparing polyimides having enhanced melt flow properties is described. The process consists of heating a mixture of a high molecular weight poly-(amic acid) or polyimide with a low molecular weight amic acid or imide additive in the range of 0.05 to 15 percent by weight of the additive. The polyimide powders so obtained show improved processability, as evidenced by lower melt viscosity by capillary rheometry. Likewise, films prepared from mixtures of polymers with additives show improved processability with earlier onset of stretching by TMA.

  9. Additional Types of Neuropathy

    MedlinePlus

    ... A A Listen En Español Additional Types of Neuropathy Charcot's Joint Charcot's Joint, also called neuropathic arthropathy, ... can stop bone destruction and aid healing. Cranial Neuropathy Cranial neuropathy affects the 12 pairs of nerves ...

  10. Food Additives and Hyperkinesis

    ERIC Educational Resources Information Center

    Wender, Ester H.

    1977-01-01

    The hypothesis that food additives are causally associated with hyperkinesis and learning disabilities in children is reviewed, and available data are summarized. Available from: American Medical Association 535 North Dearborn Street Chicago, Illinois 60610. (JG)

  11. Smog control fuel additives

    SciTech Connect

    Lundby, W.

    1993-06-29

    A method is described of controlling, reducing or eliminating, ozone and related smog resulting from photochemical reactions between ozone and automotive or industrial gases comprising the addition of iodine or compounds of iodine to hydrocarbon-base fuels prior to or during combustion in an amount of about 1 part iodine per 240 to 10,000,000 parts fuel, by weight, to be accomplished by: (a) the addition of these inhibitors during or after the refining or manufacturing process of liquid fuels; (b) the production of these inhibitors for addition into fuel tanks, such as automotive or industrial tanks; or (c) the addition of these inhibitors into combustion chambers of equipment utilizing solid fuels for the purpose of reducing ozone.

  12. The Effect of the Multivariate Box-Cox Transformation on the Power of MANOVA.

    ERIC Educational Resources Information Center

    Kirisci, Levent; Hsu, Tse-Chi

    Most of the multivariate statistical techniques rely on the assumption of multivariate normality. The effects of non-normality on multivariate tests are assumed to be negligible when variance-covariance matrices and sample sizes are equal. Therefore, in practice, investigators do not usually attempt to remove non-normality. In this simulation…

  13. A Multivariate Model for the Study of Parental Acceptance-Rejection and Child Abuse.

    ERIC Educational Resources Information Center

    Rohner, Ronald P.; Rohner, Evelyn C.

    This paper proposes a multivariate strategy for the study of parental acceptance-rejection and child abuse and describes a research study on parental rejection and child abuse which illustrates the advantages of using a multivariate, (rather than a simple-model) approach. The multivariate model is a combination of three simple models used to study…

  14. Investigating College and Graduate Students' Multivariable Reasoning in Computational Modeling

    ERIC Educational Resources Information Center

    Wu, Hsin-Kai; Wu, Pai-Hsing; Zhang, Wen-Xin; Hsu, Ying-Shao

    2013-01-01

    Drawing upon the literature in computational modeling, multivariable reasoning, and causal attribution, this study aims at characterizing multivariable reasoning practices in computational modeling and revealing the nature of understanding about multivariable causality. We recruited two freshmen, two sophomores, two juniors, two seniors, four…

  15. Some multivariable orthogonal polynomials of the Askey tableau-discrete families

    NASA Astrophysics Data System (ADS)

    Tratnik, M. V.

    1991-09-01

    A multivariable generalization is presented for all the discrete families of the Askey tableau. This significantly extends the multivariable Hahn polynomials introduced by Karlin and McGregor. The latter are recovered as a limit case from a family of multivariable Racah polynomials.

  16. Sequencing human ribs into anatomical order by quantitative multivariate methods.

    PubMed

    Cirillo, John; Henneberg, Maciej

    2012-06-01

    Little research has focussed on methods to anatomically sequence ribs. Correct anatomical sequencing of ribs assists in determining the location and distribution of regional trauma, age estimation, number of puncture wounds, number of individuals, and personal identification. The aim of the current study is to develop a method for placing fragmented and incomplete rib sets into correct anatomical position. Ribs 2-10 were used from eleven cadavers of an Australian population. Seven variables were measured from anatomical locations on the rib. General descriptive statistics were calculated for each variable along with an analysis of variance (ANOVA) and ANOVA with Bonferroni statistics. Considerable overlap was observed between ribs for univariate methods. Bivariate and multivariate methods were then applied. Results of the ANOVA with post hoc Bonferroni statistics show that ratios of various dimensions of a single rib could be used to sequence it within adjacent ribs. Using multiple regression formulae, the most accurate estimation of the anatomical rib number occurs when the entire rib is found in isolation. This however, is not always possible. Even when only the head and neck of the rib are preserved, a modified multivariate regression formula assigned 91.95% of ribs into correct anatomical position or as an adjacent rib. Using multivariate methods it is possible to sequence a single human rib with a high level of accuracy and they are superior to univariate methods. Left and right ribs were found to be highly symmetrical. Some rib dimensions were greater in males than in females, but overall the level of sexual dimorphism was low.

