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Sample records for addition multivariate analyses

  1. A Call for Conducting Multivariate Mixed Analyses

    ERIC Educational Resources Information Center

    Onwuegbuzie, Anthony J.

    2016-01-01

    Several authors have written methodological works that provide an introductory- and/or intermediate-level guide to conducting mixed analyses. Although these works have been useful for beginning and emergent mixed researchers, with very few exceptions, works are lacking that describe and illustrate advanced-level mixed analysis approaches. Thus,…

  2. Multivariate analyses of crater parameters and the classification of craters

    NASA Technical Reports Server (NTRS)

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

    1974-01-01

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

  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. Multiscale, multiorgan and multivariate complexity analyses of cardiovascular regulation.

    PubMed

    Cerutti, Sergio; Hoyer, Dirk; Voss, Andreas

    2009-04-13

    Cardiovascular system complexity is confirmed by both its generally variegated structure of physiological modelling and the richness of information detectable from processing of the signals involved in it, with strong linear and nonlinear interactions with other biological systems. In particular, this behaviour may be accordingly described by means of what we call MMM paradigm (i.e. multiscale, multiorgan and multivariate). Such an approach to the cardiovascular system emphasizes where the genesis of its complexity is potentially allocated and how it is possible to detect information from it. No doubt that processing signals from multi-leads of the same system (multivariate), from the interaction of different physiological systems (multiorgan) and integrating all this information across multiple scales (from genes, to proteins, molecules, cells, up to the whole organ) could really provide us with a more complete look at the overall phenomenon of cardiovascular system complexity, with respect to the one which is obtainable from its single constituent parts. In this paper, some examples of approaches are discussed for investigating the cardiovascular system in different time and spatial scales, in studying a different organ involvement (such as sleep, depression and multiple organ dysfunction) and in using a multivariate approach via various linear and nonlinear methods for cardiovascular risk stratification and pathology assessment.

  5. Multivariate Behavior Genetic Analyses of Aggressive Behavior Subtypes

    PubMed Central

    Yeh, Michelle T.; Coccaro, Emil F.; Jacobson, Kristen C.

    2012-01-01

    This study examined the genetic and environmental architecture underlying aggressive behavior measured by the Life History of Aggression Questionnaire (LHA; Coccaro et al. 1997a). Following preliminary phenotypic factor analysis procedures, multivariate behavioral genetics models were fit to responses from 2,925 adult twins from the PennTwins cohort on five LHA items assessing lifetime frequency of temper tantrums, indirect aggression, verbal aggression, fighting, and physical assault. The best-fitting model was a 2-factor common pathway model, indicating that these five aggressive behaviors are underpinned by two distinct etiological factors with different genetic and nonshared environmental influences. Although there was evidence of significant sex differences, the structure of the two factors appeared to be quite similar in males and females, where General Aggression and Physical Aggression factors emerged. Heritability of these factors ranged from .37 to .57, and nonshared environmental effects ranged from .43 to .63. The results of this study highlight the heterogeneous nature of the aggression construct and the need to consider differences in genetic and environmental influences on individual aggressive behaviors in a multivariate context. PMID:20432061

  6. Comparative multivariate analyses of transient otoacoustic emissions and distorsion products in normal and impaired hearing

    PubMed Central

    STAMATE, MIRELA CRISTINA; TODOR, NICOLAE; COSGAREA, MARCEL

    2015-01-01

    Background and aim The clinical utility of otoacoustic emissions as a noninvasive objective test of cochlear function has been long studied. Both transient otoacoustic emissions and distorsion products can be used to identify hearing loss, but to what extent they can be used as predictors for hearing loss is still debated. Most studies agree that multivariate analyses have better test performances than univariate analyses. The aim of the study was to determine transient otoacoustic emissions and distorsion products performance in identifying normal and impaired hearing loss, using the pure tone audiogram as a gold standard procedure and different multivariate statistical approaches. Methods The study included 105 adult subjects with normal hearing and hearing loss who underwent the same test battery: pure-tone audiometry, tympanometry, otoacoustic emission tests. We chose to use the logistic regression as a multivariate statistical technique. Three logistic regression models were developed to characterize the relations between different risk factors (age, sex, tinnitus, demographic features, cochlear status defined by otoacoustic emissions) and hearing status defined by pure-tone audiometry. The multivariate analyses allow the calculation of the logistic score, which is a combination of the inputs, weighted by coefficients, calculated within the analyses. The accuracy of each model was assessed using receiver operating characteristics curve analysis. We used the logistic score to generate receivers operating curves and to estimate the areas under the curves in order to compare different multivariate analyses. Results We compared the performance of each otoacoustic emission (transient, distorsion product) using three different multivariate analyses for each ear, when multi-frequency gold standards were used. We demonstrated that all multivariate analyses provided high values of the area under the curve proving the performance of the otoacoustic emissions. Each

  7. 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

  8. 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.

  9. 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.

  10. Divorce and dating violence revisited: multivariate analyses using Straus's conflict tactics subscores.

    PubMed

    Billingham, R E; Notebaert, N L

    1993-10-01

    514 men and 891 women college students provided information concerning behaviors both they and their partners used within the prior six months to resolve conflicts in their relationships. Multivariate analyses assessed whether experiencing the divorce of one's parents would be associated with respondents' report of their own or their partners' conflict behaviors. Students from divorced families reported higher scores for their own behavior on the Violence subscale only, while they reported higher scores for their partners on both the Verbal Aggression and Violence subscales. These results suggest that coming from a divorced family may have lasting effects on later relationships of these individuals, particularly in conflict resolution.

  11. Synthetic Multivariate Models to Accommodate Unmodeled Interfering Components During Quantitative Spectral Analyses

    SciTech Connect

    Haaland, David M.

    1999-07-14

    The analysis precision of any multivariate calibration method will be severely degraded if unmodeled sources of spectral variation are present in the unknown sample spectra. This paper describes a synthetic method for correcting for the errors generated by the presence of unmodeled components or other sources of unmodeled spectral variation. If the spectral shape of the unmodeled component can be obtained and mathematically added to the original calibration spectra, then a new synthetic multivariate calibration model can be generated to accommodate the presence of the unmodeled source of spectral variation. This new method is demonstrated for the presence of unmodeled temperature variations in the unknown sample spectra of dilute aqueous solutions of urea, creatinine, and NaCl. When constant-temperature PLS models are applied to spectra of samples of variable temperature, the standard errors of prediction (SEP) are approximately an order of magnitude higher than that of the original cross-validated SEPs of the constant-temperature partial least squares models. Synthetic models using the classical least squares estimates of temperature from pure water or variable-temperature mixture sample spectra reduce the errors significantly for the variable temperature samples. Spectrometer drift adds additional error to the analyte determinations, but a method is demonstrated that can minimize the effect of drift on prediction errors through the measurement of the spectra of a small subset of samples during both calibration and prediction. In addition, sample temperature can be predicted with high precision with this new synthetic model without the need to recalibrate using actual variable-temperature sample data. The synthetic methods eliminate the need for expensive generation of new calibration samples and collection of their spectra. The methods are quite general and can be applied using any known source of spectral variation and can be used with any multivariate

  12. Use of multivariate analyses for determining heat tolerance in Brazilian cattle.

    PubMed

    McManus, Concepta; Castanheira, Marlos; Paiva, Samuel Rezende; Louvandini, Helder; Fioravanti, Maria Clorinda Soares; Paludo, Giane Regina; Bianchini, Eliandra; Corrêa, Patricia Spoto

    2011-03-01

    Adaptability can be evaluated by the ability of an animal to adjust to environmental conditions and is especially important in extreme weather conditions such as that found in tropical Brazil. A multivariate analysis using physical and physiological traits in exotic (Nellore and Holstein) and naturalized (Junqueira, Curraleira, Mocho Nacional, Crioula Lageana, and Pantaneira) cattle breeds was carried out in the Federal District of Brazil to test and determine which traits are important in the adaptation of animal to heat stress as well as the ability of these traits and statistical techniques to separate the breeds studied. Both physical and physiological traits were measured on three occasions and included body measurements, skin and hair thickness, hair number and length, pigmentation, sweat gland area as well as heart and breathing rates, rectal temperature, sweating rate, and blood parameters. The data underwent multivariate statistical analyses, including cluster, discriminate, and canonical procedures. The tree diagram showed clear distances between the groups studied, and canonical analysis was able to separate individuals in groups. Coat traits explained little variation in physiological parameters. The traits which had higher discriminatory power included packed cell volume, shoulder height, mean corpuscular volume, body length, and heart girth. Morphological and physiological traits were able to discriminate between the breeds tested, with blood and size traits being the most important. More than 80% of animals of all breeds were correctly classified in their genetic group.

  13. 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

  14. Diagnosis of Alzheimer’s Disease Using Neuropsychological Testing Improved by Multivariate Analyses

    PubMed Central

    Chapman, Robert M.; Mapstone, Mark; Porsteinsson, Anton P.; Gardner, Margaret N.; McCrary, John W.; DeGrush, Elizabeth; Reilly, Lindsey A.; Sandoval, Tiffany C.; Guillily, Maria D.

    2010-01-01

    Neuropsychological assessment aids in the diagnosis of Alzheimer’s disease (AD) by objectively establishing cognitive impairment from standardized tests. We present new criteria for diagnosis that use weighted combined scores from multiple tests. Our method employs two multivariate analyses: Principal Components Analysis (PCA) and discriminant analysis. PCA (N = 216 subjects) created more interpretable cognitive dimensions by resolving 49 test measures in our neuropsychological battery to 13 component scores for each subject. The component scores were used to build discriminant functions that classified each participant as either an early-stage AD (N = 55) or normal elderly (N = 78). Our discriminant function performed with high accuracy, sensitivity, and specificity (nearly all >90%) in the development, a cross-validation, and a new subjects validation. When contrasted to two different traditional empirical methods for diagnosis (using cutscores and defining AD as falling below 5% on two or more test domains), our results suggested that the multivariate method was superior in classification (approximately 20% more accurate). PMID:20358452

  15. A distributed computing system for multivariate time series analyses of multichannel neurophysiological data.

    PubMed

    Müller, Andy; Osterhage, Hannes; Sowa, Robert; Andrzejak, Ralph G; Mormann, Florian; Lehnertz, Klaus

    2006-04-15

    We present a client-server application for the distributed multivariate analysis of time series using standard PCs. We here concentrate on analyses of multichannel EEG/MEG data, but our method can easily be adapted to other time series. Due to the rapid development of new analysis techniques, the focus in the design of our application was not only on computational performance, but also on high flexibility and expandability of both the client and the server programs. For this purpose, the communication between the server and the clients as well as the building of the computational tasks has been realized via the Extensible Markup Language (XML). Running our newly developed method in an asynchronous distributed environment with random availability of remote and heterogeneous resources, we tested the system's performance for a number of different univariate and bivariate analysis techniques. Results indicate that for most of the currently available analysis techniques, calculations can be performed in real time, which, in principle, allows on-line analyses at relatively low cost.

  16. 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.

  17. Design and tuning of standard additive model based fuzzy PID controllers for multivariable process systems.

    PubMed

    Harinath, Eranda; Mann, George K I

    2008-06-01

    This paper describes a design and two-level tuning method for fuzzy proportional-integral derivative (FPID) controllers for a multivariable process where the fuzzy inference uses the inference of standard additive model. The proposed method can be used for any n x n multi-input-multi-output process and guarantees closed-loop stability. In the two-level tuning scheme, the tuning follows two steps: low-level tuning followed by high-level tuning. The low-level tuning adjusts apparent linear gains, whereas the high-level tuning changes the nonlinearity in the normalized fuzzy output. In this paper, two types of FPID configurations are considered, and their performances are evaluated by using a real-time multizone temperature control problem having a 3 x 3 process system.

  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. Improving complex kinship analyses with additional STR loci.

    PubMed

    Carboni, Ilaria; Iozzi, Sara; Nutini, Anna Lucia; Torricelli, Francesca; Ricci, Ugo

    2014-11-01

    In a standard paternity testing, mother, child, and alleged father are analyzed with STR markers using commercially available kits. Since Italian civil legislation does not have thresholds to confirm a paternity, paternity is practically proven when likelihood ratio increases prior probability of paternity to posterior, accepted by court as sufficient. However, in some cases the number of markers included in a commercial kit may be insufficient to conclusively prove or disprove a relationship between individuals, especially when complex family scenarios are suspected or indirect analyses are required. Additional genetic information can increase the values of the likelihood ratio regarding the detection of true parental relationships in a pedigree, while reducing the chances of false attributions (e.g. false paternities). In these cases the introduction of a 26Plex amplification system allows to examine 23-26 additional markers depending on the commercial kit used, thus increasing the statistical power of the kinship analysis. The PCR conditions were optimized for a multiplex amplification system and a new generation CE instrument. In order to demonstrate the utility of additional STRs markers, four complex kinship cases are presented.

  1. Synthesis of arc-derived DSDP (ODP) modal sand compositions using multivariate analyses

    SciTech Connect

    Marsaglia, K.M.; Ingersoll, R.V.; Packer, B.; Gergen, L.D.

    1987-05-01

    Sands derived from Quaternary and late Tertiary arc systems can be directly correlated with the tectonic setting of the marine basin in which they were deposited (trench, arc-trench gap, back arc, etc.), adjacent arc type continental margin versus intraoceanic; dissected versus undissected), and degree of arc/basin development at the time of deposition. Rigorous compositional classifications of these sediments are useful in interpreting tectonic setting of ancient sandstones of unknown or speculative origin. Representative arc-related sand samples from DSDP (ODP) sites form the basis of this investigation; regions sampled include the Mariana, Japan, Aleutian, North American, Middle American, Southwestern Pacific, Caribbean, and Mediterranean arc-related basins. Petrographic data were uniformly gathered using the Gazzi-Dickinson point-counting method. Modal sand compositions for each arc are unique, but marked differences along certain arc systems reflect the variable tectonic history of individual arc segments. Quartzo-feldspathic sands from the Mexican segment of the Middle American trench system sharply contrast with volcanic-rich sands from the Central American segment; more subtle changes occur along the sands from the Central American segment; more subtle changes occur along the Japan and North American arc systems. In general, multivariate analyses of petrographic data indicate (1) modal compositions of sands from magmatic-arc settings can be subdivided into intraoceanic arcs, continental arcs, and intraoceanic arcs with continental influence and (2) the percentage of continental components including potassium feldspar, quartz, mica, and sedimentary and metamorphic lithic grains increases toward continental margins. This synthesis represents the most complete study of modern arc sand compositions and provides information essential to studies of ancient arc-related sandstones.

  2. Multivariate analyses of DNA index, p62c-myc, and clinicopathological status of patients with ovarian cancer.

    PubMed Central

    Curling, M; Stenning, S; Hudson, C N; Watson, J V

    1998-01-01

    AIM: To determine if either DNA index or p62c-myc is an independent prognostic variable in ovarian cancer. METHODS: Multivariate and univariate analyses of the relation between DNA index, p62c-myc, FIGO stage, histological type, tumour grade, completeness of surgery, and patient survival in ovarian cancer were examined. RESULTS: Multivariate analysis showed significant association of survival only with stage and grade. There was no relation between survival and DNA index. CONCLUSIONS: DNA index is not an independent prognostic variable in ovarian cancer. Images PMID:9771445

  3. Using multivariate decoding to go beyond contrastive analyses in consciousness research.

    PubMed

    Sandberg, Kristian; Andersen, Lau M; Overgaard, Morten

    2014-01-01

    Contrasting conditions with and without awareness has been the preferred method for investigating the neural correlates of consciousness (NCC) for decades, yet recently it has been suggested that further insights can be made by moving beyond this method, specifically by meticulously controlling that potential precursors and consequences of the NCC are not mistaken for an NCC. Here, we briefly review the advantages and potential pitfalls of existing paradigms going beyond the contrastive method, and we propose multivariate decoding of neural activity patterns as a supplement to other methods. Specifically, we emphasize the ability of multivariate decoding to detect which patterns of neural activity are consistently predictive of conscious experiences at the single trial level. This is relevant as the "NCC proper" is expected to be consistently predictive whereas processes that are consequences of consciousness may not occur on every trial (making them less predictive) and prerequisites of consciousness may be present on some trials without conscious experience (making them less predictive).

  4. Multivariable and Multigroup Receiver Operating Characteristics Curve Analyses for Qualitative and Quantitative Analysis

    DTIC Science & Technology

    2012-01-01

    presents the original experimental data collected by Fisher and includes the lengths and widths of the sepals and petals of the three iris flower ...more than two experimental groups in a systematic fashion. The classic Fisher iris flower data set is treated as one variable and two cases at a time...enhanced computer efficiency and information-rich analysis. 15. SUBJECT TERMS Multivariate analysis Sepal Univariate analysis Petal Frequency

  5. 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.

  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. 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.

  11. 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

  12. Interpretation of seasonal water quality variation in the Yeongsan Reservoir, Korea using multivariate statistical analyses.

    PubMed

    Cho, Kyung Hwa; Park, Yongeun; Kang, Joo-Hyon; Ki, Seo Jin; Cha, Sungmin; Lee, Seung Won; Kim, Joon Ha

    2009-01-01

    The Yeongsan (YS) Reservoir is an estuarine reservoir which provides surrounding areas with public goods, such as water supply for agricultural and industrial areas and flood control. Beneficial uses of the YS Reservoir, however, are recently threatened by enriched non-point and point source inputs. A series of multivariate statistical approaches including principal component analysis (PCA) were applied to extract significant characteristics contained in a large suite of water quality data (18 variables monthly recorded for 5 years); thereby to provide the important phenomenal information for establishing effective water resource management plans for the YS Reservoir. The PCA results identified the most important five principal components (PCs), explaining 71% of total variance of the original data set. The five PCs were interpreted as hydro-meteorological effect, nitrogen loading, phosphorus loading, primary production of phytoplankton, and fecal indicator bacteria (FIB) loading. Furthermore, hydro-meteorological effect and nitrogen loading could be characterized by a yearly periodicity whereas FIB loading showed an increasing trend with respect to time. The study results presented here might be useful to establish preliminary strategies for abating water quality degradation in the YS Reservoir.

  13. 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

  14. 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

  15. Multivariate Analyses of Predictors of Heavy Episodic Drinking and Drinking-Related Problems among College Students

    ERIC Educational Resources Information Center

    Fenzel, L. Mickey

    2005-01-01

    The present study examines predictors of heavy drinking frequency and drinking-related problems among more than 600 college students. Controlling for high school drinking frequency, results of multiple regression analyses showed that more frequent heavy drinking was predicted by being male and risk factors of more frequent marijuana and tobacco…

  16. 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

  17. 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.

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

    NASA Astrophysics Data System (ADS)

    Förster, Hans-Jürgen; Davis, John C.; Tischendorf, Gerhard; Seltmann, Reimar

    1999-06-01

    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-P 2O 5 Li-mica granites; high-F, low-P 2O 5 Li-mica granites; high-F, low-P 2O 5 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

  19. 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.

  20. Multivariate Analyses of Heavy Metals in Surface Soil Around an Organized Industrial Area in Eskisehir, Turkey.

    PubMed

    Malkoc, S; Yazici, B

    2017-02-01

    A total of 50 surface industrial area soil in Eskisehir, Turkey were collected and the concentrations of As, Cr, Cd, Co, Cu, Ni, Pb, Zn, Fe and Mg, at 11.34, 95.8, 1.37, 15.28, 33.06, 143.65, 14.34, 78.79 mg/kg, 188.80% and 78.70%, respectively. The EF values for As, Cu, Pb and Zn at a number of sampling sites were found to be the highest among metals. Igeo-index results show that the study area is moderately polluted with respect to As, Cd, Ni. According to guideline values of Turkey Environmental Quality Standard for Soils, there is no problem for Pb, but the Cd values are fairly high. However, Cr, Cu, Ni and Zn values mostly exceed the limits. Cluster analyses suggested that soil the contaminator values are homogenous in those sub classes. The prevention and remediation of the heavy metal soil pollution should focus on these high-risk areas in the future.

  1. Phylogenetic and Multivariate Analyses To Determine the Effects of Different Tillage and Residue Management Practices on Soil Bacterial Communities▿ †

    PubMed Central

    Ceja-Navarro, Javier A.; Rivera-Orduña, Flor N.; Patiño-Zúñiga, Leonardo; Vila-Sanjurjo, Antón; Crossa, José; Govaerts, Bram; Dendooven, Luc

    2010-01-01

    Bacterial communities are important not only in the cycling of organic compounds but also in maintaining ecosystems. Specific bacterial groups can be affected as a result of changes in environmental conditions caused by human activities, such as agricultural practices. The aim of this study was to analyze the effects of different forms of tillage and residue management on soil bacterial communities by using phylogenetic and multivariate analyses. Treatments involving zero tillage (ZT) and conventional tillage (CT) with their respective combinations of residue management, i.e., removed residue (−R) and kept residue (+R), and maize/wheat rotation, were selected from a long-term field trial started in 1991. Analysis of bacterial diversity showed that soils under zero tillage and crop residue retention (ZT/+R) had the highest levels of diversity and richness. Multivariate analysis showed that beneficial bacterial groups such as fluorescent Pseudomonas spp. and Burkholderiales were favored by residue retention (ZT/+R and CT/+R) and negatively affected by residue removal (ZT/−R). Zero-tillage treatments (ZT/+R and ZT/−R) had a positive effect on the Rhizobiales group, with its main representatives related to Methylosinus spp. known as methane-oxidizing bacteria. It can be concluded that practices that include reduced tillage and crop residue retention can be adopted as safer agricultural practices to preserve and improve the diversity of soil bacterial communities. PMID:20382808

  2. Phylogenetic and multivariate analyses to determine the effects of different tillage and residue management practices on soil bacterial communities.

    PubMed

    Ceja-Navarro, Javier A; Rivera-Orduña, Flor N; Patiño-Zúñiga, Leonardo; Vila-Sanjurjo, Antón; Crossa, José; Govaerts, Bram; Dendooven, Luc

    2010-06-01

    Bacterial communities are important not only in the cycling of organic compounds but also in maintaining ecosystems. Specific bacterial groups can be affected as a result of changes in environmental conditions caused by human activities, such as agricultural practices. The aim of this study was to analyze the effects of different forms of tillage and residue management on soil bacterial communities by using phylogenetic and multivariate analyses. Treatments involving zero tillage (ZT) and conventional tillage (CT) with their respective combinations of residue management, i.e., removed residue (-R) and kept residue (+R), and maize/wheat rotation, were selected from a long-term field trial started in 1991. Analysis of bacterial diversity showed that soils under zero tillage and crop residue retention (ZT/+R) had the highest levels of diversity and richness. Multivariate analysis showed that beneficial bacterial groups such as fluorescent Pseudomonas spp. and Burkholderiales were favored by residue retention (ZT/+R and CT/+R) and negatively affected by residue removal (ZT/-R). Zero-tillage treatments (ZT/+R and ZT/-R) had a positive effect on the Rhizobiales group, with its main representatives related to Methylosinus spp. known as methane-oxidizing bacteria. It can be concluded that practices that include reduced tillage and crop residue retention can be adopted as safer agricultural practices to preserve and improve the diversity of soil bacterial communities.

  3. Falcon: Visual analysis of large, irregularly sampled, and multivariate time series data in additive manufacturing

    DOE PAGES

    Steed, Chad A.; Halsey, William; Dehoff, Ryan; ...

    2017-02-16

    Flexible visual analysis of long, high-resolution, and irregularly sampled time series data from multiple sensor streams is a challenge in several domains. In the field of additive manufacturing, this capability is critical for realizing the full potential of large-scale 3D printers. Here, we propose a visual analytics approach that helps additive manufacturing researchers acquire a deep understanding of patterns in log and imagery data collected by 3D printers. Our specific goals include discovering patterns related to defects and system performance issues, optimizing build configurations to avoid defects, and increasing production efficiency. We introduce Falcon, a new visual analytics system thatmore » allows users to interactively explore large, time-oriented data sets from multiple linked perspectives. Falcon provides overviews, detailed views, and unique segmented time series visualizations, all with adjustable scale options. To illustrate the effectiveness of Falcon at providing thorough and efficient knowledge discovery, we present a practical case study involving experts in additive manufacturing and data from a large-scale 3D printer. The techniques described are applicable to the analysis of any quantitative time series, though the focus of this paper is on additive manufacturing.« less

  4. 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.

  5. 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

  6. Identification of trace additives in polymer materials by attenuated total reflection Fourier transform infrared mapping coupled with multivariate curve resolution.

    PubMed

    Li, Qian; Tang, Yongjiao; Yan, Zhiwei; Zhang, Pudun

    2017-03-07

    Although multivariate curve resolution (MCR) has been applied to the analysis of Fourier transform infrared (FTIR) imaging, it is still problematic to determine the number of components. The reported methods at present tend to cause the components of low concentration missed. In this paper a new idea was proposed to resolve this problem. First, MCR calculation was repeated by increasing the number of components sequentially, then each retrieved pure spectrum of as-resulted MCR component was directly compared with a real-world pixel spectrum of the local high concentration in the corresponding MCR map. One component was affirmed only if the characteristic bands of the MCR component had been included in its pixel spectrum. This idea was applied to attenuated total reflection (ATR)/FTIR mapping for identifying the trace additives in blind polymer materials and satisfactory results were acquired. The successful demonstration of this novel approach opens up new possibilities for analyzing additives in polymer materials.

  7. 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.

  8. Treatment algorithm based on the multivariate survival analyses in patients with advanced hepatocellular carcinoma treated with trans-arterial chemoembolization

    PubMed Central

    Prajapati, Hasmukh J.

    2017-01-01

    Purpose To develop the treatment algorithm from multivariate survival analyses (MVA) in patients with Barcelona clinic liver cancer (BCLC) C (advanced) Hepatocellular carcinoma (HCC) patients treated with Trans-arterial Chemoembolization (TACE). Methods Consecutive unresectable and non-tranplantable patients with advanced HCC, who received DEB TACE were studied. A total of 238 patients (mean age, 62.4yrs) was included in the study. Survivals were analyzed according to different parameters from the time of the 1st DEB TACE. Kaplan Meier and Cox Proportional Hazard model were used for survival analysis. The SS was constructed from MVA and named BCLC C HCC Prognostic (BCHP) staging system (SS). Results Overall median survival (OS) was 16.2 months. In HCC patients with venous thrombosis (VT) of large vein [main portal vein (PV), right or left PV, hepatic vein, inferior vena cava] (22.7%) versus small vein (segmental/subsegmental PV) (9.7%) versus no VT had OSs of 6.4 months versus 20 months versus 22.8 months respectively (p<0.001). On MVA, the significant independent prognostic factors (PFs) of survival were CP class, eastern cooperative oncology group (ECOG) performance status (PS), single HCC<5 cm, site of VT, metastases, serum creatinine and serum alpha-feto protein. Based on these PFs, the BCHP staging system was constructed. The OSs of stages I, II and III were 28.4 months, 11.8 months and 2.4 months accordingly (p<0.001). The treatment plan was proposed according to the different stages. Conclusion On MVA of patients with advanced HCC treated with TACE, significant independent prognostic factors (PFs) of survival were CP class, ECOG PS, single HCC<5 cm or others, site of VT, metastases, serum creatinine and serum alpha-feto protein. New BCHP SS was proposed based on MVA data to identify the suitable advanced HCC patients for TACE treatments. PMID:28170405

  9. Multivariate Quantitative Chemical Analysis

    NASA Technical Reports Server (NTRS)

    Kinchen, David G.; Capezza, Mary

    1995-01-01

    Technique of multivariate quantitative chemical analysis devised for use in determining relative proportions of two components mixed and sprayed together onto object to form thermally insulating foam. Potentially adaptable to other materials, especially in process-monitoring applications in which necessary to know and control critical properties of products via quantitative chemical analyses of products. In addition to chemical composition, also used to determine such physical properties as densities and strengths.

  10. 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.

  11. Multivariate curve resolution of incomplete fused multiset data from chromatographic and spectrophotometric analyses for drug photostability studies.

    PubMed

    De Luca, Michele; Ragno, Gaetano; Ioele, Giuseppina; Tauler, Romà

    2014-07-21

    An advanced and powerful chemometric approach is proposed for the analysis of incomplete multiset data obtained by fusion of hyphenated liquid chromatographic DAD/MS data with UV spectrophotometric data from acid-base titration and kinetic degradation experiments. Column- and row-wise augmented data blocks were combined and simultaneously processed by means of a new version of the multivariate curve resolution-alternating least squares (MCR-ALS) technique, including the simultaneous analysis of incomplete multiset data from different instrumental techniques. The proposed procedure was applied to the detailed study of the kinetic photodegradation process of the amiloride (AML) drug. All chemical species involved in the degradation and equilibrium reactions were resolved and the pH dependent kinetic pathway described.

  12. Application of mid-infrared chemical imaging and multivariate chemometrics analyses to characterise a population of microalgae cells.

    PubMed

    Tan, Suat-Teng; Balasubramanian, Rajesh Kumar; Das, Probir; Obbard, Jeffrey Philip; Chew, Wee

    2013-04-01

    A suite of multivariate chemometrics methods was applied to a mid-infrared imaging dataset of a eustigmatophyte, marine Nannochloropsis sp. microalgae strain. This includes the improved leader-follower cluster analysis (iLFCA) to interrogate spectra in an unsupervised fashion, a resonant Mie optical scatter correction algorithm (RMieS-EMSC) that improves data linearity, the band-target entropy minimization (BTEM) self-modeling curve resolution for recovering component spectra, and a multi-linear regression (MLR) for estimating relative concentrations and plotting chemical maps of component spectra. A novel Alpha-Stable probability calculation for microalgae cellular lipid-to-protein ratio Λi is introduced for estimating population characteristics.

  13. 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

  14. Prediction of beef color using time-domain nuclear magnetic resonance (TD-NMR) relaxometry data and multivariate analyses.

    PubMed

    Moreira, Luiz Felipe Pompeu Prado; Ferrari, Adriana Cristina; Moraes, Tiago Bueno; Reis, Ricardo Andrade; Colnago, Luiz Alberto; Pereira, Fabíola Manhas Verbi

    2016-05-19

    Time-domain nuclear magnetic resonance and chemometrics were used to predict color parameters, such as lightness (L*), redness (a*), and yellowness (b*) of beef (Longissimus dorsi muscle) samples. Analyzing the relaxation decays with multivariate models performed with partial least-squares regression, color quality parameters were predicted. The partial least-squares models showed low errors independent of the sample size, indicating the potentiality of the method. Minced procedure and weighing were not necessary to improve the predictive performance of the models. The reduction of transverse relaxation time (T2 ) measured by Carr-Purcell-Meiboom-Gill pulse sequence in darker beef in comparison with lighter ones can be explained by the lower relaxivity Fe(2+) present in deoxymyoglobin and oxymyoglobin (red beef) to the higher relaxivity of Fe(3+) present in metmyoglobin (brown beef). These results point that time-domain nuclear magnetic resonance spectroscopy can become a useful tool for quality assessment of beef cattle on bulk of the sample and through-packages, because this technique is also widely applied to measure sensorial parameters, such as flavor, juiciness and tenderness, and physicochemical parameters, cooking loss, fat and moisture content, and instrumental tenderness using Warner Bratzler shear force. Copyright © 2016 John Wiley & Sons, Ltd.

  15. 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.

  16. 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

  17. The roots of plant defenses: integrative multivariate analyses uncover dynamic behaviors of gene and metabolic networks of roots elicited by leaf herbivory.

    PubMed

    Gulati, Jyotasana; Baldwin, Ian T; Gaquerel, Emmanuel

    2014-03-01

    High-throughput analyses have frequently been used to characterize herbivory-induced reconfigurations in plant primary and secondary metabolism in above- and below-ground tissues, but the conclusions drawn from these analyses are often limited by the univariate methods used to analyze the data. Here we use our previously described multivariate time-series data analysis to evaluate leaf herbivory-elicited transcriptional and metabolic dynamics in the roots of Nicotiana attenuata. We observed large, but transient, systemic responses in the roots that contrasted with the pattern of co-linearity observed in the up- and downregulation of genes and metabolites across the entire time series in treated and systemic leaves. Using this newly developed approach for the analysis of whole-plant molecular responses in a time-course multivariate data set, we simultaneously analyzed stress responses in leaves and roots in response to the elicitation of a leaf. We found that transient systemic responses in roots resolved into two principal trends characterized by: (i) an inversion of root-specific semi-diurnal (12 h) transcript oscillations and (ii) transcriptional changes with major amplitude effects that translated into a distinct suite of root-specific secondary metabolites (e.g. alkaloids synthesized in the roots of N. attenuata). These findings underscore the importance of understanding tissue-specific stress responses in the correct day-night phase context and provide a holistic framework for the important role played by roots in above-ground stress responses.

  18. Structured additive distributional regression for analysing landings per unit effort in fisheries research.

    PubMed

    Mamouridis, Valeria; Klein, Nadja; Kneib, Thomas; Cadarso Suarez, Carmen; Maynou, Francesc

    2017-01-01

    We analysed the landings per unit effort (LPUE) from the Barcelona trawl fleet targeting the red shrimp (Aristeus antennatus) using novel Bayesian structured additive distributional regression to gain a better understanding of the dynamics and determinants of variation in LPUE. The data set, covering a time span of 17 years, includes fleet-dependent variables (e.g. the number of trips performed by vessels), temporal variables (inter- and intra-annual variability) and environmental variables (the North Atlantic Oscillation index). Based on structured additive distributional regression, we evaluate (i) the gain in replacing purely linear predictors by additive predictors including nonlinear effects of continuous covariates, (ii) the inclusion of vessel-specific effects based on either fixed or random effects, (iii) different types of distributions for the response, and (iv) the potential gain in not only modelling the location but also the scale/shape parameter of these distributions. Our findings support that flexible model variants are indeed able to improve the fit considerably and that additional insights can be gained. Tools to select within several model specifications and assumptions are discussed in detail as well.

  19. 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

  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 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)

  2. 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.

  3. Assessment of the Disposition of Chiral Polychlorinated Biphenyls in Female mdr 1a/b Knockout versus Wild-type Mice Using Multivariate Analyses

    PubMed Central

    Milanowski, Bartłomiej; Lulek, Janina; Lehmler, Hans-Joachim; Kania-Korwel, Izabela

    2009-01-01

    Polychlorinated biphenyls (PCBs) are present in the environment as complex mixtures, which make it challenging to identify PCB congeners that may be subject to active transport processes. Here we employ a transgenic mouse model in combination with multivariate analyses to investigate if chiral PCBs 91, 95, 132, 136, 149, 174, 176 and 183 are subject to active (enantioselective) transport by multidrug resistance (MDR) transporters. A synthetic PCB mixture containing these congeners was administered orally to female FVB or mdr1a/1b knockout mice. Due to the short half-life of chiral PCB congeners, mice were euthanized after 24 hours and PCB concentrations and enantiomeric fractions were determined in selected tissues and excreta. Principal component analysis did not reveal differences between wild-type and mdr1a/1b knockout mice. However, Hotelling T2-test revealed significantly lower PCB concentrations and a more pronounced enantiomeric enrichment in the adipose tissue of mdr1a/1b knockout mice. These differences are due to higher body weights and higher fecal fat contents of mdr1a/1b knockout mice. Analysis of the enantiomeric fractions of PCBs 91, 95, 136, 149 and 174 showed a significant enantiomeric enrichment for all five congeners in wild-type and mdr1a/1b knockout mice. Overall, by studying a PCB mixture in a transgenic mouse model in combination with a multivariate data reduction approach, PCBs 91, 95, 136, 149 and 174 could be excluded as substrates of multidrug resistance transporters 1a/b. PMID:19923000

  4. 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

  5. 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.

  6. 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.

  7. 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…

  8. 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.

  9. 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.

  10. 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

  11. 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

  12. An experiment in software reliability: Additional analyses using data from automated replications

    NASA Technical Reports Server (NTRS)

    Dunham, Janet R.; Lauterbach, Linda A.

    1988-01-01

    A study undertaken to collect software error data of laboratory quality for use in the development of credible methods for predicting the reliability of software used in life-critical applications is summarized. The software error data reported were acquired through automated repetitive run testing of three independent implementations of a launch interceptor condition module of a radar tracking problem. The results are based on 100 test applications to accumulate a sufficient sample size for error rate estimation. The data collected is used to confirm the results of two Boeing studies reported in NASA-CR-165836 Software Reliability: Repetitive Run Experimentation and Modeling, and NASA-CR-172378 Software Reliability: Additional Investigations into Modeling With Replicated Experiments, respectively. That is, the results confirm the log-linear pattern of software error rates and reject the hypothesis of equal error rates per individual fault. This rejection casts doubt on the assumption that the program's failure rate is a constant multiple of the number of residual bugs; an assumption which underlies some of the current models of software reliability. data raises new questions concerning the phenomenon of interacting faults.

  13. 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.

  14. 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.

  15. 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

  16. Non-targeted 1H NMR fingerprinting and multivariate statistical analyses for the characterisation of the geographical origin of Italian sweet cherries.

    PubMed

    Longobardi, F; Ventrella, A; Bianco, A; Catucci, L; Cafagna, I; Gallo, V; Mastrorilli, P; Agostiano, A

    2013-12-01

    In this study, non-targeted (1)H NMR fingerprinting was used in combination with multivariate statistical techniques for the classification of Italian sweet cherries based on their different geographical origins (Emilia Romagna and Puglia). As classification techniques, Soft Independent Modelling of Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Linear Discriminant Analysis (LDA) were carried out and the results were compared. For LDA, before performing a refined selection of the number/combination of variables, two different strategies for a preliminary reduction of the variable number were tested. The best average recognition and CV prediction abilities (both 100.0%) were obtained for all the LDA models, although PLS-DA also showed remarkable performances (94.6%). All the statistical models were validated by observing the prediction abilities with respect to an external set of cherry samples. The best result (94.9%) was obtained with LDA by performing a best subset selection procedure on a set of 30 principal components previously selected by a stepwise decorrelation. The metabolites that mostly contributed to the classification performances of such LDA model, were found to be malate, glucose, fructose, glutamine and succinate.

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

    PubMed

    Wolock, David M; Winter, Thomas C; McMahon, Gerard

    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.

  18. 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 ...

  19. Influences of Biodynamic and Conventional Farming Systems on Quality of Potato (Solanum Tuberosum L.) Crops: Results from Multivariate Analyses of Two Long-Term Field Trials in Sweden

    PubMed Central

    Kjellenberg, Lars; Granstedt, Artur

    2015-01-01

    The aim of this paper was to present results from two long term field experiments comparing potato samples from conventional farming systems with samples from biodynamic farming systems. The principal component analyses (PCA), consistently exhibited differences between potato samples from the two farming systems. According to the PCA, potato samples treated with inorganic fertilizers exhibited a variation positively related to amounts of crude protein, yield, cooking or tissue discoloration and extract decomposition. Potato samples treated according to biodynamic principles, with composted cow manure, were more positively related to traits such as Quality- and EAA-indices, dry matter content, taste quality, relative proportion of pure protein and biocrystallization value. Distinctions between years, crop rotation and cultivars used were sometimes more significant than differences between manuring systems. Grown after barley the potato crop exhibited better quality traits compared to when grown after ley in both the conventional and the biodynamic farming system. PMID:28231216

  20. Influences of Biodynamic and Conventional Farming Systems on Quality of Potato (Solanum Tuberosum L.) Crops: Results from Multivariate Analyses of Two Long-Term Field Trials in Sweden.

    PubMed

    Kjellenberg, Lars; Granstedt, Artur

    2015-09-15

    The aim of this paper was to present results from two long term field experiments comparing potato samples from conventional farming systems with samples from biodynamic farming systems. The principal component analyses (PCA), consistently exhibited differences between potato samples from the two farming systems. According to the PCA, potato samples treated with inorganic fertilizers exhibited a variation positively related to amounts of crude protein, yield, cooking or tissue discoloration and extract decomposition. Potato samples treated according to biodynamic principles, with composted cow manure, were more positively related to traits such as Quality- and EAA-indices, dry matter content, taste quality, relative proportion of pure protein and biocrystallization value. Distinctions between years, crop rotation and cultivars used were sometimes more significant than differences between manuring systems. Grown after barley the potato crop exhibited better quality traits compared to when grown after ley in both the conventional and the biodynamic farming system.

  1. 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.

  2. Long-term ground deformation patterns of Bucharest using multi-temporal InSAR and multivariate dynamic analyses: a possible transpressional system?

    PubMed

    Armaş, Iuliana; Mendes, Diana A; Popa, Răzvan-Gabriel; Gheorghe, Mihaela; Popovici, Diana

    2017-03-02

    The aim of this exploratory research is to capture spatial evolution patterns in the Bucharest metropolitan area using sets of single polarised synthetic aperture radar (SAR) satellite data and multi-temporal radar interferometry. Three sets of SAR data acquired during the years 1992-2010 from ERS-1/-2 and ENVISAT, and 2011-2014 from TerraSAR-X satellites were used in conjunction with the Small Baseline Subset (SBAS) and persistent scatterers (PS) high-resolution multi-temporal interferometry (InSAR) techniques to provide maps of line-of-sight displacements. The satellite-based remote sensing results were combined with results derived from classical methodologies (i.e., diachronic cartography) and field research to study possible trends in developments over former clay pits, landfill excavation sites, and industrial parks. The ground displacement trend patterns were analysed using several linear and nonlinear models, and techniques. Trends based on the estimated ground displacement are characterised by long-term memory, indicated by low noise Hurst exponents, which in the long-term form interesting attractors. We hypothesize these attractors to be tectonic stress fields generated by transpressional movements.

  3. Long-term ground deformation patterns of Bucharest using multi-temporal InSAR and multivariate dynamic analyses: a possible transpressional system?

    NASA Astrophysics Data System (ADS)

    Armaş, Iuliana; Mendes, Diana A.; Popa, Răzvan-Gabriel; Gheorghe, Mihaela; Popovici, Diana

    2017-03-01

    The aim of this exploratory research is to capture spatial evolution patterns in the Bucharest metropolitan area using sets of single polarised synthetic aperture radar (SAR) satellite data and multi-temporal radar interferometry. Three sets of SAR data acquired during the years 1992–2010 from ERS-1/-2 and ENVISAT, and 2011–2014 from TerraSAR-X satellites were used in conjunction with the Small Baseline Subset (SBAS) and persistent scatterers (PS) high-resolution multi-temporal interferometry (InSAR) techniques to provide maps of line-of-sight displacements. The satellite-based remote sensing results were combined with results derived from classical methodologies (i.e., diachronic cartography) and field research to study possible trends in developments over former clay pits, landfill excavation sites, and industrial parks. The ground displacement trend patterns were analysed using several linear and nonlinear models, and techniques. Trends based on the estimated ground displacement are characterised by long-term memory, indicated by low noise Hurst exponents, which in the long-term form interesting attractors. We hypothesize these attractors to be tectonic stress fields generated by transpressional movements.

  4. Long-term ground deformation patterns of Bucharest using multi-temporal InSAR and multivariate dynamic analyses: a possible transpressional system?

    PubMed Central

    Armaş, Iuliana; Mendes, Diana A.; Popa, Răzvan-Gabriel; Gheorghe, Mihaela; Popovici, Diana

    2017-01-01

    The aim of this exploratory research is to capture spatial evolution patterns in the Bucharest metropolitan area using sets of single polarised synthetic aperture radar (SAR) satellite data and multi-temporal radar interferometry. Three sets of SAR data acquired during the years 1992–2010 from ERS-1/-2 and ENVISAT, and 2011–2014 from TerraSAR-X satellites were used in conjunction with the Small Baseline Subset (SBAS) and persistent scatterers (PS) high-resolution multi-temporal interferometry (InSAR) techniques to provide maps of line-of-sight displacements. The satellite-based remote sensing results were combined with results derived from classical methodologies (i.e., diachronic cartography) and field research to study possible trends in developments over former clay pits, landfill excavation sites, and industrial parks. The ground displacement trend patterns were analysed using several linear and nonlinear models, and techniques. Trends based on the estimated ground displacement are characterised by long-term memory, indicated by low noise Hurst exponents, which in the long-term form interesting attractors. We hypothesize these attractors to be tectonic stress fields generated by transpressional movements. PMID:28252103

  5. Analyses of polycyclic aromatic hydrocarbon (PAH) and chiral-PAH analogues-methyl-β-cyclodextrin guest-host inclusion complexes by fluorescence spectrophotometry and multivariate regression analysis.

    PubMed

    Greene, LaVana; Elzey, Brianda; Franklin, Mariah; Fakayode, Sayo O

    2017-03-05

    The negative health impact of polycyclic aromatic hydrocarbons (PAHs) and differences in pharmacological activity of enantiomers of chiral molecules in humans highlights the need for analysis of PAHs and their chiral analogue molecules in humans. Herein, the first use of cyclodextrin guest-host inclusion complexation, fluorescence spectrophotometry, and chemometric approach to PAH (anthracene) and chiral-PAH analogue derivatives (1-(9-anthryl)-2,2,2-triflouroethanol (TFE)) analyses are reported. The binding constants (Kb), stoichiometry (n), and thermodynamic properties (Gibbs free energy (ΔG), enthalpy (ΔH), and entropy (ΔS)) of anthracene and enantiomers of TFE-methyl-β-cyclodextrin (Me-β-CD) guest-host complexes were also determined. Chemometric partial-least-square (PLS) regression analysis of emission spectra data of Me-β-CD-guest-host inclusion complexes was used for the determination of anthracene and TFE enantiomer concentrations in Me-β-CD-guest-host inclusion complex samples. The values of calculated Kb and negative ΔG suggest the thermodynamic favorability of anthracene-Me-β-CD and enantiomeric of TFE-Me-β-CD inclusion complexation reactions. However, anthracene-Me-β-CD and enantiomer TFE-Me-β-CD inclusion complexations showed notable differences in the binding affinity behaviors and thermodynamic properties. The PLS regression analysis resulted in square-correlation-coefficients of 0.997530 or better and a low LOD of 3.81×10(-7)M for anthracene and 3.48×10(-8)M for TFE enantiomers at physiological conditions. Most importantly, PLS regression accurately determined the anthracene and TFE enantiomer concentrations with an average low error of 2.31% for anthracene, 4.44% for R-TFE and 3.60% for S-TFE. The results of the study are highly significant because of its high sensitivity and accuracy for analysis of PAH and chiral PAH analogue derivatives without the need of an expensive chiral column, enantiomeric resolution, or use of a polarized

  6. Analyses of polycyclic aromatic hydrocarbon (PAH) and chiral-PAH analogues-methyl-β-cyclodextrin guest-host inclusion complexes by fluorescence spectrophotometry and multivariate regression analysis

    NASA Astrophysics Data System (ADS)

    Greene, LaVana; Elzey, Brianda; Franklin, Mariah; Fakayode, Sayo O.

    2017-03-01

    The negative health impact of polycyclic aromatic hydrocarbons (PAHs) and differences in pharmacological activity of enantiomers of chiral molecules in humans highlights the need for analysis of PAHs and their chiral analogue molecules in humans. Herein, the first use of cyclodextrin guest-host inclusion complexation, fluorescence spectrophotometry, and chemometric approach to PAH (anthracene) and chiral-PAH analogue derivatives (1-(9-anthryl)-2,2,2-triflouroethanol (TFE)) analyses are reported. The binding constants (Kb), stoichiometry (n), and thermodynamic properties (Gibbs free energy (ΔG), enthalpy (ΔH), and entropy (ΔS)) of anthracene and enantiomers of TFE-methyl-β-cyclodextrin (Me-β-CD) guest-host complexes were also determined. Chemometric partial-least-square (PLS) regression analysis of emission spectra data of Me-β-CD-guest-host inclusion complexes was used for the determination of anthracene and TFE enantiomer concentrations in Me-β-CD-guest-host inclusion complex samples. The values of calculated Kb and negative ΔG suggest the thermodynamic favorability of anthracene-Me-β-CD and enantiomeric of TFE-Me-β-CD inclusion complexation reactions. However, anthracene-Me-β-CD and enantiomer TFE-Me-β-CD inclusion complexations showed notable differences in the binding affinity behaviors and thermodynamic properties. The PLS regression analysis resulted in square-correlation-coefficients of 0.997530 or better and a low LOD of 3.81 × 10- 7 M for anthracene and 3.48 × 10- 8 M for TFE enantiomers at physiological conditions. Most importantly, PLS regression accurately determined the anthracene and TFE enantiomer concentrations with an average low error of 2.31% for anthracene, 4.44% for R-TFE and 3.60% for S-TFE. The results of the study are highly significant because of its high sensitivity and accuracy for analysis of PAH and chiral PAH analogue derivatives without the need of an expensive chiral column, enantiomeric resolution, or use of a

  7. 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.

  8. 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.

  9. 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.

  10. 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...

  11. 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

  12. 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

  13. 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…

  14. 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.

  15. Phylogenetic analyses and characterization of RNase X25 from Drosophila melanogaster suggest a conserved housekeeping role and additional functions for RNase T2 enzymes in protostomes.

    PubMed

    Ambrosio, Linda; Morriss, Stephanie; Riaz, Ayesha; Bailey, Ryan; Ding, Jian; MacIntosh, Gustavo C

    2014-01-01

    Ribonucleases belonging to the RNase T2 family are enzymes associated with the secretory pathway that are almost absolutely conserved in all eukaryotes. Studies in plants and vertebrates suggest they have an important housekeeping function in rRNA recycling. However, little is known about this family of enzymes in protostomes. We characterized RNase X25, the only RNase T2 enzyme in Drosophila melanogaster. We found that RNase X25 is the major contributor of ribonuclease activity in flies as detected by in gel assays, and has an acidic pH preference. Gene expression analyses showed that the RNase X25 transcript is present in all adult tissues and developmental stages. RNase X25 expression is elevated in response to nutritional stresses; consistent with the hypothesis that this enzyme has a housekeeping role in recycling RNA. A correlation between induction of RNase X25 expression and autophagy was observed. Moreover, induction of gene expression was triggered by oxidative stress suggesting that RNase X25 may have additional roles in stress responses. Phylogenetic analyses of this family in protostomes showed that RNase T2 genes have undergone duplication events followed by divergence in several phyla, including the loss of catalytic residues, and suggest that RNase T2 proteins have acquired novel functions. Among those, it is likely that a role in host immunosuppression evolved independently in several groups, including parasitic Platyhelminthes and parasitoid wasps. The presence of only one RNase T2 gene in the D. melanogaster genome, without any other evident secretory RNase activity detected, makes this organism an ideal system to study the cellular functions of RNase T2 proteins associated with RNA recycling and maintenance of cellular homeostasis. On the other hand, the discovery of gene duplications in several protostome genomes also presents interesting new avenues to study additional biological functions of this ancient family of proteins.

  16. Multivariable Control Systems

    DTIC Science & Technology

    1968-01-01

    one). Examples abound of systems with numerous controlled variables, and the modern tendency is toward ever greater utilization of systems and plants of this kind. We call them multivariable control systems (MCS).

  17. Analysing chemical-induced changes in macroinvertebrate communities in aquatic mesocosm experiments: a comparison of methods.

    PubMed

    Szöcs, Eduard; Van den Brink, Paul J; Lagadic, Laurent; Caquet, Thierry; Roucaute, Marc; Auber, Arnaud; Bayona, Yannick; Liess, Matthias; Ebke, Peter; Ippolito, Alessio; ter Braak, Cajo J F; Brock, Theo C M; Schäfer, Ralf B

    2015-05-01

    Mesocosm experiments that study the ecological impact of chemicals are often analysed using the multivariate method 'Principal Response Curves' (PRCs). Recently, the extension of generalised linear models (GLMs) to multivariate data was introduced as a tool to analyse community data in ecology. Moreover, data aggregation techniques that can be analysed with univariate statistics have been proposed. The aim of this study was to compare their performance. We compiled macroinvertebrate abundance datasets of mesocosm experiments designed for studying the effect of various organic chemicals, mainly pesticides, and re-analysed them. GLMs for multivariate data and selected aggregated endpoints were compared to PRCs regarding their performance and potential to identify affected taxa. In addition, we analysed the inter-replicate variability encountered in the studies. Mesocosm experiments characterised by a higher taxa richness of the community and/or lower taxonomic resolution showed a greater inter-replicate variability, whereas variability decreased the more zero counts were encountered in the samples. GLMs for multivariate data performed equally well as PRCs regarding the community response. However, compared to first axis PRCs, GLMs provided a better indication of individual taxa responding to treatments, as separate models are fitted to each taxon. Data aggregation methods performed considerably poorer compared to PRCs. Multivariate community data, which are generated during mesocosm experiments, should be analysed using multivariate methods to reveal treatment-related community-level responses. GLMs for multivariate data are an alternative to the widely used PRCs.

  18. Estimating Polygenic Models for Multivariate Data on Large Pedigrees

    PubMed Central

    Thompson, E. A.; Shaw, R. G.

    1992-01-01

    We have developed algorithms for the likelihood estimation of additive genetic models for quantitative traits on large pedigrees. The approach uses the expectation L-maximization (EM) algorithm, but avoids intensive computation. In this paper, we focus on extensions of previous work to the case of multivariate data. We exemplify the approach by analyses of bivariate data on a four-generation, 949-member pedigree of the snail Lymnaea elodes, and on a three-generation pedigree of the guppy Poecilia reticulata containing about 400 individuals. PMID:1516823

  19. 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.

  20. 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.

  1. 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.

    PubMed

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

    2016-07-05

    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.

  2. 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.

  3. Transient multivariable sensor evaluation

    DOEpatents

    Vilim, Richard B.; Heifetz, Alexander

    2017-02-21

    A method and system for performing transient multivariable sensor evaluation. The method and system includes a computer system for identifying a model form, providing training measurement data, generating a basis vector, monitoring system data from sensor, loading the system data in a non-transient memory, performing an estimation to provide desired data and comparing the system data to the desired data and outputting an alarm for a defective sensor.

  4. 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…

  5. 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.

  6. Topics in Multivariate Approximation Theory.

    DTIC Science & Technology

    1982-05-01

    include tensor products, multivariate polynomial interpolation , esp. Kergin Interpolation , and the recent developments of multivariate B-splines. t1...AMS (MOS) Subject Classifications: 41-02, 41A05, 41A10, 41A15, 41A63, 41A65 Key Words: multivariate, B-splines, Kergin interpolation , linear projectors...splines and in multivariate polynomial interpolation . These developments may well provide the theoretical foundation for efficient methods of

  7. 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

  8. Multivariate volume rendering

    SciTech Connect

    Crawfis, R.A.

    1996-03-01

    This paper presents a new technique for representing multivalued data sets defined on an integer lattice. It extends the state-of-the-art in volume rendering to include nonhomogeneous volume representations. That is, volume rendering of materials with very fine detail (e.g. translucent granite) within a voxel. Multivariate volume rendering is achieved by introducing controlled amounts of noise within the volume representation. Varying the local amount of noise within the volume is used to represent a separate scalar variable. The technique can also be used in image synthesis to create more realistic clouds and fog.

  9. 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.

  10. 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.

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

    PubMed Central

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

    2013-01-01

    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. PMID:23591519

  12. 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

  13. Halogen-free ionic liquid as an additive in zinc(II)-selective electrode: surface analyses as correlated to the membrane activity.

    PubMed

    Al-Asousi, Maryam F; Shoukry, Adel F; Bu-Olayan, Abdul Hadi

    2012-05-30

    Two conventional Zn(II) polyvinyl chloride (PVC) membrane electrodes have been prepared and characterized. They were based on dibenzo-24-crown-8 (DBC) as a neutral carrier, dioctyl phthalate (DOP) as a plasticizer, and potassium tetrakis (p-chlorophenyl) borate, KTpClPB or the halogen-free ionic liquid, tetraoctylammonium dodecylbenzene sulfonate [TOA][DBS] as an additive. The use of ionic liquid has been found to enhance the selectivity of the sensor. For each electrode, the surfaces of two membranes were investigated using X-ray photoelectron, ion-scattering spectroscopy and atomic force microscopy. One of the two membranes was conditioned by soaking it for 24 h in a 1.0×10(-3) M Zn(NO(3))(2) solution and the second was soaked in bi-distilled water for the same interval (24 h). Comparing the two surfaces indicated the following: (a) the high selectivity in case of using [TOA][DBS] as an additive is due to the extra mediation caused by the ionic liquid and (b) the working mechanism of the electrode is based on phase equilibrium at the surface of the membrane associated with ion transport through the bulk of the membrane.

  14. Angles of multivariable root loci

    NASA Technical Reports Server (NTRS)

    Thompson, P. M.; Stein, G.; Laub, A. J.

    1982-01-01

    A generalized eigenvalue problem is demonstrated to be useful for computing the multivariable root locus, particularly when obtaining the arrival angles to finite transmission zeros. The multivariable root loci are found for a linear, time-invariant output feedback problem. The problem is then employed to compute a closed-loop eigenstructure. The method of computing angles on the root locus is demonstrated, and the method is extended to a multivariable optimal root locus.

  15. Distinguishing nonpareil marketing group almond cultivars through multivariate analyses

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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 ...

  16. 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.

  17. 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.

  18. Multivariate Granger causality and generalized variance

    NASA Astrophysics Data System (ADS)

    Barrett, Adam B.; Barnett, Lionel; Seth, Anil K.

    2010-04-01

    Granger causality analysis is a popular method for inference on directed interactions in complex systems of many variables. A shortcoming of the standard framework for Granger causality is that it only allows for examination of interactions between single (univariate) variables within a system, perhaps conditioned on other variables. However, interactions do not necessarily take place between single variables but may occur among groups or “ensembles” of variables. In this study we establish a principled framework for Granger causality in the context of causal interactions among two or more multivariate sets of variables. Building on Geweke’s seminal 1982 work, we offer additional justifications for one particular form of multivariate Granger causality based on the generalized variances of residual errors. Taken together, our results support a comprehensive and theoretically consistent extension of Granger causality to the multivariate case. Treated individually, they highlight several specific advantages of the generalized variance measure, which we illustrate using applications in neuroscience as an example. We further show how the measure can be used to define “partial” Granger causality in the multivariate context and we also motivate reformulations of “causal density” and “Granger autonomy.” Our results are directly applicable to experimental data and promise to reveal new types of functional relations in complex systems, neural and otherwise.

  19. Multivariate Granger causality and generalized variance.

    PubMed

    Barrett, Adam B; Barnett, Lionel; Seth, Anil K

    2010-04-01

    Granger causality analysis is a popular method for inference on directed interactions in complex systems of many variables. A shortcoming of the standard framework for Granger causality is that it only allows for examination of interactions between single (univariate) variables within a system, perhaps conditioned on other variables. However, interactions do not necessarily take place between single variables but may occur among groups or "ensembles" of variables. In this study we establish a principled framework for Granger causality in the context of causal interactions among two or more multivariate sets of variables. Building on Geweke's seminal 1982 work, we offer additional justifications for one particular form of multivariate Granger causality based on the generalized variances of residual errors. Taken together, our results support a comprehensive and theoretically consistent extension of Granger causality to the multivariate case. Treated individually, they highlight several specific advantages of the generalized variance measure, which we illustrate using applications in neuroscience as an example. We further show how the measure can be used to define "partial" Granger causality in the multivariate context and we also motivate reformulations of "causal density" and "Granger autonomy." Our results are directly applicable to experimental data and promise to reveal new types of functional relations in complex systems, neural and otherwise.