  17. Multi-application controls: Robust nonlinear multivariable aerospace controls applications

    NASA Technical Reports Server (NTRS)

    Enns, Dale F.; Bugajski, Daniel J.; Carter, John; Antoniewicz, Bob

    1994-01-01

    This viewgraph presentation describes the general methodology used to apply Honywell's Multi-Application Control (MACH) and the specific application to the F-18 High Angle-of-Attack Research Vehicle (HARV) including piloted simulation handling qualities evaluation. The general steps include insertion of modeling data for geometry and mass properties, aerodynamics, propulsion data and assumptions, requirements and specifications, e.g. definition of control variables, handling qualities, stability margins and statements for bandwidth, control power, priorities, position and rate limits. The specific steps include choice of independent variables for least squares fits to aerodynamic and propulsion data, modifications to the management of the controls with regard to integrator windup and actuation limiting and priorities, e.g. pitch priority over roll, and command limiting to prevent departures and/or undesirable inertial coupling or inability to recover to a stable trim condition. The HARV control problem is characterized by significant nonlinearities and multivariable interactions in the low speed, high angle-of-attack, high angular rate flight regime. Systematic approaches to the control of vehicle motions modeled with coupled nonlinear equations of motion have been developed. This paper will discuss the dynamic inversion approach which explicity accounts for nonlinearities in the control design. Multiple control effectors (including aerodynamic control surfaces and thrust vectoring control) and sensors are used to control the motions of the vehicles in several degrees-of-freedom. Several maneuvers will be used to illustrate performance of MACH in the high angle-of-attack flight regime. Analytical methods for assessing the robust performance of the multivariable control system in the presence of math modeling uncertainty, disturbances, and commands have reached a high level of maturity. The structured singular value (mu) frequency response methodology is presented

  18. Multivariate Clustering of Large-Scale Scientific Simulation Data

    SciTech Connect

    Eliassi-Rad, T; Critchlow, T

    2003-06-13

    Simulations of complex scientific phenomena involve the execution of massively parallel computer programs. These simulation programs generate large-scale data sets over the spatio-temporal space. Modeling such massive data sets is an essential step in helping scientists discover new information from their computer simulations. In this paper, we present a simple but effective multivariate clustering algorithm for large-scale scientific simulation data sets. Our algorithm utilizes the cosine similarity measure to cluster the field variables in a data set. Field variables include all variables except the spatial (x, y, z) and temporal (time) variables. The exclusion of the spatial dimensions is important since ''similar'' characteristics could be located (spatially) far from each other. To scale our multivariate clustering algorithm for large-scale data sets, we take advantage of the geometrical properties of the cosine similarity measure. This allows us to reduce the modeling time from O(n{sup 2}) to O(n x g(f(u))), where n is the number of data points, f(u) is a function of the user-defined clustering threshold, and g(f(u)) is the number of data points satisfying f(u). We show that on average g(f(u)) is much less than n. Finally, even though spatial variables do not play a role in building clusters, it is desirable to associate each cluster with its correct spatial region. To achieve this, we present a linking algorithm for connecting each cluster to the appropriate nodes of the data set's topology tree (where the spatial information of the data set is stored). Our experimental evaluations on two large-scale simulation data sets illustrate the value of our multivariate clustering and linking algorithms.

  19. Multivariate Clustering of Large-Scale Simulation Data

    SciTech Connect

    Eliassi-Rad, T; Critchlow, T

    2003-03-04

    Simulations of complex scientific phenomena involve the execution of massively parallel computer programs. These simulation programs generate large-scale data sets over the spatiotemporal space. Modeling such massive data sets is an essential step in helping scientists discover new information from their computer simulations. In this paper, we present a simple but effective multivariate clustering algorithm for large-scale scientific simulation data sets. Our algorithm utilizes the cosine similarity measure to cluster the field variables in a data set. Field variables include all variables except the spatial (x, y, z) and temporal (time) variables. The exclusion of the spatial space is important since 'similar' characteristics could be located (spatially) far from each other. To scale our multivariate clustering algorithm for large-scale data sets, we take advantage of the geometrical properties of the cosine similarity measure. This allows us to reduce the modeling time from O(n{sup 2}) to O(n x g(f(u))), where n is the number of data points, f(u) is a function of the user-defined clustering threshold, and g(f(u)) is the number of data points satisfying the threshold f(u). We show that on average g(f(u)) is much less than n. Finally, even though spatial variables do not play a role in building a cluster, it is desirable to associate each cluster with its correct spatial space. To achieve this, we present a linking algorithm for connecting each cluster to the appropriate nodes of the data set's topology tree (where the spatial information of the data set is stored). Our experimental evaluations on two large-scale simulation data sets illustrate the value of our multivariate clustering and linking algorithms.

  20. Quality Reporting of Multivariable Regression Models in Observational Studies

    PubMed Central

    Real, Jordi; Forné, Carles; Roso-Llorach, Albert; Martínez-Sánchez, Jose M.

    2016-01-01

    Abstract Controlling for confounders is a crucial step in analytical observational studies, and multivariable models are widely used as statistical adjustment techniques. However, the validation of the assumptions of the multivariable regression models (MRMs) should be made clear in scientific reporting. The objective of this study is to review the quality of statistical reporting of the most commonly used MRMs (logistic, linear, and Cox regression) that were applied in analytical observational studies published between 2003 and 2014 by journals indexed in MEDLINE. Review of a representative sample of articles indexed in MEDLINE (n = 428) with observational design and use of MRMs (logistic, linear, and Cox regression). We assessed the quality of reporting about: model assumptions and goodness-of-fit, interactions, sensitivity analysis, crude and adjusted effect estimate, and specification of more than 1 adjusted model. The tests of underlying assumptions or goodness-of-fit of the MRMs used were described in 26.2% (95% CI: 22.0–30.3) of the articles and 18.5% (95% CI: 14.8–22.1) reported the interaction analysis. Reporting of all items assessed was higher in articles published in journals with a higher impact factor. A low percentage of articles indexed in MEDLINE that used multivariable techniques provided information demonstrating rigorous application of the model selected as an adjustment method. Given the importance of these methods to the final results and conclusions of observational studies, greater rigor is required in reporting the use of MRMs in the scientific literature. PMID:27196467