  20. Constitutive Analyses of Nontraditional Stabilization Additives

    DTIC Science & Technology

    2004-11-01

    Final report Approved for public release; distribution is unlimited Prepared for Headquarters, U. S. Army Corps of Engineers Washington, DC 20314-1000...program, AT22 Work Package 238, currently sponsored by Headquarters, U.S. Army Corps of Engineers (CECW-EW). This publication was prepared by personnel...Dr. Steve L. Larson and vii Ms. Barbara Tardy, Inorganics Remediation Team, EL. Mr. Tingle and associates prepared this publication under the

  1. Multivariation calibration techniques applied to NIRA (near infrared reflectance analysis) and FTIR (Fourier transform infrared) data

    NASA Astrophysics Data System (ADS)

    Long, C. L.

    1991-02-01

    Multivariate calibration techniques can reduce the time required for routine testing and can provide new methods of analysis. Multivariate calibration is commonly used with near infrared reflectance analysis (NIRA) and Fourier transform infrared (FTIR) spectroscopy. Two feasibility studies were performed to determine the capability of NIRA, using multivariate calibration techniques, to perform analyses on the types of samples that are routinely analyzed at this laboratory. The first study performed included a variety of samples and indicated that NIRA would be well-suited to perform analyses on selected materials properties such as water content and hydroxyl number on polyol samples, epoxy content on epoxy resins, water content of desiccants, and the amine values of various amine cure agents. A second study was performed to assess the capability of NIRA to perform quantitative analysis of hydroxyl numbers and water contents of hydroxyl-containing materials. Hydroxyl number and water content were selected for determination because these tests are frequently run on polyol materials and the hydroxyl number determination is time consuming. This study pointed out the necessity of obtaining calibration standards identical to the samples being analyzed for each type of polyol or other material being analyzed. Multivariate calibration techniques are frequently used with FTIR data to determine the composition of a large variety of complex mixtures. A literature search indicated many applications of multivariate calibration to FTIR data. Areas identified where quantitation by FTIR would provide a new capability are quantitation of components in epoxy and silicone resins, polychlorinated biphenyls (PCBs) in oils, and additives to polymers.

  2. Modular multivariable control improves hydrocracking

    SciTech Connect

    Chia, T.L.; Lefkowitz, I.; Tamas, P.D.

    1996-10-01

    Modular multivariable control (MMC), a system of interconnected, single process variable controllers, can be a user-friendly, reliable and cost-effective alternative to centralized, large-scale multivariable control packages. MMC properties and features derive directly from the properties of the coordinated controller which, in turn, is based on internal model control technology. MMC was applied to a hydrocracking unit involving two process variables and three controller outputs. The paper describes modular multivariable control, MMC properties, tuning considerations, application at the DCS level, constraints handling, and process application and results.

  3. Multivariate Visual Explanation for High Dimensional Datasets

    PubMed Central

    Barlowe, Scott; Zhang, Tianyi; Liu, Yujie; Yang, Jing; Jacobs, Donald

    2010-01-01

    Understanding multivariate relationships is an important task in multivariate data analysis. Unfortunately, existing multivariate visualization systems lose effectiveness when analyzing relationships among variables that span more than a few dimensions. We present a novel multivariate visual explanation approach that helps users interactively discover multivariate relationships among a large number of dimensions by integrating automatic numerical differentiation techniques and multidimensional visualization techniques. The result is an efficient workflow for multivariate analysis model construction, interactive dimension reduction, and multivariate knowledge discovery leveraging both automatic multivariate analysis and interactive multivariate data visual exploration. Case studies and a formal user study with a real dataset illustrate the effectiveness of this approach. PMID:20694164

  4. Multivariate analysis of the modifications induced by an environmental acoustic cue on rat exploratory behavior.

    PubMed

    Casarrubea, Maurizio; Sorbera, Filippina; Crescimanno, Giuseppe

    2008-03-18

    The aim of the present paper is to study by means of a multivariate analysis the modifications induced by an environmental acoustic cue on the structure of rat exploratory behavior. Adult male Wistar rats were observed during the exploration of a soundproof observation box. Each rat was acoustically stimulated after 150 s from the beginning of the experimental session, lasting 300 s, and recorded through a digital videocamera. A frame by frame analysis was thereafter carried out using a professional video-recording system. Thirteen behavioral patterns were selected: immobility, immobile-sniffing, walking, rearing, climbing, chewing, paw-licking, face-grooming, body-grooming, head-turning, tuning, oriented-sniffing, focusing. Both descriptive and multivariate analyses (cluster, stochastic, adjusted residuals) were carried out. Through descriptive statistical analysis, latencies and per cent distribution of each pattern were studied. A multivariate cluster analysis revealed the presence of three main behavioral clusters, an additional one being identified following acoustic stimulation. Multivariate stochastic analysis showed that all the patterns converged on immobile-sniffing which could represent a key component in behavioral switching processes related to environmental exploration. Moreover, through adjusted residuals, the degree of relationship among different patterns was shown according to statistic Z-distribution. Our data assign new ethological meanings to different behavioral patterns. Notably, head-turning is suggested to be considered as a generic directional search and tuning as a subtle activity of stimulus localization.

  5. Multivariate analysis in thoracic research.

    PubMed

    Mengual-Macenlle, Noemí; Marcos, Pedro J; Golpe, Rafael; González-Rivas, Diego

    2015-03-01

    Multivariate analysis is based in observation and analysis of more than one statistical outcome variable at a time. In design and analysis, the technique is used to perform trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest. The development of multivariate methods emerged to analyze large databases and increasingly complex data. Since the best way to represent the knowledge of reality is the modeling, we should use multivariate statistical methods. Multivariate methods are designed to simultaneously analyze data sets, i.e., the analysis of different variables for each person or object studied. Keep in mind at all times that all variables must be treated accurately reflect the reality of the problem addressed. There are different types of multivariate analysis and each one should be employed according to the type of variables to analyze: dependent, interdependence and structural methods. In conclusion, multivariate methods are ideal for the analysis of large data sets and to find the cause and effect relationships between variables; there is a wide range of analysis types that we can use.

  6. Multivariate analysis in thoracic research

    PubMed Central

    Mengual-Macenlle, Noemí; Marcos, Pedro J.; Golpe, Rafael

    2015-01-01

    Multivariate analysis is based in observation and analysis of more than one statistical outcome variable at a time. In design and analysis, the technique is used to perform trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest. The development of multivariate methods emerged to analyze large databases and increasingly complex data. Since the best way to represent the knowledge of reality is the modeling, we should use multivariate statistical methods. Multivariate methods are designed to simultaneously analyze data sets, i.e., the analysis of different variables for each person or object studied. Keep in mind at all times that all variables must be treated accurately reflect the reality of the problem addressed. There are different types of multivariate analysis and each one should be employed according to the type of variables to analyze: dependent, interdependence and structural methods. In conclusion, multivariate methods are ideal for the analysis of large data sets and to find the cause and effect relationships between variables; there is a wide range of analysis types that we can use. PMID:25922743

  7. 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

  8. The Evolution of Multivariate Maternal Effects

    PubMed Central

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

    2014-01-01

    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. PMID:24722346

  9. Multivariate Analog of Hays Omega-Squared.

    ERIC Educational Resources Information Center

    Sachdeva, Darshan

    The multivariate analog of Hays omega-squared for estimating the strength of the relationship in the multivariate analysis of variance has been proposed in this paper. The multivariate omega-squared is obtained through the use of Wilks' lambda test criterion. Application of multivariate omega-squared to a numerical example has been provided so as…

  10. 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)

  11. 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…

  12. 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.

  13. An Interactive Approach to Analyzing Incomplete Multivariate Data.

    ERIC Educational Resources Information Center

    Raymond, Mark R.

    This paper examines some of the problems that arise when conducting multivariate analyses with incomplete data. The literature on the effectiveness of several missing data procedures (MDP) is summarized. The most widely used MDPs are: (1) listwise deletion; (2) pairwise deletion; (3) variable mean; (4) correlational methods. No MDP should be used…

  14. 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.

  15. 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

  16. Deriving ocean climatologies with multivariate coupling

    NASA Astrophysics Data System (ADS)

    Barth, Alexander; Alvera Azcarate, Aida; Beckers, Jean-Marie

    2016-04-01

    In situ measurements of ocean properties are generally sparsely distributed and thus undersample the ocean variability. Deriving ocean climatologies is a challenging task especially for biological and chemical parameters where the number of data is, by an order of magnitude, smaller than for physical parameters. However, physical and biogeochemical parameters are related through the ocean dynamics. In particular fronts visible in physical parameters are often related to gradients in biogeochemical parameters. Ocean climatologies are generally derived for different variables independently. For biogeochemical parameters, only the very large-scale variability can be derived for poorly sampled areas. Here we present a method to derive multivariate analysis taking the relationship between physical and biogeochemical variables into account. The benefit of this procedure is showed by using model data for salinity, nitrate and phosphate of the Mediterranean Sea. The model fields are sampled at the locations of true observations (extracted from the World Ocean Database 2013) and the analysed fields are compared to the original model fields. The multivariate analysis result in a reduction of the RMS error and to a better representation of the gradients.

  17. 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.

  18. 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.

  19. Information extraction from multivariate images

    NASA Technical Reports Server (NTRS)

    Park, S. K.; Kegley, K. A.; Schiess, J. R.

    1986-01-01

    An overview of several multivariate image processing techniques is presented, with emphasis on techniques based upon the principal component transformation (PCT). Multiimages in various formats have a multivariate pixel value, associated with each pixel location, which has been scaled and quantized into a gray level vector, and the bivariate of the extent to which two images are correlated. The PCT of a multiimage decorrelates the multiimage to reduce its dimensionality and reveal its intercomponent dependencies if some off-diagonal elements are not small, and for the purposes of display the principal component images must be postprocessed into multiimage format. The principal component analysis of a multiimage is a statistical analysis based upon the PCT whose primary application is to determine the intrinsic component dimensionality of the multiimage. Computational considerations are also discussed.

  20. 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.

  1. Multivariate Bioclimatic Ecosystem Change Approaches

    DTIC Science & Technology

    2015-02-06

    Headquarters, US Army Corps of Engineers Washington, DC 20314-1000 ERDC/CERL TR-15-2 ii Abstract Changes in climatic parameters are important in that they... climatic changes on specific installations. To support this need, the research tested and evaluated the application of six multivariate approach...techniques to predict climatic changes on a specific Army installation, Fort Benning, GA. The six approaches were tested for their ability to identify

  2. A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research

    PubMed Central

    Jiang, Xiaoqian; Matheny, Michael E; Farcas, Claudiu; D’Arcy, Michel; Pearlman, Laura; Nookala, Lavanya; Day, Michele E; Kim, Katherine K; Kim, Hyeoneui; Boxwala, Aziz; El-Kareh, Robert; Kuo, Grace M; Resnic, Frederic S; Kesselman, Carl; Ohno-Machado, Lucila

    2015-01-01

    Background Centralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at each site and data exchange policies are managed in a study-centric manner. Objective The objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features, such as algorithms for distributed iterative multivariate models, a graphical interface for multivariate model specification, synchronous and asynchronous response to network queries, investigator-initiated studies, and study-based control of staff, protocols, and data sharing policies. Materials and Methods Based on the requirements gathered from statisticians, administrators, and investigators from multiple institutions, the authors developed infrastructure and tools to support multisite comparative effectiveness studies using web services for multivariate statistical estimation in the SCANNER federated network. Results The authors implemented massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared. The authors illustrated the use of these systems among institutions with highly different policies and operating under different state laws. Discussion and Conclusion Federated research networks need not limit distributed query functionality to count queries, cohort discovery, or independently estimated analytic models. Multivariate analyses can be efficiently and securely conducted without patient

  3. Clines Arc through Multivariate Morphospace.

    PubMed

    Lohman, Brian K; Berner, Daniel; Bolnick, Daniel I

    2017-04-01

    Evolutionary biologists typically represent clines as spatial gradients in a univariate character (or a principal-component axis) whose mean changes as a function of location along a transect spanning an environmental gradient or ecotone. This univariate approach may obscure the multivariate nature of phenotypic evolution across a landscape. Clines might instead be plotted as a series of vectors in multidimensional morphospace, connecting sequential geographic sites. We present a model showing that clines may trace nonlinear paths that arc through morphospace rather than elongating along a single major trajectory. Arcing clines arise because different characters diverge at different rates or locations along a geographic transect. We empirically confirm that some clines arc through morphospace, using morphological data from threespine stickleback sampled along eight independent transects from lakes down their respective outlet streams. In all eight clines, successive vectors of lake-stream divergence fluctuate in direction and magnitude in trait space, rather than pointing along a single phenotypic axis. Most clines exhibit surprisingly irregular directions of divergence as one moves downstream, although a few clines exhibit more directional arcs through morphospace. Our results highlight the multivariate complexity of clines that cannot be captured with the traditional graphical framework. We discuss hypotheses regarding the causes, and implications, of such arcing multivariate clines.

  4. 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

  5. Multivariate Analysis of Genotype–Phenotype Association

    PubMed Central

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

    2016-01-01

    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

  6. 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

  7. A Multivariate Genome-Wide Association Analysis of 10 LDL Subfractions, and Their Response to Statin Treatment, in 1868 Caucasians

    PubMed Central

    Shim, Heejung; Chasman, Daniel I.; Smith, Joshua D.; Mora, Samia; Ridker, Paul M.; Nickerson, Deborah A.; Krauss, Ronald M.; Stephens, Matthew

    2015-01-01

    We conducted a genome-wide association analysis of 7 subfractions of low density lipoproteins (LDLs) and 3 subfractions of intermediate density lipoproteins (IDLs) measured by gradient gel electrophoresis, and their response to statin treatment, in 1868 individuals of European ancestry from the Pharmacogenomics and Risk of Cardiovascular Disease study. Our analyses identified four previously-implicated loci (SORT1, APOE, LPA, and CETP) as containing variants that are very strongly associated with lipoprotein subfractions (log10Bayes Factor > 15). Subsequent conditional analyses suggest that three of these (APOE, LPA and CETP) likely harbor multiple independently associated SNPs. Further, while different variants typically showed different characteristic patterns of association with combinations of subfractions, the two SNPs in CETP show strikingly similar patterns - both in our original data and in a replication cohort - consistent with a common underlying molecular mechanism. Notably, the CETP variants are very strongly associated with LDL subfractions, despite showing no association with total LDLs in our study, illustrating the potential value of the more detailed phenotypic measurements. In contrast with these strong subfraction associations, genetic association analysis of subfraction response to statins showed much weaker signals (none exceeding log10Bayes Factor of 6). However, two SNPs (in APOE and LPA) previously-reported to be associated with LDL statin response do show some modest evidence for association in our data, and the subfraction response proles at the LPA SNP are consistent with the LPA association, with response likely being due primarily to resistance of Lp(a) particles to statin therapy. An additional important feature of our analysis is that, unlike most previous analyses of multiple related phenotypes, we analyzed the subfractions jointly, rather than one at a time. Comparisons of our multivariate analyses with standard univariate analyses

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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

  14. Multivariate image analysis in biomedicine.

    PubMed

    Nattkemper, Tim W

    2004-10-01

    In recent years, multivariate imaging techniques are developed and applied in biomedical research in an increasing degree. In research projects and in clinical studies as well m-dimensional multivariate images (MVI) are recorded and stored to databases for a subsequent analysis. The complexity of the m-dimensional data and the growing number of high throughput applications call for new strategies for the application of image processing and data mining to support the direct interactive analysis by human experts. This article provides an overview of proposed approaches for MVI analysis in biomedicine. After summarizing the biomedical MVI techniques the two level framework for MVI analysis is illustrated. Following this framework, the state-of-the-art solutions from the fields of image processing and data mining are reviewed and discussed. Motivations for MVI data mining in biology and medicine are characterized, followed by an overview of graphical and auditory approaches for interactive data exploration. The paper concludes with summarizing open problems in MVI analysis and remarks upon the future development of biomedical MVI analysis.

  15. 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)

  16. Supporting Inquiry Learning by Promoting Normative Understanding of Multivariable Causality

    ERIC Educational Resources Information Center

    Keselman, Alla

    2003-01-01

    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…

  17. 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.

  18. Shape Control in Multivariate Barycentric Rational Interpolation

    NASA Astrophysics Data System (ADS)

    Nguyen, Hoa Thang; Cuyt, Annie; Celis, Oliver Salazar

    2010-09-01

    The most stable formula for a rational interpolant for use on a finite interval is the barycentric form [1, 2]. A simple choice of the barycentric weights ensures the absence of (unwanted) poles on the real line [3]. In [4] we indicate that a more refined choice of the weights in barycentric rational interpolation can guarantee comonotonicity and coconvexity of the rational interpolant in addition to a polefree region of interest. In this presentation we generalize the above to the multivariate case. We use a product-like form of univariate barycentric rational interpolants and indicate how the location of the poles and the shape of the function can be controlled. This functionality is of importance in the construction of mathematical models that need to express a certain trend, such as in probability distributions, economics, population dynamics, tumor growth models etc.

  19. Design of feedforward controllers for multivariable plants

    NASA Technical Reports Server (NTRS)

    Seraji, H.

    1987-01-01

    Simple methods for the design of feedforward controllers to achieve steady-state disturbance rejection and command tracking in stable multivariable plants are developed in this paper. The controllers are represented by simple and low-order transfer functions and are not based on reconstruction of the states of the commands and disturbances. For unstable plants, it is shown that the present method can be applied directly when an additional feedback controller is employed to stabilize the plant. The feedback and feedforward controllers do not affect each other and can be designed independently based on the open-loop plant to achieve stability, disturbance rejection and command tracking, respectivley. Numerical examples are given for illustration.

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

    PubMed

    Gowen, Aoife A; Dorrepaal, Ronan M

    2016-07-02

    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.

  1. 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).

  2. A multivariate heuristic model for fuzzy time-series forecasting.

    PubMed

    Huarng, Kun-Huang; Yu, Tiffany Hui-Kuang; Hsu, Yu Wei

    2007-08-01

    Fuzzy time-series models have been widely applied due to their ability to handle nonlinear data directly and because no rigid assumptions for the data are needed. In addition, many such models have been shown to provide better forecasting results than their conventional counterparts. However, since most of these models require complicated matrix computations, this paper proposes the adoption of a multivariate heuristic function that can be integrated with univariate fuzzy time-series models into multivariate models. Such a multivariate heuristic function can easily be extended and integrated with various univariate models. Furthermore, the integrated model can handle multiple variables to improve forecasting results and, at the same time, avoid complicated computations due to the inclusion of multiple variables.

  3. Distance measure with improved lower bound for multivariate time series

    NASA Astrophysics Data System (ADS)

    Li, Hailin

    2017-02-01

    Lower bound function is one of the important techniques used to fast search and index time series data. Multivariate time series has two aspects of high dimensionality including the time-based dimension and the variable-based dimension. Due to the influence of variable-based dimension, a novel method is proposed to deal with the lower bound distance computation for multivariate time series. The proposed method like the traditional ones also reduces the dimensionality of time series in its first step and thus does not directly apply the lower bound function on the multivariate time series. The dimensionality reduction is that multivariate time series is reduced to univariate time series denoted as center sequences according to the principle of piecewise aggregate approximation. In addition, an extended lower bound function is designed to obtain good tightness and fast measure the distance between any two center sequences. The experimental results demonstrate that the proposed lower bound function has better tightness and improves the performance of similarity search in multivariate time series datasets.

  4. 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.…

  5. 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

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

    PubMed

    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.

  7. 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.

  8. Detrended fluctuation analysis of multivariate time series

    NASA Astrophysics Data System (ADS)

    Xiong, Hui; Shang, P.

    2017-01-01

    In this work, we generalize the detrended fluctuation analysis (DFA) to the multivariate case, named multivariate DFA (MVDFA). The validity of the proposed MVDFA is illustrated by numerical simulations on synthetic multivariate processes, where the cases that initial data are generated independently from the same system and from different systems as well as the correlated variate from one system are considered. Moreover, the proposed MVDFA works well when applied to the multi-scale analysis of the returns of stock indices in Chinese and US stock markets. Generally, connections between the multivariate system and the individual variate are uncovered, showing the solid performances of MVDFA and the multi-scale MVDFA.

  9. Multivariate Connectome-Based Symptom Mapping in Post-Stroke Patients: Networks Supporting Language and Speech

    PubMed Central

    Fridriksson, Julius; Rorden, Chris; Gleichgerrcht, Ezequiel; Bonilha, Leonardo

    2016-01-01

    Language processing relies on a widespread network of brain regions. Univariate post-stroke lesion-behavior mapping is a particularly potent method to study brain–language relationships. However, it is a concern that this method may overlook structural disconnections to seemingly spared regions and may fail to adjudicate between regions that subserve different processes but share the same vascular perfusion bed. For these reasons, more refined structural brain mapping techniques may improve the accuracy of detecting brain networks supporting language. In this study, we applied a predictive multivariate framework to investigate the relationship between language deficits in human participants with chronic aphasia and the topological distribution of structural brain damage, defined as post-stroke necrosis or cortical disconnection. We analyzed lesion maps as well as structural connectome measures of whole-brain neural network integrity to predict clinically applicable language scores from the Western Aphasia Battery (WAB). Out-of-sample prediction accuracy was comparable for both types of analyses, which revealed spatially distinct, albeit overlapping, networks of cortical regions implicated in specific aspects of speech functioning. Importantly, all WAB scores could be predicted at better-than-chance level from the connections between gray-matter regions spared by the lesion. Connectome-based analysis highlighted the role of connectivity of the temporoparietal junction as a multimodal area crucial for language tasks. Our results support that connectome-based approaches are an important complement to necrotic lesion-based approaches and should be used in combination with lesion mapping to fully elucidate whether structurally damaged or structurally disconnected regions relate to aphasic impairment and its recovery. SIGNIFICANCE STATEMENT We present a novel multivariate approach of predicting post-stroke impairment of speech and language from the integrity of the

  10. The multivariate statistical structure of DRASTIC model

    NASA Astrophysics Data System (ADS)

    Pacheco, Fernando A. L.; Sanches Fernandes, Luís F.

    2013-01-01

    SummaryAn assessment of aquifer intrinsic vulnerability was conducted in the Sordo river basin, a small watershed located in the Northeast of Portugal that drains to a lake used as public resource of drinking water. The method adopted to calculate intrinsic vulnerability was the DRASTIC model, which hinges on a weighted addition of seven hydrogeologic features, but was combined with a pioneering approach for feature reduction and adjustment of feature weights to local settings, based on a multivariate statistical method. Basically, with the adopted statistical technique-Correspondence Analysis-one identified and minimized redundancy between DRASTIC features, allowing for the calculation of a composite index based on just three of them: topography, recharge and aquifer material. The combined algorithm was coined vector-DRASTIC and proved to describe more realistically intrinsic vulnerability than DRASTC. The proof resulted from a validation of DRASTIC and vector-DRASTIC by the results of a groundwater pollution risk assessment standing on the spatial distribution of land uses and nitrate concentrations in groundwater, referred to as [NO3-]-DRASTIC method. Vector-DRASTIC and [NO3-]-DRASTIC portray the Sordo river basin as an environment with a self-capability to neutralize contaminants, preventing its propagation downstream. This observation was confirmed by long-standing low nitrate concentrations in the lake water and constitutes additional validation of vector-DRASTIC results. Nevertheless, some general recommendations are proposed in regard to agriculture management practices for water quality protection, as part of an overall watershed approach.

  11. 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.

  12. 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…

  13. Multivariate Density Estimation and Remote Sensing

    NASA Technical Reports Server (NTRS)

    Scott, D. W.

    1983-01-01

    Current efforts to develop methods and computer algorithms to effectively represent multivariate data commonly encountered in remote sensing applications are described. While this may involve scatter diagrams, multivariate representations of nonparametric probability density estimates are emphasized. The density function provides a useful graphical tool for looking at data and a useful theoretical tool for classification. This approach is called a thunderstorm data analysis.

  14. 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.

  15. 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

  16. Multivariate analysis for the optimization of polysaccharide-based nanoparticles prepared by self-assembly.

    PubMed

    Pistone, Sara; Qoragllu, Dafina; Smistad, Gro; Hiorth, Marianne

    2016-10-01

    Polysaccharide-based nanoparticles are promising carriers for drug delivery applications. The particle size influences the biodistribution of the nanoparticles; hence size distributions and polydispersity index (PDI) are critical characteristics. However, the preparation of stable particles with a low PDI is a challenging task and is usually based on empirical trials. In this study, we report the use of multivariate evaluation to optimize the formulation factors for the preparation of alginate-zinc nanoparticles by ionotropic gelation. The PDI was selected as the response variable. Particle size, size distributions, zeta potential and pH of the samples were also recorded. Two full factorial (mixed-level) designs were analyzed by partial least squares regression (PLS). In the first design, the influence of the polysaccharide and the crosslinker concentrations were studied. The results revealed that size distributions with a low PDI were obtained by using a low polysaccharide concentrations (0.03-0.05%) and a zinc concentration of 0.03% (w/w). However, a high polysaccharide concentration can be advantageous for drug delivery systems. Therefore, in the second design, a high alginate concentration was used (0.09%) and a reduction in the PDI was obtained by simultaneously increasing the ionic strength of the solvent and the zinc concentration. The multivariate analysis also revealed the interaction between the factors in terms of their effects on the PDI; hence, compared to traditional univariate analyses, the multivariate analysis allowed us to obtain a more complete understanding of the effects of the factors scrutinized. In addition, the results are considered useful in order to avoid extensive empirical tests for future formulation studies.

  17. Synergy, redundancy, and multivariate information measures: an experimentalist's perspective.

    PubMed

    Timme, Nicholas; Alford, Wesley; Flecker, Benjamin; Beggs, John M

    2014-04-01

    Information theory has long been used to quantify interactions between two variables. With the rise of complex systems research, multivariate information measures have been increasingly used to investigate interactions between groups of three or more variables, often with an emphasis on so called synergistic and redundant interactions. While bivariate information measures are commonly agreed upon, the multivariate information measures in use today have been developed by many different groups, and differ in subtle, yet significant ways. Here, we will review these multivariate information measures with special emphasis paid to their relationship to synergy and redundancy, as well as examine the differences between these measures by applying them to several simple model systems. In addition to these systems, we will illustrate the usefulness of the information measures by analyzing neural spiking data from a dissociated culture through early stages of its development. Our aim is that this work will aid other researchers as they seek the best multivariate information measure for their specific research goals and system. Finally, we have made software available online which allows the user to calculate all of the information measures discussed within this paper.

  18. 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…

  19. Multivariate optimum interpolation of surface pressure and winds over oceans

    NASA Technical Reports Server (NTRS)

    Bloom, S. C.

    1984-01-01

    The observations of surface pressure are quite sparse over oceanic areas. An effort to improve the analysis of surface pressure over oceans through the development of a multivariate surface analysis scheme which makes use of surface pressure and wind data is discussed. Although the present research used ship winds, future versions of this analysis scheme could utilize winds from additional sources, such as satellite scatterometer data.

  20. Multivariate normative comparisons using an aggregated database.

    PubMed

    Agelink van Rentergem, Joost A; Murre, Jaap M J; Huizenga, Hilde M

    2017-01-01

    In multivariate normative comparisons, a patient's profile of test scores is compared to those in a normative sample. Recently, it has been shown that these multivariate normative comparisons enhance the sensitivity of neuropsychological assessment. However, multivariate normative comparisons require multivariate normative data, which are often unavailable. In this paper, we show how a multivariate normative database can be constructed by combining healthy control group data from published neuropsychological studies. We show that three issues should be addressed to construct a multivariate normative database. First, the database may have a multilevel structure, with participants nested within studies. Second, not all tests are administered in every study, so many data may be missing. Third, a patient should be compared to controls of similar age, gender and educational background rather than to the entire normative sample. To address these issues, we propose a multilevel approach for multivariate normative comparisons that accounts for missing data and includes covariates for age, gender and educational background. Simulations show that this approach controls the number of false positives and has high sensitivity to detect genuine deviations from the norm. An empirical example is provided. Implications for other domains than neuropsychology are also discussed. To facilitate broader adoption of these methods, we provide code implementing the entire analysis in the open source software package R.

  1. Multivariate normative comparisons using an aggregated database

    PubMed Central

    Murre, Jaap M. J.; Huizenga, Hilde M.

    2017-01-01

    In multivariate normative comparisons, a patient’s profile of test scores is compared to those in a normative sample. Recently, it has been shown that these multivariate normative comparisons enhance the sensitivity of neuropsychological assessment. However, multivariate normative comparisons require multivariate normative data, which are often unavailable. In this paper, we show how a multivariate normative database can be constructed by combining healthy control group data from published neuropsychological studies. We show that three issues should be addressed to construct a multivariate normative database. First, the database may have a multilevel structure, with participants nested within studies. Second, not all tests are administered in every study, so many data may be missing. Third, a patient should be compared to controls of similar age, gender and educational background rather than to the entire normative sample. To address these issues, we propose a multilevel approach for multivariate normative comparisons that accounts for missing data and includes covariates for age, gender and educational background. Simulations show that this approach controls the number of false positives and has high sensitivity to detect genuine deviations from the norm. An empirical example is provided. Implications for other domains than neuropsychology are also discussed. To facilitate broader adoption of these methods, we provide code implementing the entire analysis in the open source software package R. PMID:28267796

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. 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).

  7. Chemical Analyses

    NASA Technical Reports Server (NTRS)

    Bulluck, J. W.; Rushing, R. A.

    1994-01-01

    As a preliminary study on the effects of chemical aging of polymer materials MERL and TRI have examined two polymeric materials that are typically used for offshore umbilical applications. These two materials were Tefzel, a copolymer of ethylene and tetrafluoroethylene, and Coflon, polyvinylidene fluoride. The Coflon specimens were cut from pipe sections and exposed to H2S at various temperatures and pressures. One of these specimens was tested for methane permeation, and another for H2S permeation. The Tefzel specimens were cut from .05 mm sheet stock material and were exposed to methanol at elevated temperature and pressure. One of these specimens was exposed to methanol permeation for 2 days at 100 C and 2500 psi. An additional specimen was exposed to liquid methanol for 3 days at 150 C and 15 Bar. Virgin specimens of each material were similarly prepared and tested.

  8. 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

  9. 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

  10. Multivariate Voronoi Outlier Detection for Time Series.

    PubMed

    Zwilling, Chris E; Wang, Michelle Yongmei

    2014-10-01

    Outlier detection is a primary step in many data mining and analysis applications, including healthcare and medical research. This paper presents a general method to identify outliers in multivariate time series based on a Voronoi diagram, which we call Multivariate Voronoi Outlier Detection (MVOD). The approach copes with outliers in a multivariate framework, via designing and extracting effective attributes or features from the data that can take parametric or nonparametric forms. Voronoi diagrams allow for automatic configuration of the neighborhood relationship of the data points, which facilitates the differentiation of outliers and non-outliers. Experimental evaluation demonstrates that our MVOD is an accurate, sensitive, and robust method for detecting outliers in multivariate time series data.

  11. Multivariate Statistical Mapping of Spectroscopic Imaging Data

    PubMed Central

    Young, K.; Govind, V.; Sharma, K.; Studholme, C.; Maudsley, A.A; Schuff, N.

    2010-01-01

    For magnetic resonance spectroscopic imaging (MRSI) studies of the brain it is important to measure the distribution of metabolites in a regionally unbiased way - that is without restrictions to apriori defined regions of interest (ROI). Since MRSI provides measures of multiple metabolites simultaneously at each voxel, there is furthermore great interest in utilizing the multidimensional nature of MRSI for gains in statistical power. Voxelwise multivariate statistical mapping is expected to address both of these issues but it has not been previously employed for SI studies of brain. The aims of this study were to: 1) develop and validate multivariate voxel based statistical mapping for MRSI and 2) demonstrate that multivariate tests can be more powerful than univariate tests in identifying patterns of altered brain metabolism. Specifically, we compared multivariate to univariate tests in identifying known regional patterns in simulated data and regional patterns of metabolite alterations due to amyotrophic lateral sclerosis, a devastating brain disease of the motor neurons. PMID:19953514

  12. Borrowing of strength and study weights in multivariate and network meta-analysis.

    PubMed

    Jackson, Dan; White, Ian R; Price, Malcolm; Copas, John; Riley, Richard D

    2015-11-06

    Multivariate and network meta-analysis have the potential for the estimated mean of one effect to borrow strength from the data on other effects of interest. The extent of this borrowing of strength is usually assessed informally. We present new mathematical definitions of 'borrowing of strength'. Our main proposal is based on a decomposition of the score statistic, which we show can be interpreted as comparing the precision of estimates from the multivariate and univariate models. Our definition of borrowing of strength therefore emulates the usual informal assessment. We also derive a method for calculating study weights, which we embed into the same framework as our borrowing of strength statistics, so that percentage study weights can accompany the results from multivariate and network meta-analyses as they do in conventional univariate meta-analyses. Our proposals are illustrated using three meta-analyses involving correlated effects for multiple outcomes, multiple risk factor associations and multiple treatments (network meta-analysis).

  13. Multivariate equivalence tests for use in pharmaceutical development.

    PubMed

    Hoffelder, Thomas; Gössl, Rüdiger; Wellek, Stefan

    2015-01-01

    Statistical equivalence analyses are well-established parts of many studies in the biomedical sciences. Also in pharmaceutical development and manufacturing equivalence testing methods are required in order to statistically establish similarities between machines, process components, or complete processes. This article presents a choice of multivariate equivalence testing procedures for normally distributed data as generalizations of existing univariate methods. In all derived methods, variability is interpreted as nuisance parameter. The use of the proposed methods in pharmaceutical development is demonstrated with a comparative analysis of dissolution profiles.

  14. Multivariate Analysis and Prediction of Dioxin-Furan ...

    EPA Pesticide Factsheets

    Peer Review Draft of Regional Methods Initiative Final Report Dioxins, which are bioaccumulative and environmentally persistent, pose an ongoing risk to human and ecosystem health. Fish constitute a significant source of dioxin exposure for humans and fish-eating wildlife. Current dioxin analytical methods are costly, time-consuming, and produce hazardous by-products. A Danish team developed a novel, multivariate statistical methodology based on the covariance of dioxin-furan congener Toxic Equivalences (TEQs) and fatty acid methyl esters (FAMEs) and applied it to North Atlantic Ocean fishmeal samples. The goal of the current study was to attempt to extend this Danish methodology to 77 whole and composite fish samples from three trophic groups: predator (whole largemouth bass), benthic (whole flathead and channel catfish) and forage fish (composite bluegill, pumpkinseed and green sunfish) from two dioxin contaminated rivers (Pocatalico R. and Kanawha R.) in West Virginia, USA. Multivariate statistical analyses, including, Principal Components Analysis (PCA), Hierarchical Clustering, and Partial Least Squares Regression (PLS), were used to assess the relationship between the FAMEs and TEQs in these dioxin contaminated freshwater fish from the Kanawha and Pocatalico Rivers. These three multivariate statistical methods all confirm that the pattern of Fatty Acid Methyl Esters (FAMEs) in these freshwater fish covaries with and is predictive of the WHO TE

  15. Application of multivariate statistical techniques in microbial ecology

    PubMed Central

    Paliy, O.; Shankar, V.

    2016-01-01

    Recent advances in high-throughput methods of molecular analyses have led to an explosion of studies generating large scale ecological datasets. Especially 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 amounts of data, powerful statistical techniques of multivariate analysis are well suited to analyze and interpret these datasets. Many different multivariate techniques are available, and often it is not clear which method should be applied to a particular dataset. 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 dataset structure. PMID:26786791

  16. Food additives

    MedlinePlus

    ... or natural. Natural food additives include: Herbs or spices to add flavor to foods Vinegar for pickling ... Certain colors improve the appearance of foods. Many spices, as well as natural and man-made flavors, ...

  17. 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.

  18. 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.

  19. 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.

  20. Multivariate epidemiological approach to salmonellosis in broiler breeder flocks.

    PubMed

    Henken, A M; Frankena, K; Goelema, J O; Graat, E A; Noordhuizen, J P

    1992-05-01

    A retrospective, case-control study into risk factors of salmonellosis was undertaken using data from 111 broiler breeder flocks assembled during a 5-yr period. The results of both univariate and multivariate analyses are presented. Many different Salmonella species were detected. Multivariate models were created based on the outcome of univariate analyses. The following variables appeared to be the most relevant: disinfection tubs, hygiene barriers, the interaction of disinfection tubs by hygiene barriers, and feed mills. The final model indicated that flocks housed at farms without a disinfection tub, with poor hygiene barriers, and receiving their feed from a small feed mill had a 46.1 times greater risk of being Salmonella-positive than flocks housed at farms with a disinfection tub, with good hygiene barriers, and receiving their feed from a large feed mill. It is concluded that the application of quantitative epidemiological methods can be valuable not only to identify potential risk factors but also to quantify their contributory effect on the disease outcome. Hence, it may be a useful tool for application in "integrated food chain quality control programs".

  1. 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

  2. A multivariable control scheme for robot manipulators

    NASA Technical Reports Server (NTRS)

    Tarokh, M.; Seraji, H.

    1991-01-01

    The article puts forward a simple scheme for multivariable control of robot manipulators to achieve trajectory tracking. The scheme is composed of an inner loop stabilizing controller and an outer loop tracking controller. The inner loop utilizes a multivariable PD controller to stabilize the robot by placing the poles of the linearized robot model at some desired locations. The outer loop employs a multivariable PID controller to achieve input-output decoupling and trajectory tracking. The gains of the PD and PID controllers are related directly to the linearized robot model by simple closed-form expressions. The controller gains are updated on-line to cope with variations in the robot model during gross motion and for payload change. Alternatively, the use of high gain controllers for gross motion and payload change is discussed. Computer simulation results are given for illustration.

  3. Schmidt decomposition and multivariate statistical analysis

    NASA Astrophysics Data System (ADS)

    Bogdanov, Yu. I.; Bogdanova, N. A.; Fastovets, D. V.; Luckichev, V. F.

    2016-12-01

    The new method of multivariate data analysis based on the complements of classical probability distribution to quantum state and Schmidt decomposition is presented. We considered Schmidt formalism application to problems of statistical correlation analysis. Correlation of photons in the beam splitter output channels, when input photons statistics is given by compound Poisson distribution is examined. The developed formalism allows us to analyze multidimensional systems and we have obtained analytical formulas for Schmidt decomposition of multivariate Gaussian states. It is shown that mathematical tools of quantum mechanics can significantly improve the classical statistical analysis. The presented formalism is the natural approach for the analysis of both classical and quantum multivariate systems and can be applied in various tasks associated with research of dependences.

  4. 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.

  5. 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.

  6. 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.

  7. Multivariate moment closure techniques for stochastic kinetic models.

    PubMed

    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.

  8. The application of multivariate data analysis in the interpretation of engineering geological parameters

    NASA Astrophysics Data System (ADS)

    Kovács, József; Bodnár, Nikolett; Török, Ákos

    2016-01-01

    The paper presents the evaluation of engineering geological laboratory test results of core drillings along the new metro line (line 4) in Budapest by using a multivariate data analysis. A data set of 30 core drillings with a total coring length of over 1500 meters was studied. Of the eleven engineering geological parameters considered in this study, only the five most reliable (void ratio, dry bulk density, angle of internal friction, cohesion and compressive strength) representing 1260 data points were used for multivariate (cluster and discriminant) analyses. To test the results of the cluster analysis discriminant analysis was used. The results suggest that the use of multivariate analyses allows the identification of different groups of sediments even when the data sets are overlapping and contain several uncertainties. The tests also prove that the use of these methods for seemingly very scattered parameters is crucial in obtaining reliable engineering geological data for design.

  9. 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.

  10. A Bayesian framework for cell-level protein network analysis for multivariate proteomics image data

    NASA Astrophysics Data System (ADS)

    Kovacheva, Violet N.; Sirinukunwattana, Korsuk; Rajpoot, Nasir M.

    2014-03-01

    The recent development of multivariate imaging techniques, such as the Toponome Imaging System (TIS), has facilitated the analysis of multiple co-localisation of proteins. This could hold the key to understanding complex phenomena such as protein-protein interaction in cancer. In this paper, we propose a Bayesian framework for cell level network analysis allowing the identification of several protein pairs having significantly higher co-expression levels in cancerous tissue samples when compared to normal colon tissue. It involves segmenting the DAPI-labeled image into cells and determining the cell phenotypes according to their protein-protein dependence profile. The cells are phenotyped using Gaussian Bayesian hierarchical clustering (GBHC) after feature selection is performed. The phenotypes are then analysed using Difference in Sums of Weighted cO-dependence Profiles (DiSWOP), which detects differences in the co-expression patterns of protein pairs. We demonstrate that the pairs highlighted by the proposed framework have high concordance with recent results using a different phenotyping method. This demonstrates that the results are independent of the clustering method used. In addition, the highlighted protein pairs are further analysed via protein interaction pathway databases and by considering the localization of high protein-protein dependence within individual samples. This suggests that the proposed approach could identify potentially functional protein complexes active in cancer progression and cell differentiation.

  11. Multivariate linkage scan for metabolic syndrome traits in families with type 2 diabetes.

    PubMed

    Edwards, Karen L; Wan, Jia Y; Hutter, Carolyn M; Fong, Pui Yee; Santorico, Stephanie A

    2011-06-01

    The purpose of this study was to evaluate evidence for linkage to interrelated quantitative features of the metabolic syndrome (MetS). Data on eight quantitative MetS traits (body weight, waist circumference, systolic and diastolic blood pressure, high-density lipoprotein (HDL) cholesterol, triglycerides (TGs), and fasting glucose and insulin measurements) and a 10 cM genome scan were available for 78 white families (n = 532 subjects). These data were used to conduct multipoint, multivariate linkage analyses, including tests for coincident linkage and complete pleiotropy. The strongest evidence for linkage from the bivariate analyses was observed on chromosome 1 (1p22.2) (HDL-TG; univariate lod score equivalent (lod(eq) = 3.99)) with stronger results from the trivariate analysis at the same location (HDL-TG-Insulin; lod(eq) = 4.32). Seven additional susceptibility regions (lod(eq) scores >1.9) were observed (1p36, 1q23, 2q21.2, 8q23.3, 14q23.2, 14q32.11, and 20p11.21). The results from this study indicate that several correlated traits of the MetS are influenced by the same gene(s) that account for some of the clustering of the MetS features.

  12. 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,…

  13. 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...

  14. Multivariable Control System Design for a Submarine,

    DTIC Science & Technology

    1984-05-01

    Open Loop Singular Values for the 5 and 1S Knot Linear Modelo *~~* b % % V’ , * % ~ .%~ C 9 ~ V. --.- V. V.-.--.--46..- S. 77’ Model S20R5 20- 10- -0...Control, Addison-Wesley, 1976, pp 65-86. 14. Kevin Boettcher, Analysis of Multivariable Control Systems with Structured Uncertainty, Area Examination

  15. Multivariate analysis: greater insights into complex systems

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Many agronomic researchers measure and collect multiple response variables in an effort to understand the more complex nature of the system being studied. Multivariate (MV) statistical methods encompass the simultaneous analysis of all random variables (RV) measured on each experimental or sampling ...

  16. Multivariate analysis of longitudinal rates of change.

    PubMed

    Bryan, Matthew; Heagerty, Patrick J

    2016-12-10

    Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed in the literature. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, 'accelerated time' methods have been developed which assume that covariates rescale time in longitudinal models for disease progression. In this manuscript, we detail an alternative multivariate model formulation that directly structures longitudinal rates of change and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants. Copyright © 2016 John Wiley & Sons, Ltd.

  17. 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.

  18. 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.

  19. 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.

  20. 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…

  1. 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…

  2. Multivariate simulation framework reveals performance of multi-trait GWAS methods

    PubMed Central

    Porter, Heather F.; O’Reilly, Paul F.

    2017-01-01

    Burgeoning availability of genome-wide association study (GWAS) results and national biobank data has led to growing interest in performing multi-trait genetic analyses. Numerous multi-trait GWAS methods that exploit either summary statistics or individual-level data have been developed, but their relative performance is unclear. Here we develop a simulation framework to model the complex networks underlying multivariate genetic epidemiology, enabling the vast model space of genetic effects on multiple correlated traits to be explored systematically. We perform a comprehensive comparison of the leading multi-trait GWAS methods, finding: (1) method performance is highly sensitive to the specific combination of genetic effects and phenotypic correlations, (2) most of the current multivariate methods have remarkably similar statistical power, and (3) multivariate methods may offer a substantial increase in the discovery of genetic variants over the standard univariate approach. We believe our findings offer the clearest picture to date of the relative performance of multi-trait GWAS methods and act as a guide for method selection. We provide a web application and open-source software program implementing our simulation framework, for: (i) further benchmarking of multivariate GWAS methods, (ii) power calculations for multivariate genetic studies, and (iii) generating data for testing any multivariate method in genetic epidemiology. PMID:28287610

  3. 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.

  4. Flexible multivariate marginal models for analyzing multivariate longitudinal data, with applications in R.

    PubMed

    Asar, Ozgür; Ilk, Ozlem

    2014-07-01

    Most of the available multivariate statistical models dictate on fitting different parameters for the covariate effects on each multiple responses. This might be unnecessary and inefficient for some cases. In this article, we propose a modelling framework for multivariate marginal models to analyze multivariate longitudinal data which provides flexible model building strategies. We show that the model handles several response families such as binomial, count and continuous. We illustrate the model on the Kenya Morbidity data set. A simulation study is conducted to examine the parameter estimates. An R package mmm2 is proposed to fit the model.

  5. Application of Multivariate Modeling for Radiation Injury Assessment: A Proof of Concept

    PubMed Central

    Bolduc, David L.; Villa, Vilmar; Sandgren, David J.; Ledney, G. David; Blakely, William F.; Bünger, Rolf

    2014-01-01

    Multivariate radiation injury estimation algorithms were formulated for estimating severe hematopoietic acute radiation syndrome (H-ARS) injury (i.e., response category three or RC3) in a rhesus monkey total-body irradiation (TBI) model. Classical CBC and serum chemistry blood parameters were examined prior to irradiation (d 0) and on d 7, 10, 14, 21, and 25 after irradiation involving 24 nonhuman primates (NHP) (Macaca mulatta) given 6.5-Gy 60Co Υ-rays (0.4 Gy min−1) TBI. A correlation matrix was formulated with the RC3 severity level designated as the “dependent variable” and independent variables down selected based on their radioresponsiveness and relatively low multicollinearity using stepwise-linear regression analyses. Final candidate independent variables included CBC counts (absolute number of neutrophils, lymphocytes, and platelets) in formulating the “CBC” RC3 estimation algorithm. Additionally, the formulation of a diagnostic CBC and serum chemistry “CBC-SCHEM” RC3 algorithm expanded upon the CBC algorithm model with the addition of hematocrit and the serum enzyme levels of aspartate aminotransferase, creatine kinase, and lactate dehydrogenase. Both algorithms estimated RC3 with over 90% predictive power. Only the CBC-SCHEM RC3 algorithm, however, met the critical three assumptions of linear least squares demonstrating slightly greater precision for radiation injury estimation, but with significantly decreased prediction error indicating increased statistical robustness. PMID:25165485

  6. 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.

  7. mmm: an R package for analyzing multivariate longitudinal data with multivariate marginal models.

    PubMed

    Asar, Özgür; İlk, Özlem

    2013-12-01

    Modeling multivariate longitudinal data has many challenges in terms of both statistical and computational aspects. Statistical challenges occur due to complex dependence structures. Computational challenges are due to the complex algorithms, the use of numerical methods, and potential convergence problems. Therefore, there is a lack of software for such data. This paper introduces an R package mmm prepared for marginal modeling of multivariate longitudinal data. Parameter estimations are achieved by generalized estimating equations approach. A real life data set is applied to illustrate the core features of the package, and sample R code snippets are provided. It is shown that the multivariate marginal models considered in this paper and mmm are valid for binary, continuous and count multivariate longitudinal responses.

  8. 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.

  9. 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.

  10. 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

  11. 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.

  12. Design of multivariable controllers for robot manipulators

    NASA Technical Reports Server (NTRS)

    Seraji, H.

    1986-01-01

    The paper presents a simple method for the design of linear multivariable controllers for multi-link robot manipulators. The control scheme consists of multivariable feedforward and feedback controllers. The feedforward controller is the minimal inverse of the linearized model of robot dynamics and contains only proportional-double-derivative (PD2) terms. This controller ensures that the manipulator joint angles track any reference trajectories. The feedback controller is of proportional-integral-derivative (PID) type and achieves pole placement. This controller reduces any initial tracking error to zero as desired and also ensures that robust steady-state tracking of step-plus-exponential trajectories is achieved by the joint angles. The two controllers are independent of each other and are designed separately based on the linearized robot model and then integrated in the overall control scheme. The proposed scheme is simple and can be implemented for real-time control of robot manipulators.

  13. 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.

  14. 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.

  15. 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.

  16. Multivariable PID Controller For Robotic Manipulator

    NASA Technical Reports Server (NTRS)

    Seraji, Homayoun; Tarokh, Mahmoud

    1990-01-01

    Gains updated during operation to cope with changes in characteristics and loads. Conceptual multivariable controller for robotic manipulator includes proportional/derivative (PD) controller in inner feedback loop, and proportional/integral/derivative (PID) controller in outer feedback loop. PD controller places poles of transfer function (in Laplace-transform space) of control system for linearized mathematical model of dynamics of robot. PID controller tracks trajectory and decouples input and output.

  17. Preliminary Multivariable Cost Model for Space Telescopes

    NASA Technical Reports Server (NTRS)

    Stahl, H. Philip

    2010-01-01

    Parametric cost models are routinely used to plan missions, compare concepts and justify technology investments. Previously, the authors published two single variable cost models based on 19 flight missions. The current paper presents the development of a multi-variable space telescopes cost model. The validity of previously published models are tested. Cost estimating relationships which are and are not significant cost drivers are identified. And, interrelationships between variables are explored

  18. Multivariable root loci on the real axis

    NASA Technical Reports Server (NTRS)

    Yagle, A. E.; Levy, B. C.

    1982-01-01

    Some methods for determining the number of branches of multivariable root loci which are located on the real axis at a given point are obtained by using frequency domain methods. An equation for the number of branches is given for the general case, and simpler results for the special cases when the transfer function G(s) has size 2 x 2, and when G(s) is symmetric, are also presented.

  19. Multi-Variable Analysis and Design Techniques.

    DTIC Science & Technology

    1981-09-01

    by A.G.J.MacFarlane 2 MULTIVARIABLE DESIGN TECHNIQUES BASED ON SINGULAR VALUE GENERALIZATIONS OF CLASSICAL CONTROL by J.C. Doyle 3 LIMITATIONS ON...prototypes to complex mathematical representations. All of these assemblages of information or information generators can loosely be termed "models...non linearities (e.g., control saturation) I neglect of high frequency dynamics. T hese approximations are well understood and in general their impact

  20. Multivariate temporal pattern analysis applied to the study of rat behavior in the elevated plus maze: methodological and conceptual highlights.

    PubMed

    Casarrubea, M; Magnusson, M S; Roy, V; Arabo, A; Sorbera, F; Santangelo, A; Faulisi, F; Crescimanno, G

    2014-08-30

    Aim of this article is to illustrate the application of a multivariate approach known as t-pattern analysis in the study of rat behavior in elevated plus maze. By means of this multivariate approach, significant relationships among behavioral events in the course of time can be described. Both quantitative and t-pattern analyses were utilized to analyze data obtained from fifteen male Wistar rats following a trial 1-trial 2 protocol. In trial 2, in comparison with the initial exposure, mean occurrences of behavioral elements performed in protected zones of the maze showed a significant increase counterbalanced by a significant decrease of mean occurrences of behavioral elements in unprotected zones. Multivariate t-pattern analysis, in trial 1, revealed the presence of 134 t-patterns of different composition. In trial 2, the temporal structure of behavior become more simple, being present only 32 different t-patterns. Behavioral strings and stripes (i.e. graphical representation of each t-pattern onset) of all t-patterns were presented both for trial 1 and trial 2 as well. Finally, percent distributions in the three zones of the maze show a clear-cut increase of t-patterns in closed arm and a significant reduction in the remaining zones. Results show that previous experience deeply modifies the temporal structure of rat behavior in the elevated plus maze. In addition, this article, by highlighting several conceptual, methodological and illustrative aspects on the utilization of t-pattern analysis, could represent a useful background to employ such a refined approach in the study of rat behavior in elevated plus maze.

  1. Compressive tracking with incremental multivariate Gaussian distribution

    NASA Astrophysics Data System (ADS)

    Li, Dongdong; Wen, Gongjian; Zhu, Gao; Zeng, Qiaoling

    2016-09-01

    Various approaches have been proposed for robust visual tracking, among which compressive tracking (CT) yields promising performance. In CT, Haar-like features are efficiently extracted with a very sparse measurement matrix and modeled as an online updated naïve Bayes classifier to account for target appearance change. The naïve Bayes classifier ignores overlap between Haar-like features and assumes that Haar-like features are independently distributed, which leads to drift in complex scenario. To address this problem, we present an extended CT algorithm, which assumes that all Haar-like features are correlated with each other and have multivariate Gaussian distribution. The mean vector and covariance matrix of multivariate normal distribution are incrementally updated with constant computational complexity to adapt to target appearance change. Each frame is associated with a temporal weight to expend less modeling power on old observation. Based on temporal weight, an update scheme with changing but convergent learning rate is derived with strict mathematic proof. Compared with CT, our extended algorithm achieves a richer representation of target appearance. The incremental multivariate Gaussian distribution is integrated into the particle filter framework to achieve better tracking performance. Extensive experiments on the CVPR2013 tracking benchmark demonstrate that our proposed tracker achieves superior performance both qualitatively and quantitatively over several state-of-the-art trackers.

  2. Control of wastewater using multivariate control chart

    NASA Astrophysics Data System (ADS)

    Nugraha, Jaka; Fatimah, Is; Prabowo, Rino Galang

    2017-03-01

    Wastewater treatment is a crucial process in industry cause untreated or improper treatment of wastewater may leads some problems affecting to the other parts of environmental aspects. For many kinds of wastewater treatments, the parameters of Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and the Total Suspend Solid (TSS) are usual parameters to be controlled as a standard. In this paper, the application of multivariate Hotteling T2 Individual was reported to control wastewater treatment. By using wastewater treatment data from PT. ICBP, east Java branch, while the fulfillment of quality standards are based on East Java Governor Regulation No. 72 Year 2013 on Standards of Quality of Waste Water Industry and / or Other Business Activities. The obtained results are COD and TSS has a correlation with BOD values with the correlation coefficient higher than 50%, and it is is also found that influence of the COD and TSS to BOD values are 82% and 1.9% respectively. Based on Multivariate control chart Individual T2 Hotteling, it is found that BOD-COD and BOD-TSS are each one subgroup that are outside the control limits. Thus, it can be said there is a process that is not multivariate controlled, but univariately the variables of BOD, COD and TSS are within specification (standard quality) that has been determined.

  3. 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.

  4. Processes and subdivisions in diogenites, a multivariate statistical analysis

    NASA Technical Reports Server (NTRS)

    Harriott, T. A.; Hewins, R. H.

    1984-01-01

    Multivariate statistical techniques used on diogenite orthopyroxene analyses show the relationships that occur within diogenites and the two orthopyroxenite components (class I and II) in the polymict diogenite Garland. Cluster analysis shows that only Peckelsheim is similar to Garland class I (Fe-rich) and the other diogenites resemble Garland class II. The unique diogenite Y 75032 may be related to type I by fractionation. Factor analysis confirms the subdivision and shows that Fe does not correlate with the weakly incompatible elements across the entire pyroxene composition range, indicating that igneous fractionation is not the process controlling total diogenite composition variation. The occurrence of two groups of diogenites is interpreted as the result of sampling or mixing of two main sequences of orthopyroxene cumulates with slightly different compositions.

  5. 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.

  6. A multivariate hierarchical Bayesian approach to measuring agreement in repeated measurement method comparison studies

    PubMed Central

    2009-01-01

    Background Assessing agreement in method comparison studies depends on two fundamentally important components; validity (the between method agreement) and reproducibility (the within method agreement). The Bland-Altman limits of agreement technique is one of the favoured approaches in medical literature for assessing between method validity. However, few researchers have adopted this approach for the assessment of both validity and reproducibility. This may be partly due to a lack of a flexible, easily implemented and readily available statistical machinery to analyse repeated measurement method comparison data. Methods Adopting the Bland-Altman framework, but using Bayesian methods, we present this statistical machinery. Two multivariate hierarchical Bayesian models are advocated, one which assumes that the underlying values for subjects remain static (exchangeable replicates) and one which assumes that the underlying values can change between repeated measurements (non-exchangeable replicates). Results We illustrate the salient advantages of these models using two separate datasets that have been previously analysed and presented; (i) assuming static underlying values analysed using both multivariate hierarchical Bayesian models, and (ii) assuming each subject's underlying value is continually changing quantity and analysed using the non-exchangeable replicate multivariate hierarchical Bayesian model. Conclusion These easily implemented models allow for full parameter uncertainty, simultaneous method comparison, handle unbalanced or missing data, and provide estimates and credible regions for all the parameters of interest. Computer code for the analyses in also presented, provided in the freely available and currently cost free software package WinBUGS. PMID:19161599

  7. 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

  8. An alternative pseudolikelihood method for multivariate random-effects meta-analysis

    PubMed Central

    Chen, Yong; Hong, Chuan; Riley, Richard D

    2015-01-01

    Recently, multivariate random-effects meta-analysis models have received a great deal of attention, despite its greater complexity compared to univariate meta-analyses. One of its advantages is its ability to account for the within-study and between-study correlations. However, the standard inference procedures, such as the maximum likelihood or maximum restricted likelihood inference, require the within-study correlations, which are usually unavailable. In addition, the standard inference procedures suffer from the problem of singular estimated covariance matrix. In this paper, we propose a pseudolikelihood method to overcome the aforementioned problems. The pseudolikelihood method does not require within-study correlations and is not prone to singular covariance matrix problem. In addition, it can properly estimate the covariance between pooled estimates for different outcomes, which enables valid inference on functions of pooled estimates, and can be applied to meta-analysis where some studies have outcomes missing completely at random. Simulation studies show that the pseudolikelihood method provides unbiased estimates for functions of pooled estimates, well-estimated standard errors, and confidence intervals with good coverage probability. Furthermore, the pseudolikelihood method is found to maintain high relative efficiency compared to that of the standard inferences with known within-study correlations. We illustrate the proposed method through three meta-analyses for comparison of prostate cancer treatment, for the association between paraoxonase 1 activities and coronary heart disease, and for the association between homocysteine level and coronary heart disease. © 2014 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. PMID:25363629

  9. Multivariate Analyses of Urban Community College Student Performance on the ACT College Outcomes Measures Program Test.

    ERIC Educational Resources Information Center

    Kitabchi, Gloria

    This study examined the relationship and relative importance of selected variables to successful performance of urban community college students on the American College Testing Program (ACT) College Outcome Measures Program (COMP). The importance of age, race, gender, type of degree, program or major category, admissions criteria and ACT…

  10. [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.

  11. Testing of the effect of missing data estimation and distribution in morphometric multivariate data analyses.

    PubMed

    Brown, Caleb Marshall; Arbour, Jessica H; Jackson, Donald A

    2012-12-01

    Missing data are an unavoidable problem in biological data sets and the performance of missing data deletion and estimation techniques in morphometric data sets is poorly understood. Here, a novel method is used to measure the introduced error of multiple techniques on a representative sample. A large sample of extant crocodilian skulls was measured and analyzed with principal component analysis (PCA). Twenty-three different proportions of missing data were introduced into the data set, estimated, analyzed, and compared with the original result using Procrustes superimposition. Previous work investigating the effects of missing data input missing values randomly, a non-biological phenomenon. Here, missing data were introduced into the data set using three methodologies: purely at random, as a function of the Euclidean distance between respective measurements (simulating anatomical regions), and as a function of the portion of the sample occupied by each taxon (simulating unequal missing data in rare taxa). Gower's distance was found to be the best performing non-estimation method, and Bayesian PCA the best performing estimation method. Specimens of the taxa with small sample sizes and those most morphologically disparate had the highest estimation error. Distribution of missing data had a significant effect on the estimation error for almost all methods and proportions. Taxonomically biased missing data tended to show similar trends to random, but with higher error rates. Anatomically biased missing data showed a much greater deviation from random than the taxonomic bias, and with magnitudes dependent on the estimation method.

  12. Multivariate Analyses of Quality Metrics for Crystal Structures in the PDB Archive.

    PubMed

    Shao, Chenghua; Yang, Huanwang; Westbrook, John D; Young, Jasmine Y; Zardecki, Christine; Burley, Stephen K

    2017-03-07

    Following deployment of an augmented validation system by the Worldwide Protein Data Bank (wwPDB) partnership, the quality of crystal structures entering the PDB has improved. Of significance are improvements in quality measures now prominently displayed in the wwPDB validation report. Comparisons of PDB depositions made before and after introduction of the new reporting system show improvements in quality measures relating to pairwise atom-atom clashes, side-chain torsion angle rotamers, and local agreement between the atomic coordinate structure model and experimental electron density data. These improvements are largely independent of resolution limit and sample molecular weight. No significant improvement in the quality of associated ligands was observed. Principal component analysis revealed that structure quality could be summarized with three measures (Rfree, real-space R factor Z score, and a combined molecular geometry quality metric), which can in turn be reduced to a single overall quality metric readily interpretable by all PDB archive users.

  13. Curve-based multivariate distance matrix regression analysis: application to genetic association analyses involving repeated measures

    PubMed Central

    Salem, Rany M.; O'Connor, Daniel T.

    2010-01-01

    Most, if not all, human phenotypes exhibit a temporal, dosage-dependent, or age effect. Despite this fact, it is rare that data are collected over time or in sequence in relevant studies of the determinants of these phenotypes. The costs and organizational sophistication necessary to collect repeated measurements or longitudinal data for a given phenotype are clearly impediments to this, but greater efforts in this area are needed if insights into human phenotypic expression are to be obtained. Appropriate data analysis methods for genetic association studies involving repeated or longitudinal measures are also needed. We consider the use of longitudinal profiles obtained from fitted functions on repeated data collections from a set of individuals whose similarities are contrasted between sets of individuals with different genotypes to test hypotheses about genetic influences on time-dependent phenotype expression. The proposed approach can accommodate uncertainty of the fitted functions, as well as weighting factors across the time points, and is easily extended to a wide variety of complex analysis settings. We showcase the proposed approach with data from a clinical study investigating human blood vessel response to tyramine. We also compare the proposed approach with standard analytic procedures and investigate its robustness and power via simulation studies. The proposed approach is found to be quite flexible and performs either as well or better than traditional statistical methods. PMID:20423962

  14. Nonlinear estimation of coherent phase vibrations for statistical signals through multivariable analyses

    NASA Astrophysics Data System (ADS)

    Deng, Linhua

    2015-07-01

    Three nonlinear analysis techniques, including cross-recurrence plot, line of synchronization, and cross-wavelet transform, are proposed to estimate the coherent phase vibrations of nonlinear and non-stationary time series. The case study utilizes the monthly averages of sunspot areas during the time interval from May 1874 to August 2014. The following prominent results are found: (1) the phase-leading hemisphere of long-term sunspot areas has changed twice in the past 140 years, indicating that the hemispheric imbalances and apparent phase differences on both hemispheres are a prevalent behavior and are not anomalous; (2) the alternating regularity of hemispheric asynchronism exhibits a cyclical pattern of 4.5+3.5 cycles, and the magnetic flux excess in a certain hemisphere during the ascending branch of a cycle can be taken as an indication of the phase-leading hemisphere in this cycle. We firmly believe that powerful nonlinear approaches are more advanced than classical linear methods when they are combined to determine the dynamic complexity of nonlinear physical systems.

  15. Multivariate Chemometrics with Regression and Classification Analyses in Heroin Profiling Based on the Chromatographic Data.

    PubMed

    B Gadžurić, Slobodan; O Podunavac Kuzmanović, Sanja; B Vraneš, Milan; Petrin, Marija; Bugarski, Tatjana; Kovačević, Strahinja Z

    2016-01-01

    The purpose of this work is to promote and facilitate forensic profiling and chemical analysis of illicit drug samples in order to determine their origin, methods of production and transfer through the country. The article is based on the gas chromatography analysis of heroin samples seized from three different locations in Serbia. Chemometric approach with appropriate statistical tools (multiple-linear regression (MLR), hierarchical cluster analysis (HCA) and Wald-Wolfowitz run (WWR) test) were applied on chromatographic data of heroin samples in order to correlate and examine the geographic origin of seized heroin samples. The best MLR models were further validated by leave-one-out technique as well as by the calculation of basic statistical parameters for the established models. To confirm the predictive power of the models, external set of heroin samples was used. High agreement between experimental and predicted values of acetyl thebaol and diacetyl morphine peak ratio, obtained in the validation procedure, indicated the good quality of derived MLR models. WWR test showed which examined heroin samples come from the same population, and HCA was applied in order to overview the similarities among the studied heroine samples.

  16. 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...

  17. Multivariate Chemometrics with Regression and Classification Analyses in Heroin Profiling Based on the Chromatographic Data

    PubMed Central

    B. Gadžurić, Slobodan; O. Podunavac Kuzmanović, Sanja; B. Vraneš, Milan; Petrin, Marija; Bugarski, Tatjana; Kovačević, Strahinja Z.

    2016-01-01

    The purpose of this work is to promote and facilitate forensic profiling and chemical analysis of illicit drug samples in order to determine their origin, methods of production and transfer through the country. The article is based on the gas chromatography analysis of heroin samples seized from three different locations in Serbia. Chemometric approach with appropriate statistical tools (multiple-linear regression (MLR), hierarchical cluster analysis (HCA) and Wald-Wolfowitz run (WWR) test) were applied on chromatographic data of heroin samples in order to correlate and examine the geographic origin of seized heroin samples. The best MLR models were further validated by leave-one-out technique as well as by the calculation of basic statistical parameters for the established models. To confirm the predictive power of the models, external set of heroin samples was used. High agreement between experimental and predicted values of acetyl thebaol and diacetyl morphine peak ratio, obtained in the validation procedure, indicated the good quality of derived MLR models. WWR test showed which examined heroin samples come from the same population, and HCA was applied in order to overview the similarities among the studied heroine samples. PMID:28243268

  18. Multivariate statistical evaluation of trace elements in groundwater in a coastal area in Shenzhen, China.

    PubMed

    Chen, Kouping; Jiao, Jiu J; Huang, Jianmin; Huang, Runqiu

    2007-06-01

    Multivariate statistical techniques are efficient ways to display complex relationships among many objects. An attempt was made to study the data of trace elements in groundwater using multivariate statistical techniques such as principal component analysis (PCA), Q-mode factor analysis and cluster analysis. The original matrix consisted of 17 trace elements estimated from 55 groundwater samples colleted in 27 wells located in a coastal area in Shenzhen, China. PCA results show that trace elements of V, Cr, As, Mo, W, and U with greatest positive loadings typically occur as soluble oxyanions in oxidizing waters, while Mn and Co with greatest negative loadings are generally more soluble within oxygen depleted groundwater. Cluster analyses demonstrate that most groundwater samples collected from the same well in the study area during summer and winter still fall into the same group. This study also demonstrates the usefulness of multivariate statistical analysis in hydrochemical studies.

  19. 10 CFR 436.24 - Uncertainty analyses.

    Code of Federal Regulations, 2012 CFR

    2012-01-01

    ... 10 Energy 3 2012-01-01 2012-01-01 false Uncertainty analyses. 436.24 Section 436.24 Energy... Procedures for Life Cycle Cost Analyses § 436.24 Uncertainty analyses. If particular items of cost data or... by conducting additional analyses using any standard engineering economics method such as...

  20. 10 CFR 436.24 - Uncertainty analyses.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

    ... 10 Energy 3 2014-01-01 2014-01-01 false Uncertainty analyses. 436.24 Section 436.24 Energy... Procedures for Life Cycle Cost Analyses § 436.24 Uncertainty analyses. If particular items of cost data or... by conducting additional analyses using any standard engineering economics method such as...

  1. 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.

  2. 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…

  3. Multi-site, multivariate weather generator using maximum entropy bootstrap

    NASA Astrophysics Data System (ADS)

    Srivastav, Roshan K.; Simonovic, Slobodan P.

    2014-05-01

    Weather generators are increasingly becoming viable alternate models to assess the effects of future climate change scenarios on water resources systems. In this study, a new multisite, multivariate maximum entropy bootstrap weather generator (MEBWG) is proposed for generating daily weather variables, which has the ability to mimic both, spatial and temporal dependence structure in addition to other historical statistics. The maximum entropy bootstrap (MEB) involves two main steps: (1) random sampling from the empirical cumulative distribution function with endpoints selected to allow limited extrapolation and (2) reordering of the random series to respect the rank ordering of the original time series (temporal dependence structure). To capture the multi-collinear structure between the weather variables and between the sites, we combine orthogonal linear transformation with MEB. Daily weather data, which include precipitation, maximum temperature and minimum temperature from 27 years of record from the Upper Thames River Basin in Ontario, Canada, are used to analyze the ability of MEBWG based weather generator. Results indicate that the statistics from the synthetic replicates were not significantly different from the observed data and the model is able to preserve the 27 CLIMDEX indices very well. The MEBWG model shows better performance in terms of extrapolation and computational efficiency when compared to multisite, multivariate K-nearest neighbour model.

  4. A Method for Comparing Multivariate Time Series with Different Dimensions

    PubMed Central

    Tapinos, Avraam; Mendes, Pedro

    2013-01-01

    In many situations it is desirable to compare dynamical systems based on their behavior. Similarity of behavior often implies similarity of internal mechanisms or dependency on common extrinsic factors. While there are widely used methods for comparing univariate time series, most dynamical systems are characterized by multivariate time series. Yet, comparison of multivariate time series has been limited to cases where they share a common dimensionality. A semi-metric is a distance function that has the properties of non-negativity, symmetry and reflexivity, but not sub-additivity. Here we develop a semi-metric – SMETS – that can be used for comparing groups of time series that may have different dimensions. To demonstrate its utility, the method is applied to dynamic models of biochemical networks and to portfolios of shares. The former is an example of a case where the dependencies between system variables are known, while in the latter the system is treated (and behaves) as a black box. PMID:23393554

  5. 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.

  6. 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

  7. 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

  8. New multivariable capabilities of the INCA program

    NASA Technical Reports Server (NTRS)

    Bauer, Frank H.; Downing, John P.; Thorpe, Christopher J.

    1989-01-01

    The INteractive Controls Analysis (INCA) program was developed at NASA's Goddard Space Flight Center to provide a user friendly, efficient environment for the design and analysis of control systems, specifically spacecraft control systems. Since its inception, INCA has found extensive use in the design, development, and analysis of control systems for spacecraft, instruments, robotics, and pointing systems. The (INCA) program was initially developed as a comprehensive classical design analysis tool for small and large order control systems. The latest version of INCA, expected to be released in February of 1990, was expanded to include the capability to perform multivariable controls analysis and design.

  9. 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.

  10. 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.

  11. 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

  12. 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

    2016-06-14

    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.

  13. 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.

  14. Multivariate sequence analysis reveals additional function impacting residues in the SDR superfamily.

    PubMed

    Tiwari, Pratibha; Singh, Noopur; Dixit, Aparna; Choudhury, Devapriya

    2014-10-01

    The "extended" type of short chain dehydrogenases/reductases (SDR), share a remarkable similarity in their tertiary structures inspite of being highly divergent in their functions and sequences. We have carried out principal component analysis (PCA) on structurally equivalent residue positions of 10 SDR families using information theoretic measures like Jensen-Shannon divergence and average shannon entropy as variables. The results classify residue positions in the SDR fold into six groups, one of which is characterized by low Shannon entropies but high Jensen-Shannon divergence against the reference family SDR1E, suggesting that these positions are responsible for the specific functional identities of individual SDR families, distinguishing them from the reference family SDR1E. Site directed mutagenesis of three residues from this group in the enzyme UDP-Galactose 4-epimerase belonging to SDR1E shows that the mutants promote the formation of NADH containing abortive complexes. Finally, molecular dynamics simulations have been used to suggest a mechanism by which the mutants interfere with the re-oxidation of NADH leading to the formation of abortive complexes.

  15. 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.

  16. 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.

  17. 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.

  18. A semiparametric multivariate and multisite weather generator

    NASA Astrophysics Data System (ADS)

    Apipattanavis, Somkiat; Podestá, Guillermo; Rajagopalan, Balaji; Katz, Richard W.

    2007-11-01

    We propose a semiparametric multivariate weather generator with greater ability to reproduce the historical statistics, especially the wet and dry spells. The proposed approach has two steps: (1) a Markov Chain for generating the precipitation state (i.e., no rain, rain, or heavy rain), and (2) a k-nearest neighbor (k-NN) bootstrap resampler for generating the multivariate weather variables. The Markov Chain captures the spell statistics while the k-NN bootstrap captures the distributional and lag-dependence statistics of the weather variables. Traditional k-NN generators tend to under-simulate the wet and dry spells that are keys to watershed and agricultural modeling for water planning and management; hence the motivation for this research. We demonstrate the utility of the proposed approach and its improvement over the traditional k-NN approach through an application to daily weather data from Pergamino in the Pampas region of Argentina. We show the applicability of the proposed framework in simulating weather scenarios conditional on the seasonal climate forecast and also at multiple sites in the Pampas region.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  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. A multivariate Bayesian model for embryonic growth.

    PubMed

    Willemsen, Sten P; Eilers, Paul H C; Steegers-Theunissen, Régine P M; Lesaffre, Emmanuel

    2015-04-15

    Most longitudinal growth curve models evaluate the evolution of each of the anthropometric measurements separately. When applied to a 'reference population', this exercise leads to univariate reference curves against which new individuals can be evaluated. However, growth should be evaluated in totality, that is, by evaluating all body characteristics jointly. Recently, Cole et al. suggested the Superimposition by Translation and Rotation (SITAR) model, which expresses individual growth curves by three subject-specific parameters indicating their deviation from a flexible overall growth curve. This model allows the characterization of normal growth in a flexible though compact manner. In this paper, we generalize the SITAR model in a Bayesian way to multiple dimensions. The multivariate SITAR model allows us to create multivariate reference regions, which is advantageous for prediction. The usefulness of the model is illustrated on longitudinal measurements of embryonic growth obtained in the first semester of pregnancy, collected in the ongoing Rotterdam Predict study. Further, we demonstrate how the model can be used to find determinants of embryonic growth.

  5. 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.

  6. Possibilities to improve the genetic evaluation of a rare breed using limited genomic information and multivariate BLUP.

    PubMed

    Pollott, G E; Charlesworth, A; Wathes, D C

    2014-05-01

    The use of molecular genetic information in the evaluation of livestock has become more common. This study looks at the efficacy of using such information to improve the genetic evaluation of a rare breed of dual-purpose cattle. Data were available in the form of pedigree information on the Gloucester cattle breed in the United Kingdom and recorded milk and beef performance on a small number of animals. In addition, molecular genetic information in the form of multi-marker, multiple regression results converted to a 1 to 10 score (Igenity scores) and 123 single nucleotide polymorphism (SNP) genotypes for 199 non-recorded animals were available. Appropriate mixed-animal models were explored for the recorded traits and these were used to calculate estimated breeding values (EBV), and their accuracies, for 6527 animals in the breed's pedigree file. Various ways to improve the accuracy of these EBV were explored. This involved using multivariate BLUP analyses, genomic estimated breeding values (GEBV) and combining Igenity scores with recorded traits in a series of bivariate genetic analyses. Using the milk recording traits as an example, the accuracy of a number of traits could be improved using multivariate analyses by up to 14%, depending on the combination of traits used. The level of increase in accuracy largely corresponded to the absolute difference between the genetic and residual correlations between two traits, but this was not always symmetrical. The use of GEBV did not increase the accuracy of milk trait EBV owing to the low proportion of variance explained by the 101 SNPs used. Using Igenity scores in bivariate analyses with the recorded data was more successful in increasing EBV accuracy. The largest increases were found in genotyped animals with no recorded performance (e.g. a 58% increase in fat weight in milk); however, the size of the increase depended on the level of the genetic correlation between the recorded trait and the Igenity score for that

  7. 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.

  8. 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

  9. 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

  10. Connections between conventional and singular-value-based multi-variable flight control system design techniques

    NASA Technical Reports Server (NTRS)

    Mcruer, D. T.; Myers, T. T.; Thompson, P. M.

    1986-01-01

    It is proposed that frequency-domain multivariable robustness techniques, when combined with classical multivariable procedures, can offer an additional means of evaluating FCS designs. A lateral-directional FCS for an advanced fighter is used as an example. Robustness to unstructured aircraft-input uncertainties is assessed using purely numerical singular-value procedures. Literal approximations for the singular values of the open-loop plant and controller and for the inverse return difference are shown to provide a means of decomposing and diagnosing robustness problems that are insoluble via purely numerical methods.

  11. Correlation between fish distribution and water qualities in the Kaname river, Japan: application of multivariate statistics

    NASA Astrophysics Data System (ADS)

    Kutsumi, M.; terada, K.; Tajima, F.; Kitano, T.

    2012-12-01

    In order to find physical and chemical environment factors which relate to the fish fauna distribution, we investigated the temporal and spatial change of water qualities and fish distributions in Kaname river, Japan. We investigated the fish distribution, physical water parameters (temperature, salinity, dissolved oxygen, Chl-a and turbidity) and chemical water parameters (nitrate, nitrite, ammonia, orthophosphoric and suspended solids). We conducted the multivariate analyses using these observational data and discussed the relationship between water environment parameters and fish habitat distribution.

  12. Application of Maxent Multivariate Analysis to Define Climate-Change Effects on Species Distributions and Changes

    DTIC Science & Technology

    2014-09-01

    program called Maxent was used to per- form range-extent analyses for two animal species of interest to Army land managers: the Red-Cockaded Woodpecker...2007 Intergovernmental Panel on Climate Change report (IPCC 2007a) indicate that global surface temperature is likely to rise between 1.1 and 6.4 °C...analysis is the basis of modeling software package called Maxent (Phillips 2006), which applies a multivariate technique called maximum entropy analysis

  13. The flyby anomaly: a multivariate analysis approach

    NASA Astrophysics Data System (ADS)

    Acedo, L.

    2017-02-01

    The flyby anomaly is the unexpected variation of the asymptotic post-encounter velocity of a spacecraft with respect to the pre-encounter velocity as it performs a slingshot manoeuvre. This effect has been detected in, at least, six flybys of the Earth but it has not appeared in other recent flybys. In order to find a pattern in these, apparently contradictory, data several phenomenological formulas have been proposed but all have failed to predict a new result in agreement with the observations. In this paper we use a multivariate dimensional analysis approach to propose a fitting of the data in terms of the local parameters at perigee, as it would occur if this anomaly comes from an unknown fifth force with latitude dependence. Under this assumption, we estimate the range of this force around 300 km.

  14. MM Algorithms for Some Discrete Multivariate Distributions.

    PubMed

    Zhou, Hua; Lange, Kenneth

    2010-09-01

    The MM (minorization-maximization) principle is a versatile tool for constructing optimization algorithms. Every EM algorithm is an MM algorithm but not vice versa. This article derives MM algorithms for maximum likelihood estimation with discrete multivariate distributions such as the Dirichlet-multinomial and Connor-Mosimann distributions, the Neerchal-Morel distribution, the negative-multinomial distribution, certain distributions on partitions, and zero-truncated and zero-inflated distributions. These MM algorithms increase the likelihood at each iteration and reliably converge to the maximum from well-chosen initial values. Because they involve no matrix inversion, the algorithms are especially pertinent to high-dimensional problems. To illustrate the performance of the MM algorithms, we compare them to Newton's method on data used to classify handwritten digits.

  15. 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.

  16. Compensator improvement for multivariable control systems

    NASA Technical Reports Server (NTRS)

    Mitchell, J. R.; Mcdaniel, W. L., Jr.; Gresham, L. L.

    1977-01-01

    A theory and the associated numerical technique are developed for an iterative design improvement of the compensation for linear, time-invariant control systems with multiple inputs and multiple outputs. A strict constraint algorithm is used in obtaining a solution of the specified constraints of the control design. The result of the research effort is the multiple input, multiple output Compensator Improvement Program (CIP). The objective of the Compensator Improvement Program is to modify in an iterative manner the free parameters of the dynamic compensation matrix so that the system satisfies frequency domain specifications. In this exposition, the underlying principles of the multivariable CIP algorithm are presented and the practical utility of the program is illustrated with space vehicle related examples.

  17. Multivariate Markov chain modeling for stock markets

    NASA Astrophysics Data System (ADS)

    Maskawa, Jun-ichi

    2003-06-01

    We study a multivariate Markov chain model as a stochastic model of the price changes of portfolios in the framework of the mean field approximation. The time series of price changes are coded into the sequences of up and down spins according to their signs. We start with the discussion for small portfolios consisting of two stock issues. The generalization of our model to arbitrary size of portfolio is constructed by a recurrence relation. The resultant form of the joint probability of the stationary state coincides with Gibbs measure assigned to each configuration of spin glass model. Through the analysis of actual portfolios, it has been shown that the synchronization of the direction of the price changes is well described by the model.

  18. Multivariable Harmonic Balance for Central Pattern Generators.

    PubMed

    Iwasaki, Tetsuya

    2008-12-01

    The central pattern generator (CPG) is a nonlinear oscillator formed by a group of neurons, providing a fundamental control mechanism underlying rhythmic movements in animal locomotion. We consider a class of CPGs modeled by a set of interconnected identical neurons. Based on the idea of multivariable harmonic balance, we show how the oscillation profile is related to the connectivity matrix that specifies the architecture and strengths of the interconnections. Specifically, the frequency, amplitudes, and phases are essentially encoded in terms of a pair of eigenvalue and eigenvector. This basic principle is used to estimate the oscillation profile of a given CPG model. Moreover, a systematic method is proposed for designing a CPG-based nonlinear oscillator that achieves a prescribed oscillation profile.

  19. Regionalization in geology by multivariate classification

    USGS Publications Warehouse

    Harff, Jan; Davis, J.C.

    1990-01-01

    The concept of multivariate classification of "geological objects" can be combined with the concept of regionalized variables to yield a procedure for typification of geological objects, such as rock units, well records, or samples. Numerical classification is followed by subdivision of the area of investigation, and culminates in a regionalization or mapping of the classification onto the plane. Regions are subdivisions of the map area which are spatially contiguous and relatively homogeneous in their geological properties. The probability of correct classification of each point within a region as being part of that region can be assessed in terms of Bayesian probability as a space-dependent function. The procedure is applied to subsurface data from western Kansas. The geologic properties used are quantitative variables, and relationships are expressed by Mahalanobis' distances. These functions could be replaced by other metrics if qualitative or binary data derived from geological descriptions or appraisals were included in the analysis. ?? 1990 International Association for Mathematical Geology.

  20. 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.

  1. Classification of adulterated honeys by multivariate analysis.

    PubMed

    Amiry, Saber; Esmaiili, Mohsen; Alizadeh, Mohammad

    2017-06-01

    In this research, honey samples were adulterated with date syrup (DS) and invert sugar syrup (IS) at three concentrations (7%, 15% and 30%). 102 adulterated samples were prepared in six batches with 17 replications for each batch. For each sample, 32 parameters including color indices, rheological, physical, and chemical parameters were determined. To classify the samples, based on type and concentrations of adulterant, a multivariate analysis was applied using principal component analysis (PCA) followed by a linear discriminant analysis (LDA). Then, 21 principal components (PCs) were selected in five sets. Approximately two-thirds were identified correctly using color indices (62.75%) or rheological properties (67.65%). A power discrimination was obtained using physical properties (97.06%), and the best separations were achieved using two sets of chemical properties (set 1: lactone, diastase activity, sucrose - 100%) (set 2: free acidity, HMF, ash - 95%).

  2. Tailored multivariate analysis for modulated enhanced diffraction

    DOE PAGES

    Caliandro, Rocco; Guccione, Pietro; Nico, Giovanni; ...

    2015-10-21

    Modulated enhanced diffraction (MED) is a technique allowing the dynamic structural characterization of crystalline materials subjected to an external stimulus, which is particularly suited forin situandoperandostructural investigations at synchrotron sources. Contributions from the (active) part of the crystal system that varies synchronously with the stimulus can be extracted by an offline analysis, which can only be applied in the case of periodic stimuli and linear system responses. In this paper a new decomposition approach based on multivariate analysis is proposed. The standard principal component analysis (PCA) is adapted to treat MED data: specific figures of merit based on their scoresmore » and loadings are found, and the directions of the principal components obtained by PCA are modified to maximize such figures of merit. As a result, a general method to decompose MED data, called optimum constrained components rotation (OCCR), is developed, which produces very precise results on simulated data, even in the case of nonperiodic stimuli and/or nonlinear responses. Furthermore, the multivariate analysis approach is able to supply in one shot both the diffraction pattern related to the active atoms (through the OCCR loadings) and the time dependence of the system response (through the OCCR scores). Furthermore, when applied to real data, OCCR was able to supply only the latter information, as the former was hindered by changes in abundances of different crystal phases, which occurred besides structural variations in the specific case considered. In order to develop a decomposition procedure able to cope with this combined effect represents the next challenge in MED analysis.« less

  3. Tailored multivariate analysis for modulated enhanced diffraction

    SciTech Connect

    Caliandro, Rocco; Guccione, Pietro; Nico, Giovanni; Tutuncu, Goknur; Hanson, Jonathan C.

    2015-10-21

    Modulated enhanced diffraction (MED) is a technique allowing the dynamic structural characterization of crystalline materials subjected to an external stimulus, which is particularly suited forin situandoperandostructural investigations at synchrotron sources. Contributions from the (active) part of the crystal system that varies synchronously with the stimulus can be extracted by an offline analysis, which can only be applied in the case of periodic stimuli and linear system responses. In this paper a new decomposition approach based on multivariate analysis is proposed. The standard principal component analysis (PCA) is adapted to treat MED data: specific figures of merit based on their scores and loadings are found, and the directions of the principal components obtained by PCA are modified to maximize such figures of merit. As a result, a general method to decompose MED data, called optimum constrained components rotation (OCCR), is developed, which produces very precise results on simulated data, even in the case of nonperiodic stimuli and/or nonlinear responses. Furthermore, the multivariate analysis approach is able to supply in one shot both the diffraction pattern related to the active atoms (through the OCCR loadings) and the time dependence of the system response (through the OCCR scores). Furthermore, when applied to real data, OCCR was able to supply only the latter information, as the former was hindered by changes in abundances of different crystal phases, which occurred besides structural variations in the specific case considered. In order to develop a decomposition procedure able to cope with this combined effect represents the next challenge in MED analysis.

  4. 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.

  5. Multivariate Meta-Analysis of Genetic Association Studies: A Simulation Study.

    PubMed

    Neupane, Binod; Beyene, Joseph

    2015-01-01

    In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between end points. However, under random-effects assumption the multivariate estimation is more complex (as it involves estimation of more parameters simultaneously) than univariate estimation, and sometimes can produce unrealistic parameter estimates. Usefulness of multivariate approach to meta-analysis of the effects of a genetic variant on two or more correlated traits is not well understood in the area of genetic association studies. In such studies, genetic variants are expected to roughly maintain Hardy-Weinberg equilibrium within studies, and also their effects on complex traits are generally very small to modest and could be heterogeneous across studies for genuine reasons. We carried out extensive simulation to explore the comparative performance of multivariate approach with most commonly used univariate inverse-variance weighted approach under random-effects assumption in various realistic meta-analytic scenarios of genetic association studies of correlated end points. We evaluated the performance with respect to relative mean bias percentage, and root mean square error (RMSE) of the estimate and coverage probability of corresponding 95% confidence interval of the effect for each end point. Our simulation results suggest that multivariate approach performs similarly or better than univariate method when correlations between end points within or between studies are at least moderate and between-study variation is similar or larger than average within-study variation for meta-analyses of 10 or more genetic studies. Multivariate approach produces estimates with smaller bias and RMSE especially for the end point that has randomly or informatively missing summary data in some individual studies, when the missing data

  6. 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.

  7. 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 ...

  8. Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure

    PubMed Central

    Li, Yanming; Zhu, Ji

    2015-01-01

    Summary We propose a multivariate sparse group lasso variable selection and estimation method for data with high-dimensional predictors as well as high-dimensional response variables. The method is carried out through a penalized multivariate multiple linear regression model with an arbitrary group structure for the regression coefficient matrix. It suits many biology studies well in detecting associations between multiple traits and multiple predictors, with each trait and each predictor embedded in some biological functioning groups such as genes, pathways or brain regions. The method is able to effectively remove unimportant groups as well as unimportant individual coefficients within important groups, particularly for large p small n problems, and is flexible in handling various complex group structures such as overlapping or nested or multilevel hierarchical structures. The method is evaluated through extensive simulations with comparisons to the conventional lasso and group lasso methods, and is applied to an eQTL association study. PMID:25732839

  9. Granger causality and information flow in multivariate processes.

    PubMed

    Blinowska, Katarzyna J; Kuś, Rafał; Kamiński, Maciej

    2004-11-01

    The multivariate versus bivariate measures of Granger causality were considered. Granger causality in the application to multivariate physiological time series has the meaning of the information flow between channels. It was shown by means of simulations and by the example of experimental electroencephalogram signals that bivariate estimates of directionality in case of mutually interdependent channels give erroneous results, therefore multivariate measures such as directed transfer function should be used for determination of the information flow.

  10. 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.

  11. Modeling multivariate survival data by a semiparametric random effects proportional odds model.

    PubMed

    Lam, K F; Lee, Y W; Leung, T L

    2002-06-01

    In this article, the focus is on the analysis of multivariate survival time data with various types of dependence structures. Examples of multivariate survival data include clustered data and repeated measurements from the same subject, such as the interrecurrence times of cancer tumors. A random effect semiparametric proportional odds model is proposed as an alternative to the proportional hazards model. The distribution of the random effects is assumed to be multivariate normal and the random effect is assumed to act additively to the baseline log-odds function. This class of models, which includes the usual shared random effects model, the additive variance components model, and the dynamic random effects model as special cases, is highly flexible and is capable of modeling a wide range of multivariate survival data. A unified estimation procedure is proposed to estimate the regression and dependence parameters simultaneously by means of a marginal-likelihood approach. Unlike the fully parametric case, the regression parameter estimate is not sensitive to the choice of correlation structure of the random effects. The marginal likelihood is approximated by the Monte Carlo method. Simulation studies are carried out to investigate the performance of the proposed method. The proposed method is applied to two well-known data sets, including clustered data and recurrent event times data.

  12. A multivariate exploration of basic symptoms.

    PubMed

    Rubino, I Alex; Ciani, Nicola

    2002-01-01

    Little is known about the relationship between the different categories of basic symptoms (BS). Researchers of the Bonn School have accurately described the progression from second-level BS (relatively characteristic BS) to first-rank Schneiderian symptoms. Using a multiple regression model, the present study tried to investigate which kind of dynamic deficiencies (DDs; uncharacteristic first-level BS) mostly lead to each type of second-level BS. A group of 108 patients with a DSM-III-R diagnosis of schizophrenia completed an inventory on BS, with all items in strict accordance with those of the Bonn Scale. Five dependent variables (cognitive thought disorders, cognitive perception disorders, cognitive action disorders, increased impressionability, cenesthesias) and four independent variables (DDs with direct negative symptoms, DDs with indirect negative symptoms, affective DDs, relational DDs) were considered. Among the significant findings, a widespread contribution of DDs with indirect negative symptoms to most of the dependent variables, and the special role of DDs with direct negative symptoms as a predictor of cognitive thought disorders, must be emphasized. Suggestions for further multivariate studies in the field of BS are presented.

  13. Multivariate semiparametric spatial methods for imaging data.

    PubMed

    Chen, Huaihou; Cao, Guanqun; Cohen, Ronald A

    2017-04-01

    Univariate semiparametric methods are often used in modeling nonlinear age trajectories for imaging data, which may result in efficiency loss and lower power for identifying important age-related effects that exist in the data. As observed in multiple neuroimaging studies, age trajectories show similar nonlinear patterns for the left and right corresponding regions and for the different parts of a big organ such as the corpus callosum. To incorporate the spatial similarity information without assuming spatial smoothness, we propose a multivariate semiparametric regression model with a spatial similarity penalty, which constrains the variation of the age trajectories among similar regions. The proposed method is applicable to both cross-sectional and longitudinal region-level imaging data. We show the asymptotic rates for the bias and covariance functions of the proposed estimator and its asymptotic normality. Our simulation studies demonstrate that by borrowing information from similar regions, the proposed spatial similarity method improves the efficiency remarkably. We apply the proposed method to two neuroimaging data examples. The results reveal that accounting for the spatial similarity leads to more accurate estimators and better functional clustering results for visualizing brain atrophy pattern.Functional clustering; Longitudinal magnetic resonance imaging (MRI); Penalized B-splines; Region of interest (ROI); Spatial penalty.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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

  19. 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.

  20. Publishing nutrition research: a review of multivariate techniques--part 1.

    PubMed

    Sheean, Patricia M; Bruemmer, Barbara; Gleason, Phillip; Harris, Jeffrey; Boushey, Carol; Van Horn, Linda

    2011-01-01

    This article is the seventh in a series reviewing the importance of research design, analyses, and epidemiology in the conduct, interpretation, and publication of nutrition research. Although there are a variety of factors to consider before conducting nutrition research, the techniques used to conduct the statistical analysis are fundamental for translating raw data into interpretable findings. The statistical approach must be considered during the design phase of any study and often involves the use of multivariate analytical techniques. Multivariate analytical techniques represent a variety of mathematical models used to measure and quantify an exposure-disease or an exposure-outcome association, taking into account important factors that can influence this relationship. The primary purpose of this review is to introduce the more commonly used multivariate techniques, including linear and logistic regression (simple and multiple), and survival analyses (Kaplan Meier plots and Cox regression). These techniques are described in detail, providing basic definitions and practical examples with nutrition relevancy. An appreciation for the general principles within and presented previously in this article series is vital for enhancing the rigor in which nutrition-related research is implemented, reviewed, and published.

  1. 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…

  2. Exploratory Tobit Factor Analysis for Multivariate Censored Data.

    ERIC Educational Resources Information Center

    Kamakura, Wagner A.; Wedel, Michel

    2001-01-01

    Proposes a class of multivariate Tobit models with a factor structure on the covariance matrix. Such models are useful in the exploratory analysis of multivariate censored data and the identification of latent variables from behavioral data. The factor structure provides a parsimonious representation of the censored data. Models are estimated with…

  3. Multivariate Seismic Calibration for the Novaya Zemlya Test Site

    DTIC Science & Technology

    1992-09-30

    every multivariate magnitude combination. A classical confidence interval is presented to estimate future yields, based on estimates of the unknown...multivariate calibration parameters. A test of TTBT compliance and a definition of the F-number, based on the confidence interval , are also provided. F

  4. 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…

  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. Multivariate Display for Quipus to Faces. Program Statistics Research.

    ERIC Educational Resources Information Center

    Wainer, Howard

    The past decade has seen a substantial growth in methods and schemes for the display of multivariate data. This paper encompasses a sketch of the history of multivariate displays, from the pre-Columbian Quipu to Chernoff's Face; examines a number of techniques; describes their construction; illustrates their use; and comments on their efficacy.…

  7. Methods for presentation and display of multivariate data

    NASA Technical Reports Server (NTRS)

    Myers, R. H.

    1981-01-01

    Methods for the presentation and display of multivariate data are discussed with emphasis placed on the multivariate analysis of variance problems and the Hotelling T(2) solution in the two-sample case. The methods utilize the concepts of stepwise discrimination analysis and the computation of partial correlation coefficients.

  8. 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…

  9. 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.

  10. 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

  11. Gravitational-wave detection using multivariate analysis

    NASA Astrophysics Data System (ADS)

    Adams, Thomas S.; Meacher, Duncan; Clark, James; Sutton, Patrick J.; Jones, Gareth; Minot, Ariana

    2013-09-01

    Searches for gravitational-wave bursts (transient signals, typically of unknown waveform) require identification of weak signals in background detector noise. The sensitivity of such searches is often critically limited by non-Gaussian noise fluctuations that are difficult to distinguish from real signals, posing a key problem for transient gravitational-wave astronomy. Current noise rejection tests are based on the analysis of a relatively small number of measured properties of the candidate signal, typically correlations between detectors. Multivariate analysis (MVA) techniques probe the full space of measured properties of events in an attempt to maximize the power to accurately classify events as signal or background. This is done by taking samples of known background events and (simulated) signal events to train the MVA classifier, which can then be applied to classify events of unknown type. We apply the boosted decision tree (BDT) MVA technique to the problem of detecting gravitational-wave bursts associated with gamma-ray bursts. We find that BDTs are able to increase the sensitive distance reach of the search by as much as 50%, corresponding to a factor of ˜3 increase in sensitive volume. This improvement is robust against trigger sky position, large sky localization error, poor data quality, and the simulated signal waveforms that are used. Critically, we find that the BDT analysis is able to detect signals that have different morphologies from those used in the classifier training and that this improvement extends to false alarm probabilities beyond the 3σ significance level. These findings indicate that MVA techniques may be used for the robust detection of gravitational-wave bursts with a priori unknown waveform.

  12. Cross-Modal Multivariate Pattern Analysis

    PubMed Central

    Meyer, Kaspar; Kaplan, Jonas T.

    2011-01-01

    Multivariate pattern analysis (MVPA) is an increasingly popular method of analyzing functional magnetic resonance imaging (fMRI) data1-4. Typically, the method is used to identify a subject's perceptual experience from neural activity in certain regions of the brain. For instance, it has been employed to predict the orientation of visual gratings a subject perceives from activity in early visual cortices5 or, analogously, the content of speech from activity in early auditory cortices6. Here, we present an extension of the classical MVPA paradigm, according to which perceptual stimuli are not predicted within, but across sensory systems. Specifically, the method we describe addresses the question of whether stimuli that evoke memory associations in modalities other than the one through which they are presented induce content-specific activity patterns in the sensory cortices of those other modalities. For instance, seeing a muted video clip of a glass vase shattering on the ground automatically triggers in most observers an auditory image of the associated sound; is the experience of this image in the "mind's ear" correlated with a specific neural activity pattern in early auditory cortices? Furthermore, is this activity pattern distinct from the pattern that could be observed if the subject were, instead, watching a video clip of a howling dog? In two previous studies7,8, we were able to predict sound- and touch-implying video clips based on neural activity in early auditory and somatosensory cortices, respectively. Our results are in line with a neuroarchitectural framework proposed by Damasio9,10, according to which the experience of mental images that are based on memories - such as hearing the shattering sound of a vase in the "mind's ear" upon seeing the corresponding video clip - is supported by the re-construction of content-specific neural activity patterns in early sensory cortices. PMID:22105246

  13. Evaluation of spatial and temporal variations in marine sediments quality using multivariate statistical techniques.

    PubMed

    Alvarez, Odalys Quevedo; Tagle, Margarita Edelia Villanueva; Pascual, Jorge L Gómez; Marín, Ma Teresa Larrea; Clemente, Ana Catalina Nuñez; Medina, Miriam Odette Cora; Palau, Raiza Rey; Alfonso, Mario Simeón Pomares

    2014-10-01

    Spatial and temporal variations of sediment quality in Matanzas Bay (Cuba) were studied by determining a total of 12 variables (Zn, Cu, Pb, As, Ni, Co, Al, Fe, Mn, V, CO₃²⁻, and total hydrocarbons (THC). Surface sediments were collected, annually, at eight stations during 2005-2008. Multivariate statistical techniques, such as principal component (PCA), cluster (CA), and lineal discriminant (LDA) analyses were applied for identification of the most significant variables influencing the environmental quality of sediments. Heavy metals (Zn, Cu, Pb, V, and As) and THC were the most significant species contributing to sediment quality variations during the sampling period. Concentrations of V and As were determined in sediments of this ecosystem for the first time. The variation of sediment environmental quality with the sampling period and the differentiation of samples in three groups along the bay were obtained. The usefulness of the multivariate statistical techniques employed for the environmental interpretation of a limited dataset was confirmed.

  14. 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.

  15. Coupling GIS and multivariate approaches to reference site selection for wadeable stream monitoring.

    PubMed

    Collier, Kevin J; Haigh, Andy; Kelly, Johlene

    2007-04-01

    Geographic Information System (GIS) was used to identify potential reference sites for wadeable stream monitoring, and multivariate analyses were applied to test whether invertebrate communities reflected a priori spatial and stream type classifications. We identified potential reference sites in segments with unmodified vegetation cover adjacent to the stream and in >85% of the upstream catchment. We then used various landcover, amenity and environmental impact databases to eliminate sites that had potential anthropogenic influences upstream and that fell into a range of access classes. Each site identified by this process was coded by four dominant stream classes and seven zones, and 119 candidate sites were randomly selected for follow-up assessment. This process yielded 16 sites conforming to reference site criteria using a conditional-probabilistic design, and these were augmented by an additional 14 existing or special interest reference sites. Non-metric multidimensional scaling (NMS) analysis of percent abundance invertebrate data indicated significant differences in community composition among some of the zones and stream classes identified a priori providing qualified support for this framework in reference site selection. NMS analysis of a range standardised condition and diversity metrics derived from the invertebrate data indicated a core set of 26 closely related sites, and four outliers that were considered atypical of reference site conditions and subsequently dropped from the network. Use of GIS linked to stream typology, available spatial databases and aerial photography greatly enhanced the objectivity and efficiency of reference site selection. The multi-metric ordination approach reduced variability among stream types and bias associated with non-random site selection, and provided an effective way to identify representative reference sites.

  16. Gene Sets Net Correlations Analysis (GSNCA): a multivariate differential coexpression test for gene sets

    PubMed Central

    Rahmatallah, Yasir; Emmert-Streib, Frank; Glazko, Galina

    2014-01-01

    Motivation: To date, gene set analysis approaches primarily focus on identifying differentially expressed gene sets (pathways). Methods for identifying differentially coexpressed pathways also exist but are mostly based on aggregated pairwise correlations or other pairwise measures of coexpression. Instead, we propose Gene Sets Net Correlations Analysis (GSNCA), a multivariate differential coexpression test that accounts for the complete correlation structure between genes. Results: In GSNCA, weight factors are assigned to genes in proportion to the genes’ cross-correlations (intergene correlations). The problem of finding the weight vectors is formulated as an eigenvector problem with a unique solution. GSNCA tests the null hypothesis that for a gene set there is no difference in the weight vectors of the genes between two conditions. In simulation studies and the analyses of experimental data, we demonstrate that GSNCA captures changes in the structure of genes’ cross-correlations rather than differences in the averaged pairwise correlations. Thus, GSNCA infers differences in coexpression networks, however, bypassing method-dependent steps of network inference. As an additional result from GSNCA, we define hub genes as genes with the largest weights and show that these genes correspond frequently to major and specific pathway regulators, as well as to genes that are most affected by the biological difference between two conditions. In summary, GSNCA is a new approach for the analysis of differentially coexpressed pathways that also evaluates the importance of the genes in the pathways, thus providing unique information that may result in the generation of novel biological hypotheses. Availability and implementation: Implementation of the GSNCA test in R is available upon request from the authors. Contact: YRahmatallah@uams.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24292935

  17. Relationship between Sedentariness and Moderate-to-Vigorous Physical Activity in Youth: A Multivariate Multilevel Study

    PubMed Central

    Gomes, Thayse Natacha; Hedeker, Donald; dos Santos, Fernanda Karina; Souza, Michele; Santos, Daniel; Pereira, Sara; Katzmarzyk, Peter T.; Maia, José

    2017-01-01

    This study aimed to jointly analyse moderate-to-vigorous physical activity (MVPA) and sedentariness, and their correlates, in children within their school contexts, using a multivariate multilevel approach. The sample comprises 499 Portuguese children (284 girls) from 23 schools. MVPA and sedentary time were estimated by accelerometer. A set of predictor variables from both child and school levels was tested. Overall, schools explained a small amount of the total variance in both MVPA (5.6%) and sedentariness (3.2%), and a correlation coefficient of −0.45 (p < 0.05) was found between MVPA and sedentariness at the child level. Number of siblings and socioeconomic status (SES) were significantly associated with both sedentariness (SES: β = 2.372 ± 1.183; siblings: β = −8.127 ± 2.759) and MPVA (SES: β = −1.535 ± 0.421; siblings: β = 2.822 ± 0.977), but with opposite signs. Body Mass Index (BMI) (β = −4.804 ± 1.898) and sex (male) (β = 21.561 ± 3.496) were only associated with MVPA. None of the school correlates were statistically significant in their joint effects to simultaneously explain sedentariness and MVPA. These results suggest that although MVPA and sedentariness may be different constructs, they are correlated and this should be taken into account when designing strategies to reduce children’s sedentariness and increase their MVPA. In addition, the small effect of the school context on this relationship highlights the important roles of child and family characteristics. PMID:28165401

  18. Relationship between Sedentariness and Moderate-to-Vigorous Physical Activity in Youth: A Multivariate Multilevel Study.

    PubMed

    Gomes, Thayse Natacha; Hedeker, Donald; Dos Santos, Fernanda Karina; Souza, Michele; Santos, Daniel; Pereira, Sara; Katzmarzyk, Peter T; Maia, José

    2017-02-04

    This study aimed to jointly analyse moderate-to-vigorous physical activity (MVPA) and sedentariness, and their correlates, in children within their school contexts, using a multivariate multilevel approach. The sample comprises 499 Portuguese children (284 girls) from 23 schools. MVPA and sedentary time were estimated by accelerometer. A set of predictor variables from both child and school levels was tested. Overall, schools explained a small amount of the total variance in both MVPA (5.6%) and sedentariness (3.2%), and a correlation coefficient of -0.45 (p < 0.05) was found between MVPA and sedentariness at the child level. Number of siblings and socioeconomic status (SES) were significantly associated with both sedentariness (SES: β = 2.372 ± 1.183; siblings: β = -8.127 ± 2.759) and MPVA (SES: β = -1.535 ± 0.421; siblings: β = 2.822 ± 0.977), but with opposite signs. Body Mass Index (BMI) (β = -4.804 ± 1.898) and sex (male) (β = 21.561 ± 3.496) were only associated with MVPA. None of the school correlates were statistically significant in their joint effects to simultaneously explain sedentariness and MVPA. These results suggest that although MVPA and sedentariness may be different constructs, they are correlated and this should be taken into account when designing strategies to reduce children's sedentariness and increase their MVPA. In addition, the small effect of the school context on this relationship highlights the important roles of child and family characteristics.

  19. Linking trace metals and agricultural land use in volcanic soils--a multivariate approach.

    PubMed

    Parelho, C; Rodrigues, A S; Cruz, J V; Garcia, P

    2014-10-15

    The concern about the environmental impacts caused by agriculture intensification is growing as large amounts of nutrients and contaminants are introduced into soil ecosystems. Volcanic soils are unique naturally fertile resources extensively used for agricultural purposes, with particular physical and chemical properties that may result in possible accumulation of toxic substances, such as metals. Within this particular geological context, the present study aims to evaluate the impact of different agricultural systems (conventional, traditional and organic) in trace metal (TM) soil pollution and define the tracers for each one. Physicochemical properties and TM contents in agricultural topsoils were determined. Enrichment Factors (EF) were calculated to distinguish geogenic and anthropogenic contribution to TM contents in agricultural soils. An ensemble of multivariate statistical analyses (PCA and FDA) was performed to reduce the multidimensional space of variables and samples, thus defining a set of TM as tracers of distinct agricultural farming systems. Results show that agricultural soils have low organic matter content (<5%) compared to reference soil (>30%); in addition, electric conductivity in conventional farming soils is higher (262.3 ± 162.6 μS cm(-1)) while pH is lower (5.8 ± 0.3). Regarding metal inputs, V, Ba and Hg soil contents are mainly of geogenic origin, while Li, P, K, Cr, Mn, Ni, Cu, Zn, As, Mo, Cd and Pb result primarily from anthropogenic inputs. Li revealed to be a tracer of agricultural pollution in conventional farming soils, whereas V allowed the discrimination of traditional farming soils. This study points to agriculture as a diffuse source of anthropogenic TM soil pollution and is the first step to identify priority chemicals affecting agricultural Andosols.

  20. 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

  1. 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.

  2. Multivariate optimum interpolation of surface pressure and surface wind over oceans

    NASA Technical Reports Server (NTRS)

    Bloom, S. C.; Baker, W. E.; Nestler, M. S.

    1984-01-01

    The present multivariate analysis method for surface pressure and winds incorporates ship wind observations into the analysis of surface pressure. For the specific case of 0000 GMT, on February 3, 1979, the additional data resulted in a global rms difference of 0.6 mb; individual maxima as larse as 5 mb occurred over the North Atlantic and East Pacific Oceans. These differences are noted to be smaller than the analysis increments to the first-guess fields.

  3. Sedimentary chemofacies characterization by means of multivariate analysis

    NASA Astrophysics Data System (ADS)

    Montero-Serrano, Jean Carlos; Palarea-Albaladejo, Javier; Martín-Fernández, Josep A.; Martínez-Santana, Manuel; Gutiérrez-Martín, José Vicente

    2010-07-01

    Multivariate statistical analysis is applied to geochemical data from three sections forming part of the stratigraphic record of the Cerro Pelado Formation (Oligocene-Miocene), in the central region of the Falcón Basin, northwestern Venezuela. Our main goal is introducing and testing a statistical protocol for the identification of chemofacies in the studied sections. The first step involves data preparation and cleaning: selection of relevant components, convenient replacement of values below the detection limit and determination of outliers. Second, a biplot analysis allows us to infer geochemical processes that can be interpreted from a paleoenvironmental point of view: detrital association, redox-organic matter association and carbonatic association. Considering such geochemical associations, a constrained cluster analysis is then carried out to determine the chemofacies for each section. According to the compositional nature of geochemical data, all statistical analysis is conducted within a log-ratio analysis framework. In addition, robust statistical methods are considered for outlier detection and biplot representation in order to smooth the influence of potential outliers on the estimates.

  4. Beyond a bigger brain: Multivariable structural brain imaging and intelligence.

    PubMed

    Ritchie, Stuart J; Booth, Tom; Valdés Hernández, Maria Del C; Corley, Janie; Maniega, Susana Muñoz; Gow, Alan J; Royle, Natalie A; Pattie, Alison; Karama, Sherif; Starr, John M; Bastin, Mark E; Wardlaw, Joanna M; Deary, Ian J

    2015-01-01

    People with larger brains tend to score higher on tests of general intelligence (g). It is unclear, however, how much variance in intelligence other brain measurements would account for if included together with brain volume in a multivariable model. We examined a large sample of individuals in their seventies (n = 672) who were administered a comprehensive cognitive test battery. Using structural equation modelling, we related six common magnetic resonance imaging-derived brain variables that represent normal and abnormal features-brain volume, cortical thickness, white matter structure, white matter hyperintensity load, iron deposits, and microbleeds-to g and to fluid intelligence. As expected, brain volume accounted for the largest portion of variance (~ 12%, depending on modelling choices). Adding the additional variables, especially cortical thickness (+~ 5%) and white matter hyperintensity load (+~ 2%), increased the predictive value of the model. Depending on modelling choices, all neuroimaging variables together accounted for 18-21% of the variance in intelligence. These results reveal which structural brain imaging measures relate to g over and above the largest contributor, total brain volume. They raise questions regarding which other neuroimaging measures might account for even more of the variance in intelligence.

  5. Multivariate neural network operators with sigmoidal activation functions.

    PubMed

    Costarelli, Danilo; Spigler, Renato

    2013-12-01

    In this paper, we study pointwise and uniform convergence, as well as order of approximation, of a family of linear positive multivariate neural network (NN) operators with sigmoidal activation functions. The order of approximation is studied for functions belonging to suitable Lipschitz classes and using a moment-type approach. The special cases of NN operators, activated by logistic, hyperbolic tangent, and ramp sigmoidal functions are considered. Multivariate NNs approximation finds applications, typically, in neurocomputing processes. Our approach to NN operators allows us to extend previous convergence results and, in some cases, to improve the order of approximation. The case of multivariate quasi-interpolation operators constructed with sigmoidal functions is also considered.

  6. Modeling Baseline Shifts in Multivariate Disease Outbreak Detection

    PubMed Central

    Que, Jialan; Tsui, Fu-Chiang

    2013-01-01

    Objective Outbreak detection algorithms monitoring only disease-relevant data streams may be prone to false alarms due to baseline shifts. In this paper, we propose a Multinomial-Generalized-Dirichlet (MGD) model to adjust for baseline shifts. Introduction Population surges or large events may cause shift of data collected by biosurveillance systems [1]. For example, the Cherry Blossom Festival brings hundreds of thousands of people to DC every year, which results in simultaneous elevations in multiple data streams (Fig. 1). In this paper, we propose an MGD model to accommodate the needs of dealing with baseline shifts. Methods Existing multivariate algorithms only model disease-relevant data streams (e.g., anti-fever medication sales or patient visits with constitutional syndrome for detection of flu outbreak). On the contrary, we also incorporate a non-disease-relevant data stream as a control factor. We assume that the counts from all data streams follow a Multinomial distribution. Given this distribution, the expected value of the distribution parameter is not subject to change during a baseline shift; however, it has to change in order to model an outbreak. Therefore, this distribution inherently adjusts for the baseline shifts. In addition, we use the generalized Dirichlet (GD) distribution to model the parameter, since GD distribution is one of the conjugate prior of Multinomial [2]. We call this model the Multinomial-Generalized-Dirichlet (MGD) model. Results We applied MGD model in our previous proposed Rank-Based Spatial Clustering (MRSC) algorithm [3]. We simulated both outbreak cases and baseline shift phenomena. The experiment includes two groups of data sets. The first includes the data sets only injected with outbreak cases, and the second includes the ones with both outbreak cases and baseline shifts. We apply MRSC algorithm and a reference method, the Multivariate Bayesian Scan Statistic (MBSS) algorithm (which only analyzes the disease

  7. Readiness Evaluations Using Multivariate Data Reduction

    DTIC Science & Technology

    1978-11-01

    Discriminant Analysis." The reader is referred to Afifi and Azen [1] for a description of the procedure. In addition, the SPSS program performs a...CONTINUE CALL CLOSER C3. "MHTRIX"- END OK - 56 T-385 REFERENCES [1] AFIFI , A. A. and S. P. AZEN (1972). Statistical Analysis: A Computer

  8. 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

  9. 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.

  10. Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

    NASA Astrophysics Data System (ADS)

    Glascock, M. D.; Neff, H.; Vaughn, K. J.

    2004-06-01

    The application of instrumental neutron activation analysis and multivariate statistics to archaeological studies of ceramics and clays is described. A small pottery data set from the Nasca culture in southern Peru is presented for illustration.

  11. Multivariate Generalized Beta Distributions with Applications to Utility Assessment.

    ERIC Educational Resources Information Center

    Libby, David L.; Novick, Melvin R.

    1982-01-01

    Two multivariate probability distributions, a generalized beta distribution and a generalized F distribution, are derived. Formulas for the moments of these distributions are given and an example of the bivariate generalized beta is presented. (Author/JKS)

  12. Multivariate Cryptography Based on Clipped Hopfield Neural Network.

    PubMed

    Wang, Jia; Cheng, Lee-Ming; Su, Tong

    2016-11-23

    Designing secure and efficient multivariate public key cryptosystems [multivariate cryptography (MVC)] to strengthen the security of RSA and ECC in conventional and quantum computational environment continues to be a challenging research in recent years. In this paper, we will describe multivariate public key cryptosystems based on extended Clipped Hopfield Neural Network (CHNN) and implement it using the MVC (CHNN-MVC) framework operated in GF(p) space. The Diffie--Hellman key exchange algorithm is extended into the matrix field, which illustrates the feasibility of its new applications in both classic and postquantum cryptography. The efficiency and security of our proposed new public key cryptosystem CHNN-MVC are simulated and found to be NP-hard. The proposed algorithm will strengthen multivariate public key cryptosystems and allows hardware realization practicality.

  13. 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.

  14. MICROSCOPE: A Software System for Multivariate Analysis.

    DTIC Science & Technology

    1984-06-01

    Design Work Unit Number 3 (Numerical Analysis and Scientific Computing) Department of Mathematics, University of Utah, Salt Lake City, Utah 84112...2 where eps and aps are random numbers between -1 and +1. The addition of 1 2 the eps term is not standard but appropriate in the present context. 2...because in investigations with MICROSCOE small numbers are often due to taking differences between very close large numbers , leading to a cancellation

  15. A Bayesian approach to multivariate measurement system assessment

    SciTech Connect

    Hamada, Michael Scott

    2016-07-01

    This article considers system assessment for multivariate measurements and presents a Bayesian approach to analyzing gauge R&R study data. The evaluation of variances for univariate measurement becomes the evaluation of covariance matrices for multivariate measurements. The Bayesian approach ensures positive definite estimates of the covariance matrices and easily provides their uncertainty. Furthermore, various measurement system assessment criteria are easily evaluated. The approach is illustrated with data from a real gauge R&R study as well as simulated data.

  16. 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.

  17. 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.

  18. What works in offender profiling? A comparison of typological, thematic, and multivariate models.

    PubMed

    Goodwill, Alasdair M; Alison, Laurence J; Beech, Anthony R

    2009-01-01

    Utilizing a sample of 85 stranger rapists, three models (Hazelwood's (1987) Power and Anger FBI model, the Behavioral Thematic evaluation of Canter, Bennell, Alison, and Reddy (2003), and the Massachusetts Treatment Center: Rape classification system revision 3 (MTC:R3, Knight & Prentky, 1990)) were contrasted with a multivariate regression approach to assess their ability to predict an offender's previous convictions from crime scene information. In respect of the three aforementioned models, logistic regression and AUC analysis indicated that the Power and Anger FBI model was the most effective, followed by the MTC:R3, and then the Behavioral Thematic evaluation. However, predictive analyses based on a multivariate approach using a mixture of crime scene behaviors, as opposed to the grouping of behaviors into themes or types as in the three models, far exceeded the predictive ability of the three models under AUC analysis. The results suggest that emphasis should be placed on further exploration of the predictive validity of each of the individual behaviors that comprise existing thematic, typological, and multivariate classification systems, especially those that are subject to inter-situational variation.

  19. The effect of filtering on Granger causality based multivariate causality measures.

    PubMed

    Florin, Esther; Gross, Joachim; Pfeifer, Johannes; Fink, Gereon R; Timmermann, Lars

    2010-04-01

    In the past, causality measures based on Granger causality have been suggested for assessing directionality in neural signals. In frequency domain analyses (power or coherence) of neural data, it is common to preprocess the time series by filtering or decimating. However, in other fields, it has been shown theoretically that filtering in combination with Granger causality may lead to spurious or missed causalities. We investigated whether this result translates to multivariate causality methods derived from Granger causality with (a) a simulation study and (b) an application to magnetoencephalographic data. To this end, we performed extensive simulations of the effect of applying different filtering techniques and evaluated the performance of five different multivariate causality measures in combination with two numerical significance measures (random permutation and leave one out method). The analysis included three of the most widely used filters (high-pass, low-pass, notch filter), four different filter types (Butterworth, Chebyshev I and II, elliptic filter), variation of filter order, decimating and interpolation. The simulation results suggest that preprocessing without a strong prior about the artifact to be removed disturbs the information content and time ordering of the data and leads to spurious and missed causalities. Only if apparent artifacts like a current or movement artifact are present, filtering out the respective disturbance seems advisable. While oversampling poses no problem, decimation by a factor greater than the minimum time shift between the time series may lead to wrong inferences. In general, the multivariate causality measures are very sensitive to data preprocessing.

  20. Structural brain connectivity and cognitive ability differences: A multivariate distance matrix regression analysis.

    PubMed

    Ponsoda, Vicente; Martínez, Kenia; Pineda-Pardo, José A; Abad, Francisco J; Olea, Julio; Román, Francisco J; Barbey, Aron K; Colom, Roberto

    2017-02-01

    Neuroimaging research involves analyses of huge amounts of biological data that might or might not be related with cognition. This relationship is usually approached using univariate methods, and, therefore, correction methods are mandatory for reducing false positives. Nevertheless, the probability of false negatives is also increased. Multivariate frameworks have been proposed for helping to alleviate this balance. Here we apply multivariate distance matrix regression for the simultaneous analysis of biological and cognitive data, namely, structural connections among 82 brain regions and several latent factors estimating cognitive performance. We tested whether cognitive differences predict distances among individuals regarding their connectivity pattern. Beginning with 3,321 connections among regions, the 36 edges better predicted by the individuals' cognitive scores were selected. Cognitive scores were related to connectivity distances in both the full (3,321) and reduced (36) connectivity patterns. The selected edges connect regions distributed across the entire brain and the network defined by these edges supports high-order cognitive processes such as (a) (fluid) executive control, (b) (crystallized) recognition, learning, and language processing, and (c) visuospatial processing. This multivariate study suggests that one widespread, but limited number, of regions in the human brain, supports high-level cognitive ability differences. Hum Brain Mapp 38:803-816, 2017. © 2016 Wiley Periodicals, Inc.

  1. 49 CFR 1180.7 - Market analyses.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 49 Transportation 8 2011-10-01 2011-10-01 false Market analyses. 1180.7 Section 1180.7..., TRACKAGE RIGHTS, AND LEASE PROCEDURES General Acquisition Procedures § 1180.7 Market analyses. (a) For... identify and address relevant markets and issues, and provide additional information as requested by...

  2. 49 CFR 1180.7 - Market analyses.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 49 Transportation 8 2010-10-01 2010-10-01 false Market analyses. 1180.7 Section 1180.7..., TRACKAGE RIGHTS, AND LEASE PROCEDURES General Acquisition Procedures § 1180.7 Market analyses. (a) For... identify and address relevant markets and issues, and provide additional information as requested by...

  3. 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...

  4. 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...

  5. 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...

  6. Association Analysis for Visual Exploration of Multivariate Scientific Data Sets.

    PubMed

    Liu, Xiaotong; Shen, Han-Wei

    2016-01-01

    The heterogeneity and complexity of multivariate characteristics poses a unique challenge to visual exploration of multivariate scientific data sets, as it requires investigating the usually hidden associations between different variables and specific scalar values to understand the data's multi-faceted properties. In this paper, we present a novel association analysis method that guides visual exploration of scalar-level associations in the multivariate context. We model the directional interactions between scalars of different variables as information flows based on association rules. We introduce the concepts of informativeness and uniqueness to describe how information flows between scalars of different variables and how they are associated with each other in the multivariate domain. Based on scalar-level associations represented by a probabilistic association graph, we propose the Multi-Scalar Informativeness-Uniqueness (MSIU) algorithm to evaluate the informativeness and uniqueness of scalars. We present an exploration framework with multiple interactive views to explore the scalars of interest with confident associations in the multivariate spatial domain, and provide guidelines for visual exploration using our framework. We demonstrate the effectiveness and usefulness of our approach through case studies using three representative multivariate scientific data sets.

  7. 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.

  8. 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.

  9. 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

  10. 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.

  11. A GPU-Based Implementation of the Firefly Algorithm for Variable Selection in Multivariate Calibration Problems

    PubMed Central

    de Paula, Lauro C. M.; Soares, Anderson S.; de Lima, Telma W.; Delbem, Alexandre C. B.; Coelho, Clarimar J.; Filho, Arlindo R. G.

    2014-01-01

    Several variable selection algorithms in multivariate calibration can be accelerated using Graphics Processing Units (GPU). Among these algorithms, the Firefly Algorithm (FA) is a recent proposed metaheuristic that may be used for variable selection. This paper presents a GPU-based FA (FA-MLR) with multiobjective formulation for variable selection in multivariate calibration problems and compares it with some traditional sequential algorithms in the literature. The advantage of the proposed implementation is demonstrated in an example involving a relatively large number of variables. The results showed that the FA-MLR, in comparison with the traditional algorithms is a more suitable choice and a relevant contribution for the variable selection problem. Additionally, the results also demonstrated that the FA-MLR performed in a GPU can be five times faster than its sequential implementation. PMID:25493625

  12. 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.

  13. 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.

  14. 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

  15. Multivariate and univariate analysis of energy balance data from lactating dairy cows.

    PubMed

    Moraes, L E; Kebreab, E; Strathe, A B; Dijkstra, J; France, J; Casper, D P; Fadel, J G

    2015-06-01

    The objectives of the study were to develop a multivariate framework for analyzing energy balance data from lactating cows and investigate potential changes in maintenance requirements and partial efficiencies of energy utilization by lactating cows over the years. The proposed model accounted for the fact that metabolizable energy intake, milk energy output, and tissue energy balance are random variables that interact mutually. The model was specified through structural equations implemented in a Bayesian framework. The structural equations, along with a model traditionally used to estimate energetic parameters, were fitted to a large database of indirect calorimetry records from lactating cows. Maintenance requirements and partial efficiencies for both models were similar to values reported in the literature. In particular, the estimated parameters (with 95% credible interval in parentheses) for the proposed model were: net energy requirement for maintenance equal to 0.36 (0.34, 0.38) MJ/kg of metabolic body weight·day; the efficiency of utilizing dietary energy for milk production and tissue gain were 0.63 (0.61, 0.64) and 0.70 (0.68, 0.72), respectively; the efficiency of utilizing body stores for milk production was 0.89 (0.87, 0.91). Furthermore, additional analyses were conducted for which energetic parameters were allowed to depend on the decade in which studies were conducted. These models investigated potential changes in maintenance requirements and partial efficiencies over the years. Canonical correlation analysis was used to investigate the association between changes in energetic parameters with additional dietary and animal characteristics available in the database. For both models, net energy requirement for maintenance and the efficiency of utilizing dietary energy for milk production and tissue gain increased in the more recent decades, whereas the efficiency of utilizing body stores for milk production remained unchanged. The increase in

  16. Chemical imaging and spectroscopy using tunable filters: Instrumentation, methodology, and multivariate analysis

    NASA Astrophysics Data System (ADS)

    Turner, John Frederick, II

    based assays. The instrument has been incorporated into a commercial microtiter plate reagent dispenser and can image the fluorescence emission from microtiter plates at rates up to 10 frames/second. The instrument design and its evaluation using model fluorophores is described in detail. The final emphasis of my research has been to explore and develop rapid multivariate analyses that complement the high throughput acquisition methods employed in our laboratory. A new technique called cosine correlation analysis (CCA) is introduced which rapidly generates image contrast based on spectral shape. The theory and implementation of CCA are described using model data and Raman image data from thermoplastic olefin and silicon semiconductor materials.

  17. Multivariate dynamical modelling of structural change during development.

    PubMed

    Ziegler, Gabriel; Ridgway, Gerard R; Blakemore, Sarah-Jayne; Ashburner, John; Penny, Will

    2017-02-15

    Here we introduce a multivariate framework for characterising longitudinal changes in structural MRI using dynamical systems. The general approach enables modelling changes of states in multiple imaging biomarkers typically observed during brain development, plasticity, ageing and degeneration, e.g. regional gray matter volume of multiple regions of interest (ROIs). Structural brain states follow intrinsic dynamics according to a linear system with additional inputs accounting for potential driving forces of brain development. In particular, the inputs to the system are specified to account for known or latent developmental growth/decline factors, e.g. due to effects of growth hormones, puberty, or sudden behavioural changes etc. Because effects of developmental factors might be region-specific, the sensitivity of each ROI to contributions of each factor is explicitly modelled. In addition to the external effects of developmental factors on regional change, the framework enables modelling and inference about directed (potentially reciprocal) interactions between brain regions, due to competition for space, or structural connectivity, and suchlike. This approach accounts for repeated measures in typical MRI studies of development and aging. Model inversion and posterior distributions are obtained using earlier established variational methods enabling Bayesian evidence-based comparisons between various models of structural change. Using this approach we demonstrate dynamic cortical changes during brain maturation between 6 and 22 years of age using a large openly available longitudinal paediatric dataset with 637 scans from 289 individuals. In particular, we model volumetric changes in 26 bilateral ROIs, which cover large portions of cortical and subcortical gray matter. We account for (1) puberty-related effects on gray matter regions; (2) effects of an early transient growth process with additional time-lag parameter; (3) sexual dimorphism by modelling parameter

  18. 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

  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. 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…

  1. 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.

  2. Flow mapping and multivariate visualization of large spatial interaction data.

    PubMed

    Guo, Diansheng

    2009-01-01

    Spatial interactions (or flows), such as population migration and disease spread, naturally form a weighted location-to-location network (graph). Such geographically embedded networks (graphs) are usually very large. For example, the county-to-county migration data in the U.S. has thousands of counties and about a million migration paths. Moreover, many variables are associated with each flow, such as the number of migrants for different age groups, income levels, and occupations. It is a challenging task to visualize such data and discover network structures, multivariate relations, and their geographic patterns simultaneously. This paper addresses these challenges by developing an integrated interactive visualization framework that consists three coupled components: (1) a spatially constrained graph partitioning method that can construct a hierarchy of geographical regions (communities), where there are more flows or connections within regions than across regions; (2) a multivariate clustering and visualization method to detect and present multivariate patterns in the aggregated region-to-region flows; and (3) a highly interactive flow mapping component to map both flow and multivariate patterns in the geographic space, at different hierarchical levels. The proposed approach can process relatively large data sets and effectively discover and visualize major flow structures and multivariate relations at the same time. User interactions are supported to facilitate the understanding of both an overview and detailed patterns.

  3. 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.

  4. 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…

  5. 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.

  6. 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.

  7. 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.

  8. Genetic divergence of rubber tree estimated by multivariate techniques and microsatellite markers

    PubMed Central

    2010-01-01

    Genetic diversity of 60 Hevea genotypes, consisting of Asiatic, Amazonian, African and IAC clones, and pertaining to the genetic breeding program of the Agronomic Institute (IAC), Brazil, was estimated. Analyses were based on phenotypic multivariate parameters and microsatellites. Five agronomic descriptors were employed in multivariate procedures, such as Standard Euclidian Distance, Tocher clustering and principal component analysis. Genetic variability among the genotypes was estimated with 68 selected polymorphic SSRs, by way of Modified Rogers Genetic Distance and UPGMA clustering. Structure software in a Bayesian approach was used in discriminating among groups. Genetic diversity was estimated through Nei's statistics. The genotypes were clustered into 12 groups according to the Tocher method, while the molecular analysis identified six groups. In the phenotypic and microsatellite analyses, the Amazonian and IAC genotypes were distributed in several groups, whereas the Asiatic were in only a few. Observed heterozygosity ranged from 0.05 to 0.96. Both high total diversity (HT' = 0.58) and high gene differentiation (G st' = 0.61) were observed, and indicated high genetic variation among the 60 genotypes, which may be useful for breeding programs. The analyzed agronomic parameters and SSRs markers were effective in assessing genetic diversity among Hevea genotypes, besides proving to be useful for characterizing genetic variability. PMID:21637487

  9. A fully Bayesian multivariate approach to before-after safety evaluation.

    PubMed

    Park, Eun Sug; Park, Jaebeom; Lomax, Timothy J

    2010-07-01

    . The fully Bayesian multivariate approach introduced in this paper has additional advantages over the corresponding univariate approaches (whether classical or Bayesian) in that the multivariate approach can recover the underlying correlation structure of the multivariate crash counts and can also lead to a more precise safety effectiveness estimate by taking into account correlations among different crash severities or types for estimation of the expected number of crashes. The new method is illustrated with the multivariate crash count data obtained from expressways in Korea for 13 years to assess the safety effectiveness of decreasing the posted speed limit.

  10. Sensitivity equation for quantitative analysis with multivariate curve resolution-alternating least-squares: theoretical and experimental approach.

    PubMed

    Bauza, María C; Ibañez, Gabriela A; Tauler, Romà; Olivieri, Alejandro C

    2012-10-16

    A new equation is derived for estimating the sensitivity when the multivariate curve resolution-alternating least-squares (MCR-ALS) method is applied to second-order multivariate calibration data. The validity of the expression is substantiated by extensive Monte Carlo noise addition simulations. The multivariate selectivity can be derived from the new sensitivity expression. Other important figures of merit, such as limit of detection, limit of quantitation, and concentration uncertainty of MCR-ALS quantitative estimations can be easily estimated from the proposed sensitivity expression and the instrumental noise. An experimental example involving the determination of an analyte in the presence of uncalibrated interfering agents is described in detail, involving second-order time-decaying sensitized lanthanide luminescence excitation spectra. The estimated figures of merit are reasonably correlated with the analytical features of the analyzed experimental system.

  11. 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

  12. 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

  13. 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

  14. 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.

  15. Virulence of Bacillus cereus: a multivariate analysis.

    PubMed

    Minnaard, J; Delfederico, L; Vasseur, V; Hollmann, A; Rolny, I; Semorile, L; Pérez, P F

    2007-05-10

    Biological activity and presence of DNA sequences related to virulence genes were studied in 21 strains of the Bacillus cereus group. The activity of spent culture supernatants and the effect of infection by vegetative bacterial cells were assessed on cultured human enterocytes (Caco-2 cells). The effect of extracellular factors on the detachment, necrosis and mitochondrial dehydrogenase activity of cultured human enterocytes was studied. Hemolytic activity on rabbit red blood cells was also evaluated and the effect of direct procaryotic-eucaryotic interactions was assessed in infection assays with vegetative bacterial cells. Concerning virulence genes, presence of the DNA sequences corresponding to the genes entS, entFM, nhe (A, B and C), sph, hbl (A, B, C and D), piplC and bceT was assessed by PCR. Ribopatterns were determined by an automated riboprinting analysis after digestion of the DNA with EcoRI. Principal component analysis and biplots were used to address the relationship between variables. Results showed a wide range of biological activities: decrease in mitochondrial dehydrogenase activity, necrosis, cell detachment and hemolytic activity. These effects were strain-dependent. Concerning the occurrence of the DNA sequences tested, different patterns were found. In addition, ribotyping showed that strains under study grouped into two main clusters. One of these clusters includes all the strains that were positive for all the DNA sequences tested. Positive and negative correlations between variables under study were evidenced. Interestingly, high detaching strains were positively correlated with the presence of the sequences entS, nheC and sph. Within gene complexes, high correlation was found between sequences of the hbl complex. In contrast, sequences of the nhe complex were not correlated. Some strains clustered together in the biplots. These strains were positive for all the DNA sequences tested and they were able to detach enterocytes upon infection

  16. Networks: On the relation of bi- and multivariate measures

    PubMed Central

    Mader, Wolfgang; Mader, Malenka; Timmer, Jens; Thiel, Marco; Schelter, Björn

    2015-01-01

    A reliable inference of networks from observations of the nodes’ dynamics is a major challenge in physics. Interdependence measures such as a the correlation coefficient or more advanced methods based on, e.g., analytic phases of signals are employed. For several of these interdependence measures, multivariate counterparts exist that promise to enable distinguishing direct and indirect connections. Here, we demonstrate analytically how bivariate measures relate to the respective multivariate ones; this knowledge will in turn be used to demonstrate the implications of thresholded bivariate measures for network inference. Particularly, we show, that random networks are falsely identified as small-world networks if observations thereof are treated by bivariate methods. We will employ the correlation coefficient as an example for such an interdependence measure. The results can be readily transferred to all interdependence measures partializing for information of thirds in their multivariate counterparts. PMID:26042994

  17. A note on rank reduction in sparse multivariate regression.

    PubMed

    Chen, Kun; Chan, Kung-Sik

    A reduced-rank regression with sparse singular value decomposition (RSSVD) approach was proposed by Chen et al. for conducting variable selection in a reduced-rank model. To jointly model the multivariate response, the method efficiently constructs a prespecified number of latent variables as some sparse linear combinations of the predictors. Here, we generalize the method to also perform rank reduction, and enable its usage in reduced-rank vector autoregressive (VAR) modeling to perform automatic rank determination and order selection. We show that in the context of stationary time-series data, the generalized approach correctly identifies both the model rank and the sparse dependence structure between the multivariate response and the predictors, with probability one asymptotically. We demonstrate the efficacy of the proposed method by simulations and analyzing a macro-economical multivariate time series using a reduced-rank VAR model.

  18. The subject-by-formulation interaction in multivariate bioequivalence.

    PubMed

    Cao, Li; Mathew, Thomas

    2007-01-01

    This paper addresses hypothesis testing problems concerning the subject-by-formulation interaction matrix for the assessment of multivariate bioequivalence. Two problems are addressed: (a) the problem of testing if the subject-by-formulation interaction matrix itself is zero, and (b) the problem of testing if suitable scalar valued functions of the subject-by-formulation interaction matrix is below a threshold. Approximate tests are developed in both cases and the accuracy of the approximation is numerically investigated. The results are illustrated with an example. Even though the literature on univariate bioequivalence testing addresses average bioequivalence, variance bioequivalence and subject-by-formulation interaction, the literature on multivariate bioequivalence deals only with the problem of average bioequivalence. This work appears to be the first attempt to address tests for the subject-by-formulation interaction matrix for testing multivariate bioequivalence.

  19. 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.

  20. Inheritance of nitrogen use efficiency in inbred progenies of tropical maize based on multivariate diallel analysis.

    PubMed

    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.

  1. 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

  2. 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

  3. Fast simulated annealing with a multivariate Cauchy distribution and the configuration's initial temperature

    NASA Astrophysics Data System (ADS)

    Lee, Chang-Yong

    2015-05-01

    We propose a multi-dimensional fast simulated annealing method based on a multivariate Cauchy probability distribution and an initial temperature estimated from the configuration's variation. While conventional multi-dimensional fast simulated annealing adopts the product of onedimensional random variables generated by a univariate Cauchy distribution, the proposed method generates a random vector from a multivariate Cauchy distribution. In this way, fast simulated annealing for a multi-dimensional problem maintains the same annealing schedule as that for the one-dimensional case. The proposed method also utilizes the initial temperature estimated from the configuration's variation to generate a candidate state in addition to the conventional initial temperature derived from the variation of the objective function for the acceptance probability. The proposed method is shown not only to guarantee a fast annealing schedule but also to enhance the search capability. The proposed method was tested against the optimization of real-valued functions. We empirically found that the configuration's initial temperature, together with multivariate Cauchy distribution, is more suitable than the conventional scheme for a fast annealing schedule. Moreover, the proposed method outperforms the conventional one in optimization problems having many variables.

  4. Atmospheric tether mission analyses

    NASA Technical Reports Server (NTRS)

    1996-01-01

    NASA is considering the use of tethered satellites to explore regions of the atmosphere inaccessible to spacecraft or high altitude research balloons. This report summarizes the Lockheed Martin Astronautics (LMA) effort for the engineering study team assessment of an Orbiter-based atmospheric tether mission. Lockheed Martin responsibilities included design recommendations for the deployer and tether, as well as tether dynamic analyses for the mission. Three tether configurations were studied including single line, multistrand (Hoytether) and tape designs.

  5. 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.

  6. 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.

  7. Minimal inversion, command matching and disturbance decoupling in multivariable systems

    NASA Technical Reports Server (NTRS)

    Seraji, H.

    1989-01-01

    The present treatment of the related problems of minimal inversion and perfect output control in linear multivariable systems uses a simple analytical expression for the inverse of a square multivariate system's transfer-function matrix to construct a minimal-order inverse of the system. Because the poles of the minimal-order inverse are the transmission zeros of the system, necessary and sufficient conditions for the inverse system's stability are simply stated in terms of the zero polynomial of the original system. A necessary and sufficient condition for the existence of the required controllers is that the plant zero polynomial be neither identical to zero nor unstable.

  8. Robust Multivariable Controller Design via Implicit Model-Following Methods.

    DTIC Science & Technology

    1983-12-01

    HD-Ri38 309 ROBUST MULTIVARIABLE CONTROLLER DESIGN VIA IMPLICIT 1/4 MODEL-FOLLOWING METHODS(U) AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH SCHOOL...aaS. a%. 1 .111 I Q~ 18 0 ROBUST MULTIVARIABLE CONTROLLER DESIGN -~ :VIA IMPLICIT MODEL-FOLLOWING METHODS ’.% THESIS , AFIT/GE/EE/83D-48 William G... CONTROLLER DESIGN VIA IMPLICIT MODEL-FOLLOWING METHODS THESIS AFIT/GE/EE/83D-48 William G. Miller Capt USAF ,. Approved for pubi release; distribution

  9. 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.

  10. 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

  11. Integrated environmental monitoring and multivariate data analysis-A case study.

    PubMed

    Eide, Ingvar; Westad, Frank; Nilssen, Ingunn; de Freitas, Felipe Sales; Dos Santos, Natalia Gomes; Dos Santos, Francisco; Cabral, Marcelo Montenegro; Bicego, Marcia Caruso; Figueira, Rubens; Johnsen, Ståle

    2017-03-01

    The present article describes integration of environmental monitoring and discharge data and interpretation using multivariate statistics, principal component analysis (PCA), and partial least squares (PLS) regression. The monitoring was carried out at the Peregrino oil field off the coast of Brazil. One sensor platform and 3 sediment traps were placed on the seabed. The sensors measured current speed and direction, turbidity, temperature, and conductivity. The sediment trap samples were used to determine suspended particulate matter that was characterized with respect to a number of chemical parameters (26 alkanes, 16 PAHs, N, C, calcium carbonate, and Ba). Data on discharges of drill cuttings and water-based drilling fluid were provided on a daily basis. The monitoring was carried out during 7 campaigns from June 2010 to October 2012, each lasting 2 to 3 months due to the capacity of the sediment traps. The data from the campaigns were preprocessed, combined, and interpreted using multivariate statistics. No systematic difference could be observed between campaigns or traps despite the fact that the first campaign was carried out before drilling, and 1 of 3 sediment traps was located in an area not expected to be influenced by the discharges. There was a strong covariation between suspended particulate matter and total N and organic C suggesting that the majority of the sediment samples had a natural and biogenic origin. Furthermore, the multivariate regression showed no correlation between discharges of drill cuttings and sediment trap or turbidity data taking current speed and direction into consideration. Because of this lack of correlation with discharges from the drilling location, a more detailed evaluation of chemical indicators providing information about origin was carried out in addition to numerical modeling of dispersion and deposition. The chemical indicators and the modeling of dispersion and deposition support the conclusions from the multivariate

  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. 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.…

  14. 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

  15. Additive Similarity Trees

    ERIC Educational Resources Information Center

    Sattath, Shmuel; Tversky, Amos

    1977-01-01

    Tree representations of similarity data are investigated. Hierarchical clustering is critically examined, and a more general procedure, called the additive tree, is presented. The additive tree representation is then compared to multidimensional scaling. (Author/JKS)

  16. 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

  17. Multivariate stochastic analysis for Monthly hydrological time series at Cuyahoga River Basin

    NASA Astrophysics Data System (ADS)

    zhang, L.

    2011-12-01

    Copula has become a very powerful statistic and stochastic methodology in case of the multivariate analysis in Environmental and Water resources Engineering. In recent years, the popular one-parameter Archimedean copulas, e.g. Gumbel-Houggard copula, Cook-Johnson copula, Frank copula, the meta-elliptical copula, e.g. Gaussian Copula, Student-T copula, etc. have been applied in multivariate hydrological analyses, e.g. multivariate rainfall (rainfall intensity, duration and depth), flood (peak discharge, duration and volume), and drought analyses (drought length, mean and minimum SPI values, and drought mean areal extent). Copula has also been applied in the flood frequency analysis at the confluences of river systems by taking into account the dependence among upstream gauge stations rather than by using the hydrological routing technique. In most of the studies above, the annual time series have been considered as stationary signal which the time series have been assumed as independent identically distributed (i.i.d.) random variables. But in reality, hydrological time series, especially the daily and monthly hydrological time series, cannot be considered as i.i.d. random variables due to the periodicity existed in the data structure. Also, the stationary assumption is also under question due to the Climate Change and Land Use and Land Cover (LULC) change in the fast years. To this end, it is necessary to revaluate the classic approach for the study of hydrological time series by relaxing the stationary assumption by the use of nonstationary approach. Also as to the study of the dependence structure for the hydrological time series, the assumption of same type of univariate distribution also needs to be relaxed by adopting the copula theory. In this paper, the univariate monthly hydrological time series will be studied through the nonstationary time series analysis approach. The dependence structure of the multivariate monthly hydrological time series will be

  18. 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.

  19. 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.

  20. 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,…

  1. 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…

  2. 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.

  3. 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…

  4. 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...

  5. 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.…

  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. Estimating the decomposition of predictive information in multivariate systems.

    PubMed

    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.

  8. 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…

  9. An operator of composition for the multivariate copula construction

    NASA Astrophysics Data System (ADS)

    Bína, Vladislav

    2014-12-01

    The necessity to interconnect several observed or measured quantities arises in many fields of science. And this need led, as early as in the 1940s and 1950s, to the proposals of different solutions, e.g., the IPF Procedure or copula apparatus. Multidimensional copulas alone appeared to be insufficiently flexible to model complex dependency structures in multivariate distributions, and therefore other solutions were introduced, e.g., vines. In this paper the author defines a general method of continuous density composition, providing a flexible possibility to build a multivariate continuous distribution from low-dimensional "pieces". The model of multivariate distribution is based on iterative application of the operator of composition, and it is shown that, under the assumption of consistency of the composed probability densities, the application of the operator of composition maximizes the differential entropy in the space of all common extensions of both operands' probability densities. Furthermore, a special case of compositional models is introduced here based on Archimedean copulas, and thus a copula-based approach to the modeling of multivariate dependence structure is presented as an alternative to the vines.

  10. Selection and Ranking Procedures for Multivariate Normal Populations.

    DTIC Science & Technology

    The paper deals with selection and ranking procedures for multivariate normal populations. Procedures for selecting a subset containing the (unknown) population with the smallest generalized variance, the largest Mahalanobis distance function and the largest (smallest) multiple correlation coefficient are described. The paper also surveys other known results in ranking problems for these populations and mentions some unsolved problems. (Modified author abstract)

  11. Canonical Analysis as a Generalized Regression Technique for Multivariate Analysis.

    ERIC Educational Resources Information Center

    Williams, John D.

    The use of characteristic coding (dummy coding) is made in showing solutions to four multivariate problems using canonical analysis. The canonical variates can be themselves analyzed by the use of multiple linear regression. When the canonical variates are used as criteria in a multiple linear regression, the R2 values are equal to 0, where 0 is…

  12. Temporal MDS Plots for Analysis of Multivariate Data.

    PubMed

    Jäckle, Dominik; Fischer, Fabian; Schreck, Tobias; Keim, Daniel A

    2016-01-01

    Multivariate time series data can be found in many application domains. Examples include data from computer networks, healthcare, social networks, or financial markets. Often, patterns in such data evolve over time among multiple dimensions and are hard to detect. Dimensionality reduction methods such as PCA and MDS allow analysis and visualization of multivariate data, but per se do not provide means to explore multivariate patterns over time. We propose Temporal Multidimensional Scaling (TMDS), a novel visualization technique that computes temporal one-dimensional MDS plots for multivariate data which evolve over time. Using a sliding window approach, MDS is computed for each data window separately, and the results are plotted sequentially along the time axis, taking care of plot alignment. Our TMDS plots enable visual identification of patterns based on multidimensional similarity of the data evolving over time. We demonstrate the usefulness of our approach in the field of network security and show in two case studies how users can iteratively explore the data to identify previously unknown, temporally evolving patterns.

  13. Total Information in Multivariate Data from Dual Scaling Perspectives

    ERIC Educational Resources Information Center

    Nishisato, Shizuhiko

    2003-01-01

    It is an established matter that the total information in multivariate data is defined as the sum of eigenvalues of the variance-covariance matrix. In this article, we challenge this time-honored tradition and look at another definition of the total information in data from a dual scaling perspective. This proposal is a step toward unifying the…

  14. 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…

  15. 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.

  16. A High-Dimensional Nonparametric Multivariate Test for Mean Vector

    PubMed Central

    Wang, Lan; Peng, Bo; Li, Runze

    2015-01-01

    This work is concerned with testing the population mean vector of nonnormal high-dimensional multivariate data. Several tests for high-dimensional mean vector, based on modifying the classical Hotelling T2 test, have been proposed in the literature. Despite their usefulness, they tend to have unsatisfactory power performance for heavy-tailed multivariate data, which frequently arise in genomics and quantitative finance. This paper proposes a novel high-dimensional nonparametric test for the population mean vector for a general class of multivariate distributions. With the aid of new tools in modern probability theory, we proved that the limiting null distribution of the proposed test is normal under mild conditions when p is substantially larger than n. We further study the local power of the proposed test and compare its relative efficiency with a modified Hotelling T2 test for high-dimensional data. An interesting finding is that the newly proposed test can have even more substantial power gain with large p than the traditional nonparametric multivariate test does with finite fixed p. We study the finite sample performance of the proposed test via Monte Carlo simulations. We further illustrate its application by an empirical analysis of a genomics data set. PMID:26848205

  17. Learning Adaptive Forecasting Models from Irregularly Sampled Multivariate Clinical Data.

    PubMed

    Liu, Zitao; Hauskrecht, Milos

    2016-02-01

    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.

  18. 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.

  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. 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 ...

  1. 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...

  2. 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

  3. Multivariate Epi-splines and Evolving Function Identification Problems

    DTIC Science & Technology

    2015-04-15

    MULTIVARIATE EPI- SPLINES AND EVOLVING FUNCTION IDENTIFICATION PROBLEMS∗ Johannes O. Royset Roger J-B Wets Operations Research Department Department...fitting, and estimation. The paper develops piecewise polynomial functions, called epi- splines , that approximate any lsc function to an arbitrary...level of accuracy. Epi- splines provide the foundation for the solution of a rich class of function identification problems that incorporate general

  4. 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…

  5. 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…

  6. 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…

  7. 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…

  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. An Evaluation of the Multivariate Methodology of the Project.

    ERIC Educational Resources Information Center

    Harman, Harry H.

    Presented at a symposium on "The Structure of Concept Attainment Abilities Project: Final Report and Critique," this paper provides the methodological aspects of the project. The discussion centers around a "Guide to the Multivariate Methods," which is provided in the paper. The basic guide-posts are the types of analysis and the types of content.…

  10. 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…

  11. Stabilization of linear multivariable systems by output feedback.

    NASA Technical Reports Server (NTRS)

    Mcbrinn, D. E.; Roy, R. J.

    1972-01-01

    A method is developed for improving the stability of linear multivariable systems using output feedback. The technique, which utilizes a gradient approach, has been mechanized in a digital computer program. Illustrative results are given for a seven-state two-feedback model of the Saturn V booster.

  12. 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.

  13. Multivariate Linear Models of the Multitrait-Multimethod Matrix.

    ERIC Educational Resources Information Center

    Wothke, Werner

    Several multivariate statistical methodologies have been proposed to ensure objective and quantitative evaluation of the multitrait-multimethod matrix. The paper examines the performance of confirmatory factor analysis and covariance component models. It is shown, both empirically and formally, that confirmatory factor analysis is not a reliable…

  14. Voxelwise multivariate analysis of multimodality magnetic resonance imaging.

    PubMed

    Naylor, Melissa G; Cardenas, Valerie A; Tosun, Duygu; Schuff, Norbert; Weiner, Michael; Schwartzman, Armin

    2014-03-01

    Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remain a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available.

  15. Evaluation of Meterorite Amono Acid Analysis Data Using Multivariate Techniques

    NASA Technical Reports Server (NTRS)

    McDonald, G.; Storrie-Lombardi, M.; Nealson, K.

    1999-01-01

    The amino acid distributions in the Murchison carbonaceous chondrite, Mars meteorite ALH84001, and ice from the Allan Hills region of Antarctica are shown, using a multivariate technique known as Principal Component Analysis (PCA), to be statistically distinct from the average amino acid compostion of 101 terrestrial protein superfamilies.

  16. Polyimide processing additives

    NASA Technical Reports Server (NTRS)

    Pratt, J. R.; St. Clair, T. L.; Burks, H. D.; Stoakley, D. M.

    1987-01-01

    A method has been found for enhancing the melt flow of thermoplastic polyimides during processing. A high molecular weight 422 copoly(amic acid) or copolyimide was fused with approximately 0.05 to 5 pct by weight of a low molecular weight amic acid or imide additive, and this melt was studied by capillary rheometry. Excellent flow and improved composite properties on graphite resulted from the addition of a PMDA-aniline additive to LARC-TPI. Solution viscosity studies imply that amic acid additives temporarily lower molecular weight and, hence, enlarge the processing window. Thus, compositions containing the additive have a lower melt viscosity for a longer time than those unmodified.

  17. [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.

  18. 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

  19. 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

  20. 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

  1. 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.

  2. Evaluation of the processes affecting vertical water chemistry in an alluvial aquifer of Mankyeong Watershed, Korea, using multivariate statistical analyses

    NASA Astrophysics Data System (ADS)

    Choi, Byoung-Young; Kim, Hyeon-Jung; Kim, Kangjoo; Kim, Seok-Hwi; Jeong, Hwa-Jin; Park, Eungyu; Yun, Seong-Taek

    2008-03-01

    Vertical variations of redox chemistry and groundwater quality were investigated in an alluvial aquifer beneath an agricultural area, in which deep groundwaters are free of NO3, Fe, and Mn problems that are frequently encountered during the development of alluvial groundwaters. This study was performed to identify and evaluate vertical chemical processes attenuating these chemical species in the study area. For this study, the processes affecting groundwater chemistry were identified by factor analysis (FA) and the groundwater samples collected from six multilevel samplers were hierarchically classified into three different redox zones by cluster analysis (CA) based on the similarity of geochemical features. FA results indicated three major factors affecting the overall water chemistry: agricultural activities (factor 1), redox reactions (factor 2), and remnant seawater (factor 3). The groundwater quality in the study area was revealed to be controlled by a series of different redox reactions, resulting in different redox zones as a function of depth. It was also revealed that the low Fe and Mn levels in the groundwater of the deeper part are associated with sulfate reduction, which led to precipitation of Fe as iron sulfide and adsorption of Mn on it.

  3. 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.

  4. A multivariate tobit analysis of highway accident-injury-severity rates.

    PubMed

    Anastasopoulos, Panagiotis Ch; Shankar, Venky N; Haddock, John E; Mannering, Fred L

    2012-03-01

    Relatively recent research has illustrated the potential that tobit regression has in studying factors that affect vehicle accident rates (accidents per distance traveled) on specific roadway segments. Tobit regression has been used because accident rates on specific roadway segments are continuous data that are left-censored at zero (they are censored because accidents may not be observed on all roadway segments during the period over which data are collected). This censoring may arise from a number of sources, one of which being the possibility that less severe crashes may be under-reported and thus may be less likely to appear in crash databases. Traditional tobit-regression analyses have dealt with the overall accident rate (all crashes regardless of injury severity), so the issue of censoring by the severity of crashes has not been addressed. However, a tobit-regression approach that considers accident rates by injury-severity level, such as the rate of no-injury, possible injury and injury accidents per distance traveled (as opposed to all accidents regardless of injury-severity), can potentially provide new insights, and address the possibility that censoring may vary by crash-injury severity. Using five-year data from highways in Washington State, this paper estimates a multivariate tobit model of accident-injury-severity rates that addresses the possibility of differential censoring across injury-severity levels, while also accounting for the possible contemporaneous error correlation resulting from commonly shared unobserved characteristics across roadway segments. The empirical results show that the multivariate tobit model outperforms its univariate counterpart, is practically equivalent to the multivariate negative binomial model, and has the potential to provide a fuller understanding of the factors determining accident-injury-severity rates on specific roadway segments.

  5. 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.

  6. 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

  7. 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.

  8. An Investigation of Multivariate Adaptive Regression Splines for Modeling and Analysis of Univariate and Semi-Multivariate Time Series Systems

    DTIC Science & Technology

    1991-09-01

    GRAFSTAT from IBM Research; I am grateful to Dr . Peter Welch for supplying GRAFSTAT. To P.A.W. Lewis, Thank you for your support, confidence and...34Multivariate Adaptive Regression Splines", Annals of Statistics, v. 19, no. 2, pp. 1-142, 1991. Geib , A., Applied Optimal Estimation, M.I.T. Press, Cambridge

  9. 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.

  10. Genetic Analyses of Integrin Signaling

    PubMed Central

    Wickström, Sara A.; Radovanac, Korana; Fässler, Reinhard

    2011-01-01

    The development of multicellular organisms, as well as maintenance of organ architecture and function, requires robust regulation of cell fates. This is in part achieved by conserved signaling pathways through which cells process extracellular information and translate this information into changes in proliferation, differentiation, migration, and cell shape. Gene deletion studies in higher eukaryotes have assigned critical roles for components of the extracellular matrix (ECM) and their cellular receptors in a vast number of developmental processes, indicating that a large proportion of this signaling is regulated by cell-ECM interactions. In addition, genetic alterations in components of this signaling axis play causative roles in several human diseases. This review will discuss what genetic analyses in mice and lower organisms have taught us about adhesion signaling in development and disease. PMID:21421914

  11. Multivariate statistical approach to the temporal and spatial patterns of selected bioindicators observed in the North Sea during the years 1995-1997

    NASA Astrophysics Data System (ADS)

    Schmolke, S. R.; Broeg, K.; Zander, S.; Bissinger, V.; Hansen, P. D.; Kress, N.; Herut, B.; Jantzen, E.; Krüner, G.; Sturm, A.; Körting, W.; von Westernhagen, H.

    A comprehensive database, containing biological and chemical information, collected in the framework of the bilateral interdisciplinary MARS project (''biological indicators of natural and man-made changes in marine and coastal waters'') during the years 1995-1997 in the coastal environment of the North Sea, was subjected to a multivariate statistical evaluation. The MARS project was designated to combine a variety of approaches and to develop a set of methods for the employment of biological indicators in pollution monitoring and environmental quality assessment. In total, nine ship cruises to four coastal sampling sites were conducted; 765 fish and 384 mussel samples were analysed for biological and chemical parameters. Additional information on the chemical background at the sampling sites was derived from sediment samples, collected at each of the four sampling sites. Based on the available chemical data in sediments and black mussel (Mytilus edulis) a pollution gradient between the selected sites, was established. The chemical body burden of flounder (Platichthys flesus) from these sites, though, did not reflect this gradient equally clear. In contrast, the biological information derived from measurements in fish samples displayed significant a regional as well as a temporal pattern. A multivariate bioindicator data matrix was evaluated employing a factor analysis model to identify relations between selected biological indicators, and to improve the understanding of a regional and temporal component in the parameter response. In a second approach, applying the k-means algorithm on the data matrix, two significantly different clusters of samples, characterised by the current health status of the fish, were extracted. Using this classification a temporal, and in the second order, a less pronounced spatial effect was evident. In particular, during July 1996, a clear sign of deteriorating environmental conditions was extracted from the biological data matrix.

  12. A comparison study of multivariate fixed models and Gene Association with Multiple Traits (GAMuT) for next-generation sequencing.

    PubMed

    Chiu, Chi-Yang; Jung, Jeesun; Wang, Yifan; Weeks, Daniel E; Wilson, Alexander F; Bailey-Wilson, Joan E; Amos, Christopher I; Mills, James L; Boehnke, Michael; Xiong, Momiao; Fan, Ruzong

    2017-01-01

    In this paper, extensive simulations are performed to compare two statistical methods to analyze multiple correlated quantitative phenotypes: (1) approximate F-distributed tests of multivariate functional linear models (MFLM) and additive models of multivariate analysis of variance (MANOVA), and (2) Gene Association with Multiple Traits (GAMuT) for association testing of high-dimensional genotype data. It is shown that approximate F-distributed tests of MFLM and MANOVA have higher power and are more appropriate for major gene association analysis (i.e., scenarios in which some genetic variants have relatively large effects on the phenotypes); GAMuT has higher power and is more appropriate for analyzing polygenic effects (i.e., effects from a large number of genetic variants each of which contributes a small amount to the phenotypes). MFLM and MANOVA are very flexible and can be used to perform association analysis for (i) rare variants, (ii) common variants, and (iii) a combination of rare and common variants. Although GAMuT was designed to analyze rare variants, it can be applied to analyze a combination of rare and common variants and it performs well when (1) the number of genetic variants is large and (2) each variant contributes a small amount to the phenotypes (i.e., polygenes). MFLM and MANOVA are fixed effect models that perform well for major gene association analysis. GAMuT can be viewed as an extension of sequence kernel association tests (SKAT). Both GAMuT and SKAT are more appropriate for analyzing polygenic effects and they perform well not only in the rare variant case, but also in the case of a combination of rare and common variants. Data analyses of European cohorts and the Trinity Students Study are presented to compare the performance of the two methods.

  13. 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.

  14. The interprocess NIR sampling as an alternative approach to multivariate statistical process control for identifying sources of product-quality variability.

    PubMed

    Marković, Snežana; Kerč, Janez; Horvat, Matej

    2017-03-01

    We are presenting a new approach of identifying sources of variability within a manufacturing process by NIR measurements of samples of intermediate material after each consecutive unit operation (interprocess NIR sampling technique). In addition, we summarize the development of a multivariate statistical process control (MSPC) model for the production of enteric-coated pellet product of the proton-pump inhibitor class. By developing provisional NIR calibration models, the identification of critical process points yields comparable results to the established MSPC modeling procedure. Both approaches are shown to lead to the same conclusion, identifying parameters of extrusion/spheronization and characteristics of lactose that have the greatest influence on the end-product's enteric coating performance. The proposed approach enables quicker and easier identification of variability sources during manufacturing process, especially in cases when historical process data is not straightforwardly available. In the presented case the changes of lactose characteristics are influencing the performance of the extrusion/spheronization process step. The pellet cores produced by using one (considered as less suitable) lactose source were on average larger and more fragile, leading to consequent breakage of the cores during subsequent fluid bed operations. These results were confirmed by additional experimental analyses illuminating the underlying mechanism of fracture of oblong pellets during the pellet coating process leading to compromised film coating.

  15. Polylactides in additive biomanufacturing.

    PubMed

    Poh, Patrina S P; Chhaya, Mohit P; Wunner, Felix M; De-Juan-Pardo, Elena M; Schilling, Arndt F; Schantz, Jan-Thorsten; van Griensven, Martijn; Hutmacher, Dietmar W

    2016-12-15

    New advanced manufacturing technologies under the alias of additive biomanufacturing allow the design and fabrication of a range of products from pre-operative models, cutting guides and medical devices to scaffolds. The process of printing in 3 dimensions of cells, extracellular matrix (ECM) and biomaterials (bioinks, powders, etc.) to generate in vitro and/or in vivo tissue analogue structures has been termed bioprinting. To further advance in additive biomanufacturing, there are many aspects that we can learn from the wider additive manufacturing (AM) industry, which have progressed tremendously since its introduction into the manufacturing sector. First, this review gives an overview of additive manufacturing and both industry and academia efforts in addressing specific challenges in the AM technologies to drive toward AM-enabled industrial revolution. After which, considerations of poly(lactides) as a biomaterial in additive biomanufacturing are discussed. Challenges in wider additive biomanufacturing field are discussed in terms of (a) biomaterials; (b) computer-aided design, engineering and manufacturing; (c) AM and additive biomanufacturing printers hardware; and (d) system integration. Finally, the outlook for additive biomanufacturing was discussed.

  16. Additive Manufactured Product Integrity

    NASA Technical Reports Server (NTRS)

    Waller, Jess; Wells, Doug; James, Steve; Nichols, Charles

    2017-01-01

    NASA is providing key leadership in an international effort linking NASA and non-NASA resources to speed adoption of additive manufacturing (AM) to meet NASA's mission goals. Participants include industry, NASA's space partners, other government agencies, standards organizations and academia. Nondestructive Evaluation (NDE) is identified as a universal need for all aspects of additive manufacturing.

  17. [Psychosocial predictors of metabolic instability in brittle diabetes--a multivariate time series analysis].

    PubMed

    Brosig, B; Leweke, F; Milch, W; Eckhard, M; Reimer, C

    2001-06-01

    The term "brittle diabetes" denotes the unstable course of an insulin-dependent diabetes characterised by frequent hypo- or hyperglycaemic crises. The aim of this study is to demonstrate empirically how psychosocial parameters interact with metabolic instability in a paradigmatic case of juvenile brittle diabetes. By means of a structured diary study, blood sugar values, moods (SAM), body symptoms (GBB), the daily hustle and hassle, helping therapeutic alliance (HAQ) and the aspects of setting were registered. Resulting time series (112 days each) were ARIMA-analysed by a multivariate approach. It could be shown that the mean variance of daily blood sugar values as an indicator of brittleness was predicted by moods, body complaints and by a family session as setting factor (p < 0.05, for corresponding predictors). Feelings of dominance preceded an increase of blood sugar variance, whereas depressive moods, anger and body symptoms were associated with metabolic instability. A family therapy session also resulted in an increase of the mean blood sugar variance. The model accounted for almost 30% of the total variance of the dependent variable (R-square-adjusted, p < 0.0001). The potential of multivariate time-series as a means to demonstrate psychosomatic interrelations is discussed. We believe that the results may also contribute to an empirically rooted understanding of psychodynamic processes in psychosomatoses.

  18. Multivariate Bayesian analysis of Gaussian, right censored Gaussian, ordered categorical and binary traits using Gibbs sampling.

    PubMed

    Korsgaard, Inge Riis; Lund, Mogens Sandø; Sorensen, Daniel; Gianola, Daniel; Madsen, Per; Jensen, Just

    2003-01-01

    A fully Bayesian analysis using Gibbs sampling and data augmentation in a multivariate model of Gaussian, right censored, and grouped Gaussian traits is described. The grouped Gaussian traits are either ordered categorical traits (with more than two categories) or binary traits, where the grouping is determined via thresholds on the underlying Gaussian scale, the liability scale. Allowances are made for unequal models, unknown covariance matrices and missing data. Having outlined the theory, strategies for implementation are reviewed. These include joint sampling of location parameters; efficient sampling from the fully conditional posterior distribution of augmented data, a multivariate truncated normal distribution; and sampling from the conditional inverse Wishart distribution, the fully conditional posterior distribution of the residual covariance matrix. Finally, a simulated dataset was analysed to illustrate the methodology. This paper concentrates on a model where residuals associated with liabilities of the binary traits are assumed to be independent. A Bayesian analysis using Gibbs sampling is outlined for the model where this assumption is relaxed.

  19. 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

  20. Multivariate Analysis and Quantitation of (17)O-NMR in Primary Alcohol Mixtures

    SciTech Connect

    Alam, M.Kathleen; Alam, Todd M.

    1999-07-01

    Multivariate techniques were used to address the quantification of {sup 17}O-NMR (nuclear magnetic resonance) spectra for a series of primary alcohol mixtures. Due to highly overlapping resonances, quantitative spectral evaluation using standard integration and deconvolution techniques proved difficult. Multivariate evaluation of the {sup 17}O-NMR spectral data obtained for 26 mixtures of five primary alcohols demonstrated that obtaining information about spectral overlap and interferences allowed the development of more accurate models. Initial partial least squares (PLS) models developed for the {sup 17}O-NMR data collected from the primary alcohol mixtures resulted in very poor precision, with signal overlap between the different chemical species suspected of being the primary contributor to the error. To directly evaluate the question of spectral overlap in these alcohol mixtures, net analyte signal (NAS) analyses were performed. The NAS results indicate that alcohols with similar chain lengths produced severely overlapping {sup 17}O-NMR resonances. Grouping the alcohols based on chain length allowed more accurate and robust calibration models to be developed.

  1. 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.

  2. 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.

  3. Linking multimetric and multivariate approaches to assess the ecological condition of streams.

    PubMed

    Collier, Kevin J

    2009-10-01

    Few attempts have been made to combine multimetric and multivariate analyses for bioassessment despite recognition that an integrated method could yield powerful tools for bioassessment. An approach is described that integrates eight macroinvertebrate community metrics into a Principal Components Analysis to develop a Multivariate Condition Score (MCS) from a calibration dataset of 511 samples. The MCS is compared to an Index of Biotic Integrity (IBI) derived using the same metrics based on the ratio to the reference site mean. Both approaches were highly correlated although the MCS appeared to offer greater potential for discriminating a wider range of impaired conditions. Both the MCS and IBI displayed low temporal variability within reference sites, and were able to distinguish between reference conditions and low levels of catchment modification and local habitat degradation, although neither discriminated among three levels of low impact. Pseudosamples developed to test the response of the metric aggregation approaches to organic enrichment, urban, mining, pastoral and logging stressor scenarios ranked pressures in the same order, but the MCS provided a lower score for the urban scenario and a higher score for the pastoral scenario. The MCS was calculated for an independent test dataset of urban and reference sites, and yielded similar results to the IBI. Although both methods performed comparably, the MCS approach may have some advantages because it removes the subjectivity of assigning thresholds for scoring biological condition, and it appears to discriminate a wider range of degraded conditions.

  4. Additive interaction between heterogeneous environmental ...

    EPA Pesticide Factsheets

    BACKGROUND Environmental exposures often occur in tandem; however, epidemiological research often focuses on singular exposures. Statistical interactions among broad, well-characterized environmental domains have not yet been evaluated in association with health. We address this gap by conducting a county-level cross-sectional analysis of interactions between Environmental Quality Index (EQI) domain indices on preterm birth in the Unites States from 2000-2005.METHODS: The EQI, a county-level index constructed for the 2000-2005 time period, was constructed from five domain-specific indices (air, water, land, built and sociodemographic) using principal component analyses. County-level preterm birth rates (n=3141) were estimated using live births from the National Center for Health Statistics. Linear regression was used to estimate prevalence differences (PD) and 95% confidence intervals (CI) comparing worse environmental quality to the better quality for each model for a) each individual domain main effect b) the interaction contrast and c) the two main effects plus interaction effect (i.e. the “net effect”) to show departure from additive interaction for the all U.S counties. Analyses were also performed for subgroupings by four urban/rural strata. RESULTS: We found the suggestion of antagonistic interactions but no synergism, along with several purely additive (i.e., no interaction) associations. In the non-stratified model, we observed antagonistic interac

  5. A novel transmission-based test of association for multivariate phenotypes: an application to systolic and diastolic blood pressure levels

    PubMed Central

    2014-01-01

    Unlike case-control studies, family-based tests for association are protected against population stratification. Complex genetic traits are often governed by quantitative precursors and it has been argued that it may be a more powerful strategy to analyze these quantitative precursors instead of the clinical end point trait. Although methods have been developed for family-based association tests for single quantitative traits, it is of interest to develop such methods for multivariate phenotypes. We propose a novel transmission-based approach based on a trio design using a simple logistic regression to test for association with a multivariate phenotype. We use our proposed method to analyze data on systolic and diastolic blood pressure levels provided in Genetic Analysis Workshop 18. However, we find that the bivariate analysis of the two phenotypes did not provide more promising results compared to univariate analyses, suggesting a possibility of a different set of major genetic variants modulating the two phenotypes. PMID:25519341

  6. Adding local components to global functions for continuous covariates in multivariable regression modeling.

    PubMed

    Binder, H; Sauerbrei, W

    2010-03-30

    When global techniques, based on fractional polynomials (FPs), are employed for modeling potentially nonlinear effects of several continuous covariates on a response, accessible model equations are obtained. However, local features might be missed. Therefore, a procedure is introduced, which systematically checks model fits, obtained by the multivariable fractional polynomial (MFP) approach, for overlooked local features. Statistically significant local polynomials are then parsimoniously added. This approach, called MFP + L, is seen to result in an effective control of the Type I error with respect to the addition of local components in a small simulation study with univariate and multivariable settings. Prediction performance is compared with that of a penalized regression spline technique. In a setting unfavorable for FPs, the latter outperforms the MFP approach, if there is much information in the data. However, the addition of local features reduces this performance difference. There is only a small detrimental effect in settings where the MFP approach performs better. In an application example with children's respiratory health data, fits from the spline-based approach indicate many local features, but MFP + L adds only few significant features, which seem to have good support in the data. The proposed approach may be expected to be superior in settings with local features, but retains the good properties of the MFP approach in a large number of settings where global functions are sufficient.

  7. 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.

  8. 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.

  9. Symbolic observability coefficients for univariate and multivariate analysis

    NASA Astrophysics Data System (ADS)

    Letellier, Christophe; Aguirre, Luis A.

    2009-06-01

    In practical problems, the observability of a system not only depends on the choice of observable(s) but also on the space which is reconstructed. In fact starting from a given set of observables, the reconstructed space is not unique, since the dimension can be varied and, in the case of multivariate measurement functions, there are various ways to combine the measured observables. Using a graphical approach recently introduced, we analytically compute symbolic observability coefficients which allow to choose from the system equations the best observable, in the case of scalar reconstructions, and the best way to combine the observables in the case of multivariate reconstructions. It is shown how the proposed coefficients are also helpful for analysis in higher dimension.

  10. Face recognition using tridiagonal matrix enhanced multivariance products representation

    NASA Astrophysics Data System (ADS)

    Ã-zay, Evrim Korkmaz

    2017-01-01

    This study aims to retrieve face images from a database according to a target face image. For this purpose, Tridiagonal Matrix Enhanced Multivariance Products Representation (TMEMPR) is taken into consideration. TMEMPR is a recursive algorithm based on Enhanced Multivariance Products Representation (EMPR). TMEMPR decomposes a matrix into three components which are a matrix of left support terms, a tridiagonal matrix of weight parameters for each recursion, and a matrix of right support terms, respectively. In this sense, there is an analogy between Singular Value Decomposition (SVD) and TMEMPR. However TMEMPR is a more flexible algorithm since its initial support terms (or vectors) can be chosen as desired. Low computational complexity is another advantage of TMEMPR because the algorithm has been constructed with recursions of certain arithmetic operations without requiring any iteration. The algorithm has been trained and tested with ORL face image database with 400 different grayscale images of 40 different people. TMEMPR's performance has been compared with SVD's performance as a result.

  11. Estimation of Sparse Directed Acyclic Graphs for Multivariate Counts Data

    PubMed Central

    Han, Sung Won; Zhong, Hua

    2016-01-01

    Summary The next-generation sequencing data, called high throughput sequencing data, are recorded as count data, which is generally far from normal distribution. Under the assumption that the count data follow the Poisson log-normal distribution, this paper 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

  12. [Anomaly Detection of Multivariate Time Series Based on Riemannian Manifolds].

    PubMed

    Xu, Yonghong; Hou, Xiaoying; Li Shuting; Cui, Jie

    2015-06-01

    Multivariate time series problems widely exist in production and life in the society. Anomaly detection has provided people with a lot of valuable information in financial, hydrological, meteorological fields, and the research areas of earthquake, video surveillance, medicine and others. In order to quickly and efficiently find exceptions in time sequence so that it can be presented in front of people in an intuitive way, we in this study combined the Riemannian manifold with statistical process control charts, based on sliding window, with a description of the covariance matrix as the time sequence, to achieve the multivariate time series of anomaly detection and its visualization. We made MA analog data flow and abnormal electrocardiogram data from MIT-BIH as experimental objects, and verified the anomaly detection method. The results showed that the method was reasonable and effective.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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

  18. Studying Resist Stochastics with the Multivariate Poisson Propagation Model

    DOE PAGES

    Naulleau, Patrick; Anderson, Christopher; Chao, Weilun; ...

    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.

  19. 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.

  20. Multivariate Scattered Data Derivative Generation by Functional Minimization,

    DTIC Science & Technology

    1984-06-01

    situation is complicated by the fact that the interpolant is expressed in terms of barycentric coordinates, whereas the derivatives L entering as data are...minimizing our piepewise quintic C1 interpolant can be used as data for the second of the two piecewise rational C 2 Lnterpolants described in Alfeld...ABSTRACT /Many multivariate interpolation schemes require as data values of derivatives that are not available in a practical application, and that therefore